What is an unconscious representation?

In contemporary cognitive neuroscience, we often encounter the term “unconscious representations” (e.g., Shea & Frith, 2016). These are not just representations that could be conscious but just so happen to be unconscious; they are a distinct type of cognitive representation, where their status as unconscious is inherent to the kind of representations they happen to be. That is, being unconscious is part of the representational format in question.

This conception of unconsciousness differs from a type of established folk model inherited from Freud’s early twentieth-century armchair psychology. Here, the unconscious is just a place (e.g., “in the mind”) where representations go (by implication, consciousness is also a mind place, just a place different from the unconscious place). According to this picture, representations are unconscious as long as they are in that place (e.g., the unconscious), usually banished there by some speculative mental process (itself unconscious!) called “repression.” Because being unconscious is a contingent and not inherent aspect of those representations, they could lose it by being “brought back” to consciousness via some therapeutic intervention. This type of “folk Freudianism” is a deleterious brain cramp that needs to be abandoned if the aim is to understand how contemporary cognitive neuroscientists understand unconscious representations. Even when cleansed of its Freudian associations, as in more recent talk of the “cognitive unconscious” (Reber, 1993) or the “unconscious mind” (Bargh & Morsella, 2008), the idea of the unconscious as a “place” (or “brain system”) where representations reside is misleading and should be jettisoned. Instead, we should speak of unconsciousness as an inherent property of some representations in the brain.

Let’s reiterate. The unconscious is not a place where previously conscious representations go. Instead, it is a fundamental property of the vehicles in which some representations (crucial for various cognitive functions) are instantiated in the brain. These representations are inherently unconscious. They cannot be “brought” “into” consciousness (a seductive remnant of the unconscious-as-place conceptual metaphor) by any conceivable procedure. However, cognitive scientists and linguists can generate public representations (e.g., instantiated in some kind of linguistic theory or neuroscientific computational model) that redescribe these unconscious representations to establish their content (e.g., what they are designed to represent) and function (their role in the cognitive economy of the agent). These public representations, clearly phenomenally accessible to all as conscious agents, are convenient representational redescriptions (RRs) of the content of what are inherently unconscious representations.

For instance, psycholinguists sometimes try to reconstruct the underlying unconscious representations we use to parse the linguistic input’s syntactic and phonetic structure during language comprehension by transforming them (or, more accurately, their best guesses as to which these are) into all forms of public representations, like the ubiquitous sentence tree diagrams of generative linguistics or the pictorial diagrams of cognitive grammar (Jackendoff, 1987; Langacker, 2008). These public representations are not intended to be the exact or literal analogs of the unconscious representations people employ to parse the syntactic structure of a sentence. For one, they are in a different format (digital or paper and pencil diagrams) distinct from the target unconscious representations, which exist as (perhaps structurally similar) activation and connection patterns in neuronal assemblies. However, public representations of unconscious representation used in cognitive-scientific theorizing are designed to preserve the representational content of the underlying unconscious representations (the what of what is being represented) across distinct vehicles. So, both a sentence tree grammar and the underlying unconscious representations that enable us to parse the syntactic structure of a sentence represent the same content (e.g., the target sentence’s synaptic structure).

If unconscious representations are inherently unconscious, how do we even know they exist? Perhaps all the diagrams produced by linguists and cognitive scientists are just made up, referring to nothing since we cannot observe unconscious representations. There are two responses to this worry.

First, the idea of unconscious neural representations as “unobservable posits” is greatly exaggerated because unobservability (unlike unconsciousness!) happens to be not an inherent but merely a contingent property of unconscious brain representations. This is because the unobservability of any entity, including unconscious representations, is a two-place relational property, always relative to our historically fluid capacities (and limitations) as scientific observers. This means that just like in other scientific fields like high-energy physics, molecular biology, or astronomy, previously unobservable entities can cross the threshold of observability after some kind of technological advancement occurs in our observational instruments. Some contemporary cognitive neuroscientists argue that this is precisely what has happened to unconscious brain representations, which are now, given advances in fMRI technology, as observable as apples, tables, and chairs (Thomson & Piccinini, 2018).

Second, even if we treat unconscious representations as classic unobservables, we still can still have a solid warrant for their existence based on their (presumably explanatorily successful) role in our best cognitive theories and models. For instance, the only way to rationally reconstruct how cognitive neuroscientists proceed as scientists is thus via our old friend abduction (inference to the best explanation). Like every other scientist, cognitive neuroscientists proceed from observing an initially puzzling phenomenon to trying to understand the generative mechanisms that produce that phenomenon and, thus, solve the puzzle.

In the case of unconscious representations, the observed phenomenon is usually some kind of initially puzzling human ability or capacity no one doubts exists—like the ability to parse the phonetic structure of words or the syntactic structure of sentences or engage in fine-grained motor control. Unconscious representations then come in as proper parts of the underlying cognitive mechanisms posited by the neuroscientific theory, whose operations account for the phenomenon in question, thus solving the puzzle of how people “can do that” (Craver, 1998). Cognitive scientists thus posit the existence of unconscious representations as part of this underlying mechanism because they provide the best explanation, thus accounting for the puzzle of people’s ability to exercise the target capacity. Inference to the best explanation thus justifies both the act of the positing and the reality of the (for the sake of argument “unobservable”) unconscious representations featuring in our most explanatory successful models of how people can exercise a given capacity (Boyd, 1983).

To sum up, unconscious representations, like those accounting for your capacity to parse the phonetic structure of every word in this paragraph as you read it, are a completely uncontroversial part of the scientific ontology of contemporary cognitive neuroscience. They feature centrally in almost every mechanistic model of every cognitive capacity and ability we have, providing a solid scientific account of some of the essential functions of the mind. Their status as “unobservable” has been overstated since now we have routine access to them as observable entities and even the processes in which they participate.

References

Bargh, J. A., & Morsella, E. (2008). The Unconscious Mind. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 3(1), 73–79.

Boyd, R. N. (1983). On the Current Status of the Issue of Scientific Realism. In C. G. Hempel, H. Putnam, & W. K. Essler (Eds.), Methodology, Epistemology, and Philosophy of Science: Essays in Honour of Wolfgang Stegmüller on the Occasion of His 60th Birthday, June 3rd, 1983 (pp. 45–90). Springer Netherlands.

Craver, C. F. (1998). Neural Mechanisms: On the Structure, Function, and Development of Theories in Neurobiology [University of Pittsburgh]. https://philpapers.org/rec/CRANMO-2

Reber, A. S. (1993). Implicit Learning and Tacit Knowledge: An Essay on the Cognitive Unconscious. Oxford University Press.

Shea, N., & Frith, C. D. (2016). Dual-process theories and consciousness: the case for “Type Zero” cognition. Neuroscience of Consciousness, 2016(1), niw005.

Thomson, E., & Piccinini, G. (2018). Neural representations observed. Minds and Machines, 28(1), 191–235.

The Promise of Affective Science and the Sociology of Emotions

The sociology of emotions is a curious subfield. On the one hand, the recognition that the study of emotions (and their dynamics) overlap with nearly every single thing sociologists care to study suggests they deserve central casting in the myriad studies that fill journals and monographs (Turner and Stets 2006). On the other hand, the sociology of emotions remains stuck in neutral, waiting for the sort of “renaissance” experienced by cognition when cultural sociology “discovered” schemas (DiMaggio 1997) and dual-process models (Lizardo et al. 2016, Vaisey 2009). This sort of paradox makes some sense, for emotions, or what founding sociologists like Cooley called sentiments, have nearly always been a part of the discipline. Weber’s most important typologies included affectual action and charismatic authority; as early as The Division of Labour, Durkheim had emotions front and center in his theory of deviance and crime; and, the aforementioned Cooley premised his entire social psychology on pride and shame transforming self into a moral thing. But, simultaneously, the study or use of emotions in sociological analysis remained mired in false Cartesian binaries (see Damasio 1994) that propped up misogynistic commitments to dichotomizing cognition (masculine) and affect (feminine), while also being tainted by association with Freudian psychoanalysis.

The 1970s saw these old barriers erode, as social psychologists—especially symbolic interactionists of a variety of flavors—began to mine the emotional veins of self (Shott 1979), roles/identities (Burke and Reitzes 1981), situations (Heise 1977), structure (Kemper 1978), and performance/expectations (Hochschild 1979—for the sake of argument, I put Hochschild here even though she [so far as I know] nor I would really call her a symbolic interactionist). Over the course of the next few decades, the most important theoretical and empirical work explaining how and why solidarity between individuals, as well as between individuals and groups, is produced and maintained centered emotions (Collins 1988, 2004, Lawler 1992, Lawler et al. 2009, Turner 2007). These works drew from Durkheim and picked up threads of Goffman’s (1956, 1967) that “felt” more important than sometimes even Goffman let on, while often like Turner’s evolutionary work on emotions or Collin’s interaction ritual chains, borrowing from nascent brain science. But, beyond these, work in the sociology of emotions remained relatively the same as it had in the earliest innovative days while its contribution beyond the sociology of emotions was held back.

Omar and I (2020) have argued previously that one of the glaring problems is that the sociology of emotions remains rooted in the Cartesian separation of mind and body that haunts social science. Emotions are, generally speaking, treated as mediating variables—e.g., signals that one’s cognitive appraisal of a situation does not match the information received about the situation (Burke and Stets 2009, Robinson 2014)—or dependent variables—e.g., emotions are things to be managed through cognitive or linguistic work (Hochschild 1983). A third option, which also treats emotions as dependent variables, posits that relational patterns like superordinate-subordinate constrain emotions either by structural fiat (Kemper 1978) or via cultural beliefs about what incumbents in these positions should and can do (Ridgeway 2006). What if the next frontier for emotions scholarship considers emotions and affect (the sociocultural labels we learn and the neurophysiological/biological response to stimuli) as independent variables?

Some Important Facts

Studying an intrapersonal force or dynamic is not radical, as cultural sociology has largely accepted the fact that cognitive mechanisms are at the root of a theory of action (Vaisey 2009). Action is caused, at least in some way, shape, or form by cognition without doing violence to the social factors beyond the organism. Affect, however, remains on the sidelines despite several key facts.

Affect, as a motivating force of motor response, is older than cognition (Panskepp 1998). Evolution appears to have worked heavily on the subcortical emotion centers in mammals to encourage both the active pursuit of life-sustaining resources and the avoidance/aversion to painful life-destroying resources. And, given the exceptionally enlarged emotional architecture in our brains (in comparison to our closest cousins, gorillas and chimps), it is plausible to suggest emotions played an outsized role in humans developing and expanding their cultural repertoire for language, kinship, social organization, and so forth. In other words, emotions have been causal, historically speaking.
Undoubtedly, they are causal still today. First, the subcortical areas of the brain play an important role in memory (which is the root of a social self, for instance) (LeDoux 2000). Second, human brain imaging reveals that affect is not resigned to subcortical areas of the brain, but is actually deeply integrated with areas usually reserved for cognition (Davidson 2003). Emotions, then, can control our cognition and behavior, command it in some cases (e.g., a panic attack), and, at the very least coordinate with cognitive functions. Any theory of action that fails to account for affect is dubious is unable to realistically explain social or solitary behavior cognition (Blakemore and Vuilleumier 2017).
Consequently, the vast majority of social psychological processes such as comparison, appraisal, or reflection as well as the vast majority of “causal” explanations sociologists employ like values, interests, or ideology are inextricably tied to affect. If we can no more make a decision about which toothpaste to buy without affect then we should not be surprised that comparing and choosing social objects requires affect as well.
A point Lizardo and I make is that sociologists too often rely on cognitive appraisals of emotions, focusing on self-reports about valence (negative/positive), intensity, mood (longer lasting feelings), and psychologized language like loneliness. However, emotions are visceral, bodily things (Adolphs et al. 2003), and sociologists cannot only borrow from psychological research and methods on emotions.
Emotions may be “social constructs” in so far as a given group of people produce and reproduce labels for different bodily feelings experienced in different situations and which carry different meanings about the (a) appropriateness of those feelings, (b) expectations for their expression or suppression, and (c) “rules” about the duration and intensity of situationally-triggered emotions. However, much of this applies to either highly institutionalized settings, like formal ceremonies (e.g., funerals), where ritual participants approach the “center” of the community and the center must be protected from moral transgression (Shils 1975) or routinized encounters where interaction itself is ritualized (Goffman 1967, Collins 2004). But the need for rules and expectations implies that affect, if left to its own devices, can wreak havoc. Moreover, it ignores the diverse array of solitary actions that consume a significant portion of our daily lives (Cohen 2015), as well as ignores the fact that emotions are often things others “use” as means of affecting others’ feelings, thoughts, and actions (Thoits 1996).

Implications

If my argument that emotion’s scholarship has largely stalled is correct, but emotions are central to individual and social life, what are we to do? Of the myriad directions one could suggest, I will emphasize four that feel most consanguine to sociological inquiry.

The first suggestion picks up on a larger set of questions being raised recently by sociologists of youth and education around the largely abandoned conceptual process of socialization (Guhin et al. 2021). Once a central explanatory framework for understanding how a society “out there” could find its way inside each of us, socialization, like most bits and pieces of functionalism, was tossed out with the icky water. Prematurely, it would seem because it has not been replaced meaningfully, which has subsequently constrained a once-vibrant area of interest: child (and adolescent) development from a sociological perspective. Studying emotions and emotional socialization seems fruitful for so many reasons. For one, the rules and the patterning of emotions-behaviors is really only an adult trait. Childhood and adolescence is a period of unbridled affect, as anyone with a toddler knows well. How do we teach emotion regulation? How is this teaching process distributed across classic demographic and socioeconomic categories? How effective are social forces versus natural brain development for emotion regulation? What about teaching emotion dysregulation? Finally, the most interesting set of questions revolve around social emotions like guilt, shame, pride, and empathy (Decety and Howard 2013). At this point, sociology has ceded these culturally-coded emotions to psychological research, despite the unique methodological tools sociologists possess. For example, studying a high school’s ecosystem and status hierarchy seems an incredibly important pathway to understanding shame and pride, empathy and sympathy. Here, kids are learning, supposedly, the rules of the affectual game. Rather than reduce their experiences to DSM labels like anxiety or depression, why not expand the lens through which we view mundane and spectacular youth experiences?
A second related, implication centers on what I would call emotional styles or biographies. Sociologists are familiar with these sorts of metaphors, as groups have “styles” (Eliasoph and Lichterman 2003) or biographies shaped by a collective memory. These sorts of styles or biographies shape many things like the ways parents and children interface with teachers and the educational system more generally (Lareau 2003). Research has suggested that different personality types appear to correlate with different affectual “styles,” which suggests there is something neurophysiological about doing emotions (Montag et al. 2021). My best guess is that there are social forces that play a role as well, but oddly, mainstream sociologists rarely bother to ask about emotions—likely a reflection of the ingrained Cartesian binary and not negligence on the part of social scientists.
Shifting gears, a third implication builds on the dual processes models approach (Vaisey 2009, Lizardo et al. 2016) and the elephant-rider metaphor. The metaphor itself is designed to explain how implicit cultural knowledge (the elephant) is largely responsible for the direction the rider takes. Deliberate, conscious action is possible but less impactful. But, what guides the elephant? To date, the answer has largely been deeply internalized values or nondeclarative knowledge, but how do we acquire those? How does the brain sort through the variety of potential ideas, scripts, frames, or schema available? And, once internalized, how does the brain choose between different schema or knowledge? Emotions are part of the answer, as affectually tagged memories are most intensely, most readily, and quickly recalled (Catani et al. 2013). But, the rider’s level of effort in directing the elephant is no less shaped by affect. In fact, emotions appear to have a dual process related to deliberate, intentional action as well (Blakemore and Vuilleumier 2017). On the one hand, internal, affectual sensations can become associated with patterned behavior, That is, recognizable affectual sensations signals “action readiness [in order to] prepare and guide the body for action” (p. 300). On the other hand, there are preconscious motivation systems that evolved to seek positive resources and avoid their negative counterparts. A child touches a hot stove and does not need their parents to teach them never to touch that stove again. Whenever they get near a stove they will become more alert and cautious. Of course, these aversions can become pathological (and no less conscious), leading to all sorts of strange phobias and disorders. The point, however, is that emotions are causal in two different ways for the rider, which seems an important addition to the dual-process models perspective, as does the consideration of how affect coordinates, controls, and sometimes commands the so-called automatic cognition that is the elephant.
The final implication speaks directly to the methodological tools we use. For the most part, emotions are measured through self-report (Stets and Carter 2012), which often conflate cognitive appraisals of emotions with emotions and affect. I would point the reader towards highly innovative efforts, like those found in Katz (1999), Collins’ (2004), and Scheff’s (1990) work, respectively. All of these use some form of ultra-micro methods that make employ audio-visual technology, careful observation, and in some cases, linguistic analyses. But, these are simply a starting point, sources of inspired analytic strategy. Ethnographic techniques are easily repurposed to include emotions and affect, as careful observation of bodily display, language, and situational cues are hallmarks of good ethnographic work (Summers-Effler 2009). Even users of quantitative methods should think more carefully about how to ask about emotions, even if that means including basic questions for the sake of explorative social science.

In short, emotions remain central to understanding and explaining how we think and act, but also remain mired in antiquated notions of mind-body, rationality-irrationality, and masculine-feminine. Moreover, old insecurities surrounding the differences between psychological and sociological social psychology—which are simply microcosms of broader insecurities writ large in sociology—have generally prohibited the conceptualization of emotions as independent, causal variables, delimiting the directions the sociology of emotion may go. The next frontier, arguably, is incorporating affective sciences into the study of emotions, and allowing brain science to speak to sociology and vice versa.

References

Abrutyn, Seth and Omar Lizardo. 2020. “Grief, Care, and Play: Theorizing the Affective Roots of the Social Self.” Advances in Group Processes 37:79-108.

Adolphs, Ralph, Daniel Tranel and Antonio R. Damasio. 2003. “Dissociable Neural Systems for Recognizing Emotions.” Brain and Cognition 52:61-69.

Blakemore, Rebekah L. and Patrik Vuilleumier. 2017. “An Emotional Call to Action: Integrating Affective Neuroscience in Models of Motor Control.” Emotion Review 9(4):299-309.

Burke, Peter J. and Donald C. Reitzes. 1981. “The Link between Identities and Role Performance.” Social Psychology Quarterly 44(2):83-92.

Burke, Peter J. and Jan E. Stets. 2009. Identity Theory. New York: Oxford University Press.

Catani, Marco, Flavio Dell’Acqua and Michel Thiebaut De Schotten. 2013. “A Revised Limbic System Model for Memory, Emotion and Behaviour.” Neuroscience & Biobehavioral Reviews 37(8):1724-37.

Cohen, Ira J. 2015. Solitary Action: Acting on Our Own in Everyday Life. Oxford: Oxford University Press.

Collins, Randall. 1988. “The Micro Contribution to Macro Sociology.” Sociological Theory 6(2):242-53.

—. 2004. Interaction Ritual Chains. Princeton: Princeton University Press.

Damasio, Antonio. 1994. Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Avon Books.

Davidson, Richard J. 2003. “Seven Sins in the Study of Emotion: Correctives from Affective Neuroscience.” Brain and Cognition 52:129-32.

Decety, Jean and Lauren H. Howard. 2013. “The Role of Affect in the Neurodevelopment of Morality.” Child Development Perspectives 7(1):49-54.

DiMaggio, Paul. 1997. “Culture and Cognition.” Annual Review of Sociology 23:268-87.

Eliasoph, Nina and Paul Lichterman. 2003. “Culture in Interaction.” American Journal of Sociology 108(4):735-94.

Goffman, Erving. 1956. “Embarrassment and Social Organization.” American Journal of Sociology 22(3):264-71.

—. 1967. Interaction Ritual: Essays on Face-to-Face Behavior. New York: Pantheon Books.

Guhin, Jeff, Jessica McCrory Calacro and Cynthia Miller-Idriss. 2021. “Whatever Happened to Socialization?”. Annual Review of Sociology 47:109-29.

Heise, David. 1977. “Social Action as the Control of Affect.” Behavioral Sciences 22(3):163-77.

Hochschild, Arlie. 1979. “Emotion Work, Feeling Rules, and Social Structure.” American Journal of Sociology 85(3):551-72.

—. 1983. The Managed Heart: Commercialization of Human Feeling. Berkeley: University of California Press.

Katz, Jack. 1999. How Emotions Work. Chicago: University of Chicago.

Kemper, Theodore. 1978. A Social Interactional Theory of Emotions. New York: John Wiley and Sons.

Lareau, Annette. 2003. Unequal Childhoods: Class, Race, and Family Life. Berkeley: University of California Press.

Lawler, Edward J. 1992. “Affective Attachments to Nested Groups: Choice-Process Theory.” American Sociological Review 57(3):327-39.

Lawler, Edward J., Shane Thye and Jeongkoo Yoon. 2009. Social Commitments in a Depersonalized World. New York: Russell Sage.

LeDoux, Joseph. 2000. “Cognitive-Emotional Interactions: Listening to the Brain.” Pp. 129-55 in Cognitive Neuroscience of Emotion, edited by R. D. Lane and L. Nadel. New York: Oxford University Press.

Lizardo, Omar, Robert Mowry, Brandon Sepulvado, Dustin S. Stoltz, Marshall A. Taylor, Justin Van Ness and Michael Wood. 2016. “What Are Dual Process Models? Implications for Cultural Analysis in Sociology.” Sociological Theory 34(4):287-310.

Montag, Christian, Jon D. Elhai and Kenneth L. Davis. 2021. “A Comprehensive Review of Studies Using the Affective Neuroscience Personality Scales in the Psychological and Psychiatric Sciences.” Neuroscience & Biobehavioral Reviews 125:160-67.

Panskepp, Jaak. 1998. Affective Neuroscience: The Foundations of Human and Animal Emotions. Oxford: Oxford University Press.

Ridgeway, Cecilia L. 2006. “Expectation States Theory and Emotion.” Pp. 374-67 in Handbook of the Sociology of Emotions, edited by J. E. Stets and J. H. Turner. New York: Springer.

Robinson, Dawn, T. 2014. “The Role of Cultural Meanings and Situated Interaction in Shaping Emotion.” Emotion Review 8(3):189-95.

Scheff, Thomas. 1990. Microsociology: Discourse, Emotion and Social Structure. Chicago: The University of Chicago Press.

Shils, Edward. 1975. “Ritual and Crisis.” Pp. 153-63 in Center and Periphery: Essays in Macrosociology, edited by E. Shils. Chicago: University of Chicago Press.

Shott, Susan. 1979. “Emotion and Social Life: A Symbolic Interactionist Analysis.” American Journal of Sociology 84(6):1317-34.

Stets, Jan E. and Michael J. Carter. 2012. “A Theory of the Self for the Sociology of Morality.” American Sociological Review 77(1):120-40.

Summers-Effler, Erika. 2009. Laughing Saints and Righteous Heroes. Chicago: University of Chicago Press.

Thoits, Peggy A. 1996. “Managing the Emotions of Others.” Symbolic Interaction 19(2):85-109.

Turner, Jonathan H. 2007. Human Emotions: A Sociological Theory. New York: Routledge.

Turner, Jonathan H. and Jan E. Stets, eds. 2006. Handbook of the Sociology of Emotions. New York: Springer.

Vaisey, Stephen. 2009. “Motivation and Justification: A Dual Process Model of Culture in Action.” American Journal of Sociology 114(+):1675-715.

Sociology’s Motivation Problem (Part II)

In a previous post, we outlined the three critical mistakes sociologists make in theorizing about motivation. We referred to them as the mono-motivational, social-psychological, and list-making fallacies. In this post, we briefly summarize each fallacy. We follow with a more extended discussion on how recent interdisciplinary work in social, cognitive, affective, and motivational neuroscience can provide new analytic tools to move the sociological theory of motivation forward while preventing falling into theoretical cul-de-sac previous work fell into.

Mono-Motivations

The first refers to the sociological penchant to attribute a single “master” motivation to people. Sociologists and social psychologists naturally prefer that this master motivation is of the social kind and that people are primarily driven by social motivations. These range from the usual functionalist penchant to say that people are motivated to conform to the norms imposed by the society that Wrong (1963) castigated, to the Mills-inspired approach that both denies “motivations” exist as motor-springs of action, while simultaneously assuming people are motivated to produce “accounts” of their actions conforming to cultural expectations. Another version of the mono-motivational story links up to the social psychology of “need states.” In this approach, people have an “uber” motivation to “belong” to groups, form strong social ties, and the like (Baumeister and Leary 1995). A special case is “dual” motivation stories in which two “uber” motivations, one social and one anti-social, or one social and one “instrumental,” fight out for supremacy in an endless Manichean struggle (Durkheim 2005; Freud 1989; Kadushin 2002).

Drive-Reduction

The second fallacy is a more general version of this last point. This idea—central to most social and personality psychology work since the early 20th century—argues motivation can be understood as a process by which unmet needs or drives generate an unpleasant state, which people are then motivated to “reduce” or “eliminate.” This general “drive-reduction” model was first developed in behaviorist animal psychology but then generalized to the study of human motivation with the development of “control models” of human behavior after the 1950s (Carver and Scheier 1998; Heise 1977; Powers 1973). The control model imagery provides the ideal formal specification of the drive-reduction model. In this imagery, people can be thought of as “human thermostats.” A “drive” or an unmet “need” (e.g., being lonely) is a deviation from the setpoint (e.g., belongingness). Finally, human motivation is geared toward re-establishing the previous balance (finding some company)–e.g., modern affect and identity control models in social psychology (Burke and Stets 2009; Smith-Lovin and Heise 1988) are built on these foundations. Note that social-psychological control models are also mono-motivational models. They postulate a single abstract motivation (e.g., reduction of “deflection” or identity verification). Most research shows how their motivation (and method of appraising its veracity) is primary to other research programs’ motivation (Burke and Stets 1999). The social psychology behind structuration theory, ethnomethodology, and some versions of social construction, in which people are motivated to re-establish ontological security, facticity, cognitive order, and the like when threatened, also rely on the same underlying imagery (Fararo 2001).

List-Making

Finally, we noted that multi-motivational (list) models move beyond some drawbacks of mono-motivational and drive-reduction models. The most sophisticated one, developed in sociology by Jonathan Turner (2010), poses the interplay of a multiplicity of motivations operating in every face-to-face encounter. Motivations range from the cognitive to the affective to the instrumental. However, while the multidimensional aspect of Turner’s approach is appreciated, it does inherit some weaknesses of the drive-reduction and control models that it draws upon. One problem is that the “list” of motivations, regardless of how “fundamental” the analyst thinks these are, comes from pre-existing theory, which means it is unlikely that those lists will exhaustively cover all the sources of motivated action. The lists are inherently limited and occlude both the particularity of motivation, the open-ended nature of the objects of motivation, and the situated nature of most motivated action. The other problem with Turner’s model, shared by most social-psychological models, is the assumption that people are motivated to contain or reduce abstract need states. Under this imagery, both the dynamics of motivation and the end states (usually psychological) that people pursue in motivated action are internal. The actual object people are motivated to seek drops out of the picture altogether.

Overall, we think that the search for “fundamental” motivations, whether of the omnipotent or additive variety, is a red-herring. People are motivated by many things, and it is unlikely that this will fall into analytically neat “fundamental” types. Moreover, what is fundamental for one person, can be peripheral for another because “fundamentality” is determined by a history of learning and accumulating rewarding and non-rewarding experiences with specific objects (and by the psychological and biological potential to constitute them as rewards). Another limitation of conventional approaches is that most motivation is reactive rather than proactive. People are not motivated to act until their needs for facticity are threatened, or their identities fail to be verified, or they end up getting the short end of the deal in exchange. In a strange sense, sociologists have elevated the avoidance or, more typically, removing pain at the expense of the pursuit or enjoyment of pleasure. By relying on removing or avoiding pain and focusing on externalities only, the sociology of motivation fails the fundamental question of why one person pursues one thing and another person other things, even when faced with similar environmental prompts (Kringelbach and Berridge 2016). In short, what is missing from the social psychology of motivation is both a way to theorize the specific pursuit of particular objects, activities and events and an account of motivated action that puts motivation first — that is, in which motivated action emerges pro-actively rather than re-actively.

Moving Beyond the Fallacies

Beyond Mono-Motivations

Moving beyond mono-motivations is the easiest. “Typing” motivations at an abstract level does not get us very far in this endeavor (Martin and Lembo 2020), so the best fix is just to acknowledge both the diversity and the specificity of motivators, so we don’t fall into the penchant to say that people are motivated to pursue psychological abstractions (like “ontological security” or “belongingness”), let alone a single one of these. Put differently, people are motivated to pursue a multiplicity of objects and lines of action, and the candidate “motivators” are massively diverse. Some are social, some are pro-social, some are anti-social, some are egoistic, others altruistic, and, yes, some are psychological. A good rule of thumb is that if you cannot tell us what people are motivated by — where “what” has to be a concrete object, event, or experience (e.g., that I get tenure) — then you need to move down the “ladder of abstraction” and tell us precisely what you think people are pursuing (Sartori 1984).

The same goes for the crypto-mono-motivational approach inspired by Mills, where people are master-motivated to produce “accounts” of their actions. Sometimes people may be motivated to do this; other times, they are not. The essential analytic point is that we need to separate motive or motivation talk from motivation proper. To foreshadow, motivation has everything to do with objects and rewards and nothing to do with justifications. This is not difficult to imagine. In quantitative research, in particular, but also retrospective and historical qualitative research, motive talk may be the only data available. Understanding the normative frames or motivation schemata actors use to interpret their behavior remains a relevant and essential subject of study (Franzese 2013; Hewitt 2013). Nevertheless, we should not assume post hoc accounts are causal or even verge on tapping into causally relevant factors.

Beyond Drive-Reduction

However, we have seen that you can conquer the mono-motivational monster while remaining trapped by the constraint of the dominant model of motivation in social psychology — the drive to reduce discomfort, pain, and the like. For instance, if I think meaning maintenance is such a need, when people experience hard to interpret events (e.g., a mother killing her child), then I can posit that they are motivated to reduce the uncomfortable state of deflection this event has produced. There is no question that some motivational processes are of this (reactive) sort. However, taking this as the paradigm for motivation is an analytic mistake. Most motivated action is the proactive pursuit of specific objects, events, persons, or states of affairs; it is, by definition, intentional, guided, and controlled (Miller Tate 2019). The initiation of motivated action need-not (and usually is not) preceded by a “need” state. Instead, it is preceded by an event that activates a memory of the desired object. Later, by a plan (which could also be stored in long-term memory as a habit if repeatedly rehearsed before) that provides a flexible behavioral template for the person to pursue it.

But what makes objects desired or desirable? This is a question for which contemporary motivational neuroscience’s answer is deceptively simple, but, we think, extraordinarily generative. Objects become the object of motivation when they are constituted as rewards (Schroeder, 2004). Objects are constituted as rewards when, after seeking them out, they lead to satisfying (e.g., pleasurable) experiences in a given context. This is followed by a learning process (reinforcement) in which we bind the experienced qualities of the object to the pleasurable experience while also storing for future use the extent to which the positive experience matches, exceeds, or falls short of the pleasure we predicted we were going to get (where “prediction” can be both implicit or explicit). In this way, objects go, via repeated travels through this cycle, from being “neutral” (non-motivating) to being capable of triggering motivated action (we start “wanting” the object spontaneously or without much effort).

An object with the capacity to lead to motivated action following positive consummatory experiences is thus constituted (construed, categorized) as a reward in future encounters so that the object begins to function as a salient incentive. We can then speak of the object as being represented (by that person) as a reward, with reward-representations leading to motivated action once they are activated (either by the environment or by the person) on future occasions (Schroeder 2004; Winkielman and Berridge 2003).

The basic lesson here is that only objects constituted as rewards have the causal power to energize action. Abstract “need-states,” uncomfortable drives, experiences of “deflection,” or “lack of meaning,” “ennui,” “ontological (in)security,” or “loneliness,” are not objects. Therefore, they cannot be constituted as rewards. By implication, they cannot count as causes energizing people to act. However, an apple, a glass of water, a beer, hanging out with your best friend, molly, reaching the solution to a challenging crossword puzzle, publishing a paper, getting released from prison, earning praise from your advisor, cocaine, getting a bunch of Twitter likes, and a zillion others (the list is open-ended) are objects (e.g., they are either things or events). Therefore, they can all be constituted—under the right circumstances, by particular people—as rewards. By implication, they count as causes that energize people to act.

If you are still mourning the death of homeostatic or drive reduction theories of motivation, think of the last time you stuffed yourself with chocolate lava cake after a hearty dinner. You sure weren’t hungry any longer (thus, there was no “drive” to “reduce”). You probably were beyond your set point for satiation (so the “error” was probably going in the wrong direction. However, you still ate the cake because you either had looked forward to dessert from the start, the cake itself looked delicious; or, even more likely, you might have eaten the cake although the “hedonic impact” (the pleasure experience) was actually much more muted than you thought. All rewards (psychological, social, and the like) work like that (Berridge 2004, 2018).

Beyond Fundamental Motives

From the perspective of modern motivational science, we can think of standard fundamental motivation theories as incompletely articulated models of motivation. Thus, people are not motivated to attain abstract states (e.g., trust or predictability) qua external states. The hidden scope condition here is that as long as trust and predictability lead to psychologically rewarding objects, people will be motivated to try to organize their external environment such that those states obtain. Making explicit this scope condition also shows the futility of delving for “universal” motives of this kind. Thus, it is fair to suggest people will be motivated to try to live in trusting and predictable worlds, but there is nothing necessary about this; if trust and predictability fail to be psychologically rewarding, then people will not be motivated to pursue these external conditions.

For instance, people high on the personality trait usually labeled “openness to experience,” find (moderately) unpredictable environments psychologically rewarding and overly predictable environments non-rewarding. As such, these people will be driven to pursue lines of action that do not conform to the idea of “ontological security” as a general motivator. Jumping from planes, hanging out with grizzly bears, or diving around lethal ocean life, none of which are conducive to ontological (or physical) security, can be constituted as rewards by some people. In that case, people will be motivated to seek out these lines of action. The analytic mistake here is to think of the (usually) rewarding line of action as the “motivation,” when in fact it is the (contingent, not necessary) link between the external state and the internal reward (the real motivator) that makes the former a condition to be striven for.

In the same way, it is essential to not assume that just because something “sounds good,” from the armchair, that it will be a universal motivator. Take, for instance, the oft-discussed case of “belongingness.” It might seem redundant and unnecessary to specify that social ties or group belonging can be constituted as psychological rewards (Baumeister and Leary 1995; Kadushin 2002). But if the full extent of human variation is considered, it is easy to see that they may not be. For instance, recent work in the neuropsychology of autism and the autism-spectrum shows (Carré et al. 2015; Supekar et al. 2018) there is a portion (how large remains unclear) of the population for whom interpersonal relations are either less rewarding or non-rewarding (compared to tangible rewards (Gale, Eikeseth, and Klintwall 2019)), and a smaller proportion for whom they might actually be aversive (so it is the avoidance or cessation of belonging or connection that actually counts as a reward). Interpersonal relationships are generally rewarding for “neurotypical” people because a (developmental, genetic, epigenetic) mechanism has made them so. If this mechanism is either disrupted by, for instance, brain injury or the onset of mental illness (or is non-existent from early on during development as with autistic individuals), then belongingness ceases to be a “fundamental” motivation.

Throwing Out the Lists

In this last respect, many of the criticisms of fundamental motivations apply to the list-makers. Because of the contingent link between external state (e.g., trust, security, belonging) and reward, it is unlikely that any of the other so-called “fundamental motivations,” that have been proposed in psychology and sociology (e.g., need for “power,” “influence,” “status,” “altruism,” “trust,” and the like) by people who like to write down “lists” of motives are fundamental. This is especially the case for “fundamental” motives theorized as “needs for” some concrete state of affairs. Thus, all of these candidate motives will fail Baumeister and Leary’s criterion of being “universal in the sense of applying to all people” (p. 498). Instead, most of the motives appearing in these sorts of lists and proposals can be best thought of as states, processes, and external conditions commonly (in the probabilistic sense) linked to objects typically constituted as rewards and thus likely to be pursued by most (but not all) people. Diversity, both in terms of “neurodiversity” and diversity of experience and learning history, and institutional location and historical context is the rule rather than the exception.

Turner’s (2010) list inherits this weakness. Still, it stands out because it does not seek an exhaustive list of drives we have—mostly because he accepts the underlying homeostatic control model seeing a finite number of needs being salient in micro-interaction and because he does not prioritize the items on the list. On the one hand, this is commendable. It adds flexibility to the social scientist: we could add more things to the list as identity verification, trust, facticity, reciprocal fairness, and belongingness are not the only things that might matter. Furthermore, this flexibility does not negate the utility of his list because he does locate the motivational forces, even if he does not specify their neurobiological foundations, inside our heads and bodies. On the other hand, because Turner’s list seeks to contextualize psychological needs within a larger constellation of nested social spaces, it cannot explain a wide array of behaviors that fall outside the interaction or encounter unit in which his microsociology situates itself. Drug or food addiction goes unexplained, as do situations between two or more people who are not motivated by, say, trust, but get along just fine, and so does the ability to make sense of why some scientists pursue celebrity status at all costs while others operate within the rules of their professional field.

From Fundamental “Motivations” to Fundamental Motivational Processes

Ultimately, lists or not, drive-states or not, the fundamental weakness in sociological theories of motivation is the omission of reward and, importantly, the neurophysiological connections between reward/object/schema work. This is perhaps the most controversial thing we can posit to sociologists, given their aversion to intrapersonal dynamics and to any hint at reductionism. But, despite our best efforts to resist over-psychologization and over-economization, sociology’s candidates for motivation continue to psychologize and economize (and, worse, oversocialize), but with very little connection to empirical research on the mechanics of motivation or reflective thought on what, why, and how people are actually compelled to do things. Rewards, then, are central to the explanatory story (Kringelbach and Berridge 2016). Controversial as it may be, it is the best path forward for exercising sociology of the (explicit and implicit) vestiges of a long-standing and venerable tradition, in which analysts sit at their desks trying to come up with the one, or for more modest cogitators, the definitive top list of, motivation and motivations, respectively. Incorporating control-theoretic versions of early twentieth-century homeostatic models or philosophical speculation about “ontological security” did not help matters in this particular regard.

Luckily for us, contemporary work in affective, cognitive, and motivational neuroscience (and increasingly the overlap of these fields with social neuroscience and social and personality psychology) suggests a fundamental theoretical reorientation in the way we think of motivation in broader social and human sciences. Thus, instead of “fundamental motivations,” we propose that the focus should move to the study of fundamental motivational processes, with the understanding that there is a massive (perhaps non-enumerable) set of objects that could count as “motivators.”

What are these processes? In the earlier discussion, we have made reference to a few of them. Note, for instance, that in the cycle leading objects to be constituted as rewards, there is a seeking phase where we engage in (flexible—either habitual or intentional) motivated activity to attain the object and a consummatory phase—where we enjoy the object. There is also a post-consummation (or satiatory) phase, where we store linkages between the pleasure experienced (if any) to update the “reward status” of the object and where we compare what we thought we were going to get to what we got. Using folk psychological labels for these phases of motivation, we can say that the fundamental motivational processes leading objects to be constituted as rewards are wanting (seeking), liking, and learning. Thus, pleasure is an aspect or “phase” (to use Dewey’s locution) of motivated action, not the whole of it.

In short, it is this cycle (and, as we will see in a follow-up post, each phase’s neurobiological dissociability), our ability to anticipate — right or wrongly — rewarding experiences with an object (or set of similarly classed objects), and the actual reward itself that constitutes a theory of motivation or motivational processes. Any object can come to intentionally guide and control our motor impulses or become a source of habitually motivated activity. In a follow-up post, we will discuss these fundamental motivational processes, how they are linked together—and most importantly, how they come apart—and the more significant implications the reward-focused approach has for the study of motivated action in institutional settings.

References

Baumeister, R. F., and M. R. Leary. 1995. “The Need to Belong: Desire for Interpersonal Attachments as a Fundamental Human Motivation.” Psychological Bulletin 117(3):497–529.

Berridge, Kent C. 2004. “Motivation Concepts in Behavioral Neuroscience.” Physiology & Behavior 81(2):179–209.

Berridge, Kent C. 2018. “Evolving Concepts of Emotion and Motivation.” Frontiers in Psychology 9:1647.

Burke, Peter J., and Jan E. Stets. 1999. “Trust and Commitment through Self-Verification.” Social Psychology Quarterly 62(4):347–66.

Burke, Peter J., and Jan E. Stets. 2009. Identity Theory. Oxford: Oxford University Press.

Carré, Arnaud, Coralie Chevallier, Laurence Robel, Caroline Barry, Anne-Solène Maria, Lydia Pouga, Anne Philippe, François Pinabel, and Sylvie Berthoz. 2015. “Tracking Social Motivation Systems Deficits: The Affective Neuroscience View of Autism.” Journal of Autism and Developmental Disorders 45(10):3351–63.

Carver, Charles S., and Michael F. Scheier. 1998. On the Self-Regulation of Behavior. Cambridge University Press.

Durkheim, Emile. 2005. “The Dualism of Human Nature and Its Social Conditions.” Durkheimian Studies 11(1). doi: 10.3167/175223005783472211.

Fararo, Thomas J. 2001. Social Action Systems: Foundation and Synthesis in Sociological Theory. Greenwood Publishing Group.

Franzese, Alexis T. 2013. “Motivation, Motives, and Individual Agency.” Pp. 281–318 in Handbook of Social Psychology, edited by J. DeLamater and A. Ward. Dordrecht: Springer Netherlands.

Freud, Sigmund. 1989. The Ego and the Id. WW Norton & Company.

Gale, Catherine M., Svein Eikeseth, and Lars Klintwall. 2019. “Children with Autism Show Atypical Preference for Non-Social Stimuli.” Scientific Reports 9(1):10355.

Heise, David R. 1977. “Social Action as the Control of Affect.” Systems Research: The Official Journal of the International Federation for Systems Research 22(3):163–77.

Hewitt, John P. 2013. “Dramaturgy and Motivation: Motive Talk, Accounts, and Disclaimers.” Pp. 109–36 in The Drama of Social Life: A Dramaturgical Handbook, edited by C. Edgley. New York: Routledge.

Kadushin, Charles. 2002. “The Motivational Foundation of Social Networks.” Social Networks 24(1):77–91.

Kringelbach, Morten L., and Kent C. Berridge. 2016. “Neuroscience of Reward, Motivation, and Drive.” Pp. 23–35 in Recent Developments in Neuroscience Research on Human Motivation. Vol. 19, Advances in Motivation and Achievement. Emerald Group Publishing Limited.

Martin, John Levi, and Alessandra Lembo. 2020. “On the Other Side of Values.” The American Journal of Sociology 126(1):52–98.

Miller Tate, Alex James. 2019. “A Predictive Processing Theory of Motivation.” Synthese. doi: 10.1007/s11229-019-02354-y.

Powers, William Treval. 1973. Behavior: The Control of Perception. Aldine Publishing Company.

Sartori, Giovanni. 1984. “Guidelines for Concept Analysis.” Pp. 15–85 in Social Science Concepts: A Systematic Analysis, edited by G. Sartori. Beverly Hills, CA: Sage.

Schroeder, Timothy. 2004. Three Faces of Desire. Oxford University Press.

Smith-Lovin, Lynn, and David R. Heise. 1988. Analyzing Social Interaction: Advances in Affect Control Theory. Taylor & Francis.

Supekar, Kaustubh, John Kochalka, Marie Schaer, Holly Wakeman, Shaozheng Qin, Aarthi Padmanabhan, and Vinod Menon. 2018. “Deficits in Mesolimbic Reward Pathway Underlie Social Interaction Impairments in Children with Autism.” Brain: A Journal of Neurology 141(9):2795–2805.

Turner, Jonathan H. 2010. “Motivational Dynamics in Encounters.” P. ` in Theoretical Principles of Sociology, Volume 2: Microdynamics, edited by J. H. Turner. New York, NY: Springer New York.

Winkielman, Piotr, and Kent Berridge. 2003. “Irrational Wanting and Subrational Liking: How Rudimentary Motivational and Affective Processes Shape Preferences and Choices.” Political Psychology 24(4):657–80.

Wrong, Dennis H. 1963. “Human Nature and the Perspective of Sociology.” Social Research 30(3):300–318.

Cognition and Cultural Kinds

What the proper relationship should be between “culture” and “cognition” has been a fundamental issue ever since the emergence of psychology as a hybrid science in the middle of the nineteenth century (Cole, 1996). This question became even more pressing with the consolidation of anthropology and sociology as standalone socio-cultural sciences in the late nineteenth century (Ignatow, 2012; Turner, 2007). Initially, the terms of the debate were set when Wundtian psychology, having lost its “cultural” wing, became established in the English speaking world (and the U.S. in particular) as a quasi-experimental science centered on individual mental processes, thus ceding the unruly realm of the cultural to whoever dared take it (something that a reluctant anthropology, with a big push from functionalist sociology, ultimately did, but not until the middle of the twentieth century, only to drop it again at the end of Millenium (Kuper, 2009) just as it was being picked up again by an enthusiastic sociology). The changing fates of distinct meta-methodological traditions in psychology through the twentieth century (e.g., introspectionist, to behaviorism, to information processing, to neural computation) has done little to alter this, despite sporadic calls to revitalize the ecological, cultural, or “socio-cultural” wing of psychology in the intervening years (Bruner, 1990; Cole, 1996; Neisser, 1967)

In anthropology and sociology, the early mid-twentieth century saw the development of a variety of approaches, from Sapir and Boas-inspired Psychological Anthropology to Parsons’s functionalist sociology, that attempted to integrate the psychological with the socio-cultural (usually under the auspices of a psychoanalytic conceptualization of the former domain). As noted previously, by the 1960s and 1970s, psychological integration movements had lost steam in both disciplines, with perspectives conceiving of culture in mainly anti-psychological (or non-psychological) terms taking center stage. Meanwhile, psychology continued its march toward the full naturalization of mental phenomena, first under the banner of the computer metaphor of first-generation cognitive science (and the associated conception of cognition as computation over symbolic mental representations), and today under the idea of full or partial integration with the sciences of the brain yielding the interfield of cognitive neuroscience (united by the hybrid ideas of cognition as neural computation over biologically realized representations in the brain (Churchland & Sejnowski, 1990)).

Cognition in Anthropology and Sociology

The Emergence of Cognitive Anthropology

But the domain of the psychological was never completely eradicated from the socio-cultural sciences. Instead, anthropology and sociology developed small islands dedicated to the link between psychology (now indexed by the idea of “cognition”) and culture. This happened first in anthropology via the development, by Ward Goodenough and a subsequent generation of students and collaborators (Goodenough, 2003), of a “cognitive anthropology,” that took language as the main model of what culture was (inspired by American structuralist linguistics), centered on the ethnosemantics of folk categories, and was aided by the method componential analysis (decomposition into semantic features differentiating terms from one another) of linguistic terms belonging to specific practical domains. This methodological approach was later followed by the “consensus analysis” of Romney Kimball and associates (D’Andrade, 1995).

Today, the primary representative of a cognitive approach in anthropology is the “cultural models” school developed in the work of Dorothy Holland, Naomi Quinn, Claudia Strauss, and Bradd Shore. This approach emerged during the 1980s and 1990s via the incorporation of a (rediscovered from Jean Piaget and Frederic Bartlett) notion of “schemata” in artificial intelligence and first-generation cognitive science (which developed the related notions of “script”), and the importation of the idea of “cognitive models” from the then emerging cognitive movement in linguistics (Holland, 1987), as represented primarily in the work of George Lakoff (1987). This conception of schemata and cultural models was later supplemented by the incorporation of new understandings of how agents come to internalize culture as a set of distributed, multimodal, sub-symbolic, context-sensitive, but always meaningful representations constitutive of personal culture (Strauss & Quinn, 1997), inspired by connectionist models of cognition developed by the cognitive scientist David Rumelhart and associates in the 1980s (McClelland et al., 1986).

A critical insight in this regard developed, somewhat independently, by the anthropologists Maurice Bloch (1991) and Strauss and Quinn (1997), is that the core theoretical takeaway of Pierre Bourdieu’s reflections in Outline of a Theory of Practice is that the practice-based model of cultural internalization and deployment developed therein was mostly consistent with this emerging “connectionist” understanding of how cultural schemata where implemented in the brain as primarily non-linguistic, multimodal, distributed representations in a connectionist architecture, operating as tacit knowledge, and equally internalized via experienced-based, mostly implicit processes.

The Emergence of the “New” Cognitive Sociology

Renewed engagements with cognition in sociology, occurring later than in anthropology, have been the beneficiary of all of these interdisciplinary developments. After the ethnomethodological false start of the 1970s (Cicourel, 1974), cognitive sociology went into hibernation until it was jump-started in the 1990s by scholars such as, inter alia, Eviatar Zerubavel (1999), Karen Cerulo (1998), and Paul DiMaggio (1997).

DiMaggio’s highly cited review paper was particularly pivotal. In that paper, DiMaggio made three points that “stuck” and heralded the current era of “cultural cognitive sociology”:

The first one, now hardly disputed by anyone, is that sociologists interested in how culture works and how it affects action cannot afford to ignore cognition. The reason DiMaggio pointed to was logical: Claims about culture entail claims about cognition. As such, “[s]ociologists who write about the ways that culture enters into everyday life necessarily make assumptions about cognitive processes,” (italics mine) that therefore it is always better if they got more transparent and more explicit on what those cognitive presuppositions are (1997: 266ff).
The second point is that while these underlying cognitive presuppositions are seldom directly scrutinized by sociologists (they are “meta-theoretical” to sociologists’ higher level substantive concerns), they “are keenly empirical from the standpoint of cognitive psychology” (1997: 266). This means that rather than being seen as part of the (non-empirical) presuppositional background of cultural theory (Alexander, 1982), they are capable of adjudication and evaluation by setting them against what the best empirical research in cognitive psychology has to say. The underlying message is that we can compare a given pair of cultural theories and see which one seems to be more consistent with the evidence in cognitive science to decide which one to go with (as DiMaggio himself did in the paper for “latent variable” and toolkit theories of how culture works). Thus, cognitive psychology could play a regulatory and largely salutary work in cultural theorizing, helping to adjudicate otherwise impossible to settle debates (Vaisey, 2009, 2019; Vaisey & Frye, 2017).
Finally, DiMaggio argued that the cognitive theory developed by the school of cultural models in cognitive anthropology, and the centerpiece notion of “schema” was the best way for sociologists to think about how the culture people internalize is mentally organized (1997: 269ff). Additionally, DiMaggio noted, in line with the then consolidating “dual process” perspective in cognitive and social psychology (Smith & DeCoster, 2000), that internalized schemata can come to affect action in two ideal-typical ways, one automatic and efficient, and the other deliberate, explicit, and effortful. Thus, in one fell swoop, DiMaggio set the research agenda in the field for the next twenty years (and to this day). In particular, the isolation of schemas as a central concept linking the concerns of cognitive science and sociology, and of dual-process models of cultural use as being a skeleton key to a lot of the “culture in action” problems that had accreted in sociology throughout the post-Parsonian era, proved profoundly prescient leading to an efflorescence of empirical, measurement, and theoretical work on both schemas and dual-process cognition in cultural sociology(e.g., Boutyline & Soter, 2020; Cerulo, 2018; Frye, 2017; Goldberg, 2011; Hunzaker & Valentino, 2019; Leschziner, 2019; Leschziner & Green, 2013; Lizardo et al., 2016; Miles, 2015, 2018; Taylor et al., 2019; Vaisey, 2009; Wood et al., 2018).

In all, interest in the link between culture and cognition and the role and import of cognitive processes and mechanisms for core questions in sociology has only grown in the last two decades in sociology, with a critical mass of scholars now identifying themselves as doing active research on cognition and cognitive processes. As the cultural sociologist Matthew Norton (2020, p. 46) has recently noted, in sociology, “the encounter with cognitive science has ushered in something of a cognitive turn, or at least a robust cognitive option, for cultural sociological theory and analysis.” The resurgence of the cognitive in sociology means that the question of the relationship between culture and cognitive acquires renewed urgency.

References

Alexander, J. (1982). Theoretical Logic in Sociology: Positivism, Presupposition and Current Controversies (Vol. 1). University of California Press.

Bloch, M. (1991). Language, Anthropology and Cognitive Science. Man, 26(2), 183–198.

Boutyline, A., & Soter, L. (2020). Cultural Schemas: What They Are, How to Find Them, and What to Do Once You’ve Caught One. https://doi.org/10.31235/osf.io/ksf3v

Bruner, J. S. (1990). Acts of Meaning. Harvard University Press.

Cerulo, K. A. (1998). Deciphering Violence: The Cognitive Structure of Right and Wrong. Psychology Press.

Cerulo, K. A. (2018). Scents and Sensibility: Olfaction, Sense-Making, and Meaning Attribution. American Sociological Review, 83(2), 361–389.

Churchland, P. S., & Sejnowski, T. J. (1990). Neural Representation and Neural Computation. Philosophical Perspectives. A Supplement to Nous, 4, 343–382.

Cicourel, A. V. (1974). Cognitive sociology: Language and meaning in social interaction. Free Press.

Cole, M. (1996). Cultural psychology: A once and future discipline. Harvard University Press.

D’Andrade, R. G. (1995). The Development of Cognitive Anthropology. Cambridge University Press.

DiMaggio, P. (1997). Culture and Cognition. Annual Review of Sociology, 23, 263–287.

Frye, M. (2017). Cultural Meanings and the Aggregation of Actions: The Case of Sex and Schooling in Malawi. American Sociological Review, 82(5), 945–976.

Goldberg, A. (2011). Mapping Shared Understandings Using Relational Class Analysis: The Case of the Cultural Omnivore Reexamined. The American Journal of Sociology, 116(5), 1397–1436.

Goodenough, W. H. (2003). In Pursuit of Culture. Annual Review of Anthropology, 32(1), 1–12.

Holland, D. (1987). Cultural Models in Language and Thought. Cambridge University Press.

Hunzaker, M. B. F., & Valentino, L. (2019). Mapping Cultural Schemas: From Theory to Method. American Sociological Review, 84(5), 950–981.

Ignatow, G. (2012). Mauss’s lectures to psychologists: A case for holistic sociology. Journal of Classical Sociology. http://jcs.sagepub.com/content/12/1/3.short

Kuper, A. (2009). Culture: The Anthropologists’ Account. Harvard University Press.

Lakoff, G. (1987). Women, Fire and Dangerous Things: What Concepts Reveal about the Mind. Chicago University Press.

Leschziner, V. (2019). The Specter of Schemas: Uncovering the Meanings and Uses of Schemas in Sociology. Unpublished Manuscript.

Leschziner, V., & Green, A. I. (2013). Thinking about Food and Sex: Deliberate Cognition in the Routine Practices of a Field. Sociological Theory, 31(2), 116–144.

Lizardo, O., Mowry, R., Sepulvado, B., Stoltz, D. S., Taylor, M. A., Van Ness, J., & Wood, M. (2016). What are dual process models? Implications for cultural analysis in sociology. Sociological Theory, 34(4), 287–310.

McClelland, J. L., Rumelhart, D. E., Group, P. R., & Others. (1986). Parallel distributed processing. Explorations in the Microstructure of Cognition, 2, 216–271.

Miles, A. (2015). The (Re)genesis of Values: Examining the Importance of Values for Action. American Sociological Review, 80(4), 680–704.

Miles, A. (2018). An Assessment of Methods for Measuring Automatic Cognition. In W Brekhus And (Ed.), Oxford Handbook of Cognitive Sociology, e (p. forthcoming). Oxford University Press.

Neisser, U. (1967). Cognitive psychology. Appleton-Century-Crofts.

Norton, M. (2020). Cultural sociology meets the cognitive wild: advantages of the distributed cognition framework for analyzing the intersection of culture and cognition. American Journal of Cultural Sociology, 8(1), 45–62.

Smith, E. R., & DeCoster, J. (2000). Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 4(2), 108–131.

Strauss, C., & Quinn, N. (1997). A cognitive theory of cultural meaning (Vol. 9). Cambridge University Press.

Taylor, M. A., Stoltz, D. S., & McDonnell, T. E. (2019). Binding significance to form: Cultural objects, neural binding, and cultural change. Poetics , 73, 1–16.

Turner, S. P. (2007). Social Theory as a Cognitive Neuroscience. European Journal of Social Theory, 10(3), 357–374.

Vaisey, S. (2009). Motivation and Justification: A Dual-Process Model of Culture in Action. American Journal of Sociology, 114(6), 1675–1715.

Vaisey, S. (2019). From Contradiction to Coherence: Theory Building in the Sociology of Culture. https://doi.org/10.31235/osf.io/9mwfc

Vaisey, S., & Frye, M. (2017). The Old One-Two: Preserving Analytical Dualism in Psychological Sociology. https://doi.org/10.31235/osf.io/p2w5c

Wood, M. L., Stoltz, D. S., Van Ness, J., & Taylor, M. A. (2018). Schemas and Frames. Sociological Theory, 36 (3), 244–261.

Zerubavel, E. (1999). Social Mindscapes: An Invitation to Cognitive Sociology. Harvard University Press.

Habitus and Learning to Learn: Part III

Language, Habitus, and Cultural Cognition

The recasting of habitus as a neuro-cognitive structure conducive to learning opens up promising avenues otherwise foreclosed in traditional cultural theory (see here and here for previous discussion). However, it also opens up some analytical difficulties, especially when it comes to the role of language and linguistic symbols in cultural cognition. Two observations deserve to be made in this respect.

First, language (and linguistic symbols) are the products of habitus; yet, the underlying procedural capacities productive of language (as practice) and linguistic symbols (as objectified products) cannot themselves be linguistic. This is actually a good thing. If external linguistic symbols were the product of a set of internal structures that also had the status of language-like symbols, we would get ourselves into an infinite regress, as we would have to ask what establishes the meaning of those symbols. This is a version of Harnad’s (1990) “symbol grounding” problem, as this is known in cognitive science and artificial intelligence.

As both Wittgenstein and Searle have proposed in different ways, the only way to forestall this regress is to posit a non-representational, non-symbolic “background” where the buck stops. This backgground is then generative of structures that end up having representational and symbolic properties (such as linguistic symbols). I propose that the neuro-cognitive habitus is such a “background” (Hutto 2012), as it was precisely to deal with this problem in the sociology of knowledge that led Bourdieu to resort to this (ironically scholastic) construct (Lizardo 2013).

Do We Think “With” Language?

Second, it appears to us (phenomenologically) that we think using (or via the medium) of language. That is, thought presents itself as a sort of “internal conversation” happening using internal linguistic symbols which may even have the same dialogical structure of dyadic or interactive conversations we have with others (Archer 2003). In fact, in the symbolic interactionist/pragmatist tradition of Mead and in the “activity theory” of Vygotsky, interactive or dialogic conversation comes first, and internal conversations with ourselves later. From this perspective, the origins of the self (conceived as a symbolic representation the agent constructs of themselves) are both dialogic, linguistic, and even “semiotic” (Wiley 1994).

Insofar as the habitus makes possible our direct, embodied engagement with the world, then it is the locus of thinking or at least a type of thinking that allows for practice, action, and problem-solving. The problem is that the kind of thinking that happens via language does not seem to have the properties required for the “online” control of action and practical engagement with the world (Jeannerod 2001). If habitus engages a particular type of thinking, and even if there is a type of “cultural-cognition” happenning via habitus, then it has tobe a sort of non-linguistic cultural cognition.

This means we need to make conceptual space for a type of cognition that still deserves the label of thinking, that is affected by culture and experience, but that is not linguistic in its essence or mode of functioning. The basic proposal is that this is the base-level non-linguistic cultural cognition is made possible by habitus, and that the most substantial cultural effects on the way we think happen because culture affects this non-declarative procedural type of thinking (Cohen and Leung 2009).

From the perspective of traditional lines of cultural theory having long roots in sociology and anthropology, suggesting the existence of a type of thinking that does not rely on language, and much less making this type of thinking more basic than the linguistic one, is an odd proposal (Bloch 1986). In the standard approach, culture is equated with language, thinking is equated with language use, and cultural effects on cognition are reduced to the impact of cultural patterns in the way we use language to make sense of the world and to talk to ourselves, and others (Biernacki 2000).

A neural recasting of habitus reminds us that, while culture also affects the way that we use language to think (Boroditsky 2001), insofar linguistic cognition is grounded on non-linguistic cognition, equating the entirety of culture’s effects on thinking to its impact on the way we use language to think “offline” when decoupled from action in the world would be an analytic mistake.

Two Ways of Engaging the World

As now well-established by work in the dual-process framework in social and cognitive psychology (Lizardo et al. 2016), we can distinguish between two ways in which culture-driven cognition (or “culture in thinking”) operates. One relies on the use and manipulation of explicit symbolic tokens that can be combined in a linear order into higher-order structures, such as the sentences of a natural language. These linguistic symbols have the potential to stand in arbitrary relations to the things that they represent. This type of cognition is serial, slow, and in many ways, “cognitively costly” (Whitehouse 2004:55).

The habitus does not typically rely on this type of linguistic, sentential processing to “get action” in the world (Glenberg 1997). Insofar as the habitus shapes and produces culture, the role of linguistic symbols in cultural analysis has to be rethought (Lizardo et al. 2019). One premise that is undoubtedly on the wrong track is that personal culture embodied in habitus is, in its essence, linguistic or is primarily symbolic in a quasi-linguistic sense (Lizardo 2012).

In its place, I propose that habitus operates at a non-linguistic level. But what exactly does this entail? In contrast to the linguistic theory of internalized personal culture, the habitus relies on cognitive resources that aer imagistic, perceptual and “analog.” The neural structures constitutive of habitus learn (and thus “internalize” culture) by extracting higher-order patterns from the world that are meaningful at a direct experiential level. The linkages between these patterns are not arbitrary but are constrained to be directly tied to previous experience, so that they can be used to deal successfully with subsequent experiences sharing similar structure (Bar 2007).

In this last respect, the habitus recognizes connections between practical symbolic structures when these are compatible with its experiential history. Habitus uses the structural features of previous experience, directly linked to our status as embodied, spatial and temporal creatures, to bring order, predictability, and regularity to the most diverse action domains (Bourdieu 1990a).

The (Emergence of) the Scholastic Point of View

In a neural reconceptualization of habitus, language, linguistic structures and linguaform modes of expression are put in their place as supported by analog structures derived from experience. In fact, as shown in modern cognitive linguistics, most of the features of spoken language usually thought of as being endowed with some sort of mysterious, autonomous and ineffable “linguistic” or “semiologic” quality are grounded in the type of embodied, directly perceptual encoding and processing of meanings that is characteristic of habitus (Langacker 1991).

The status of modes of cognitive processing highly reliant on language in the cognitive economy of the social agent and the cultural economy of the social world has been overblown in social and cultural theory (using the misleading imprimatur of Ferdinand De Saussure). A neural recasting of habitus as a learning to learn structure reminds us that the foundations of meaning and culture are non-linguistic, non-propositional, non-sentential, and in a strong sense not symbolic, since they retain an intuitive, easily recoverable perceptual logic grounded in non-discursive forms of thinking, perception, and activity (Bloch 1991).

How Habitus Keeps Track of Experience

Following a connectionist rethinking of the notion of mental representation proposed in the previous post, I propose that the habitus “stores” experiential traces in terms of what has been referred to as what the philosopher Andy Clark has referred to as “super-positional storage; “[t]he basic idea of superposition is straightforward. Two representations are superposed if the resources used to represent item 1 are [at least partially] coextensive with those used to represent item 2” (Clark 1993: 17).

This observation carries an important analytical consequence, insofar as the dominant theory of culture today—the linguistic or semiotic theory—tacitly presupposes that the way in which cultural information is stored by persons resembles and is constrained to match those modes of storage and representation that are characteristic of linguistic symbols. This includes, amodality (the non-analogic nature of representational vehicles) and partial separability of the conceptual resources that are devoted to represent different slices of experience. For instance, under the standard model there is little (if no) overlap between the underlying conceptual resources used to represent the (more abstract) notion of “agency” and the (more concrete) notion of “movement.”

But if habitus uses overlapping resources to capture the structure of experience, then it must encode similarities in experiential content directly and thus arbitrariness is ruled out as a plausible encoding strategy: “[t]he semantic…similarity between representational contents is echoed as a similarity between representational vehicles. Within such a scheme, the representations of individual items is nonarbitrary” (Clark 1993: 19). This means that the habitus will attempt to deal with more abstract categories removed from experience and linked to seemingly arbitrary non-linguistic symbols by mapping them to less arbitrary categories linked to experience. In this respect, there will be substantial overlap between the conceptualization of freedom and movement, with the latter serving as the ground providing semantic support for our thinking about the former (Glenberg 1997).

This means that whatever strategic (from a cognitive viewpoint) structural signatures are found in the relevant experiential domain, will have an analogue in the structural representation of that domain that comes to be encoded in the neural structure of habitus. Here, the structure of the underlying neural representation is determined by experience. In the traditional account, the experience is “neutral” and some exogenous cultural grid, with no necessary relation to experience is imposed on this sensory “flux.” This is what Martin (2011) has referred as “the grid of perception” theory of culture.

The neural recasting of habitus offered here provides an alternative to this approach, which highlights the primary role experience without subordinating it to a “higher” order set of cultural categories, standing above (and apart) from experience.

Natural Born Categories

As noted, the habitus stores traces of long-term procedural knowledge in the synaptic weights coding for the correlated features of the objects, events and persons repeatedly encountered in our everyday dealings. The ability of habitus to extract the relevant structural and statistical features from experience (and only these), along with the super-positional encoding of experiential information, leads naturally to the notion of habitus as a categorizing engine, in which categories take prototype structure, with central (exemplar) members (sharing most of the relevant features) toward the center and less prototypical members in the periphery. The extraction of prototype-based categories via habitus allows us to understand and act upon experiential domains sharing similar structural features using overlapping cognitive resources.

In addition, whenever a given slice of experience comes to recurrently present the agent with the same set of underlying regularities, a general “category” will be extracted by the habitus. This category, comprising both entity (object) and event (process) prototypes, will be composed of contextually embodied features corresponding to those given by experience. At the same time they are capable of being transferred (“transposed”) to domains of experience that share similar structural features. “Schematic transposition” is thus a natural consequence of the way habitus is transformed by, and subsequently organizes, experience.

References

Archer, M. S. 2003. Structure, Agency and the Internal Conversation. Cambridge University Press.

Bar, M. (2007). The proactive brain: using analogies and associations to generate predictions. Trends in cognitive sciences, 11(7), 280-289.

Biernacki, Richard. 2000. “Language and the Shift from Signs to Practices in Cultural Inquiry.” History and Theory 39(3):289–310.

Bloch, Maurice. 1986. “From Cognition to Ideology.” Pp. 21–48. in Knowledge and Power: Anthropological and Sociological Approaches, edited by R. Fardon. Edinburgh: Scottish University Press.

Bloch, Maurice. 1991. “Language, Anthropology and Cognitive Science.” Man 26(2):183–98.

Bourdieu, Pierre. 1990a. The Logic of Practice. Stanford University Press.

Bourdieu, Pierre. 1990b. “The Scholastic Point of View.” Cultural Anthropology: Journal of the Society for Cultural Anthropology 5(4):380–91.

Clark, Andy. 1993. Associative Engines: Connectionism, Concepts, and Representational Change. MIT Press.

Cohen, Dov and Angela K. Y. Leung. 2009. “The Hard Embodiment of Culture.” European Journal of Social Psychology 39(7):1278–89.

Glenberg, Arthur M. 1997. “What Memory Is for: Creating Meaning in the Service of Action.” The Behavioral and Brain Sciences 20(01):41–50.

Harnad, Stevan. 1990. “The Symbol Grounding Problem.” Physica D. Nonlinear Phenomena 42(1):335–46.

Hutto, Daniel D. 2012. “Exposing the Background: Deep and Local.” Pp. 37–56 in Knowing without Thinking: Mind, Action, Cognition and the Phenomenon of the Background, edited by Z. Radman. London: Palgrave Macmillan UK.

Jeannerod, M. 2001. “Neural Simulation of Action: A Unifying Mechanism for Motor Cognition.” NeuroImage 14(1 Pt 2):S103–9.

Joas, Hans. 1996. The Creativity of Action. University of Chicago Press.

Langacker, R. W. 1991. Foundations of Cognitive Grammar: Descriptive Application. Vol. 2. Stanford: Stanford University Press.

Lizardo, O. 2012. “Embodied Culture as Procedure: Cognitive Science and the Link between Subjective and Objective Culture.” Habits, Culture and Practice: Paths to Sustainable.

Lizardo, Omar. 2013. “Habitus.” In Encyclopedia of Philosophy and the Social Sciences, edited by Byron Kaldis, 405–7. Thousand Oaks: Sage.

Lizardo, O. 2016. “Cultural Symbols and Cultural Power.” Qualitative Sociology. https://link.springer.com/content/pdf/10.1007/s11133-016-9329-4.pdf.

Lizardo, Omar, Robert Mowry, Brandon Sepulvado, Dustin S. Stoltz, Marshall A. Taylor, Justin Van Ness, and Michael Wood. 2016. “What Are Dual Process Models? Implications for Cultural Analysis in Sociology.” Sociological Theory 34(4):287–310.

Lizardo, Omar, Brandon Sepulvado, Dustin S. Stoltz, and Marshall A. Taylor. 2019. “What Can Cognitive Neuroscience Do for Cultural Sociology?” American Journal of Cultural Sociology 1–26.

Lizardo, Omar and Michael Strand. 2010. “Skills, Toolkits, Contexts and Institutions: Clarifying the Relationship between Different Approaches to Cognition in Cultural Sociology.” Poetics 38(2):205–28.

Martin, John Levi. 2011. The Explanation of Social Action. Oxford University Press.

Whitehouse, Harvey. 2004. Modes of Religiosity: A Cognitive Theory of Religious Transmission. New York: AltaMira Press.

Wiley, Norbert. 1994. The Semiotic Self. Chicago: University of Chicago Press.

Habitus and Learning to Learn: Part II

Beyond the Content-Storage Metaphor

The underlying neural structures constitutive of habitus are procedural (Kolers & Roediger, 1984), based on motor-schemas constructed from the experience of interacting with persons, objects, and material culture in the socio-physical world (Gallese & Lakoff, 2005; Malafouris, 2013). Habitus affords the capacity to learn because we are embodied beings endowed with the capacities and liabilities afforded by our sensory receptors and motor effectors. In this respect, the neurocognitive recasting of habitus is thoroughly consistent with the “embodied and embedded” turn in contemporary cognitive science.

Traditional accounts of learning rely primarily on the content-storage metaphor (Roediger, 1980). Under this classical conceptualization, experience modifies our cognitive makeup mainly via the recording of content-bearing representations into some sort of mental system dedicated to their inscription and “storage,” most plausibly what cognitive psychologists refer to as “long-term memory.” Because the habitus is seen as the locus of social and experiential learning, and as a sort of repository of past experience, it is tempting to conceptualize it using this content-storage metaphor.

In the current formulation, the metaphor of long-term memory storage emerges as a highly misleading one, and one that would severely limit the conceptual potential of the notion of habitus. In its place, I propose that the habitus contains the “record” of past experiences but it does not store these records as a set of individualized content-bearing “facts” or “propositions” to be accessed as (declarative) “knowledge” or as (episodic) memories that can be recalled in the form of a recreation of previous experiences (Michaelian, 2016). Explicit forms of memory are reconstructive rather than restorative, and rely on the procedural traces encoded in habitus.

The same goes for the procedures generative of goals and plans of action the conscious positing of a future project (Williams, Huang, & Bargh, 2009). The (consciously posited) goal-oriented model of action, rather than being the fundamental framework’ that constrains the very capacity to make meaningful statements about action, as Talcott Parsons (1937) once proposed, is reinterpreted under a habitus-based conception of action as a cognitively unnatural activity (Bourdieu, 2000). Thus, the deliberative positing of a possible future rather than being taken as the point of departure or as the privileged site where a special sort of “agency” is located, must be re-conceptualized, as a puzzling, context-dependent phenomenon in need of special explanation.

Offline Cognition as Habitual Reconstruction

Recent work in the psychology of memory and “mental time travel” support the idea that both the seeming recollection of past events, the imagining of counterfactual and hypothetical scenarios, and the simulation of possible future events, all share an underlying neural basis and even share some recognizable features at the level of phenomenology. Rather than being faithful records of past experiences, autobiographical memories are as reconstructive and hypothetical as the (embodied) simulation and situated conceptualization of future experiences (Michaelian, 2011). What all of these socio-cognitive states do seem to share is a suspension of our (default) embodied engagement with the world (Glenberg, 1997). As such, they represent exceptional states removed at least one step away from “action” and not the core prototypical cases upon which to build a coherent model of action. Habit-based action made possible by habitus is the default, and these other more contemplative and intellectualist mode the exception.

Nevertheless, it would be a mistake to posit to sharp a divide between habitus and scholastic contemplation of possible futures, counterfactual states, or representational pasts. All of these more intellectualist and content-ful states are rooted in habitus, if only indirectly. The habitus provides the underlying set of capacities making possible the (re)creation of mental “content” on the spot, via processes of situated conceptualization, embodied simulation, and affective-looping (Barsalou, 2005; Damasio, 1999). Nevertheless, while the online activation of facts and memories —for instance during an interview setting—is made possible via habitus, these objectified products are not to be taken as the constituents of habitus.

Habitus and Learning to Learn

In this respect, the habitus stores nothing that can be legitimately referred to as “content.” Instead, the primary form of learning that organizes the neural structures constitutive of habitus is the one that sets the stage for, and actually makes possible, the traditional forms of episodic and declarative learning-s, and the context-sensitive recreation of those contents, which come later in ontogenetic development. When the habitus forms and acquires structure in childhood what the person is doing is in essence “learning to learn.”

As noted in the previous post, the notion of learning to learn has a somewhat obscure pedigree in social theory, but it has figured prominently in the accounts given by Gregory Bateson, who called “deutero-learning,” and in Hayek’s proposal of a groundbreaking theory of perception in the Sensory Order. In both of these accounts, learning is not taken for granted as a pre-existing feature’ of the human agent, but the very ability to be modified by the world is conceived as something that must be produced by our immersion and coupling to the world. The world must prepare the agent to learn before learning can take place.

The standard model of learning takes what Bourdieu referred to as the “scholastic” situation as its primary exemplar. Under this characterization, to learn is to commit a content-bearing proposition (e.g. a belief or statement) to memory. The problem with this conception, as Bourdieu noted, is that it takes for granted the tremendeous amount of previous development, immersion, and “connection-weight setting” that happend in the previous (home) environment to prepare the person for these forms of scholastic learning. The proposed habitus-based model of learning takes the decidedly non-scholastic case of skill-acquisition as its primary exemplar of learning (Dreyfus, 1996; Polanyi, 1958).

Procedural learning, in this sense, results in the picking up of the structural features that characterize the most repetitive (and thus experientially consistent) patterns of the early environment. This is learning about the formal structure of the early world not a passive recording of facts. The structure of habitus primarily mirrors the systematic, repetitive structure of the world in terms of the overall constitution (e.g., empirical and relational co-occurrences) and temporal rhythms of the environment, especially that characteristic of the earliest experiences (e.g., the environment that predates “learning” as traditionally conceived).

Subsequent experiences will then be actively fitted into this pre-experiential (but nonetheless produced by experience) neural structure. In connectionist terms, the procedural learning giving rise to habitus is essentially equivalent “setting the weights” that will remain a durable, relatively resistant to change, part of our neuro-cognitive architecture. These weights partially fix our overall style of perception, appreciation and classification of all subsequent experience. As Philosopher Paul Churchland puts it,

…the brain represents the general or lasting features of the world with a lasting configuration of its myriad synaptic connections strengths. That configuration of carefully turned connections dictates how the brain will react to the world…To acquire those capacities for recognition and response is to learn about the general causal structure of the world, or at least, of that small part of it that is relevant to one’s own practical concerns. That knowledge is embodied in the peculiar configuration of one’s…synaptic connections. During learning and development in childhood, these connection strengths, or “weights” as they are often called, are to progressively more useful values. These adjustments…are steered most dramatically by the unique experience that each child encounters (1996, p. 5)

Accordingly, and in contrast to the view construing habitus as a mnemonic repository of experiential contents the connectionist recasting of habitus as the set of synaptic weights coming to structure further experiential activation, reveals that the habitus stores coarse-grained structural patterns keyed to “reflect” previously encountered environmental regularities and not fine-grained experiential content.

The experiential content that the person is exposed to further down the developmental line will be made sense of using the (perceived, classified and made part of practical action schemes) synaptic weights acquired in early experience. Thus, as a precondition for subsequent experience and (skillful) practical action in the world, pre-experiential learning and adjustment have to happen first. The notion of habitus is useful precisely because it captures an ontogenetic reality: the fact that this learning to learn is sticky and produces durable cognitive structures that modulate the way in which persons are allowed to be further modified by experience.

As the cognitive scientist Margaret Wilson puts it:

Research on skill-learning and expertise has primarily been conducted in the context of understanding how skills are acquired. What has been neglected is the fact that when the experiment is done, or when the real-life skill has been mastered, it leaves behind a permanently changed cognitive system. This may not matter much in the case of learning a single video game or a strategy for solving Sudoku; but the cumulative effect of a lifetime of numerous expertises may result in a dramatically different cognitive landscape across individuals.
(Wilson 2008: 182)

If the active construction, initializing, and relative equilibration (“setting the weights”) of pre-experiential neural structures necessary for making sense of further experience was not an ontogenetic reality and a presupposition for traditional forms of learning, the notion of habitus would not be a superfluous, gratuitous adjunct in social theory. But the cognitive reality is that “the rate of synaptic change does seem to go down steadily with increasing age”(Churchland 1996: 6). This statement is not incompatible with recent findings of neural “plasticity” lasting throughout adulthood, but it does force the analyst to distinguish different types of plasticity in ontogenetic time and the new capacities they are attuned to and result in. This means that a structured habitus is the ineluctable result of any type of (normal) development. Thus, exposure to repeated regularities will create a well-honed habitus reflective of the structure of the regularities encountered early on. It is in this sense that the habitus cannot but be a product of early experiential (socio-physical) realities.

References

Barsalou, L. W. (2005). Situated conceptualization. Handbook of Categorization in Cognitive Science, 619, 650.

Bourdieu, P. (2000). Pascalian Meditations. Stanford University Press.

Churchland, P. M. (1996). The Engine of Reason, the Seat of the Soul: A Philosophical Journey Into the Brain. MIT Press.

Damasio, A. R. (1999). The Feeling of what Happens: Body and Emotion in the Making of Consciousness. Harcourt Brace.

Dreyfus, H. L. (1996). The current relevance of Merleau-Ponty’s phenomenology of embodiment. The Electronic Journal of Analytic Philosophy, 4(4), 1–16.

Gallese, V., & Lakoff, G. (2005). The Brain’s concepts: the role of the Sensory-motor system in conceptual knowledge. Cognitive Neuropsychology, 22(3), 455–479.

Glenberg, A. M. (1997). What memory is for: Creating meaning in the service of action. The Behavioral and Brain Sciences, 20(01), 41–50.

Kolers, P. A., & Roediger, H. L., III. (1984). Procedures of mind. Journal of Verbal Learning and Verbal Behavior, 23(4), 425–449.

Malafouris, L. (2013). How Things Shape the Mind: A Theory of Material Engagement. MIT Press.

Michaelian, K. (2011). Generative memory. Philosophical Psychology, 24(3), 323–342.

Michaelian, K. (2016). Mental Time Travel: Episodic Memory and Our Knowledge of the Personal Past. MIT Press.

Parsons, T. (1937). The Structure of Social Action. New York: Free Press.

Polanyi, M. (1958). Personal knowledge, towards a post critical epistemology. Chicago, IL: University of.

Roediger, H. L., 3rd. (1980). Memory metaphors in cognitive psychology. Memory & Cognition, 8(3), 231–246.

Williams, L. E., Huang, J. Y., & Bargh, J. A. (2009). The Scaffolded Mind: Higher mental processes are grounded in early experience of the physical world. European Journal of Social Psychology, 39(7), 1257–1267.

Wilson, Margaret. 2010. “The Re-Tooled Mind: How Culture Re-Engineers Cognition.” Social Cognitive and Affective Neuroscience 5 (2-3): 180–87.

Habitus and Learning to Learn: Part I

In this and subsequent posts, I will attempt to revise, reconceptualize and update the concept of habitus using the theoretical and empirical resources of contemporary cognitive neuroscience and cognitive social science.

I see this step as necessary if this Bourdieusian notion is to have a future in social theory. Conversely, if no such recasting is coherent or successful, then it might be time to retire the idea of habitus.

My reconstruction of habitus in what follows is necessarily selective. I keep historical and conceptual exegesis to a minimum (see e.g. Lizardo 2004 for that), and I will not engage in an attempt to convince you that the concept of habitus is a useful one in social science research. I presume that my undertaking this effort presupposes that the notion of habitus is useful and that its “updating” in terms of contemporary advances in the cognitive sciences is a worthwhile exercise.

There is a theoretical payoff in this endeavor. By connecting the notion of habitus as a conceptual tool for social analysis with emerging developments in the cognitive and neurosciences a lot of standing problems in social scientific conceptualizations of cognition, perception, categorization, and action are shown to be either pseudo-problems, or are resolved in more satisfactory ways than in proposals made from non-cognitive standpoints. In what follows, I address a series of the theoretical issues that I believe are properly recast using a version of the habitus concept informed by cognitive neuroscience, beginning with the notion of “learning” and ending with a reconsideration of the notion of categories and categorization.

The habitus as a “learning to learn” cognitive structure

The habitus is a set of durable cognitive structures that develop in order to allow the person to exploit the most general features of experience most effectively. These structures are constitutive of our capacity to develop an intuitive, routine grasp of events, entities, and their inter-relations and yet are also the product of experience. In neuroscientific terms, this presupposes “a durable transformation of the body through the reinforcement or weakening of synaptic connections” (Bourdieu 2000, 133).

As the economist and social theorist Friedrich Hayek once put it, “the apparatus by means of which we learn about the external world is itself the product of a kind of experience” (Hayek 1952, 165). The cognitive structures constitutive of habitus themselves, are a product of a special kind of learning, the process of “learning to learn” (something that the anthropologist Gregory Bateson (1972) once referred to as “deutero-learning”). From this point of view, “the process of experience does not begin with sensations or perceptions, but necessarily precedes them: it operates on physiological events and arranges them into a structure or order which becomes the basis of their `mental’ significance” (Hayek 1952: 166).

The experience-generated cognitive structures constitutive of habitus are designed to capture the most significant axes of variation–in essence the abstract causal and temporal signatures–of the early environment (Foster 2018). They make possible subsequent practical exploitation and even the fairly unnatural contemplative “recording” of later experiences in the form of episodic and semantic learning. The habitus itself is not a repository of “contents” in the traditional sense (e.g., a “storehouse” of individuated beliefs, attitudes, and the like) but it is generative of our ability to actively retrieve the experiential, mnemonic and imaginative qualities that form the core of our everyday experience.

Beyond Plasticity

From the point of view of a neuro-cognitive construal of habitus as a learning-to-learn structure, extant notions of learning (or socialization) in sociology come off as limited. Most consist of general accounts regarding the “plasticity” of the organism (Berger and Luckmann 1966), and are usually anxious to separate whatever is innate or biologically specified from that which comes from experience. At the extreme, we find accounts suggesting that nothing specific comes from biology and that all specific content is, therefore, “learned.”

Most social theorists, after making sure to set down this rather crude division, are satisfied in having secured a place for the cultural and social sciences in having delimited the scope of that which can be directly given by “biology.” Most analysts are then satisfied to establish broad statements about how humans are unique because so much of their cultural equipment has to be acquired from the world via experience, or how the human animal is essentially incomplete, or how biological evolution and the biological “inner code” requires reliance on externalized, epigenetic cultural codes for its full expression and development (Geertz 1973).

The actual experiential and cognitive mechanisms making possible learning in the first place and the constraints that these mechanisms pose on any socio-cultural theory of learning are thought of as exogenous. Learning from experience just “happens” and the role of social science is simply to keep track, document and acknowledge the existence of the external origins of the contents so learned.

What is missing from these standard accounts? First, that persons are capable of learning or that the brain is plastic is a very important but preliminary point. Only the most narrowly misinformed nativist argument would fall when confronted with this fact. Second, the issue is not whether persons learn, but how to account for this ability without begging the question. In this respect, standard definitions of culture as that which is learned and standard definitions of persons as essentially “cultural animals,” are well-taken, but ultimately fail to make a substantively consequential statement. This views are limited because they fail to distinguish between different forms of learning, the accomplishment of which are presuppositions for others.

A neurocognitive conception of habitus can serve to re-specify the notion of learning in cultural analysis in a useful way. From the point of view of a neuroscientifically informed social theory (Turner 2007), it is not enough to acknowledge the commonplace observation that persons are modified by experience or that the current set of skills and abilities that a person commands is indeed a product of modification by experience. Instead, the key is to specify what exactly this modification consists of, and how it differs, for instance, from the experiential sort of “modification” we are constantly exposed to in our everyday life by virtue of being creatures capable of consciousness, or the modification that happens when learn a new propositional fact, or when form a new episodic memory as a result of being involved in some biographically salient event.

The neurocognitive recasting of habitus as learning-to-learn structure improves the standard account of learning by suggesting that all learning requires the early, systematic, and relatively durable modification of the person as a categorizing and perceiving agent. That is, before learning of the “usual” kind can begin (e.g. learning about propositional facts to be “stored” in semantic memory) a different sort of “learning” has to occur: the person must learn to form the pre-experiential structures that will have the function of bringing forth or disclosing a comprehensible world (in the phenomenological sense). This “deutero-learning” needs to be distinguished from the sort of recurrent experience-linked modification resulting in the acquisition of episodic (having a factual account of our personal biography) or propositional or declarative knowledge (knowledge that).

In a follow-up post, I’ll develop the implications of this distinction for contemporary understandings of enculturation and socialization in cultural analysis.

References

Bateson, Gregory. 1972. Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology. University of Chicago Press.

Berger, Peter L., and Thomas Luckmann. 1966. The Social Construction of Reality: A Treatise in the Sociology of Knowledge. Anchor Books. New York: Doubleday.

Bourdieu, Pierre. 2000. Pascalian Meditations. Stanford University Press.

Foster, Jacob G. 2018. “Culture and Computation: Steps to a Probably Approximately Correct Theory of Culture.” Poetics 68 (June): 144–54.

Geertz, Clifford. 1973. The Interpretation of Cultures: Selected Essays. New York: Basic Books.

Hayek, F. A. 1952. The Sensory Order: An Inquiry Into the Foundations of Theoretical Psychology. University of Chicago Press.

Lizardo, Omar. 2004. “The Cognitive Origins of Bourdieu’s Habitus.” Journal for the Theory of Social Behaviour 34 (4): 375–401.

Turner, Stephen P. 2007. “Social Theory as a Cognitive Neuroscience.” European Journal of Social Theory 10 (3): 357–74.

“Learning By Nodes”: Dendritic Learning and What It Means (Or Not) for Cultural Sociology

In a paper published earlier this year in Scientific Reports and further discussed in a later ACS Chemical Neuroscience article, a group of researchers argues that learning might not function like we previously thought. The researchers (Sardi et al. 2018a, 2018b) explain that the dominant conceptualization in cognitive neuroscience of how learning works—synaptic learning, or “Hebbian learning” (Hebb 1949)—is wrong. Instead, using a series of computational models and experiments with synaptic blockers and neuronal cultures (see Sardi et al. 2018a:4-7), the authors find evidence for a different type of learning—what they refer to as “dendritic learning.” Just as “Copernicus was the first to articulate loudly that the earth revolves around the sun and not vice versa, even though all the accumulated astronomical evidence at that time fit the old postulation,” the researchers proclaim, as are they the first to “[swim] against conventional wisdom” of Hebbian learning theory (2018b:1231).

Of what consequence is this newfound process of dendritic learning for cultural sociology? Should we care at all? I’ll try to briefly describe some of the potential consequences of dendritic learning for cultural sociology; but, spoiler alert, I am not sure one way or the other if these consequences amount to being consequential for how we do sociology. But perhaps taking a peek at what dendritic learning is and how it is different from conventional understandings of how learning works is a nice place to start.

Figure 1. Are We Witnessing a “Revolution of the Cognitive Spheres”?
Note: Image from Copernicus’ On the Revolutions of the Heavenly Spheres (Palca 2011).

LINKS VS. NODES

Going on 70 years, the prevailing explanation for how learning works has been synaptic learning. Building from Hebb’s (1949) The Organization of Behavior, the idea behind synaptic learning is that if there is an activity that stimulates a neuron which in turn stimulates another neuron, and if that activity is repeated over time, then the first neuron becomes a more efficient stimulator of the second neuron and the two become more strongly connected in the brain.

Neuron-neuron stimulation occurs through synapses, the chemical (usually) or electrical (less frequently) structural gaps between neurons transmitting information across them. Synaptic learning, then, is a type of “activity-dependent synaptic plasticity” (Choe 2015:1305). Repeated practices or exposures to a certain stimulus modifies the synaptic strength between the two neurons: when the practice/exposure is repeated, the two neurons become more tightly associated in the brain, and when the practice/exposure is not repeated, the association weakens. This process occurs relatively slowly.

Synaptic learning is the inspiration behind the old adage that “neurons that fire together wire together.” Until very recently, this was the way we assumed new neural coalitions formed in biological neural networks. Consider an example from Luke Muehlhauser over on the Less Wrong blog (Muehlhauser 2011). Think back to Pavlov’s experiment on classical conditioning (Pavlov 1910): a dog is given food when the researcher rings a bell, and the timing between the bell ringing and the presentation of food is manipulated. At first, there is no association between the neurons stimulated by bell ringing and the neurons that trigger salivation; they are, ostensibly, mutually exclusive actions. However, if the researcher rings the bell and the food is presented to the dog at the same time (or in close enough time intervals), the neurons that fire when food is present and the neurons that fire with bell ringing are activated together. Over repeated trials, the synapses between “bell ringing” and “salivation” neurons become stronger and, eventually, simply ringing the bell induces salivation without the presentation of food (see Figure 2).

Figure 2. Synaptic Learning with Pavlov’s Experiment
Note: Reprinted from Less Wrong blog (Muehlhauser 2011).

Sardi and colleagues refer to synaptic learning as “learning by links” (Sardi et al. 2018a:1), since learning occurs through the synapses that link the neurons together. Their research, however, suggests a different type of learning—dendritic learning, also known as “learning by nodes” (Sardi et al. 2018a:2). In short, with this mode of learning, the workhorse of the neuron for learning purposes is not the synapses, but instead the dendrites. In a neuron cell, dendrites are the long, treelike extensions that connect the cell body (the soma, which contains the cell nucleus) to the synapses that themselves “connect” the neuron to other neurons.

Take a look at Figure 3, a neuron cell’s anatomy. The dendrites are responsible for taking in information from other neurons and passing it along into the soma, while the axon is responsible for passing the information on to other neurons via the axon terminals—which are themselves connected to the next neuron’s dendrites through synapses, thus propagating information transmission across the neural network. Without dendrites, information cannot be transmitted into the body of the neuron: e.g., damaged or abnormal dendrites are linked to brain under-connectivity issues associated with autism (Martínez-Cerdeño, Maezawa, and Jin 2016). Trying to construct new neural networks without dendrites is like trying to have group deliberation with all talk and no listening.

Figure 3. A Neuron’s Anatomy
Note: Reprinted from OpenStax (2018), redirected from Khan Academy (2018).

So, how does dendritic learning differ functionally from synaptic learning? While synaptic learning is based on the idea of synaptic plasticity, dendritic learning revolves around the notion of (you guessed it) a sort of dendritic plasticity: given increasing or decreasing levels of exposure to a neuron-activating stimulus, the extent of the neuron’s “dendritic excitability” can grow or diminish while the strength of the synapses remain relatively constant (Neuroskeptic 2018).

Consider Figure 4. Across both panels, the teardrop object at the bottom represents the neuron cell body, which is where the firing happens if the input signals from the dendrites are strong enough for an outgoing signal to be pushed from the cell body down through the axon and into the dendrites of the next neuron. The long treelike branches are the dendrites, and the tips are the synapses that connect the neuron’s dendrites to the axon terminals of other (not shown) neurons. The left panel illustrates conventional synaptic learning, where the synapses themselves are weighted (indicated by the red valves at the tips of the branches) upward or downward depending on the extent of stimulus exposure. The right panel shows dendritic learning: it is the extent to which a neuron’s dendrites are in a high state of stimulation, and not the strength of the synapses linking the neuron to other neurons, that determines the strength of the input signal and therefore whether or not the neuron fires. In dendritic learning, then, there are far fewer “learning parameters,” since the dendrites are responsible for the learning and not the synapses (see the right panel of Figure 4) (ScienceDaily 2018).

Figure 4. Synaptic Learning (left) vs. Dendritic Learning (right)
Note: Reprinted from ScienceDaily (2018).

IMPLICATIONS (?) FOR CULTURAL SOCIOLOGY

The “Neuroskeptic” over at Discover Magazine reviewed the evidence from the Sardi et al. papers and suggests that “[a]t best they have shown that dendritic learning also happens [in addition to synaptic learning],” and that “[they] don’t think Copernicus has returned to earth just yet” (Neuroskeptic 2018). I agree with Neuroskeptic in terms of what this means for neuroscience, largely because they are the neuroscientist and I am not. That said, there does seem to be the potential for some implications for how we do cultural sociology. But the potential may be greater for some subfields than for others.

I’m Not Sure What this Adds for How Sociologists Study Learning

The existence of dendritic learning has at least two major implications for cognitive neuroscience. First, learning may happen at much faster timescales than previously thought. Second, weak synapses matter a lot. In terms of timescale, it seems that the brain isn’t that bad at quick adaptation—at least relative to traditional Hebbian learning. As Sardi and colleagues note, “[t]his dynamic brain activity leads to the capability that when we think about an issue several times we may find different solutions” (Shrourou 2018). For the importance of weak synapses, the researchers point out that dendritic strengths are “self-oscillating” (2018b:1231), where weak synapses effectively “temper” the dendritic weights and prevent them from taking on extreme values. In other words, “dendritic learning enables stabilization around intermediate [dendritic strength] values” (Sardi et al. 2018a:4). These implications are pretty important for neuroscientists and medical researchers studying various diseases of the brain (Sardi et al. 2018b:1231-32).

What does all this mean for cultural sociologists? It might be too early to tell. Dendritic learning might be faster than synaptic learning, but the time scales in the experiments are in much smaller intervals (minutes) than the learning processes of interest to sociologists. The researchers note that future studies should “investigate . . . [dendritic learning] efficiency and available learning time scales in more realistic scenarios” (2018b:1231), so it’s an empirical question whether or not the learning speed differentials between synaptic and dendritic learning are a wash with longer timescales. So, in terms of theoretical leverage, dendritic learning may or may not offer much over and above how we already talk about learning in culture and cognition studies (see Lizardo et al. 2016:293-95). At the end of the day, for cultural sociologists it may all look like GOFILT—Good Old Fashioned Implicit Learning Theory—in which case the difference between synaptic and dendritic learning can be taken as ontologically true but analytically inconsequential. Only time (pun) will tell.

The Payoff May Come Sooner for Computational Social Science

In addition to understanding the learning processes behind biological neural networks and brain disorders, Sardi and colleagues also note that this “paradigm shift” matters for developing machine learning algorithms built to mimic human learning (2018b:1231). In natural language processing, for instance, if synaptic learning isn’t the baseline model of human learning (itself an empirical question), then perhaps analytical strategies that build associations between terms or documents based on term frequencies and co-occurrences aren’t based on the best cognitive model for machine learning.

But at face value I’m skeptical of this last proposition—I like word count methods for analyzing meaning, others do too (Nelson 2014; Underwood 2013), and I’ve read enough papers that make defensible claims using them to sell me on their continued use. That said, we have not seen dendritic learning rules implemented into machine learning algorithms yet (but see Sardi et al. 2018a:2-3 for an example of dendritic learning rules in a series of perceptron models), and it might prove particularly consequential in deep learning tasks and artificial neural network models. These sort of machine learning algorithms have not gained much traction in sociology, though, so, for now, it seems that the utility of distinguishing between synaptic and dendritic learning for culture and cognition studies is truly a waiting game.

I can continue all of my work without making these distinctions, and I suspect that most of the people reading this post are in the same position.

REFERENCES

Choe, Yoonsuck. 2015. “Hebbian Learning.” Pp. 1305-09 in Encyclopedia of Computational Neuroscience, edited by D. Jaeger and R. Jung. New York: Springer.

Hebb, Donald O. 1949. The Organization of Behavior: An Neuropsychological Theory. New York: Wiley.

Khan Academy. 2018. “Overview of Neuron Structure and Function.” Khan Academy. Retrieved October 16, 2018 (https://www.khanacademy.org/science/biology/human-biology/neuron-nervous-system/a/overview-of-neuron-structure-and-function).

Martínez-Cerdeño, Verónica, Izumi Maezawa, and Lee-Way Jin. 2016. “Dendrites in Autism Spectrum Disorders.” Pp. 525-43 in Dendrites: Development and Disease, edited by K. Emoto, R. Wong, E. Huang, and C. Hoogenraad. Tokyo: Springer.

Muehlhauser, Luke. 2011. “A Crash Course in the Neuroscience of Human Motivation.” Less Wrong. Retrieved October 16, 2018 (https://www.lesswrong.com/posts/hN2aRnu798yas5b2k/a-crash-course-in-the-neuroscience-of-human-motivation).

Nelson, Laura K. 2014. “Computer-Assisted Content Analysis and Sociology: What You Should Know.” Bad Hessian. Retrieved October 17, 2018 (http://badhessian.org/2014/01/computer-assisted-content-analysis-and-sociology-what-you-should-know/).

Neuroskeptic. 2018. “Is ‘Dendritic Learning’ How the Brain Works?” Discover Magazine. Retrieved October 16, 2018 (http://blogs.discovermagazine.com/neuroskeptic/2018/05/11/dendritic-learning/#.W8aX4P5KjdT).

OpenStax. 2018. “Neurons and Glial Cells.” OpenStax CNX. Retrieved October 16, 2018 (https://cnx.org/contents/GFy_h8cu@9.87:c9j4p0aj@3/Neurons-and-Glial-Cells).

Palca, Joe. 2011. “For Copernicus, A ‘Perfect Heaven’ Put Sun At Center.” NPR: Morning Edition. Retrieved October 16, 2018 (https://www.npr.org/2011/11/08/141931239/for-copernicus-a-perfect-heaven-put-sun-at-center).

Pavlov, Ivan. 1910. The Work of the Digestive Glands. London: C. Griffin & Company.

Sardi, Shira, Roni Vardi, Amir Goldental, Anton Sheinin, Herut Uzan, and Ido Kanter. 2018a. “Adaptive Nodes Enrich Nonlinear Cooperative Learning Beyond Traditional Adaptation By Links.” Scientific Reports 8(1):5100.

Sardi, Shira, Roni Vardi, Amir Goldental, Yael Tugendhaft, Herut Uzan, and Ido Kanter. 2018b. “Dendritic Learning as a Paradigm Shift in Brain Learning.” ACS Chemical Neuroscience 9:1230-32.

ScienceDaily. 2018. “The Brain Learns Completely Differently than We’ve Assumed Since the 20th Century.” ScienceDaily. Retrieved October 16, 2018 (https://www.sciencedaily.com/releases/2018/03/180323084818.htm).

Shrourou, Alina. 2018. “Dendritic Learning Occurs Much Faster and In Closer Proximity to Neurons, Shows Study.” News Medical: Life Sciences. Retrieved October 16, 2018 (https://www.news-medical.net/news/20180830/Dendritic-learning-occurs-much-faster-and-in-closer-proximity-to-neurons-shows-study.aspx).

Underwood, Ted. 2013. “Wordcounts Are Amazing.” The Stone and the Shell. Retrieved October 17, 2018 (https://tedunderwood.com/2013/02/20/wordcounts-are-amazing/).

Limits of innateness: Are we born to see faces?

Sociologists tend to be skeptical of claims individuals are consistent across situations, as a recent exchange on Twitter exemplifies. This exchange was partially spurred by revelations that the famous Stanford Prison Experiment (which supposedly showed people will quickly engage in behaviors commensurate with their assigned roles even if it means being cruel to others), was even more problematic than previously thought.

The question of individual “durability” is sometimes framed as “nature vs nurture,” and this is certainly a part of the matter. In sociology, however, this skepticism of “durability” often goes much further than innateness, and sometimes leads sociologists to suggest individuals are inchoate blobs until situations come along to construct us (or interlocutors may resort to obfuscation by touting the truism that humans are always in a situation). If pushed on the topic, however, even the staunchest situationalist would likely concede that humans are born with some qualities, and the real question is what are the limits of such innateness? What kinds of qualities of people can be innate? To what extent are these innate qualities human universals? And, if we are “born with it” can “it” change and how and to what extent? In Stephen Turner’s new Cognitive Science and the Social, he puts the matter succinctly:

“…children quickly acquire the ability to speak grammatically. This seems to imply that they already had this ability in some form, such as a universal set of rules of language stored in the brain. If one begins with this problem, one wants a model of the brain as “language ready.” But why stop there? Why think that only grammatical rules are innate? One can expand this notion to the idea of the “culture-ready” brain, one that is poised and equipped to acquire a culture” (2018:44–45).

As I’ve previously discussed, the search for either the universal rules or specialized module for language has, thus far, failed. Nevertheless, most humans must be “language-ready” in the minimal sense of having the ability to acquire the ability to speak and understand speech. But, answering the question of where innateness ends and enculturation begins is not easy. Even for those without the disciplinary inclination toward strongly situationalist arguments.

Are we born to see faces?

How we identify faces is a good place to explore this difficulty: Do we learn to identify faces or are we born to see faces? And, if we are born to see faces, is this ability refined through use and to what extent? Enter: the fusiform face area (FFA). Just like language, the FFA is often used as evidence for the more general arguments of functional localization and domain specificity. This argument goes: facial recognition is produced not by generic cognitive processes involved in vision (or other generic processes), but rather an inborn special-purpose module.

One reason why faces are an even better candidate for grappling with the question of innateness than is language is that the human fetus is exposed to language while in the womb. Human fetuses gain some sense of prosody, tonality, and as a result, a basic sense of grammar in the course of development in utero. There is no comparable exposure to faces, however. Another reason is, as the Gestalt psychologists argued, faces have an irreducible structure such that they are perceived as complete wholes even when viewing only a part — “the whole is something else than the sum of its parts, because summing is a meaningless procedure, whereas the whole-part relationship is meaningful” (Koffka 1935:176).

Facial recognition encompasses two related functions: distinguishing faces from non-face objects and distinguishing among faces. The key debate within this area of cognitive neuroscience is whether there is a module that is specialized for one or both of these processes (Kanwisher, McDermott, and Chun 1997; Kanwisher and Yovel 2006), as opposed to a distributed and generic cognitive process (Haxby et al. 2001). This debate goes back to the observation that humans struggle to recognize and remember faces that are upside down, which seemed to be the case for faces more so than any other non-face object (Diamond and Carey 1986) — suggesting something about faces made them unique. The proposal facial recognition was the result of a specialized module, however, begins with a relatively recent paper by Kanwisher et al. (1997). Using functional magnetic resonance imaging (which I’ve discussed in detail in previous posts), 15 subjects were shown various common objects as well as faces. They found in 12 of those subjects a specific area of the brain was more active when they saw faces than when they saw non-face objects. On its face, it seems like reasonable evidence humans are born with a module necessary for identifying faces.

However, when one squares this claim with the underlying logic of fMRI—it is used to (a) measure relative activation, not an on/off process, and (b) voxel and temporal resolution is far too coarse to conclude a region is homogeneously activated—the claim that the FFA is a functionally specialized module for facial recognition weakens considerably. These areas are not entirely inactive when viewing non-face objects. Indeed, relative to baseline activation, subsequent research found the FFA is significantly more active when viewing various objects (Grill-Spector, Sayres, and Ress 2006). Specifically, the level of specificity of the stimulus (e.g. faces tend to be individuals whereas chairs tend to be generic) and the participants level of expertise with the stimulus (e.g. car and bird enthusiasts) predicted greater relative activation (Gauthier et al. 2000; Rhodes et al. 2004).

Finally, if we are born to distinguish faces from non-faces, the ability to distinguish among faces is considerably trained by early socialization, and such socialization introduces a lot of variation among people. For example, one of the earliest attempts to measure facial recognition concluded, “that women are perhaps superior to men in the test; that salespeople are superior to students and farm people; that fraternity people are perhaps superior to non-fraternity people…” (Howells 1938:127).

Subsequent research in this vein found individuals are better at distinguishing among their racial/ethnic ingroups than their outgroups. In an early study of black and white students from a predominantly black university and a predominantly white university, researchers found participants more easily discriminated among faces of their own race. They also found “white faces were found more discriminable” overall, which they suggest may be the result of “the distribution of social experience is such that both black persons and white persons will have had more exposure to white faces than black faces in public media…” (Malpass and Kravitz 1969:332). Summarizing more recent work, Kubota et al. (2012) state “participants process outgroup members primarily at the category level (race group) at the expense of encoding individuating information because of differences in category expertise or motivated ingroup attention.”

Why should sociologists care?

To summarize, the claim that facial recognition emerges from an innate functionally-specialized cognitive module is weakened in three ways: the FFA responds to more generic features faces share with other objects; the FFA is implicated in a distributed neural network rather than solely a discrete module; the FFA is used for non-facial recognition functions; and finally, facial recognition is trained by our (social) experience. Why should sociologists care? I think there are three reasons. First, innateness is not deterministic or specific but rather constraining and generic. Second, these constraints ripple throughout our social experience, forming the contours of cultural tropes, but are not immutable. Third, limited innateness does not mean individuals are not durable across situations, even (near) universally so.

A dispositional and distributed theory of cognition and action accounts for object recognition by its use: “information about salient properties of an object—such as what it looks like, how it moves, and how it is used—is stored in sensory and motor systems active when that information was acquired” (Martin 2007:25). This is commensurate with the broad approach many of the posts on this blog have been working with. Perhaps, however, there is a special class of objects for which this is not exactly the case. In other words, the admittedly weak innateness of distinguishing unfamiliar faces from non-face objects is, perhaps, the evidence we are “born with” some forms of nondeclarative knowledge (Lizardo 2017).

Such nondeclarative knowledge, however, may be re-purposed for cultural ends. Following the logic of neural exaption, discussed in a previous post, humans can be born with predispositions, especially related to very generic cognitive processes, which are further trained, refined, and recycled for novel uses, novel uses which are nevertheless constrained in a way that yields testable predictions. A fascinating example related to facial perception is anthropomorphization. If rudimentary facial recognition is innate (and therefore, probably evolutionarily old), this inherently social-cognitive process is being reused for non-social purposes (i.e. non-social in the restricted sense of interpersonal interaction). This facial recognition network—together with other neuronal networks—is used to identify people and predict their behavior, and this may be adapted to non-human animate and inanimate objects, like natural forces, as well as anonymous social structures, like financial markets.

What this means, following the logic of neural reuse and conceptual metaphor theory, is that the target domain (e.g. derivative markets, earthquakes) is “contaminated” by predispositions which originally dealt with the source domain (here, interpersonal interaction). This means attempting to imagine the intentions of thousands of unknown traders as if inferring the intentions of an interlocutor may lead traders to “ride” financial bubbles (De Martino et al. 2013). Therefore, what is and is not innate is a messy question to answer — even by those without a disciplinary distrust of innateness claims. Although cognitive neuroscientists are making headway, it remains an empirical question which objects are recognized innately and the extent to which the object recognition is robust to enculturation and neural recycling.

More importantly, the question of individual durability across situations should not be reduced solely to “nature vs nurture.” That is, we must grapple with the question of once these processes are so trained in an individual (during “primary socialization”), how easily can they be re-trained, if at all? In John Levi Martin’s Thinking Through Theory (2014:249), the third of his “Newest Rules of Sociological Method” is pessimistic in this regard: “Most of what people think of as cultural change is actually changes in the compositions of populations.” That is, even if we were to bar the possibility of innateness in any strong sense, once individuals reach a certain age they are likely to be fairly consistent across situations, with little chance of altering in fundamental ways.

REFERENCES

De Martino, Benedetto, John P. O’Doherty, Debajyoti Ray, Peter Bossaerts, and Colin Camerer. 2013. “In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles.” Neuron 79(6):1222–31.

Diamond, Rhea and Susan Carey. 1986. “Why Faces Are and Are Not Special: An Effect of Expertise.” Journal of Experimental Psychology. General 115(2):107.

Gauthier, I., P. Skudlarski, J. C. Gore, and A. W. Anderson. 2000. “Expertise for Cars and Birds Recruits Brain Areas Involved in Face Recognition.” Nature Neuroscience 3(2):191–97.

Grill-Spector, Kalanit, Rory Sayres, and David Ress. 2006. “High-Resolution Imaging Reveals Highly Selective Nonface Clusters in the Fusiform Face Area.” Nature Neuroscience 9(9):1177–85.

Haxby, J. V., M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini. 2001. “Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex.” Science 293(5539):2425–30.

Howells, Thomas H. 1938. “A Study of Ability to Recognize Faces.” Journal of Abnormal and Social Psychology 33(1):124.

Kanwisher, Nancy and Galit Yovel. 2006. “The Fusiform Face Area: A Cortical Region Specialized for the Perception of Faces.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 361(1476):2109–28.

Kanwisher, N., J. McDermott, and M. M. Chun. 1997. “The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception.” The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 17(11):4302–11.

Koffka, Kurt. 1935. Principles of Gestalt Psychology. New York: Harcourt, Brace.Kubota, Jennifer T., Mahzarin R. Banaji, and Elizabeth A. Phelps. 2012. “The Neuroscience of Race.” Nature Neuroscience 15(7):940–48.

Lizardo, Omar. 2017. “Improving Cultural Analysis Considering Personal Culture in Its Declarative and Nondeclarative Modes.” American Sociological Review 0003122416675175.

Malpass, R. S. and J. Kravitz. 1969. “Recognition for Faces of Own and Other Race.” Journal of Personality and Social Psychology 13(4):330–34.

Martin, Alex. 2007. “The Representation of Object Concepts in the Brain.” Annual Review of Psychology 58(1):25–45.

Martin, John Levi. 2014. Thinking Through Theory. W. W. Norton, Incorporated.

Rhodes, Gillian, Graham Byatt, Patricia T. Michie, and Aina Puce. 2004. “Is the Fusiform Face Area Specialized for Faces, Individuation, or Expert Individuation?” Journal of Cognitive Neuroscience 16(2):189–203.

Turner, Stephen P. 2018. Cognitive Science and the Social: A Primer. Routledge.

Beyond Good Old-Fashioned Ideology Theory, Part Two

In part one, I examined two recent frameworks for understanding ideology (Jost and Martin) and explained how both serve as alternatives to the good old-fashioned ideology theory (GOFIT). Ultimately, I concluded that Martin’s (2015) model has specific advantages over Jost’s (2006) model, though the connection between ideology and “practical mastery of ideologically-relevant social relations” needs to be fleshed out. This is particularly true because any strong concentration on social relations seems to preclude any serious attention to cognition. But without it, the argument is vulnerable to crying foul over reductionism.

In this post, I sketch a model of cognition that checks the boxes of GOFIT ideology: distorting, invested with power, supports unequal social relations. But it is different for reasons I specify below. To do this, I use a famous experiment in neuroscience—Michael Gazzaniga’s “split-brain” hypothesis— and draw an analogue between it and a possible non-GOFIT ideology.

Galanter, Gerstenhaber … and Geertz

But before doing that, it seems reasonable to ask about the purpose of even attempting a non-GOFIT ideology. Is GOFIT a strawman? Why is it problematic? To answer these questions, and to indicate why a holistic revision of ideology away from GOFIT seems to be in order, consider Clifford Geertz and his essay (1973) “Ideology as a cultural system,” which presents what is to date arguably the most influential, non-Marxist approach to ideology in the social sciences. Geertz’s burden is to make ideology relevant by providing it with a “nonevaluative” form. And the way he does this, using modular or computational cognition, is what I want to focus on.

Ideology here is not tantamount to oversimplified, inaccurate, “fake news” style distortion that is, above all and categorically, what science is not. But if it isn’t to be censured like this, then for Geertz ideology must be a symbolic phenomenon that has something to do with how “symbolic systems” make meaning in the world, and in turn serve to guide action (e.g. “models of, models for”). To make this argument, he does, in fact, make ideology cognitive by drawing from a psychological model: Eugene Galanter and Murray Gerstenhaber’s [1956] “On Thought: The Extrinsic Theory.”

As Geertz summarizes:

thought consists of the construction and manipulation of symbol systems, which are employed as models of other systems, physical, organic, social, psychological, and so forth, in such a way that the structure of these other systems– and, in the favorable ease, how they may therefore be expected to behave–is, as we say “understood.” Thinking, conceptualization, formulation, comprehension, understanding, or what-have-you, consists not of ghostly happenings in the head but of a matching of the states and processes of symbolic models against the states and processes of the wider world … (214)

Geertz returns to this same argument in arguably his most thorough approach to the culture concept (“The Growth of Culture and the Evolution of Mind”). Importantly, there too he does not conceive of culture or symbols absent a psychological referent, which he consistently draws from Galanter and Gerstenhaber.

Whatever their other differences, both so-called cognitive and so-called expressive symbols or symbol-systems have, then, at least one thing in common: they are extrinsic sources of information in terms of which human life can be patterned–extrapersonal mechanisms for the perception, understanding, judgment, and manipulation of the world. Culture patterns–religious, philosophical, aesthetic, scientific, ideological–are “programs”; they provide a template or blueprint for the organization of social and psychological processes, much as genetic systems provide such a template for the organization of organic processes (Geertz, 216)

How does this apply to ideology? It makes ideology a symbolic system for building an internal model. Geertz is distinctively not anti-psychological here but instead seems to double down on the “extrinsic theory of thought” to define culture as a symbol system through which agents construct models of and for some system out in the world, effectively programming their response to that system. Ideology refers to the symbol system that does this for the political system:

The function of ideology is to make an autonomous politics possible by providing the authoritative concepts that render it meaningful, the suasive images by means of which it can be sensibly grasped … Whatever else ideologies may be–projections of unacknowledged fears, disguises for ulterior motives, phatic expressions of group solidarity–they are, most distinctively, maps of problematic social reality and matrices for the creation of collective conscience (Geertz, 218, 220)

Geertz mentions the example of the Taft-Hartley Act (restricting labor unionizing) that carries the ideological label the “slave labor act.” Geertz emphasizes how ideology works according to how well or how poorly the model (“slave labor act”) “symbolically coerces … the discordant meanings [of its object] into a unitary conceptual framework” (210-211).

If GOFIT is a set of assumptions widely held about ideology, then we probably find little to disagree with in Geertz’s argument, at least at first glance. Much of it should ring true. If we object to anything it might be the heavy-handed language that Geertz uses that evokes modular or computational cognition (e.g. “programs”). But maybe Geertz himself is not responsible for this. His sources, Galanter and Gerstenhaber, were explicit in making these assumptions about cognition, and this I want to argue is important for a specific reason.

To Galanter and Gerstenhaber, “model” clearly meant the sort of three-dimensional scale models that scientists construct in order to understand large-scale physical phenomena. In this sense, they solved the “problem of human thinking” by defining it as a lesser version of idealized scientific thinking. And they were not alone in that pursuit. At least initially, cognition was presented as antithetical to behaviorism in psychology by allying itself with resources that were quite deliberate and quite reflexive: “[mid-century] cognitive scientists … looked for human nature by holding an image of what they were looking for in their [own] minds. The image they held was none other than their own self-image … ‘good academic thinking’ [became the] model of human thinking” (Cohen-Cole 2005).

This is not only the context for Geertz’s theory of ideology. His understanding of “symbol systems” writ large cannot be removed from this specific gloss on and an extension of “good academic thinking.” For our purposes, this should beg the question of whether using symbol systems to form internal models about the external world and to manipulate and creatively construe those models as equivalent to “symbolic action” should be the template or basis for defining ideology on nonevaluative grounds, that is to say, for defining ideology in the way that Geertz himself does: as cognitive.

Ideology and the Split-Brain

What I will try to do now, after this long preamble, is sketch a different possible cognitive basis for a theory of ideology, one that I think is compatible with Martin’s (2015) field-theoretic approach to ideology discussed in part one of this post. It develops a cognitive interpretation of what “practically mastery of ideologically relevant social relations” might mean. It also situates Marx as the contrary of Geertz by making social relations a necessary condition for ideology as a cognitive phenomenon, not something that needs to be bracketed (or pigeonholed as “strain” or “interest”) for ideology to be cognitive.

This different basis is Gazzaniga’s research (1967; 1998; Gazzaniga and Ledoux 1978) on the split-brain and the process of confabulation of meaning on the basis of incomplete visual input. It is important to mention that I use the split-brain as an analogue (in “good academic thinking” terms) to convey what ideology might mean as a cognitive phenomenon if it is not a symbol system. I do not imply that ideology requires a split-brain as a physical input.

For Gazzaniga, the two sides of the brain effectively constituted two separate spheres of consciousness, but this could only be truly appreciated when the corpus callosum was severed (what used to be a procedure for epileptic patients) and the two sides of the brain were rendered independent from each other. When this happened, the visual field was bissected as the brain stopped communicating information together that came through the right and left visual fields (hereafter RVF and LVF). What was observable in the RVF was received independently from what was observable in the LVF. As Gazzaniga found, the brain is multi-modal. The left hemisphere is the center of language about visual input. So when a word or image was flashed to the RVF and the information was received by the left hemisphere, the patient could provide an accurate report. When a word or image was flashed to the LVF, the patient could only confabulate because the non-integrated brain could not combine the visual information with the language functions of the left hemisphere. The split-brain patient effectively “didn’t see anything,” even though she could still connect visual cues to related pictures on command.

When visual information is presented to a split-brain, the mystery is how the verbal left hemisphere attempts to make sense of what the non-verbal right hemisphere is doing. This is the recipe for confabulations or “false memories” as Gazzaniga (1998) puts it, because here we witness the effects of the “interpreter mechanism.”

Thus, when the RVF and LVF of a split-brain patient were shown pictures of a house in the snow and a chicken’s claw, and the patient was asked to point to relevant pictures based on these visual cues, she pointed to a snow shovel and a chicken head respectively. Here is the interesting part:

the right hemisphere—that is, the left hand—correctly picked the shovel for the snowstorm; the right hand, controlled by the left hemisphere, correctly picked the chicken to go with the bird’s foot. Then we asked the patient why the left hand— or right hemisphere—was pointing to the shovel. Because only the left hemisphere retains the ability to talk, it answered. But because it could not know why the right hemisphere was doing what it was doing, it made up a story about what it could see—namely, the chicken. It said the right hemisphere chose the shovel to clean out a chicken shed (Gazzaniga 1998: 53; emphasis added).

“It made up a story” refers here to the verbal left hemisphere attempting to make sense of why right hemisphere had been directed toward a shovel. Flashing a picture to right hemisphere lacked any narrative ability, and yet the split-brain patient could still point at a relevant image even though this did not “pass through” language.

The argument here is that this serves as a good analogue for a theory of ideology that does not make computational or modular commitments. The important point is that confabulation is not just some made up story, but what the split-brain patient believes because his brain has filled in the blank (e.g. “I chose the shovel because I need to shovel out the chicken coop”). Ideology as a cognitive phenomenon does not, in this sense, mean programming the political system according to an extrinsic symbol system; in other words, building an internal model (a three-dimensional one) of that system and drawing entailments from it, as any good scientist would do. To be “in ideology” means filling in the blank as the normal way to cognitively cope with disconnected inputs, some with a “phonological representation,” others that are “nonspeaking.”

The Split-Brain and Social Relations

We can theorize that where practical mastery of social relations becomes important, in particular, social relations that are “ideologically-relevant,” it is because they generate an equivalent of a split-brain effect and its “interpreter mechanism.” In social relations arranged as fields, practical mastery consists of the “felt motivation of impulsion … to attach impulsion … to positions … [and have] the ethical or imperative nature of such motivations [be] akin to a social object, external and (locally) intersubjectively valid, that is, valid conditional on position and history” (Martin 2011: 312).

Fields refer to one type of social relation conducive to ideological effects, particularly if they are organized on quasi-Schmittian grounds of opponents and allies (Martin 2015). Marx is clear that other types of social relation (like capital) are specifically resistant to influence by any sort of cognitive mediation. Still, he achieves some understanding of those social relations by examining their “being thought … [through] abstractions” (see Marx 1973: 143). For instance, the commodity fetish can be seen as analogous to a split-brain effect: the “social relation between things” is an LVF interpretation, while the “social relation between people” is equivalent to an RVF input. A split-brain is an analogue of mental structures that correspond to these objective (social) structures.

Taking the split-brain as the basis (not the “extrinsic theory”) for ideology as a (non-GOFIT) cognitive phenomenon, then, we can speculate that only certain social relations (fields, capital) have an ideological effect. The ideological effect they do have is because they generate a split-brain scenario with disconnected inputs. Agents are subject to social relations in which they do not have direct access (RVF). They fill in the blank of the effect of those inputs through “abstractions,” i.e. explicit endorsements or propositional attitudes that take linguistic form, often mistaken on their own terms as ideology (LVF).

To be continued … [note: Zizek (2017: 119ff) also finds the split-brain useful for thinking about ideology, though his argument confounds and mystifies with Pokemon Go]

References

Cohen-Cole, Jamie. (2005). “The Reflexivity of Cognitive Science: The Scientist as a Model of Human Nature.” History of the Human Sciences 18: 107-139.

Galanter, Eugene and Murray Gerstenhaber. (1956). “On Thought: The Extrinsic Theory.” Psychological Review 63: 218-227.

Gazzaniga, Michael. (1967). “The Split-Brain in Man.” Scientific American 217: 24-29.

_____. (1998). “The Split-Brain Revisited.” Scientific American 279: 51-55.

Gazzaniga, Michael and Joseph LeDoux. (1978). The Integrated Mind. Plenum Press.

Geertz, Clifford. (1973). “Ideology as a Cultural System.” in Interpretation of Cultures.

Jost, John. (2006). “The End of the End of Ideology.” American Psychologist 61: 651-670.

Martin, John Levi. (2015). “What is Ideology?” Sociologica 77: 9-31.

_____. (2011). The Explanation of Social Action. Oxford.

Marx, Karl. (1973). The Grundrisse. Penguin.

Zizek, Slavoj. (2017). Incontinence of the Void. MIT