A Taxonomy of Artifactual (Cultural) Kinds

In previous posts, I made a broad distinction between the two “families” of cultural kinds. This distinction was based on the way they fundamentally interact with people. Some cultural kinds do their work because they can be learned or internalized by people. Other cultural kinds do their work not because people internalize them but because they can be wielded or manipulated. For the most part, these last exist outside people (or at least being potentially separable from people’s bodies). We referred to the former as cultural-cognitive kinds (or cognitive kinds for short) and to the latter as artifactual cultural kinds (or artifactual kinds for short).

Most of the cultural stuff that exists outside of people (so-called “public culture”) is either an artifact, whether simple or complex (usually referred to as “material culture”), a systematic or improvised coupling between a person and an artifact (usually mediated by an internalized cultural kind such as a learned skill or ability), or a more extended socio-material ensemble (Hutchins, 1995; Malafouris, 2013), consisting of the distributed agglomeration of artifacts, people, and the knowledge (both explicit and implicit) required to use the artifacts in the setting for particular purposes, whether instrumental, expressive, or performative. Traditional cultural theory in sociology and anthropology tends to embody purpose in internalized cultural-cognitive kinds such as beliefs, goals, and values. However, an argument can be made that nothing embodies purpose (and even teleology) more directly than artifactual kinds designed to accomplish concrete ends (Malafouris, 2013).

Subsequent posts were dedicated to the process via which people internalize cultural-cognitive kinds. These reflections yielded an emergent and intuitive typology within the broad “family” of cultural cognitive kinds. Some cognitive kinds are like beliefs, encoding explicit declarations or propositions. Other cognitive kinds are more like skills or abilities and are difficult to verbalize in explicit form. A third form is in-between, more like concepts, encoding general semantic knowledge (both schematic and detail-rich) of the explicit and implicit aspects of categories. Riffing on a classic distinction in the philosophy of mind and action, we referred to the first kind as “knowlege-that,” the second kind as “knowlege-how,” and the third one as “knowledge-what.” The idea is that this provides an admittedly rough but exhaustive taxonomy of cultural-cognitive kinds as people internalize them.

Given this, it is easy to form the impression that artifactual (public) cultural kinds are an undifferentiated mass. However, recent work in cognitive science and philosophy has endeavored to provide a more differentiated taxonomic picture of the various forms artifactual kinds can take (Fasoli, 2018; Heersmink, 2021; Viola, 2021). In a forthcoming paper in a special issue of Topics in Cognitive Science dedicated to “the cognitive science of tools and techniques,” Richard Heersmink (2021) provides a useful generic typology of artifactual cultural kinds that aims for the same level of generality and exhaustiveness, concerning artifactual cultural kinds, as the knowledge-that/how/what typology concerning cultural-cognitive kinds.

Heersmink (2021) defines an artifact in the broadest sense as “material objects or structures that are made to be used to achieve an aim.” Heersmink differentiates between four broad families of artifacts: Embodied, perceptual, cognitive, and affective. To each type of artifact corresponds a specific set of skills of abilities people develop when they become good and proficient at using them, which Heersmink refers to as techniques (an approach in the same spirit as Mauss, 1973). Thus, there are embodied techniques, perceptual techniques, and so forth.

Artifact/technique is an important distinction, which separates the “cognitive” family of cultural kinds from the artifactual one. However, they tend to be run together in the literature. For instance, Hutchins (1995, p.) refers to the internalized (ability) component corresponding to the use of an external artifact as an “internal artifact.” However, this is confusing and blurs an important analytic line. As Heersmink (2013, p. 468) noted in earlier work,

it is clarifying to make a distinction between technology and technique. A technology (or artifact) is usually defined as a physical object intentionally designed, made, and used for a particular purpose, whereas a technique (or skill) is a method or procedure for doing something. Both technologies and techniques are intentionally developed and used for some purpose and are in that sense artificial, i.e., human-made. However, it is important to note, or so I claim, that they are not both artifactual. Only technologies are artifactual in that they are designed and manufactured physical objects and in this sense what Hutchins refers to as internal artifacts, such as perceptual strategies, can best be seen as cognitive techniques, rather than as internal artifacts. Moreover, given that these cognitive techniques are learned from other navigators and are thus first external to the embodied agent, it is perhaps more accurate to refer to them as internalized cognitive techniques, rather than as internal cognitive techniques.

Being “artifactual,” and thus usable (e.g., made by people but external to people and embodied in material objects but not “internalizable” by people) is diagnostic for artifacts as public cultural kinds. In the same way, being “internalizable,” is diagnostic for cognitive kinds such as skills, know-how, and abilities. This (internalizability criterion) is the distinguishing marker that separates them from artifactual kinds. Both are cultural kinds because they are the historical product of human ingenuity and invention.

Embodied artifacts are the “prototypical” of the category since they show up mainly as tools we use to get stuff accomplished. In philosophy and social theory, “Heidegger’s hammer,” and Merleau-Ponty’s “blind person’s cane” are the standard examples. Enumerating specific exemplars of the category is of course an endless task, as it includes any material object that can be used to accomplish a goal (e.g., pencils, shovels, fly swatters, brooms, skateboards, keyboards, etc.). It also includes using objects not designed for a given function to accomplish a particular goal (as when we use a hammer as a doorstop). While the “proper function” of a hammer is to drive nails through a surface, it can also be used for a myriad of improvised goals, and the same goes for pretty much every embodied artifact. Concerning the person-artifact interface, the critical phenomenological transition with regard to embodied artifacts happens when we become proficient at using them after repeatedly interacting with them (or more commonly being taught by an expert user how to use them). This results in internalization, via either socialization or enculturation, of artifact-specific skills or abilities facilitating person-and-artifact couplings. Once this coupling is established, the artifact or tool becomes transparent. It is experienced as a natural extension of the body. Following Heidegger, artifacts that have achieved this level of transparency are referred as “equipment” (Dreyfus, 1984).

Perceptual artifacts are used to correct, enhance, extend, and in some cases substitute our natural perceptual abilities. Reading glasses or hearing aids are a standard (corrective) example and telescopes or binoculars a standard (enhancing/extending) example. Merleau-Ponty’s blind man’s cane can be thought of as an embodied artifact that becomes a perceptual artifact via cross-modal substitution; tactile information comes to play the functional role for non-sighted persons that visual information plays for sighted people via the mediation of the artifact. In some cases, perceptual artifacts can be engineered so that they can make available to us aspects of the world that are naturally inaccessible to us (e.g., lightwaves in the infrared range of the spectrum). This is a type of enhancement that goes beyond amplifying the usual range of our standard perceptual techniques.

Naturally, cognitive artifacts have received a tremendous amount of attention in cognitive science and the philosophy of mind (Clark, 2008). Heersmink defines them as “…human-made, material objects or structures that functionally contribute to performing a cognitive task” (Heersmink, 2021, p. 10). Cognitive artifacts have even been used as “intuition pumps,” to show how cognition and cognitive activity can be thought of as (sometimes) occurring “outside the head,” using artifactual vehicles (e.g., a notepad or an abacus) used by people to perform cognitive tasks such as remembering and calculating (Clark & Chalmers, 1998), yielding the hypothesis of “extended cognition.” Independently of their role in this particular line of investigation, cognitive artifacts are central to the study of culture. Cognitive artifacts such as calculators, maps, multiplication tables, computers, and the like are ubiquitous in our everyday lives, facilitating a virtually open-ended range of cognitive, navigational, and calculative activities that would be either very difficult or impossible to do without them.

Affective artifacts refer to “material…objects that have the capacity to alter the affective condition of the agent” (Piredda, 2019, p. 550). Under this definition, affective artifacts are pervasive and may even precede cognitive artifacts in human evolution (Langer, 1967). They include most of the human-designed implements for the production of expressive and aesthetic symbols (e.g., music, visual arts, poetry, and the like) such as musical instruments, as well as the product of their use such as aesthetic objects and performances. Language (typically a cognitive artifact), when used in particular ways to evoke affect and emotion, becomes an affective artifact. When used to evoke feeling and emotion in a ritual or aesthetic performance, or when the voice is used for a similar purpose in singing, people’s bodies and their effectors can become the affective artifact par excellence.

As Heersmink notes, these taxonomic distinctions do not imply that many artifacts end up being hybrids, performing multiple functions at once. Thus, many perceptual artifacts (e.g., a microscope) also perform cognitive functions. Cognitive artifacts (such as a family photograph) may bring up emotionally charged autobiographical memories, thus performing affective functions. Merleau-Ponty’s blind man’s cane, as noted, is both an embodied and a perceptual artifact. Artifacts can also be linked in chains, such that one kind of artifact helps us use another one. The most coupling is embodied artifacts and cognitive artifacts; for instance, mice and keyboards help us interact with computers as cognitive artifacts. Most artifacts as used in everyday dealings consist of such hybrids or multiple chains of artifact families.

References

Clark, A. (2008). Supersizing the Mind: Embodiment, Action, and Cognitive Extension. Oxford University Press,.

Clark, A., & Chalmers, D. (1998). The Extended Mind. Analysis, 58(1), 7–19.

Dreyfus, H. L. (1984). Between Technē and Technology: The Ambiguous Place of Equipment in Being and Time. Tulane Studies in Philosophy, 32, 23–35.

Fasoli, M. (2018). Substitutive, Complementary and Constitutive Cognitive Artifacts: Developing an Interaction-Centered Approach. Review of Philosophy and Psychology, 9(3), 671–687.

Hutchins, E. (1995). Cognition in the Wild. MIT Press.

Heersmink, R. (2013). A Taxonomy of Cognitive Artifacts: Function, Information, and Categories. Review of Philosophy and Psychology, 4(3), 465–481.

Heersmink, R. (2021). Varieties of artifacts: Embodied, perceptual, cognitive, and affective. Retrieved May 23, 2021, from https://philpapers.org/archive/HEEVOA.pdf

Langer, S. K. K. (1967). Mind: an essay on human feeling. Johns Hopkins Press.

Mauss, M. (1973). Techniques of the body. Economy and Society, 2(1), 70–88. (Original work published 1935)

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

Piredda, G. (2020). What is an affective artifact? A further development in situated affectivity. Phenomenology and the Cognitive Sciences, 19(3), 549–567.

Viola, M. (2021). Three Varieties of Affective Artifacts: Feeling, Evaluative and Motivational Artifacts. https://doi.org/10.3389/fpsyg.2016.00266

On Looping Effects

In this post, I sketch out some preliminary ideas for introducing repetition into theories of social formation and for situating cognition at their base. The major principle for this endeavor is what I (unoriginally) propose as loops. More originally, I argue that loops take at least three different forms and that not all looping effects are created equal. Loops are equal, however, in putting the onus on repetition as a source of scaled orders and formations (e.g. “structures,” “enclosures,” “molds,” “modulations”) rather than on generality.

A loop, quite simply, is a generative process with one necessary condition: a non-identity between two parts, but two parts that repeat in their connection. A repeating loop makes a scaled order particular rather than general, because it is fundamentally a sequence. Loops might be broadly instantiated, but they cannot find equivalences everywhere. As repetitions, they need the same ingredients. As connections and cycles, they remain distinctive rather than ordinary.

Douglas (1986: 100-02) suggests that a looping effect need not directly involve a human agent at all; they are mostly natural. Knowledge of microbes leads to medications; when those medications are applied to microbes, the microbes adapt. This explains a clear feedback relation, but it is not a loop: microbes change because of knowledge about them; but the change needs no repetition. It is adaptive instead.

The thing about a loop is that it must repeat (again and again). A loop must generate its own momentum. This link between repetition and loops has been neglected to date. Such neglect means that looping effects are discussed separately from the prevalence of scaled orders and formations, for instance those that are “disciplinary” versus those that are “control,” leaving both of fuzzy meaning and provenance, used interchangeably. When we concentrate on looping effects as repetitions, it becomes clear that orders and formations of scale (e.g. not presumed to be general; that cannot find equivalences everywhere) rest largely on an edifice of cognition that, in practice, does not need to remain implicit in order to be effective.

Hacking loops (enclosures, molds)

For Hacking (1995; 2006), loops come from the process of classification and categorization that feeds a dynamic nominalism. Classifications are made by people about people, as an index of traits, of the properties displayed by the latter. In a Hacking loop, classifications made by people about people loop into their target and alter it. The classifiers “create certain kinds of people that in a certain sense did not exist before.” The onus rests on the name given to these traits that collects them together. Hacking loops therefore represent a form of nominalism: they need not become entangled with real kinds or make any difference for them. What matters more is the name, its legitimation by expertise, its elaboration by institutions, its officialization by bureaucracy, all of which reinforces its external, public and legitimate presence.

As Hacking puts it:

In 1955 “multiple personalities” was not a way to be a person, people did not experience themselves in this way, they did not interact with their friends, their families, their employers, their counsellors, in this way; but in 1985 this was a way to be a person, to experience oneself, to live in society (2006).

The intervention of 30 years meant the formulation of this name, the proliferation of knowledge and the accumulation of references under its heading, all giving it a stable external presence, by indexing various evident things (e.g. “this is that”) in a distinguishable way. While the traits of multiple personality (or manic depression, anxiety disorder, etc) might have preceded the name, this is not Hacking’s point: as a “way to be a person,” multiple personality needed a name that could index these traits and thereafter be a way of indexing of oneself (e.g. “that is me”). Such a sequence (this is that → that is me) becomes fully reversible (that is me → this is that). A name, increasingly standardized, rationalized and externalized (e.g. “discourse”) makes up and stabilizes people by feeding back into their identification (this is that ←→ that is me).

To be a certain type of person, to live in society as that person, to be interacted with as that person, and most importantly to experience oneself as that person occurs through a Hacking loop. Importantly, all of these effects require only an external process rather than a change of “inner life.” A name is “deep” in a contingent sense: the loop rests upon the identification of those who are indexed. It makes no substantive difference for the traits that are indexed that they now receive a name. In fact, Hacking loops seem to require this given the proliferation of names and entire professions and forms of expertise dedicated to classification. However, it does make a difference for those who are classified and named. Hacking loops create an enclosure or mold, from which there might be no escape so long as the name is externally maintained.

Mutually sustaining relations (structures)

A different loop is proposed by Sewell in what we might call his principle of mutually sustaining relations. The terminology here concentrates on a “mutually sustaining” loop between two different kinds of things (schemas and resources), as mentioned in the following influential formula and applied (famously) to a theory of structure:

Structures … are constituted by mutually sustaining cultural schemas and sets of resources that empower and constrain social action and tend to be reproduced by that action. Agents are empowered by structures, both by the knowledge of cultural schemas that enables them to mobilize resources and by the access to resources that enables them to enact schemas (27).

As Sewell implies, schemas and resources mutually sustain each other through a repeating connection. A schema remains an effect of resources, just resources are the effect of schemas.

When the priest transforms the host and wine into the body and blood of Christ and administers the host to communicants, the communicants are suffused by a sense of spiritual well-being. Communion therefore demonstrates to the communicants the reality and power of the rule of apostolic succession that made the priest a priest. In short, if resources are instantiations or embodiments of schemas, they therefore inculcate and justify the schemas as well (13)

Unlike a Hacking loop, Sewell’s “mutually sustaining” loop does suggest a deep effect, as the very constitution of a set of properties as “resources” are schema dependent, just as the constitution of mental categories as “schemas” are resource dependent. A resource is equivalent to the traits that a Hacking loop collects under the heading of a name, but a schema does not “name” them. Sewell characterizes the mutually sustaining link as instead “reading” or “interpreting.” A resource needs to be read as a resource in order to be a resource. A schema does the reading. A schema, presumably, is not a schema if it does not read or interpret resources. The loop can be initiated through either end: resource accumulation to a schema (resource → schema) or schema accumulation to a resource (schema → resource). A loop becomes difficult to sustain in cases that allow for too much agency (e.g. transposition of schemas), which prevents an unambiguous rendering of resources.

In cases where there are limited schemas for “reading” and “interpreting” a resource, and this is in turn “sustained” by limited resources for other possible schemas, a “structure” will result. A structure is distinguishable from a “mold” or “enclosure” in a Hacking loop. Structure, by contrast, suggests not only a potential source of resistance but also the limits of meaning. This entails the “depth” of structure as opposed to the externality of a mold. Structure refers to inner life, which it substantially depends on shaping and altering. The surface-level chaos of capitalism, for instance, only signals the depth of a schema ←→ resource loop: the schematic and repeating transformation of use- to exchange-value is a necessary condition for “resource” in this context; resources, meanwhile, accumulate to “schemas” that involve a use-to-exchange transformation.

We should expect structures to change through disruption to an established loop, via the interchangeability and replacement of both parts of structural loops (schemas and resources). This creates demands on inner life through transpositions that likely appear “impractical” in their interpretations and reading of things. The chance of resource accumulation keeps the possibility of structural change open.

Expectations-chances loops (modulations)

Hacking loops and Sewell’s mutually sustaining loops are both known well-enough by this point as to render the above discussion boring by comparison. To finish this post, I want to make two proposals: first, that Hacking’s “molds” and “castings” and Sewell’s “structures” are both loops found within a disciplinary order. This suggests a relative limit on their generality, though equally they remain contingent on repetition (as loops). Second, I want to understand a disciplinary order as distinct from a control order based on a different loop that engages cognition differently than naming, reading, or interpreting (Deleuze 1992). This is a expectations-chances loop that works according to (objective) prediction and (subjective) guessing (see Bourdieu 1973: 64).

In one version of this loop, the tale is told indicatively as follows:

Acrimonious debates about the calculative abilities of individuals and the limits of human rationality have given way to an empirical matter-of-factness about measuring action in real life, and indeed in real time. The computers won, but not because we were able to build abstract models and complex simulations of human reasoning. They bypassed the problem of the agent’s inner life altogether. The new machines do not need to be able to think; they just need to be able to learn. Correspondingly, ideas about action have changed (Fourcade and Healy 2017: 24).

Hence, a proposal for non-intentional action becomes applicable to data-gathering mechanisms, but the “index” is different in this scenario, as it includes “inner life” no longer. “Culture” is an association rather than internalized pattern generator. It does not have effects, but rather stands for a history of traces:

When people are presumptively rational, behavioral failure comes primarily from the lack of sufficient information, from noise, poor signaling or limited information-processing abilities. But when information is plentiful, and the focus is on behavior, all that is left are concrete, practical actions, often recast as good or bad ‘choices’ by the agentic perspective dominant in common sense and economic discourse. The vast amounts of concrete data about actual ‘decisions’ people make offer many possibilities of judgment, especially when the end product is an individual score or rating. Outcomes are thus likely to be experienced as morally deserved positions, based on one’s prior good actions and good taste.

A theory of action remains, then, even despite the absence of inner life; because data is simply action. Data can modulate action through a “herding” or directing effect, creating futures based on past performance and subsequent encoding. Since there is no inner life, classification is based on information collected at junctures that create possible futures. The causes of action are not of interest (only that action happens), though there are consequences to action. This can exercise a disciplinary effect through anticipation, as facilitated by the rationalization of trials. Since there is no ideal model (or name gathering of characteristics a priori), however, this is not integral to control. There is only the fact that one must have been through certain trials and then out of them.

Predictions made through data protocols interface with predictions made in action. Trials introduce uncertainties that meet with anticipations; a certain future is achievable when possibilities are presented algorithmically and displace an otherwise “wild’ cognition. Control becomes an algorithmic modulation of future possibilities rather than a generative modulation of guesses.

The systematic production of “good matches” is based on controls exercised on the means of prediction from both ends: the expropriation of the means of prediction and the controlled distribution of what they predict. This keeps the loop closed between the (objective) provision of possibilities and (subjective) anticipations or guesses, making “this matching feel all the more natural because it comes from within—from cues about ourselves that we volunteered, or erratically left behind, or that were extracted from us in various parts of the digital infrastructure” (Fourcade and Healy 17).

Modulation takes place through cognitive loops, constructing a “self-deforming cast that will continually change from one moment to the other, or a sieve whose mesh will transmute from point to point” (Deleuze 4). Conventionally, the connection between “schema” and power is content-laden and substantive: it provides a way to “read” resources (Sewell 13). An expectations-chances loop finds no equivalent to “reading” (or interpreting or naming); the key process is guessing instead. A non-individual recorder or record-keeper (qua technology) can guess even if it cannot read, and it can adapt its guesses, improve them. Here looping is incompatible with “molding” or “casting”; “structure” is static by comparison. After all, you can know when you leave the “cast” and its standard no longer applies.

The theory of power embedded in a schema-resources loop puts the onus on schemas that “read” resources; this is where we find agency. In a disciplinary context, an ideal or standard (a telos) is enforced and sought after. In control contexts, such a standard goes missing. Trials are not examinations. A model is volunteered rather than enforced. An individual is a record, though there is no record-keeping individual (“examiner” or “recorder”). Rather than being incorporated into a structure (through schemas), agents are made precise as a code or classification. They do not exercise effects (structural or otherwise) but are given possible futures. They are not shoehorned into the fixed parameters of a schema. They bootstrap themselves into sequences that look increasingly like their own good matches.

Conclusion

We should therefore expect the genesis and transposition of expectations just as we do those of schemas or names, in looping connection with chances, as a way of inviting chance in or taming it. But there is a catch. The consequence of a “controlled” expectations-chances loop can be similar to the amnesiac returning to memory after several long years: “My God! What did I do in all those years?” (Bourdieu [1995] quoting Deleuze 1993). Consider, along exactly similar lines, a “coming to” after diving down an algorithmically modulated rabbit-hole. The explanation must be cognitive because this occurs through repeating loops. Disciplinary formations can achieve (reflexive) “consciousness” and nothing will change; the same is not true for control formations.

References

Bourdieu, Pierre. (1973). “Three forms of theoretical knowledge.” Social Science Information 12: 53-80.

Bourdieu, Pierre. (1995). The State Nobility. Stanford University Press.

Deleuze, Gilles. (1992). “Postscript on societies of control.” October 59: 3-7.

Deleuze, Gilles. (1993). The Fold: Leibniz and the Baroque. University of Minnesota Press.

Douglas, Mary. (1986). How Institutions Think. Syracuse University Press.

Fourcade, Marion and Kieran Healy. (2017). “Seeing like a market.” Socio-Economic Review 15: 9-29.

Hacking, Ian. (1995). “The looping effects of human kinds.” Pp. 351-394 in Causal Cognition: A Multidisciplinary Debate.

Hacking, Ian. (2006). “Making up people.” LRB 28.

Sewell, William. (1992). “A theory of structure: duality, agency and transformation.” American Journal of Sociology 98: 1-29.

Can Schemas Motivate?

In an influential paper entitled “Schemas and Motivation,” the cognitive anthropologist Roy D’Andrade once remarked on the curious lack of relation (with reference to anthropological theory)

…between culture and action. Of course, one can say ‘people do what they do because their culture makes them do it.’ The problem with this formulation is that it does not explain anything. Do people always do what their culture tells them to? If they do, why do they? If they don’t, why don’t they? And how does culture make them do it? Unless there is some specification of how culture ‘makes’ people do what they do, no explanation has been given (1992: 23).

D’Andrade’s overall observation, namely, that cultural theory is not worth its salt unless it tells us how culture links to action, is important and worth making, as I noted in a previous post. As social scientists, we care about culture to the extent that it helps us explain what people do. In the same way, D’Andrade’s dismissal of the “naive” or unqualified version of the “culture causes action” (CCA) thesis is on the right track. In its unqualified form, CCA is explanatorily vacuous because it is completely unconstrained and does not specify the mechanisms via which such causal effects are supposed to happen.

D’Andrade notes that the “explanatory gap” he points to is particularly salient when it comes to trying to explain why people put effort and striving in engaging in some lines of action at the expense of others. For D’Andrade, there is a “standard account” that posits that culture helps in action selection (and persistence) because culture helps to motivate people to pursue one line of activity over others. But it

…remains unclear how culture is connected to motivational strivings. Without an account of the relation between culture and motivation, we may have an intuitive sense that there are culturally based strivings, but we have no explanation of this (1992: 23).

D’Andrade observes that to link culture and motivation, we must clarify what we mean by motivation. He proposes a quick and dirty definition based on the usual “folk” understanding. For D’Andrade, “motivation is experienced as a desire or wish, followed by a feeling of satisfaction if the desire is fulfilled.” Thus, motivation is intimately linked to the folk category of desire (as more recently argued by Schroeder 2004). Motivation also has to do with internal processes that “energize” or activate people to act in a given setting (as more recently argued by Turner 2010); “[a]long with this increase in activity there is is typically a striving for something—a goal directedness in behavior” (24).

Thus, motivation is the persistent, energized pursuit of goals, where the latter pertains to the fulfillment of desires. D’Andrade goes on to review models of motivation that were prominent in mid-twentieth century psychological science, namely, those conceptualizing motivation in terms of “drive reduction” (e.g., satiation of hunger, thirst, and the like) and those conceptualizing motivation in terms of “need-fulfullment” where the “needs” concern usually very long (or open-ended) lists of abstract things, states, or relations people might desire to pursue (e.g., achievement, autonomy, affiliation, order, dominance, and the like).

D’Andrade profers three (correct) critiques of such models.

First, a “list of motives” approach is incapable of capturing the open-ended nature of human desire (Schroeder 2004). Essentially, there is nothing that cannot be conceptualized as a “need,” which means that analysts will be forced to include all kinds of heterogeneous, incompatible, and contradictory goals (e.g., needs for “abasement,” and “enhancement”) into any presumably exhaustive (but largely unstructured) list. Both arbitrariness and the lack of structure make these lists suspect.
Second, motive lists will always be incomplete. Such lists will be necessarily partial and tilted towards the “needs” or strivings that make sense to WEIRD populations. They will necessarily lack cross-cultural (or even historical) coverage and thus will be powerless to account for the full observed empirical variation of motives and motivations exhibited by people.
Third, there are very few “trans-situational” motives. Most motivations are contextually specific; they are inclinations or dispositions to pursue particular goals in particular settings. That is why lists of motives end up degenerating into lists of personality-like traits. Saying someone has a trans-situational “need for dominance” is no different from saying that they will be aggressive in all or most settings. But as modern personality research shows (Cervone 2005), there are few (or no) trans-situational personality traits, needs, or strivings. List of motives approaches cannot capture the “situated” nature of human motivation.

D’Andrade also points to the difficulty of measuring motives (a problem shared by all theories of motives). At one point, analysts inspired by the list of motives approach relied on discredited instruments taken from psychodynamic theories (e.g., inkblot tests). Today, the workhorse measurement method is self-report, whether in surveys or interviews, as these approaches are more likely to capture the cultural and contextual specificity of motives. Regardless, the main point is that without calibrating standard social science techniques to detect people’s wishes, desires, goals, and strivings, the search for motives grounded in a solid empirical footing will continue to be elusive.

Motives as Schemas

D’Andrade provides a swift solution to these problems: Conceptualize motives as schemas. Thinking of motives as schemas is useful, according to D’Andrade, because of three (representational) properties schemas have.

First, schemas can capture the processual and interpretive nature of many motives and motivations. In particular, schemas are useful for representing categorical domains with “prototype” organization, are readily memorable, and are used to fill in the blanks in context. Human motivation is one such domain. Representing goals in schematic format thus makes them cognitively available and usable.
Second, D’Andrade claims that schemas “have the potential of instigating action” (29). Although as we will see, he never quite cashes in on this claim. He points to the American “schema of achievement” as an example. D’Andrade notes that this schema does more than just representing the concept of achievement; it also functions as a “goal” for people. Albeit a goal of varying strength depending on the specificities of the situation in which it is activated.
Third, goal-schemas differ in their level of autonomy. This means that both motivations to engage in relatively short-term actions that are the means to larger goals, and more pervasive goals people pursue at longer time scales (perhaps lasting a lifetime) can be represented as schemas. In this way, low autonomy goals are embedded within larger projects. For instance, we activate the driving the car schema in order to make it to the PTA meeting, which satisfies a higher-order motive for affiliation or social integration. However, others (high autonomy) goals operate more or less as pervasive or chronically active (e.g., dominance, achievement). People for whom a given goal is in a high state of activation are likely to interpret even ambiguous cues in situations as prompts to engage in actions that are consistent with those goals.
Fourth, schemas differ in their schematicity, with some more specific or lower-level schemas nested within higher-level ones (thus reproducing standard categorical taxonomies). This, for D’Andrade, solves the problem of unstructured lists of motives. Instead of coming as an unstructured (and arbitrary) list, motives are structurally organized as hierarchies, with some of the vague needs and motivations (power, achievement, affiliation, and the like) being at the top, and then more specific action-guiding schemas (become a CEO, join the local PTA) at lower levels. For D’Andrade, goal-schemas at a lower-level of schematicity (and thus higher in specificity) are more context-driven, while higher-level (and thus more schematic) goal-schemas function as the pervasive “goals” of classical theories of motivation. These (autonomous) motives function “as a person’s most general goals,” or “master motives” (30). They are not directly connected to action (because many particular actions would be consistent with the schema). Still, They are connected to specific actions via more particular goal-schemas.

In sum, for D’Andrade, schemas solve many problems for anyone seeking to link culture, cognition, and motivation. Thinking of goals as having schematic representation in human memory allows us to understand human action as the result of cognitive structures activated in the situation, used by the person for categorization and interpretation, which ultimately “instigate” action. This context dependence accounts for situational variations in motivated action within-persons. At the same time, since motives differ in both autonomy and specificity, schemas can also represent pervasive, chronically active motivations that transcend situations. In contrast to the list of motives approach, the schema approach allows to properly theorize people’s goals as being part of an “overall interpretive system,” in which goals interrelate in structured ways, such as the hierarchical organization of lower-level goals nested within more schematic master motives. Finally, because schematic representation is a general representational format (capable of capturing anything that can be conceptually represented), there is no one “list” of motives; instead, “there are at least as many kinds of motives as there kinds of goal-schemas” (32). This accounts for cross-cultural variability in motivations since many goal-schemas will be specific to particular settings and locations. Schematic representation also facilitates the social-scientific job of identifying motives empirically. When motives are conceptualized as schemas, this task becomes the same as the more general endeavor of identifying schemas in text, discourse, and talk (Mohr et al. 2020; Quinn 2016).

How do Goal-Schemas Motivate?

D’Andrade’s argument that goals can be stored in human memory in the form of (more or less) schematic representations endowed with systematic organization is compelling. That is, D’Andrade provides (one) story of how one aspect of human motivation (the goals towards which we strive) is internalized as personal culture in the forms of a particular set of representations. However, representation is necessary but not sufficient for motivation. For a mental state or structure to be motivational, it must have the power to cause action. D’Andrade uses various metaphors to refer to this power in the paper, such as “instigate.” However, it is unclear how exactly a goal representation can be motivational. After all, we can have many goals represented in memory (or even currently active) without any of those goals “moving” us to act.

Toward the end of the paper, D’Andrade gives another shot to explaining how an internalized goal-schema can be motivating. Here, he moves to a different metaphor: The idea that some internalized goal-schemas have “directive force.” Directive force can be thought of in the (Durkheimian) sense of a given representation exercising a “sense of [moral] obligation” in people. But for D’Andrade, this is actually “a special case of the more general phenomenon of motivation.” And therefore, schemas are “equally central to things people wanted directly—love, friendship, success…some of these schemas turn out to have their own obligations as well as their direct and indirect rewards” (36). D’Andrade then notes that these provide a link between the conception of motives as goal-schemas and Melford Spiro’s model of “levels” of internalization of cultural beliefs. Schemas endowed with motivational force would thus be those that have the “deepest” levels of internalization in Spiro’s sense. According to Spiro, people can go from simply being “acquainted,” with some set of public representations (level 1), to accepting them as half-hearted cliches (level 2), to adopting them as part of their stock of personal beliefs (level 3), to having them motivate and guide their action in everyday life (level 4) (D’Andrade, 1995, pp. 227–228). Only culture “taken up” at levels 3 and 4 counts as “internalized,” in a way that could plausibly “motivate” action.

However, there is a problem here. The idea of internalization “depth” that D’Andrade, Spiro, along with other psychological anthropologists (Quinn et al., 2018a, 2018b) talk about is not a generic internalization story (in the sense discussed in a previous post). Instead, it is a special-purpose story that only applies to culture internalized as explicit, verbalizable belief; essentially knowledge-that (as distinguished from knowledge-what; see here for further discussion of knowledge-what). In a later publication, D’Andrade made this clear, noting that “[a]t the third level [of internalization], individuals hold their beliefs to be true, correct, or right” (1995, pp. 228, italics added). But as described by D’Andrade, goal-schemas are not a type of knowledge-that. Instead, they are a type of (categorical or conceptual) knowledge-what, endowed with all the characteristics of concepts when internalized in long-term memory and used for the same tasks (interpretation, property induction, inference, categorization, and the like).

In this last respect, “levels” of internalization can be plausibly distinguished for beliefs concerning the “commitment” dimension. But this “levels imagery breaks down when it comes to the internalization of conceptual knowledge-what. It is nonsensical to say that people have a “lightly held” concept of achievement, affiliation, power, self-enhancement, and the like. Individual differences in internalized knowledge-what can be made, but the relevant dimension of internalization is not “depth” or “commitment” but something like “elaborateness.” Experts in a domain have more elaborate concepts of the entities and activities in that domain, not “deeper” ones. People for whom achievement is important may also have a more elaborate conceptual network (and perhaps hierarchical schema taxonomy) connecting various achievement-related goals and actions across various settings.

Overall, the metaphor of “cultural depth,” while taken as a general-purpose account of cultural internalization (Sewell, 1992; Swidler, 2001), is a special-purpose story applicable to certain forms of knowledge that, like beliefs, encoding propositions about the world. While distinctions between different internalization modes can be made concerning knowledge-what, these will have very little to do with the idea of “depth,” or strength of commitment. In the end, it is unclear whether a schema is the sort of internalized culture to which the idea of levels of commitment applies. But more generally, it is doubtful that one can get a theory of motivation from a theory of degrees of commitment to such entities as beliefs or propositions. This account of motivation is not only overly intellectualist (as it restricts itself to consciously held belief), it is also not compatible with the very definition of motivation that D’Andrade began the paper with (where motivation is defined in terms of desire, want, pleasure, and reward). This commitment theory of motivation is also incompatible with the schema theory of representation that D’Andrade pursues in the paper. While motivation does undoubtedly have a representational component (something cannot be a motivation unless it is represented cognitively by the agent in some way) that role remains obscured in D’Andrade’s treatment.

Conclusion

Overall, D’Andrade’s critique of the “list of motives” approach is well-taken, as is his suggestion that thinking of goals is represented in long-term memory in the form of schemas. D’Andrade thus provides an instructive account of how thinking about the format of mental representation can help us rethink some central concepts in cultural analysis such as “goals” or “ends.” The paper’s key message is still a sound one; to link culture and action, you need to have a story of how culture is internalized and represented in memory. Mental representation (of goals, needs, desires, objects) is key because there can be no motivation without representation (Schroeder 2004). This approach can be extended by considering that schemas are only one way to represent goals in memory. After all, there is no reason why (following Rupert 2011) goals could also be represented by a panoply of other types of representation described by cognitive scientists, including (already considered) propositional beliefs, episodic memories, action-oriented representations, embodied representations, perceptual symbols, and many others.

However, to connect culture represented at the personal level to action, we need a substantive account of how mental states can be implicated in the causation of action; essentially we need a theory of motivation. Unfortunately, D’Andrade never closes the gap between the general representational proposal and actual motivational mechanisms. Nowhere are we told how purely representational, conceptual, or schematic mental representations can go on to “energize” or sustain motivated action in context. Missing are key elements that any theory of motivation should have (and which were embedded in D’Andrade’s very definition of the concept), such as wants, striving, desire, reward, pleasure, reinforcement, and learning (Kringelbach and Berridge 2016). Instead, D’Andrade never moves from purely metaphorical versions of how a purely representational state links to action, for instance, speaking of schematically represented goals can “instigate” action once activated (which sounds like a covert, and largely unsatisfactory, “ideomotor” account of the link between represented goals action of the “monkey represents, monkey does” type). This cannot deal with the fact that people in a given setting walk around with many goal representations that never become motivational. Ultimately, it is unclear why some goal representations have this instigating virtue and others do not.

When he tries to get more concrete, D’Andrade provides a (familiar to sociologist) story: the goal representations that motivate are the ones that have been “deeply” internalized. But beyond the fact that this is just another (spatial) metaphor, the account D’Andrade provides, based on Spiro’s theory of internalization, does not even match the representational format he spent the entire paper arguing goals are represented in: Conceptual knowledge-what combining procedural and declarative components. Instead, the Spiro levels account for a special-purpose internalization story applicable to “beliefs.” Even in the case of belief, it is unclear whether the Spiro story actually tells us how beliefs motivate without relying on circularities and tautologies. That is, it seems like the deeply internalized beliefs (levels 3 and 4) are the ones causally implicated in the production of action, but as we saw earlier, this is literally the definition of what it is for a mental state to be motivating. We are not given an “origin” story of why some belief-like mental states acquire this power.

This is not to pile on D’Andrade (or Spiro). The problem of linking culture and action via motivation is a tough one. But as argued before, even if some solutions previously provided are not up to par, we can agree on what the general outlines of a satisfactory solution can be. In this post, we have learned that having an account of cultural internalization or how culture is represented in memory is not enough. This is especially the case when linking culture and motivation, because motivation while incorporating a representational component, is not exhausted by it. Thus a theory that links culture to action must also be a theory of motivation, as D’Andrade observed. Motivation is key, because it tells us which slice of the culture that people have internalized has causal effects on action and which one will not.

One problem is that contemporary social science does not have satisfactory conceptions of motivation (relying on outdated drive-reduction or “need” models). D’Andrade’s account in which “motivation is experienced as a desire or wish, followed by a feeling of satisfaction if the desire is fulfilled,” and is linked to internal processes that “energize” or activate people to act such that there is typically a striving for something—a goal directedness in behavior” (24) is not a bad one as a starter pack. However, as noted, none of these elements end up (striving, wish, pleasure, fulfillment) end up being linked to schemas as candidate motivating (and not just representational) structures in D’Andrade’s classic paper. Future posts will be dedicated to cracking the puzzle of motivation and linking it to cultural analysis.

References

Cervone, D. (2005). Personality architecture: within-person structures and processes. Annual Review of Psychology, 56, 423–452.

D’Andrade, R. G. (1992). Schemas and motivation. In R. G. D’Andrade & C. Strauss (Eds.), Human motives and cultural models. (pp. 23–44). Cambridge University Press.

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

Kringelbach, M. L., & Berridge, K. C. (2016). Neuroscience of Reward, Motivation, and Drive. In Recent Developments in Neuroscience Research on Human Motivation (Vol. 19, pp. 23–35). Emerald Group Publishing Limited.

Mohr, J. W., Bail, C. A., Frye, M., Lena, J. C., Lizardo, O., McDonnell, T. E., Mische, A., Tavory, I., & Wherry, F. F. (2020). Measuring Culture. Columbia University Press.

Quinn, N. (2016). Finding Culture in Talk: A Collection of Methods. Springer.

Rupert, R. D. (2011). Embodiment, Consciousness, and the Massively Representational Mind. Philosophical Topics, 39(1), 99–120.

Schroeder, T. (2004). Three Faces of Desire. Oxford University Press.

Sewell, W. H., Jr. (1992). A Theory of Structure: Duality, Agency, and Transformation. The American Journal of Sociology, 98(1), 1–29.

Swidler, A. (2001). Talk of love: How culture matters. University of Chicago Press.

Turner, J. H. (2010). Motivational Dynamics in Encounters. In J. H. Turner (Ed.), Theoretical Principles of Sociology, Volume 2: Microdynamics (pp. 193–235). Springer New York.

Culture and Action, or Why Action Theory is not Optional

The main reason social scientists study culture is because of the (sometimes implicit) hypothesis that culture “affects” or “causes” action (Swidler 2001a, 2001b; Vaisey 2009). If culture was a causally inert cloud of stuff floating around doing nothing, it would not be worth anyone’s attention. That is, cultural theory and action theory are not independent pursuits. Social scientists who study culture have implicit or explicit action theories. Social scientists interested in the “explanation of action” have to propose a story (even if it is only to dismiss it) of how culture enters into such an explanation. More ambitiously, an explicit and coherent theory of culture should be linked to an explicit and coherent theory of action (Parsons 1951, 1972). The action theory part of cultural theory tells us how culture actually performs its causal work.

This means that culture is involved in the explanation of action is not a trivial or self-evident statement. However, it seems to have been treated as such in the history of cultural and action theory in anthropology and sociology, with some exceptions. Whether the statement even makes sense depends on what we mean by “culture” in the first place. Consider the simplest version of the thesis:

CCA:

Culture causes action.

One problem with this (very broad and vague) version of the thesis is that the default (folksy) meanings of the term culture usually imply the existence of some type of “collective mental” phenomenon. This could be, for instance, some kind of belief system, weltanschauung, or collective worldview (Turner 1994, 2014). The default meaning of “action,” on the other hand, is at the individual level. People are doing things, and more literally moving their bodies thus and so to achieve particular goals (e.g., Max Weber’s proverbial woodcutter chopping wood). In the case of CCA, therefore, we have some sort of ghostly, collective mental thing, exercising a direct causal effect on people’s action via unknown mechanisms. This type of “emanationist” picture via which culture exerts effects (e.g., “constraint”) on people was popular in idealist philosophical circles in the 19th-century and anthropological theory in the early twentieth century. It is unclear whether the thesis is conceptually coherent as stated (because it involves ontologically suspect collective abstracta bandying about real people Martin 2015), let alone whether it can ever be stated in a way that can be productively put to the test empirically.

It was not until social and behavioral scientists with interest in both action and cultural theory (such as Talcott Parsons) scrutinized the weaknesses of CCA that its main flaws began to be addressed. One obvious problem is that, even if you think that culture is a collective mental thing, and even if you believe that culture causally affects what people do, it cannot exercise unmediated or direct effects on action. Instead, we need to postulate an indirect causal effect mediated by an individual-level mechanism. The story can then go like this: People internalize collective public culture in the form of mental representations. This reduplicated internalized culture then causes people’s actions.

Thus, the problem of the cultural causation action (a “cultural theory” issue) is rendered equivalent to the problem of the mental causation of action (an “action theory” problem). Proposing a coherent action theory story (or grabbing one off-the-shelf from the storehouse of folk stories) then gives you the solution to the problem of how culture causes action, as long as you have your cultural internalization story straight.

This yields the slightly more complicated, but relatively less problematic, version of the cultural causation of action thesis:

CCA*:

Culture exists as a body of beliefs and ideas external to people.
People internalize external culture so that it becomes personal beliefs and ideas.
Personal beliefs and ideas cause action via an action theory story.

As Swidler (2001b: 75) points out, this is more or less the story of the cultural causation of action that Talcott Parsons developed in a great big heap of writings starting in the early 1950s, when he joined his earlier theory of action (developed in the 1930s) to an analytic concept of culture as a system of collective “patterns” he distilled from the anthropology of the time (1972). For theorists like Parsons, therefore, “the influence of culture depended on showing that certain cultural elements, whether ideas or values, actually operated subjectively, in the heads of actors.”

As Swidler also points out, subsequent cultural analysis in the social sciences became discomfited with the idea of culture being in people’s heads. The complaints seem to have been twofold. Cultural analysts rebelled against CCA*(1) by noting that conceptualizing culture exclusively as abstract symbolic patterns was limited. Culture could also be discursive, or semiotic, or even material. The other versions of public culture can have causal effects on how people act without necessarily going through the internalization process. These alternative variants of how culture shows up outside people not fitting the CCA* story, and not needing to be lodged in people’s heads to affect action can, as Swidler (2001a) does, be used to tell a story of culture affecting action from “the outside in.” Accordingly, in rebelling against the theories of internalization provided by CCA* theorists, cultural analysts in sociology sought other ways in which culture could have causal effects on action that did not rely on internalization stories.

For a while, these seemed like knock-down arguments against CCA* type stories. With the advantage of hindsight, it is not clear whether those were good reasons for completely abandoning the idea that culture operates via internalized beliefs and values (Vaisey 2009; Patterson 2014; Wuthnow 2008). While we can acknowledge that some forms of public culture don’t need to go through people’s heads to affect their actions, a good swath of them actually do (Strauss and Quinn 1997). Ultimately, many of the stories that abandoned CCA* type postulates seem more like changing the subject, and therefore left open a lot of the culture in action problems that CCA* theorists tackled head-on (Strauss and Quinn 1997; Quinn et al. 2018; Patterson 2014). Today, there has been a resurgence of theorizing culture as operating via internalized, or “personal” mechanisms, seeking to avoid the weaknesses of earlier versions of CCA*. For instance, such theories draw on schema theory or dual-process models from cognitive science to show how culture can have (indirect) effects on action as internalized by people.

In this post, I will not address postulates (1) and (2) of CCA*. I will only note that there are ways to conceive of external or public culture in perfectly respectable naturalistic ways that do not make it a ghostly, ontologically suspect entity hovering over people. There are also perfectly respectable ways, consistent with what we know of the cultural neuroscience of learning, to reconceptualize the idea of the internalization of public culture by people. This process also loses the mysterious and problematic cast it acquired in classical cultural theory. As such, there is a path that can get us from CCA*(1) to CCA*(3). Presuming that we have coherent conceptions of public culture and a coherent internalization story, we still need to do the analytic work of providing a story of how internalized mental contents cause action. This is where cultural theorists, even those resurgent “neo-internalization” theorists (Vaisey 2009; Lizardo 2017), have done the least analytic work. However, without an action theory story, there cannot be a “culture causes action” (CCA) story either.

The Standard Action Theory Story

An action theory story is a causal story of how mental states can be (proper, not deviant) causes of action. First, for a mental state to be a cause of action, it has to be the right type of mental state. Mental states with the power to cause action are usually referred to as “motivating,” states. Action theorists in the contemporary philosophy of action disagree on which states (under the usual folk psychological taxonomy of the mental) are motivating in this sense. Humeans say, for instance, that purely representational or cognitive states (like beliefs) cannot be motivational. Instead, only specific types of states, endowed with some sort of conative or affective “oomph” (like wants and desires), can be motivational. Non-Humeans argue that things like beliefs or normative conceptions can be motivational in the sense of being proper causes of action under the right set of conditions. Action here is defined in a commonsensical manner to refer to goal-directed movements of the body (so no reflexes or tics).

What I will refer to as the “standard” action theory story (see Douskos 2017) has been best developed for the case of intentional action. As stated, CCA* is not restricted to intentional actions. It just says that culture can cause action via the mediation of internalized mental states. A lot of recent cultural theory uses a version of CCA*. The internalized mental states take the form of habits, tacit knowledge, skills, etc., to say that culture causes non-intentional actions via the mediation of these types of states. Regardless, I will begin with the standard intentional story, sometimes referred to as “Good Old Fashioned Action Theory” (GOFAT) (Martin 2015; Turner 2018), since if we can make this story work (or at least state the story in a way that could ostensibly work under a charitable interpretation), then it could be possible to derive the non-intentional cases as systematic deviations from the standard case. Besides, it is useful to begin here since “culture causes action” stories were first developed for the intentional case (Parsons 1951). It is only more recently that practice-based versions of CCA stories have been developed for the case of non-intentional action. Still, even here, people have not been prone to state these stories as action theory stories proper (see Lizardo and Strand 2010).

So what is the standard action theory story? It goes like this. Actions begin with the formation of an intention to perform a certain activity in a given context. The intention is an abstract characterization of what the action will be and, most importantly, the action’s goal. Intentions thus have both representational (belief-like) and “motivational” (desire-like) components (which should make both Humeans and non-Humeans happy). Unlike beliefs, however, which are supposed to represent what the actual world is like, intentions represent what a future state of the world will be (if the intention is accomplished). Thus, if I wake up and think to myself, “I will chop some wood this morning,” this mental state counts as an intention because it specifies (represents) the action that I will perform (however sketchily) and stipulates that I have a “pro-attitude” towards that action (I want to chop the wood) (the basics of this story in contemporary action theory are due to Davidson 1980). So unlike desires, which could be things that we want to do but we are not necessarily committed to doing, intentions imply a commitment to engaging in the action represented by the intention.

Intentions are (typically consciously reportable) representational states because they have propositional content. An action is intentional just in case “what we do causally ensues from mental states with pertinent content” (Douskos 2017: 1129). So, if someone asks what I’m doing with this ax, I can always answer that I intend to use it to “chop some wood.” In that respect, intentions provide reasons for (causes of) action and rationalize action (e.g., make it interpretable after the fact). Note that it is precisely this “contentful” status of intentions that provides the link to their being causal effects of internalized cultural beliefs. In fact, under the sociological version of the standard story, intentions get their contents from the internalized beliefs about what is proper or customary to want to do. Once formed, intentions, by having a specific content, cause the tokening of specific sensorimotor representations of the actions that would properly satisfy their content. For instance, an intention to chop wood causes the tokening of specific mental representations concerning placing large pieces of wood in a chopping block, grabbing an ax, wielding in a way that will strike the wood, and so forth. It is in this way that intentions as mental states can be proper causes of action.

But what is being a “proper” cause of action? In the usual parlance of quantitative social scientists, it means being a non-spurious cause of the action. Thus, just like correlation is necessary but not sufficient for causation, preceding (or accompanying) the action is a necessary but not sufficient condition for an intention to be a proper cause of the action. This is because even though intention X can precede action Y, there can be a third factor, Z, that happens after X, but before Y, which is the actual cause of the action. Thus, if I form an intention to chop wood, place the wood in the chopping block, grab the ax, but exactly at that moment, I have a hallucination in which the piece of wood turns into a giant spider which I then try to kill with the ax, then the intention, even though it preceded the action, and even though the action was accomplished (I chopped the wood in the attempt to kill the imaginary spider) is not a proper cause of the resulting action. Instead, the pathological perceptual state was.

Thus, intentions cannot just be “prior” to action. They must be “in charge” of executing the action during the entire duration of the intention-driven action. If “intentions” were to take a break during action execution, this could threaten their being proper causes as other mental causes of action could then sneak in to do the job, rendering the intention spurious as a cause. Intentions, under the standard story, cannot just be initiators of action. They must also sustain the action until its completion: They are action-guiding mental states (Pacherie 2006).

This has led several philosophers to propose a distinction between the role intentions play before action and their role during intentional action. Pacherie (2006) refers to these as “dual intention” theories; these differentiate between constructs such as prior, future-directed, or prospective intentions, which are mental states happening prior to action that “set” the goals for intentional action, and such constructs as “intentions-in-action,” present-directed, or immediate intentions, which are mental states that accompany action during its execution and make sure that the actual act accords with the previously formed prior intention.

Culture and Intention

Classic sources of the standard action theory story in sociology focused on the role of culture in shaping and determining the content of prior intentions. Here the contemporary theory of action in philosophy makes a couple of points consistent with this classical sociological tradition. First, as Bratman (1984) noted, one thing that intentions do is that they serve as “terminators of practical reasoning.” Once someone forms an intention to do X, they stop batting around ideas as to what to do. Intentions stop the (potentially endless) deliberation as to what to do. If I decide to chop wood in the morning, then that determines my morning plans.

The main difference between sociological and other versions of the standard story is the search for cultural patterning across the intentions that people form. Sociological action theorists think of the consequences of a shared culture (e.g., a unified or coherent belief system) for personal action to provide people with a set of common overall intentions. This is how the social-scientific concept of “values,” is used to this day by heirs of this tradition. Values are “conceptions of the desirable” (Kluckhohn 1951:395), or in the standard folk psychological taxonomy, (relatively abstract) beliefs about what is best to want (thus combining representational and motivational components). In this story, the content of people’s specific intentions can be inherited from the more abstract values that they have internalized.

There is a problem here (which I won’t get into detail in this post) of how to derive specific intentions from abstract values (see Martin and Lembo 2020). An abstract value (e.g., self-transcendence, respect for tradition, and the like) can have many specific realizations at the level of concrete action intentions. In the same way, the same concrete intention (to chop wood) can be the realization of distinct abstract values (e.g., competitive economic achievement, spiritual self-realization via the practice of Zen). These one-to-many and the many-to-one problems are, however, not particular to values as a cultural element. It is pervasive in the standard action theory story, reproducing itself in the relationship between a “concrete” intention (e.g., chop wood) and the specific motor programs or bodily movements that realize that intention. Here we can see that chopping wood can have many practical realizations for the same person on different occasions and across different people sharing the same intention. In the same way, the same concrete set of bodily movements can be the realization of distinct intentions.

The other thing that prior intentions do, according to Bratman, is that they prompt practical reasoning about the best means to accomplish the goals encoded in the intention. This is consistent with classical sociological action theory, which poses another role for a set of shared cultural elements that function as “terminators” of this second bout of practical reasoning: Norms. While an a-cultural or purely Machiavellian actor can theoretically wonder about the best way to accomplish a goal in a relatively unconstrained way, normative considerations collapse this deliberative choice space since they rule out most of the potentially feasible ways to accomplish something as out of bounds due to normative considerations. In this way, institutionalized norms serve as heuristics for reasoning because they prevent people from reconsidering the means every time they form an intention. Instead, the default is to go with the normatively appropriate way to perform the intentional action.

To sum up, according to the standard story, internalized culture plays a central role in action that is (properly) driven by intentions as mental causes of action, thus providing a mechanism via which the third link of the CCA* story can be realized. First, internalized cultural beliefs about what is best to want end up setting the goals of most prior intentions for people. Under this story, people internalize motivational mental states that prescribe what they should strive for. These prior intentions then serve as the templates guiding intentions-in-action as they occur. This means that culture has “direct” causal effects on prior intentions as causally effective mental states and “indirect” causal effects on intentions-in-action via prior intentions. Intentions-in-action then directly affect the motor programs tokened to execute the specific bodily movements that realize the prior intention (Pacherie 2006).

Second, internalized culture collapses the search space for proper ways of achieving the prescribed goals. This is done via the construct of norms which are “canned” or “preset” ways of doing things that have the stamp of collective approval, legitimacy, and so forth. Thus, people are motivated to go with the normatively prescribed way rather than think up the best or most efficient way to achieve goals every time they think up a prior intention. In this way, norms directly affect the intentions-in-action that people pursue because they provide content to the mental states that represent the best manner in which intentional goals are to be achieved.

This is a neat story. It is also the story everyone in contemporary sociology, with some notable exceptions, hates (Martin 2015; Whitford 2002; Swidler 2001b) perhaps because it is too neat. My point here has not been to heap hate on this story for the umpteenth time. Instead, it has been to reconstruct the standard story as charitably as possible, showing the linkages between classical action theory in sociology and the contemporary theory of action in the philosophy of mind. The basic idea is that if we are going to tell heterodox stories, the content of the story can change, but not the format. If we are going to say that culture causes action, you cannot skip the step where you specify what type of culture you are talking about, how people internalize it, and how once internalized, this culture links up to some sort of mental cause of action. In future posts, we will see examples of what such heterodox stories might look like.

References

Bratman, M. (1984). Two Faces of Intention. The Philosophical Review, 93(3), 375–405.

Douskos, C. (2017). Habit and intention. Philosophia, 45(3), 1129-1148.

Kluckhohn, C. (1951). Values and Value-Orientations in the Theory of Action: An Exploration in Definition and Classification. In T. Parsons & E. A. Shils (Eds.), Toward a General Theory of Action: Theoretical Foundations for the Social Sciences (pp. 388–433.). Harvard University Press.

Lizardo, O. (2017). Improving Cultural Analysis: Considering Personal Culture in its Declarative and Nondeclarative Modes. American Sociological Review, 82(1), 88–115.

Lizardo, O., & Strand, M. (2010). Skills, toolkits, contexts and institutions: Clarifying the relationship between different approaches to cognition in cultural sociology. Poetics , 38(2), 205–228.

Martin, J. L. (2015). Thinking Through Theory. W.W. Norton, Incorporated.

Martin, J. L., & Lembo, A. (2020). On the Other Side of Values. The American Journal of Sociology, 126(1), 52–98.

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

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Patterson, O. (2014). Making Sense of Culture. Annual Review of Sociology, 40(1), 1–30.

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Quinn, N., Sirota, K. G., & Stromberg, P. G. (2018). Introduction: How This Volume Imagines Itself. In N. Quinn (Ed.), Advances in Culture Theory from Psychological Anthropology (pp. 1–19). Springer International Publishing.

Swidler, A. (2001a). Talk of love: How culture matters. University of Chicago Press.

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Wuthnow, R. (2008). The sociological study of values. Sociological Forum , 23(2), 333–343.

Simmel as a Theorist of Habit

The Journal of Classical Sociology has recently made available online a new translation, by John D. Boy, of Simmel’s classic essay on “The Metropolis and the Life of the Spirit” (better known to sociologists and urban studies people in previous translations as “The Metropolis and Mental Life”). Boy has an intriguing argument, in the translation’s introductory remarks, for why returning to Simmel’s original “spiritual” language and moving away from the “psychological” language of early translators (e.g., the German “geist” could be translated as either “spirit” or “mind”) is more faithful to Simmel’s original intellectual context and aims.

Here I would like to focus on a neglected aspect of the essay, namely, the implicit theory of habit (and its relation to the intellect and emotions) that Simmel deploys in the introductory paragraphs to set up the main argument that follows. Thus, this post can be read as a companion to previous disquisitions on habit and habit theory in this blog (see here, here, and here) and as a supplement to Charles Camic’s (1986) earlier point about the centrality of the concept of habit for most of the classical social theorists in sociology (Simmel is not one of the theorists treated at length in Camic’s classic paper) and the related story of how the idea was excised from the sociological vocabulary in the post-Parsonian period. In fact, concerning Simmel’s essay on the metropolis, in particular, it bears mentioning that one of the very earliest works influenced by Simmel’s approach (published in American Journal of Sociology in 1912) took the title “The Urban Habit of Mind” (Woolston, 1912).

Simmel on Habit and Metropolitan Life

Simmel argues that the rapid succession of novel and unpredictable stimuli in the city breaks previous habits of sensation developed in a non-urban context. Therefore, Simmel subscribes to the idea that habits are more easily developed whenever people are exposed to repetitive, internally consistent stimuli. In the more predictable non-urban setting, where each new sensation is a lot like the previous one, people can develop habits of sensibility that render them less susceptible to experience sensations in a powerful way. Simmel thus subscribes to the psychological principle that, as we develop habits of sensibility via the exposure to repetitive sensations, these fade from consciousness: “Lasting sensations, slight differences and their succession according to the regularity of habit require less consciousness” (Simmel, 2020, p. 6, emphasis added).

The city disrupts this equilibrium. It does so primarily by increasing the novelty and the unpredictability of sensory stimulation. This “intensification of nervous stimulation” is brought about “by the rapid and constant chance of external and internal sensations” (ibid, italics in original). Thus, the converse psychological principle applies: If habits are created via exposure to repetition, then exposure to novelty and non-repetition increases “consciousness” (which Simmel conceptualizes here as opposed to habit). For Simmel, people “are creatures of difference; their consciousness is stimulated by the difference between the current sensation and the ones preceding it” (ibid, emphasis added).

The disruption of habits of sensation in the city via the intensification of sensory stimulation serves as the primary psychological contrast to small-town life:

In producing these psychological conditions in every crossing of the street and in the tempo and multiplicity of its economic, occupational and social life, the metropolis creates a strong contrast to small-town and country life with its slower, more habitual, more regular rhythm in the very sensory foundation of the life of our souls, due to the far larger segment of our consciousness it occupies given our constitution as creatures of difference (ibid, boldface added).

This sets up a contrast, Simmel argues, between the calculative intellect (which Simmel associates with non-habitual cognition) and more spontaneous affect and emotion, which Simmel associates with the “more unconscious” strata of the psyche. In this way, small-town life “is founded upon relationships of disposition and emotion that have their root in the more unconscious strata of the soul and are more likely to grow out of the quiet regularity of uninterrupted habits” (ibid, emphasis added).

Thus, Simmel makes another equation here, linking habit to emotion, affect, and drives (and other residents of a more vitalistic, “dynamic” unconscious) and habit, which is separated from mental functions associated with intellect, which, for Simmel, are the more “transparent and conscious higher strata” of our inner life. This dualistic approach to habit, which distinguishes it from higher intellectual functions, seems to owe a lot to Maine de Biran’s early nineteenth-century reflections on the subject, which also made such a distinction between habit and the intellect (de Biran, 1970; see the discussion in Sinclair, 2011), one that would be criticized by Félix Ravaisson (2008).

Simmel’s reasoning and series of dualistic linkages here lead him to an odd, and seldom noted, conclusion: People who live in the city, insofar as they are forced to use “the intellect” to perform actions that would otherwise (in a non-urban context) be driven by habit, are therefore less “habit-driven” than non-urban people! This what is behind his famous “protective organ” argument, whose linkage to the habit/intellect contrast has not been noted before. For Simmel, city dwellers have to develop a way to deal with the sensory barrage in a way that prevents them from “reacting according to…[their] disposition.” Instead, “the typical metropolitan person relies primarily on…[their] intellect” (ibid). And “this intellectuality, which we have recognized as a defense of subjective life against the assault of the metropolis, becomes entangled with numerous other phenomena” (ibid).

Conclusion

The phenomena that Simmel went on to link to urban life, inclusive of the money economy, the blasé attitude, individualism, liberty, the division of labor, cosmopolitanism, fashion, and the rest, are well-known to students of Simmel’s foundational essay. Less well-known, however, are how the core premises of the piece are built on Simmel’s much-neglected (but explicitly laid out) assumptions of how the habit links to the intellect, consciousness, sensation, and emotion.

References

Camic, C. (1986). The Matter of Habit. The American Journal of Sociology, 91(5), 1039–1087.

de Biran, P. M. (1970). The Influence of Habit on the Faculty of Thinking. Greenwood.

Ravaisson, F. (2008). Of Habit. Bloomsbury Publishing.

Simmel, G. (2020). The metropolis and the life of spirit. Journal of Classical Sociology, 1468795X20980638.

Sinclair, M. (2011). Ravaisson and the Force of Habit. Journal of the History of Philosophy, 49(1), 65–85.

Woolston, H. B. (1912). The Urban Habit of Mind. The American Journal of Sociology, 17(5), 602–614.

Habit versus Skill

Habit versus Skill Ascriptions

Habit and skill tend to be run together in social theory and the philosophy of action (Dalton, 2004). However, there are good conceptual and empirical reasons to keep them distinct (Douskos, 2017b). Notably, the ascription of skill and habits entail different things about action, and only one (habit) is explanatory in the way outlined in a previous post.

When we ascribe a skill to an actor, we are usually interested in making a purely descriptive statement of “capacity ownership” but not putting the action in a larger explanatory scheme. This is generally because skill ascriptions, in contrast to dispositional habit ascriptions, usually speak of potential and not occurrent actions. When we ascribe a skill to an actor, we are simply saying that they can perform it, not that they regularly do so in response to the solicitations of a given context. This gets at the difference between capacity and tendency ascriptions (Schwitzgebel, 2013). Thus, when we say that a person is proficient at something (e.g., playing the piano, tennis, proving mathematical theorems), we do not necessarily mean they are in the regular habit of doing it. A person can possess a skill (being proficient at speaking a foreign language) without being in the habit of exercising it. In this case, the skill (while possessed) does not count as a habit.

In this way, the requirement of having a history of previous repetition and exercise does not work in the same way for habits and skills (Douskos, 2017a). In the case of skills, the link between past repetition and current exercise is a matter of the contingent way biological nervous systems “learn” given their natural constitution (e.g., via Hebbian tuning requiring multiple exposures). If we lived in a world like that portrayed in the science fiction film The Matrix, where skills (e.g., being able to fly a helicopter) can be downloaded directly into the motor cortex of people hooked up to the system in a matter of seconds, then a history of repetition would not be required for skill possession. This is different from the conceptual linkage between a history of repetition and habit ascription. When we explain an action by saying it is a habit, we are necessarily placing it in such a causal history, which requires by conceptual necessity a history of previous repetition (Douskos, 2017a, p. 509).

The same goes for the dispositional nature of actions we call habits. The explanatory advantage of habit explanation is the tight link to context, which allows us to refer to people’s inclinations even before we see them occurring. Thus, action counts as a habit when the agent is disposed to produce it in a given context (as well as reasonably similar contexts). In the case of skill, a person can have the capacity without having the disposition to exercise it in any given context. A skill can become a habit by acquiring this dispositional profile (we get into the habit of playing the piano in the evenings), but it need not have this dispositional profile (we can know how to play the piano without it being triggered regularly by a given context).

In sum, even though current skill possession implies some previous history of skill acquisition via repetitive activity, it does not mean that the skill exercise is a regular practice right now (habit). Nor do we mean the skill is exercised regularly when the person encounters a given set of conditions (disposition). Only habits have these two features.

In this last sense, dispositional (habit) ascriptions are more general than skill ascriptions since they need to be added if we want to explain the occurrence of skilled action. Thus, we may differentiate ascriptions of habitual skills to explain a given action from pure capacity ascriptions that simply posit a person’s capacity to do something. Also, habits can explain action, even if nothing about the action is exceptionally skillful. For instance, we can account for Sam’s habit of regularly driving at 8:00 am by pointing out that the action is a component of Sam’s “driving to work habit,” even if Sam is not a skillful driver. In this sense, calling something a habit implies a holistic and historical take on the action (indicating a regular history of repetition and disposition manifestation) that is partially orthogonal to how well (in the normative sense of skill) an action is performed. Thus, there are both skillful and not necessarily skillful (but still “automatic”) types of habit ascriptions, both of which can be used to explain action.

Habit, Techniques, and Skill

In a recent paper, Matthews (2017) argues that the core or prototypical members of the habit category are what Marcel Mauss called techniques (1973). Ways of being proficient at an action (e.g., tying your shoes), acquired via an enculturation process requiring training and repetition (see here for further discussion). These include both “behavioral” techniques, such as playing the piano, typing, riding a bike, and “perceptual” or “mental” techniques. However, the latter is less central members of the habit category for most people (despite being as pervasive as overt action habits) since habit is usually associated with over action or practice, even though both overt and covert “actions” can become habitual (Matthews 2017: 399). However, the most maximal conception of habits can easily extend the concept to the standard mental items (such as beliefs, desires, emotions, and the like) that figure as part of folk psychology. In that respect, there is no reason to restrict the use of habit to overt actions, even when acknowledging that semantically, over behaviors are more central members of the habit category than covert mental actions, such as believing a proposition or making an aesthetic or moral judgment.

Habitual actions, due to repetition and reinforcement, tend to acquire the facility and fluidity that we associate with skills, even though not all habits are necessarily skillful. So if habits are techniques, they tend toward the skilled end of performance, or at least toward the “good enough” end in performing their assigned function. However, the conceptual distinction between habits and skills needs to be kept since habit ascriptions and skill ascriptions buy you different things from an action theory point of view (Douskos, 2017b). A habit ascription entails conceptually entails a previous history of repetition, regularity of current performance, and a dispositional profile tied to context. It is habit ascription, not skill ascriptions, that offers a workable alternative to the intentionalist idiom when it comes to the explanation of action. All that is implied by skill is flexibility, fluidity, and proficiency in acting. As such, skills are a type of action (e.g., more or less skillful) but in themselves are not a resource for explaining action.

The main reason some analysts tend to insist on the “skilled” nature of most habits, however, is to move away from the misleading idea that only fixed, repetitive action patterns count as habits (Pollard, 2006). Habit theorists in the American-pragmatist (e.g., Deweyian) or French-Aristotelian (Ravaisson, Bourdieu, Merleau-Ponty) mold like to emphasize that when they speak of habit, they speak of flexible dispositions that adapt to their current context of enactment (and thus are different on each occasion) and not mechanical repetitions. As such, sometimes, we find these theorists equating habits and skills or proposing that all habits are skillful or creative (Dalton, 2004).

However, it seems like considering habits as dispositions clarifies their flexible, non-repetitive, non-mechanical nature, without getting into the conceptual hot water (and ultimately unproductive conundra) that equating habits and skills does (Douskos, 2017a, 2017b). As such, I propose to place proficiency as a core characteristic of habit, not skill. Proficiency is a weaker criterion because, while respecting the classic observation that the repetition of habitual action results in facilitation, it does not imply that such facilitation necessarily leads to “skillful” enactment. As noted, many habits are not particularly skillful but get to the point of being “good enough” to get the job done.

References

Dalton, B. (2004). Creativity, Habit, and the Social Products of Creative Action: Revising Joas, Incorporating Bourdieu. Sociological Theory, 22(4), 603–622.

Douskos, C. (2017a). Pollard on Habits of Action. International Journal of Philosophical Studies, 25(4), 504–524.

Douskos, C. (2017b). The spontaneousness of skill and the impulsivity of habit. Synthese. https://doi.org/10.1007/s11229-017-1658-7

Mauss, M. (1973). Techniques of the body∗. Economy and Society, 2(1), 70–88.

Pollard, B. (2006). Explaining Actions with Habits. American Philosophical Quarterly, 43(1), 57–69.

Schwitzgebel, E. (2013). A Dispositional Approach to Attitudes: Thinking Outside of the Belief Box. In N. Nottelmann (Ed.), New Essays on Belief: Constitution, Content and Structure (pp. 75–99). Palgrave Macmillan UK.

Habit as Prediction

In a previous post, Mike Strand points to the significant rise of the “predictive turn” in the sciences of action and cognition under the banner of “predictive processing” (Clark, 2015; Wiese & Metzinger, 2017). This turn is consequential, according to Mike, because it takes prediction and turns it from something that analysts, forecasters (and increasingly automated algorithms) do from something that everyone does as the result of routine activity and everyday coping with worldly affairs. According to Mike:

To put it simply, predictive processing makes prediction the primary function of the brain. The brain evolved to allow for the optimal form of engagement with a contingent and probabilistic environment that is never in a steady state. Given that our grey matter is locked away inside a thick layer of protective bone (e.g., the skull), it has no direct way of perceiving or “understanding” what is coming at it from the outside world. What it does have are the senses, which themselves evolved to gather information about that environment. Predictive processing says, in essence, that the brain can have “knowledge” of its environment by building the equivalent of a model and using it to constantly generating predictions about what the incoming sensory information could be. This works in a continuous way, both at the level of the neuron and synapse, and at the level of the whole organism. The brain does not “represent” what it is dealing with, then, but it uses associations, co-occurrences, tendencies and rhythms to predict what it is dealing with.

In this post, I would like to continue the conversation on the central role of prediction in the explanation of action and cognition that Mike started by linking it to some previous discussions on the nature and role of habit in action and the explanation of action (see here, here, and here). The essential point that I wish to make here is that there is a close link between habit and prediction. This claim may sound counterintuitive at first. The reason is that the primary way that habit and practice have been incorporated into contemporary action theory is by making habit, in its “repetitive” or “iterative” aspect, a phase or facet of action that looks mainly backward to the past (e.g., Emirbayer & Mische, 1998). Because prediction is necessarily future-oriented, most analysts think of it as also necessarily non-habitual and thus point to other non-habit like processes, such as Schutzian “projection,” that implies a break with habitual iteration. These analysts presume that there is a natural antithesis between habit and iteration (which at best may bring the past into the present) and anticipation of forthcoming futures.

Rethinking Habit for Prediction

The idea that habit is antithetical to prediction makes sense, as far as it goes, but only because it hews closely to a conception of habit that accentuates the “iterative” or repetitive side. But there are more encompassing conceptions of the role of habit in action that emphasize an iterative side to habit and an adaptive, and even “anticipatory” side. Here I focus on one such intellectual legacy of thinking about habit, which remains mostly unknown in contemporary action theory in sociology. It was developed by a cadre of thinkers, mainly in France, beginning in the early nineteenth century and extending into the early twentieth century. This approach to the notion of habit characteristically combined elements of Aristotelian, Roman-stoic, scholastic, British-empiricist, Scottish-commonsense, French-rationalist, and German-idealist philosophy, and then-novel developments in neurophysiology such as the work of Xavier Bichat. Its two leading exponents were Pierre Maine de Biran (1970) and the largely neglected (but see Carlisle (2010) and Sinclair (2019)) work of Félix Ravaisson (2008). These thinkers exercised a broad influence in the way habit was conceptualized in the French tradition, extending its influence into the work of the philosophers Albert Lemoine, Henry Bergson, and more notably, Maurice Merlau-Ponty (Sinclair, 2018).

The Double Law of Habit

The primary contribution of these two thinkers, especially Ravaisson, was developing the double law of habit. This was the proposal that habit (conceptualized as behavioral or environmental repetition) had “contradictory” effects on the “passive” (sensory, feeling) and the active (skill, action) faculties: “sensation, continued or repeated, fades, is gradually obscured and ends by disappearing without leaving a trace. Repeated movement [on the other hand] gradually becomes more precise, more prompt, and easier” (de Biran, 1970, p. 219)

In other words, facilitation in the realm of perception leads to “habituation,” meaning that experience becomes less capable of capturing attention. We become inured to the sensory flow, or in the case of experience that generate feelings (e.g., of pleasure, disgust, and so forth), the feelings “fade” in intensity (e.g., think of the difference between a first-year medical student and an experienced surgeon in the presence of a corpse). This is an argument that was deployed by Simmel to explain the “deadening” effect of urbanism on sensory discrimination and emotional reaction, generative of what he called the “blase attitude” in his classic essay on the “Metropolis and the Life of the Spirit.”

When it comes to action, on the other hand, habituation via repetition leads to the opposite of passivity; namely, facilitation of the activity (becoming faster, more precise, more self-assured) and the creation of an automatic disposition (e.g., triggered in partial or complete independence from a feeling of “willing” the action) equipped with its own inertia and bound to continue to its consummation unless interrupted. Habituated action “becomes more of a tendency, an inclination” (Ravaisson 2008: 51). This is the double face (or “law”) of habit.

Prediction as Attenuation

Trying to puzzle out these apparently contradictory effects of habituation led to a lot of head-scratching (and creative theorizing) both on the part of de Biran and Ravaisson and subsequent epigones like Bergson, Heidegger, Merleau-Ponty, and Ricoeur. Nevertheless, it becomes clear that a solution to the “double-law” puzzles emerges when the predictive dimension of both perception and action is brought to the fore. The case of “perceptual attenuation” considered below, for instance, provides the mechanism for the “fading” of the vibrancy of experience whenever we become proficient at canceling out the error produced by those experiences via top-down predictions (Hohwy, 2013). Here the “top” are generative hierarchical models instantiated across different layers in the cortex, and the bottom is incoming sensory stimulation from the world (where the job of the model is to infer the hidden causes of such stimulation).

That is, as experience is repeated and the distributed, hierarchical generative models tune their parameters to effectively figure out what’s coming before it comes, we begin to preemptively cancel out prediction error. Cancelation of prediction error leads to subsequent perceptual attenuation, such that incoming sensory information no longer commands (or requires) attention. The result is that attention is freed to concentrate on other more pressing things (e.g., the parts of the experience that are still producing precise error and thus demand it). In this respect, sensory and feeling attenuation is the price we pay for becoming good at predicting what the world offers. Prediction is at the basis of “passive” habituation (the first face of the double law).

Prediction as Facilitation

But what about the facilitation side? Here prediction, in the form of what is known as active inference, is also at play. However, this time, instead of prediction in the service of canceling out error from exteroceptive signals, the acquisition of skill turns into our capacity to cancel out prediction error emanating from our action in the world, for instance, via proprioceptive signals that track the sensory consequences of our activity. Repeated activity leads us to form increasingly accurate generative models of our action (the dynamic motor trajectory of our bodies and their effectors) in a particular environment. This means that we can anticipate what we are going to do before we do it, leading to the loss (via the mechanism described above) of the feeling of “effort” or even “willing” at the point of action initiation (Wegner, 2002), which is a phenomenological signature of habitual activity.

This is consistent with the idea that Parsonian “effort” rather than being the sine qua non of truly “free” action partially unmoored from its “conditions” (as the Kantian legacy led Parsons to implicitly assume) actually points to poorly performed (because badly predicted) action, in other words, action that is driven by generative models that are not very good at anticipating our next move. This is action that is at war with the environment not because it is “independent” from it, but because (due to lack of habituation an attunement to its objective structure of probabilities) is partially at war with it, and thus disconnected from its offerings (Silver, 2011).

The connection between habit and prediction becomes clear. On the one hand, repetition results in the attenuation of sensory input. While this was usually referred to as the “passive” side of the double-law, we can now see, drawing on recent work on predictive processing, that this is only a seeming passivity. At the subpersonal level, attenuation happens via the successful operation of well-honed generative models of the environmental causes of the input, working continuously to cancel out those incoming signals that they successfully predict. These models are one set of “habitual tracks” laid out by our experience of consistent patterns of experience.

On the “active” side, which is more clearly recognized as “habit,” proficiency in action execution also comes via prediction, but this time, instead of predicting how the distal structure of the world, we predict the same world we “self-fulfill,” as we act. Moving in the world feels like something to us (proprioception), and as we repeat activities, we become proficient in predicting the very sensory stimulation that we generate via our actions. The two sides of the double-law, which show up in contemporary predictive cognitive science as the difference between “perceptual” and “active” inference (Pezzulo et al., 2015; Wiese & Metzinger, 2017), are thus built on the predictive capacities of habits. This was something that was anticipated by Ravaisson when he noted that

[A] sort of obscure activity that increasingly anticipates both the impression of external objects in sensibility and the will in activity. In activity this reproduces the action itself; in sensibility it does not reproduce the sensation, the passion…but class for it, invokes it; in a certain sense it implores the sensation (Ravaisson 2008: 51).

Habit is thus the confluence of what has been called perceptual inference (predicting incoming signals by tuning a generative model of their causes) and active inference (self-fulfilling incoming signals via action so that they conform to the model that already exist), in other words, prediction as it facilitates our engaged coping with the world, is the nature of habit. More accurately, to the extent that we can predict the world, we do so via habit.

References

Carlisle, C. (2010). Between Freedom and Necessity: Félix Ravaisson on Habit and the Moral Life. Inquiry: A Journal of Medical Care Organization, Provision, and Financing, 53(2), 123–145.

Clark, A. (2015). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford University Press.

de Biran, P. M. (1970). The Influence of Habit on the Faculty of Thinking. Greenwood.

Emirbayer, M., & Mische, A. (1998). What is agency? The American Journal of Sociology, 103(4), 962–1023.

Hohwy, J. (2013). The Predictive Mind. Oxford University Press.

Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active Inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology, 134, 17–35.

Ravaisson, F. (2008). Of Habit. Bloomsbury Publishing.

Silver, D. (2011). The moodiness of action. Sociological Theory, 29(3), 199–222.

Sinclair, M. (2018). Habit and time in nineteenth-century French philosophy: Albert Lemoine between Bergson and Ravaisson. British Journal for the History of Philosophy: BJHP: The Journal of the British Society for the History of Philosophy, 26(1), 131–153.

Sinclair, M. (2019). Being Inclined: Félix Ravaisson’s Philosophy of Habit. Oxford University Press.

Wegner, D. M. (2002). The Illusion of Conscious Will. MIT Press.

Wiese, W., & Metzinger, T. (2017). Vanilla PP for Philosophers: A Primer on Predictive Processing. In T. Metzinger & W. Wiese (Eds.), Philosophy and Predictive Processing.

Ontic Monism versus Pluralism in Cultural Theory

As discussed in a previous post, bundling ontic claims about culture have been used to argue that culture is a single kind of thing and demarcate the boundaries of cultural kinds. This can be referred to as ontic monism about cultural kinds. Thus, a theorist might say, following Kroeber (1917), Parsons (1951), or Geertz (1973), that culture is primarily ideational or symbolic. This means that it is made out of “ideal” or “symbolic” stuff (an ontic compositional claim), and the nature of this stuff makes it different from other non-ideal (e.g., “material”) stuff.

These theorists might even go so far as to say that because culture is composed only of ideal stuff, the notion of “material culture” is a category mistake. The ontic claim here is cultural kinds are disjunctive from physical kinds (a negative “culture is not” ontic claim (Reed 2017)), such that is something is material, it is ipso facto, not culture. The positive ontic claim is that being “ideal” or “symbolic” is a mark of the cultural, such that if we know something is an idea or a symbol, we also know that it is a cultural kind.

For instance, the anthropologist Leslie White (1959: 238) noted the penchant for “idealist” culture theorists in early anthropology to reach the negative ontological conclusion regarding the notion of material culture in a classic paper on the culture concept:

Those who define culture in terms of ideas, or as an abstraction, or as behavior, find themselves obliged logically to declare that material objects are not, and cannot be, culture. “Strictly speaking,” says Hoebel (1956: 176), “material culture is really not culture at all.” Taylor (1948: 102, 98) goes farther: “…the concept of ’material culture’ is fallacious” because “culture is a mental phenomenon.” Beals and Hoijer (1953: 210): ‘…culture is an abstraction from behavior and not to be confused with acts of behavior or with material artifacts, such as tools…”

Along the same lines, Bidney (1968: 130-131) observes,

The idealists…maintain that the cultural heritage consists primarily of ideas or communicated intelligence and symbolic expression since they hold that only ideas or symbols may be communicated and transmitted. For the cultural idealists, therefore, so-called material culture is a contradiction in terms, since for them the real cultural entities, or units, are the conceptual ideas, or norms, not the particular artifacts which exemplify or embody them.

A still influential definition of culture comes from the anthropologist Ward Goodenough, for whom

C]ulture consists of whatever it is one has to know or believe in order to operate in a manner acceptable to its members. Culture is not a material phenomenon; it does not consist of things, people, behavior, or emotions. It is rather an organization of these things. It is the form of things that people have in mind, their models for perceiving, relating, and otherwise interpreting them” (1957, p. 167)

Here Goodenough makes a positive monist ontic claim (cultural kinds are ultimately mental, and consist of cognitive models internalized by people) and a corresponding negative ontic claim (culture is not things, people, or behavior).

Ontic Pluralism

Ontic monism represents a classic line of theorizing about cultural kinds. The basic message is that culture is a single kind of thing, and thus sharply contrasts, in terms of ontology, with other kinds of things in the world (Reed 2017).

But this is not the only approach we can take. A venerable tradition of cultural theory, closer to that inaugurated by the anthropologist Franz Boas (and farther back to E. B. Tylor), allows for what I will refer to as ontic pluralism in the conceptualization of what culture is. One such rendering is given by the anthropologist Roger Keesing in a once-influential review, who noted that for pluralists

[c]ultures are systems (of socially transmitted behavior patterns) that serve to relate human communities to their ecological settings. These ways-of-life-of communities include technologies and modes of economic organization, settlement patterns, modes of social grouping and political organization, religious beliefs and practices, and so on.” (Keesing, 1974, p. 75).

This perspective combines compositional multiplicity (culture is ideal, behavioral, artifactual, etc.), with qualified versions of both sharedness and systemness where these properties are made more or less likely depending on the “kind of cultural kind” we are talking about. Additionally, the ontic pluralist is perforce non-exclusivist when it comes to locational claims (some cultural kinds are “in” people and other kinds are “in” the world). In the same way, they are likely to make different claims about the historical provenance of the different kinds (different cultural kinds have distinct, but related, etiologies).

This yields synthetic attempts such as the one defended by the anthropologists Claudia Strauss and Naomi Quinn across a variety of publications (Strauss & Quinn, 1997) and the sociologist Orlando Patterson in recent work.

For Patterson (2014, p. 5),

A synthetic analysis that defines both what culture is and does and the nature of the whole [cultural] beast over and beyond its favored parts may be achieved—still using the parable of the blind people and the elephant—by listening carefully to each person’s account of the part of the elephant they are touching and analyzing.

In the same way, culture and cognition scholars such as Norbert Ross (2004:8) defend a version of ontic pluralism about cultural kinds when they conceive of culture as

[A]n emerging phenomenon evolving out of shared cognitions that themselves arise out of individual interactions with both the social and physical environment. The natural and physical environments include both institutions and physical objects (natural as well as artificial).

Overall, ontic pluralism implies that things can count as cultural kinds despite big differences in physical realization, underlying properties, and worldly location. Ross’s distinction between culture that is internalized by people (in the forms of cognitive states) and that which is physically manifested in terms of physical objects and artifacts is fairly common among pluralist theorists who note that culture consists of both “mental and material” elements (Adams & Markus, 2004, p. 342). As such, it can serve as the basis for building a useful ontology of cultural kinds that acknowledges their “motley” status.

References

Adams, G., & Markus, H. R. (2004). Toward a conception of culture suitable for a social psychology of culture. The Psychological Foundations of Culture, 335–360.

Bidney, D. (1968). Theoretical Anthropology. Transaction Publishers.

Geertz, C. (1973). The interpretation of cultures: Selected essays. Basic books.

Goodenough, W. H. (1957). Cultural Anthropology and Linguistics, By Ward H. Goodenough.

Keesing, R. M. (1974). Theories of culture. Annual Review of Anthropology, 3(1), 73–97.

Kroeber, A. L. (1917). The Superorganic. American Anthropologist, 19(2), 163–213.

Patterson, O. (2014). Making Sense of Culture. Annual Review of Sociology, 40(1), 1–30.

Parsons, T. (1951). The Social System. The Free Press.

Reed, I. A. (2017). On the very idea of cultural sociology. In Claudio E. Benzecry, Monika Krause, Isaac Ariail Reed (Ed.), Social Theory Now (pp. 18–41). University of Chicago Press.

Ross, N. (2004). Culture and Cognition: Implications for Theory and Method. SAGE.

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

White, L. A. (1959). The Concept of Culture. American Anthropologist, 61(2), 227–251.

Causal mechanisms in the cognitive social sciences

The social sciences and the cognitive sciences have grown closer together during recent decades. This is manifested in the emergence and expansion of new research fields, such as social cognitive neuroscience (Cacioppo et al. 2012; Lieberman 2017), cognitive sociology (Brekhus & Ignatow 2019), behavioral economics (Dhami 2016), and new approaches in cognitive anthropology (Bloch 2012; Hutchins 1995; Sperber 1996). However, increasing interactions between the cognitive and social sciences also raise many pressing philosophical and methodological issues about interdisciplinary integration and division of labor between disciplines. In our recent article (Sarkia, Kaidesoja & Hyyryläinen 2020), we argue that mechanistic philosophy of science can contribute to analyzing these challenges and responding to them.

According to mechanistic philosophy of science (hereafter: MPS), the primary way in which scientists explain complex cognitive and social phenomena is by describing causal mechanisms that produce, underlie, or maintain these phenomena (e.g. Bechtel 2008; Glennan 2017; Hedström & Ylikoski 2010). Commonly cited examples of semi-general social mechanisms include those that generate self-fulfilling prophecies, cumulative advantage, residential segregation, collective action, and diffusion patterns in social networks. Cognitive and neural mechanisms addressed in the cognitive sciences include those underlying perceptual processes, memory functions, learning, imagination, and social cognition.

In this post, we take a closer look at causal mechanisms and mechanistic explanations. We also indicate some ways in which MPS could help to bridge the gap between the social and the cognitive sciences. The text partially draws on our article that provides a more detailed account of mechanistic explanations in the cognitive social sciences (Sarkia, Kaidesoja & Hyyryläinen 2020: 3-8).

Mechanisms

A ‘minimal’ account of mechanisms says that a mechanism for a phenomenon “consists of entities (or parts) whose activities and interactions are organized so as to be responsible for the phenomenon” (Glennan 2017: 17). Entities are particular things (in a broad sense) in the world and activities always take place in some entity. The entities that are studied in different sciences are highly diverse, ranging from molecules to brains and complex social systems. Entities may engage in activities either by themselves or in concert with other entities. When the activities of two or more entities influence each other, they interact. In a mechanism that is responsible for some phenomenon, its constituent entities and activities, as well as their interactions, are organized in a way that allows them to produce, maintain or underlie the phenomenon, meaning that there are specific constitutive and causal relations between these constituent entities and activities. This minimal account of mechanisms makes clear that mechanisms are different from universal laws, correlations between variables (or other empirical regularities), and functions that items may perform in some larger system. Advocates of MPS have also provided accounts of mechanisms that are more specific, but most of them are compatible with the minimal account (e.g. Glennan & Illari 2018).

MPS regards mechanisms as hierarchical in the sense that lower-level mechanisms operate as parts of higher-level mechanisms (e.g. Craver & Darden 2013; Glennan 2017). When scientists investigate a mechanism that is responsible for a specific phenomenon, they commonly assume that there are underlying mechanisms that allow the constituent entities of the mechanism to engage in the activities that they engage in. Conversely, a mechanism identified at a lower level of mechanistic organization is typically embedded in some broader (or higher-level) mechanism that affects its functioning. For example, a mechanism underlying the working memory of a particular person may operate as a part of the social mechanism of collaborative learning in which the person is engaged in a common learning task with her classmates. Social and cognitive scientists often implicitly or explicitly attribute different types of cognitive capacities to people, such as the capacities to act intentionally, to communicate using spoken or written language, and to remember things from the past. As Stuart Glennan (2017: 51–52) argues, the capacities of complex entities are mechanism-dependent in the sense that the organized interactions of their parts are responsible for the capacities of the whole entity, which may manifest themselves only in suitable environments. For example, the capacity for speech is dependent on the organized interactions of neural mechanisms and manifested in embodied communicative interactions with other people.

According to MPS, mechanisms are identified on the basis of the phenomena that they contribute to (e.g. Craver & Darden 2013; Hedström & Ylikoski 2010; Glennan 2017). For example, cognitive neuroscientists investigate the neural mechanisms underlying working memory and visual perception (Bechtel, 2008), while social scientists study the social mechanisms of self-fulfilling prophecy and urban segregation (Hedström, 2005). They both use empirically established phenomena to delimit the boundaries of the mechanism under investigation and to identify the entities and activities that are relevant for explaining the phenomenon in question.

When they study highly complex systems, such as biological organisms or social groups, scientists may also get different mechanistic decompositions of the same system when they focus on different phenomena in the system (Glennan 2017: 37–38). But once they have identified a phenomenon in a system, the boundaries of the mechanism that is responsible for it are determinate and do not depend on the ways the mechanism is represented. An important implication of this is that mechanistic levels are always relative to some phenomenon of interest, meaning that there are no global levels of mechanisms. From this, it follows that cognitive social scientists should be cautious regarding the methodological value of highly abstract mechanism types, such as ‘biological mechanism’, ‘psychological mechanism’ and ‘social mechanism’ since they tend to refer to heterogeneous arrays of mechanisms rather than to fixed ‘ontological levels of reality’.

Mechanistic Explanations

While mechanisms are always particular and spatiotemporally local, cognitive and social scientists are interested in making generalizations about them and classifying them into kinds. According to MPS, scientists achieve generality by constructing models about classes of particular mechanisms. In scientific practice, mechanistic models may take many different forms, such as qualitative descriptions, diagrams, equations, or computational simulations. What they share in common is that they can be used to ‘describe (in some degree and some respect) the [target] mechanism that is responsible for some phenomenon’ (Glennan 2017: 66). An important way to construct general models is by abstracting away from the details of particular mechanisms and idealizing some of their features. For example, many models of social mechanisms not only abstract away from most neural and cognitive mechanisms that underlie the interactions of individual actors but may also include idealized descriptions of the cognitive capacities of actual human beings (cf. Hedström, 2005; Hedström & Ylikoski 2010). Abstractions omit details regarding the target mechanism while idealizations distort some features of the target mechanism (Craver & Darden 2013: 33–34, 94; Glennan 2017: 73–74). There is no general criterion regarding the acceptability of abstractions and idealizations in a mechanistic model – rather, the appropriateness of particular abstractions and idealizations should be decided in a case-by-case manner depending on the epistemic aims of the researcher (Craver & Kaplan 2018; Glennan 2017).

In MPS, scientific explanations are understood in terms of mechanistic models that scientists use –in combination with other relevant explanatory factors – to represent those mechanisms that underlie, maintain or produce the phenomenon that they aim to explain (e.g. Bechtel 2008; Craver & Darden 2013; Glennan 2017). Mechanistic explanations may unify phenomena that were earlier regarded as unconnected by revealing that their underlying mechanisms are similar. Mechanistic explanations may also split phenomena that were earlier regarded as similar by revealing that their underlying mechanisms are different.

In the context of the cognitive social sciences, some researchers have recognized the identification of cognitive mechanisms underlying social phenomena as a central argument for the cognitive social sciences (e.g. Sun 2017; Thagard 2019), while others have argued in favor of greater unification (e.g. Gintis 2007), complementarity (e.g. Zerubavel 1997) or mutual constraints (e.g. Bloch 2012) between the cognitive and social sciences without appeal to mechanistic philosophy of science. We have discussed different arguments for the cognitive social sciences in more detail in an earlier article (Kaidesoja et al. 2019) and a blog post that was based on it. However, when evaluating mechanistic explanations for social phenomena, it is important to recognize that such explanations do not reduce the phenomena to be explained to some lower level. Rather, they help us to understand how the phenomena to be explained arise from the organized interactions of its constituent entities and activities in a specific environment. This means that mechanistic explanations often cite mechanisms at many different levels in a local mechanistic hierarchy.

Some critics of MPS have claimed that advocates of this view assume that more detailed mechanistic explanations are always better (e.g. Batterman & Rice 2014), although the latter have explicitly distanced their views from this idea (e.g. Glennan 2017; Craver & Kaplan 2018). Even if it is clear that a mechanistic explanation should describe some entities and activities that contribute to the phenomenon to be explained, mechanistic explanations may vary with respect to their completeness, and the epistemic purposes of researchers should be taken into account when assessing the relevance of adding more detail to a mechanistic model. Accordingly, in their well-known article on causal mechanisms in the social sciences, Peter Hedström and Petri Ylikoski (2010: 60) conclude that ‘only those aspects of cognition that are relevant for the explanatory task at hand should be included in the explanation, and the explanatory task thus determines how rich the psychological assumptions must be’. Cognitive explanations of social phenomena may accordingly involve various degrees of realism and complexity, and more detailed multi-level explanations are not automatically more satisfactory than explanations that focus on a more straightforward or selective subset of causes.

Conclusion

This brief account of causal mechanisms and mechanistic explanations already provides some ideas on how to integrate the social sciences with the cognitive sciences. In the simplest case, mechanisms studied in the cognitive and social sciences can be organized in a hierarchical manner such that cognitive scientists model those cognitive and neural mechanisms that directly underlie those cognitive capacities and activities of social actors that are assumed in social scientists’ models about social mechanisms. However, few mechanistic models in the cognitive and social sciences can be organized into vertical relations of this kind. It is often the case, for example, that cognitive scientific and social scientific models address partially overlapping phenomena in different spatiotemporal scales by using different conceptual frameworks and research methods (e.g. Bloch 2012; Lizardo et al 2020; Turner 2018). This means that there are still significant conceptual gaps and methodological discrepancies that cognitive social scientists need to address in their explanatory practices. In our paper, we used MPS to address some of these difficulties and applied it in three case studies about the cognitive social sciences. In a follow-up post, we discuss our case studies and their lessons.

References

Batterman, RW, and Rice C (2014) “Minimal model explanations.” Philosophy of Science 81(3): 349–76.

Bechtel W (2008) Mental Mechanisms: Philosophical Perspectives on Cognitive Neuroscience. Routledge: London.

Bloch M (2012) Anthropology and the Cognitive Challenge. Cambridge: Cambridge University Press.

Brekhus W and Ignatow G (eds) (2019) Oxford Handbook of Cognitive Sociology. Oxford: Oxford University Press.

Cacioppo J, Berntson G and Decety J (2012) “A history of social neuroscience.” In: Kruglanski A and Stroebe W (eds) Handbook of the History of Social Psychology. New York: Psychology Press, pp.123-136.

Craver C and Darden L (2013) In Search of Mechanisms: Discoveries Across the Life Sciences. Chicago: University of Chicago Press.

Craver C and Kaplan D (2018) “Are more details better? On the norms of completeness for mechanistic explanations.” The British Journal for the Philosophy of Science, 1(71): 287–319

Dhami S (2016) The Foundations of Behavioral Economic Analysis. Oxford University Press.

Gintis H. (2007) A framework for the unification of the behavioral sciences. Behavioral and Brain Sciences, 30: 1–16.

Glennan S (2017) The New Mechanical Philosophy. Oxford: Oxford University Press.

Glennan S and Illari P (eds) (2018) The Routledge Handbook of Mechanisms and Mechanical Philosophy. London: Routledge.

Hedström, P (2005) Dissecting the Social: On the Principles of Analytical Sociology. Cambridge: Cambridge University Press.

Hedström P and Ylikoski P (2010) “Causal mechanisms in the social sciences.” Annual Reviews in Sociology 39: 46-67.

Hutchins E (1995) Cognition in the wild. Cambridge: MIT Press.

Kaidesoja T, Sarkia M and Hyyryläinen M (2019) “Arguments for the cognitive social sciences.” Journal for the Theory of Social Behavior 49(4):1-16. https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsb.12226

Lieberman M (2017) “Social cognitive neuroscience: A review of core processes.” Annual Review of Psychology 58: 259–289.

Lizardo O, Sepulvado B, Stoltz D and Taylor M (2020) “What can cognitive neuroscience do for cultural sociology.” American Journal of Cultural Sociology 8: 3–28.

Sarkia M, Kaidesoja T and Hyyryläinen M (2020) “Mechanistic explanations in the cognitive social sciences: Lessons from three case studies.” Social Science Information. https://doi.org/10.1177%2F0539018420968742

Sperber D (1996) Explaining Culture: a Naturalistic Approach. Oxford: Blackwell.

Sun R (2012) “Prolegomena to the cognitive social sciences.” In R. Sun (ed) Grounding Social Sciences in Cognitive Sciences. Cambridge (MA): MIT Press, pp. 3–32.

Thagard P (2019) Mind-Society: From Brains to Social Sciences and Professions. Oxford: Oxford University Press.

Turner SP (2018) Cognitive Science and the Social. London: Routledge.

Zerubavel, E (1997) Social Mindscapes: An Invitation to Cognitive Sociology. Cambridge, MA: Harvard University Press.

Did John Dewey Put Prediction into Action?

Prediction does not appear, at first, to be something that a sociologist, or really any analyst of anything, can safely ascribe to those (or that) which they are studying without running afoul of about a thousand different stringent rules that define how probability can be used for the purposes of generating knowledge. If we follow the likes of Ian Hacking (1975) and Lorraine Daston (1988) (among others), then “modern fact-making” has a lot to do with ways of using probability, especially for the purposes of making predictions. To the degree that this transforms probability into prediction, as referring to the epistemic practices that analysts use to generate a knowledge claim, this usage actually places limits on what probability can mean, how prediction can be used and where we might find it. If we don’t have certain epistemic practices (e.g. a nice regression analysis) then we can’t say that prediction is occurring anywhere if we are not doing it ourselves.

As Hacking and Daston indicate, however, for probability to be limited almost entirely to epistemic practices in this sense would appear strange to those who can stake any sort of claim to having “discovered” probability, especially Blaise Pascal. He, for one, did not understand probability to be limited to efforts at making predictions for the purposes of knowledge. For Pascal, probability had direct analogues in lived experience (without calculation) in the form of senses of risk and high stakes, and the perceived fairness of outcome, particularly in games of chance. If this seems unusual to us now, given the strictures we place on probability and prediction, these points are far less unusual for what is fast appearing as a major paradigm in cognitive science, namely predictive processing (see Clark 2013; Friston 2009; Wiese and Metzinger 2017; Williams 2018; Hohwy 2020).

To put it simply, predictive processing makes prediction the primary function of the brain. The brain evolved to allow for the optimal form of engagement with a contingent and probabilistic environment that is never in a steady state. Given that our grey matter is locked away inside a thick layer of protective bone (e.g. the skull), it has no direct way of perceiving or “understanding” what is coming at it from the outside world. What it does have are the senses, which themselves evolved to gather information about that environment. Predictive processing says, in essence, that the brain can have “knowledge” of its environment by building the equivalent of a model and using it to constantly generating predictions about what the incoming sensory information could be. This works in a continuous way, both at the level of the neuron and synapse, and at the level of the whole organism. The brain does not “represent” what it is dealing with, then, but it uses associations, co-occurrences, tendencies and rhythms to predict what it is dealing with.

All of this is contingent on making the equivalent of constant, future-oriented but past-deriving, best guesses. When those guesses are wrong, this generates error, which forms the content of our perceptions. In other words, what we perceive and consciously attend to is the leftover error of our generative models and their predictions of our sensory input. When those guesses are right, by contrast, we don’t have perceptual content because there is no error. The generative models we build are themselves multi-tiered, and the predictions they make work at several different levels of composition. A full explanation of predictive processing far exceeds the limits of this post. But this, in particular, is worth mentioning because it means that a generative model is not static or unchanging. Quite the contrary, generative models constantly change (at some compositional level) in order to better ensure prediction error minimization.

Some of these points will probably not sound that unusual. The relationship between minimized perceptual content and action is commonly referred to in discussions of embodiment and moral intuition, for example. What probably sounds very unusual, however, is the central role given here to prediction.

As mentioned, prediction has been essentially cordoned off in the protected sphere of knowledge, to be used only by specialists wielding specialist tools and training. While it can be done by the folk, we (the analysts) love to point out how they do it poorly. On the off chance they happen to predict correctly (e.g. gambling on the long shot), this is celebrated as the exception that proves the rule. After all, the folk do not have our epistemic practices or training. All they have is their (subjective) experience and biases. In fact, brandishing those presumably bad at predicting by those with increasingly sophisticated techniques to make predictions on increasingly large datasets has become par for the course in the era of “analytics” (Hofman, Sharma and Watts 2017), and this particular symbolic power is now wielded quite overtly in a variety of fields (like baseball). Thus, to take prediction away from action could have, all along, been just another way of saying that because we (the analysts) predict and they (the folk) do not predict or do so poorly, they need us.

But is this the case? Predictive processing poses a serious question to this assumption and, with it, the role that prediction plays in making sociological knowledge different from folk knowledge. There is also a bit of history worth mentioning. The assumption that prediction plays only a negligible part in action, while other things like values and beliefs play a big part, comes from Talcott Parsons, who explicitly set out to marginalize prediction (1937: 64). Sociologists are rightfully in the mood of poo-pooing Parsons and have been for quite some time; but any proposal to put prediction into action remains just as heretical today as it did to Parsons in the 1930s. As one of his major points about action, the presumption that prediction can play no direct or significant role in action has still not been revisited let alone revised.

The purpose of this post is simply to sketch out the suggestion that we can even do this (e.g. put prediction into action) without falling over our feet and retreating sheepishly to the safety of the domain the Parsons carved out for us should we ever wish to talk about “action” again. Far be it from me to attempt to do this on my own. So for the purposes of illustration, a few pages from John Dewey’s Logic: The Theory of Inquiry (1938: 101-116) (and few from Human Nature and Conduct [1922]) will be enlisted for the task. I will argue that, in these pages, which are themselves famous because in them Dewey gives specific proposals about the process or stages of inquiry, Dewey does put prediction into action, and he does so in a way that does not seem that controversial; though, for any legitimate contemporary meaning of “prediction,” these are heretical claims.

For Dewey, in contrast to Parsons, the action situation is not neatly parsed into the “objective state of affairs” that could be described with scientific precision by an external observer (and for which prediction is appropriate) and the “subjective point of view” of the actor (for which, by implication, prediction does not apply, lest we “squeeze out” the creative, voluntaristic element). Instead, the “state of affairs” is, according to Dewey (1922, p. 100ff), irreducibly composed of an entanglement of both objective and subjective elements. The very act of perception of a given state of affairs on the part of the actor introduces such a subjective element (for Parsons perception was not necessarily part of the subjective element of the action schema).

Perception is not just purely spectatorial or contemplationist, then, but serves as the “initial stage” in a dynamic action cycle. Perception is for something, and this something is anticipation and prediction. Thus, “the terminal outcome when anticipated (as it is when a moving cause of affairs is perceived) becomes an end-in-view, an aim, purpose, a prediction usable as a plan in shaping the course of events” (Dewey 1922:101, italics added). In a stronger sense, for Dewey perceptions are predictions, which in their turn are ends-in-view. Perceptions are “projections of possible consequences; they are ends-in-view. The in-viewness of ends is as much conditioning by antecedent natural conditions as is perception of contemporary objets external to the organism, trees and stones or whatever” (102).

For Dewey (1938), this can extend even further into what arguably remains his most influential contribution to pragmatist thought: the process of inquiry, as it “enters into every aspect of every area” of life (101). Inquiry, as Dewey defines it, is the “controlled or directed transformation of an indeterminate situation into one that is so determinate in its constituent distinctions and relations as to convert the elements of the original situation into a unified whole” (104-105). This filters into all subsequent understandings of pragmatist problem-solving.

The “indeterminate situation” (105) that provides antecedent conditions for inquiry is constituted by doubt, but this is not a purely subjective state (“in us”). Doubt refers to our placement in a situation that is doubtful because we cannot respond to it as we are accustomed: “the particular quality of what pervades the given materials, constituting them a situation … is a unique doubtfulness which makes that situation to be just and only the situation it is” (105). Specifically this means that we cannot form ends-in-view with respect to the situation, though we can “[respond] to it … [in] blind and wild overt activities.” As Dewey stresses, “it is the situation that has these traits,” which means that we are simply a part of the situation in being doubtful; one part of the total configuration. To simply “change our mind” with respect to the doubtful situation is hardly enough to change it, though with any indeterminate situation, we might respond by carrying through a “withdrawal from reality.” The only thing that will really be effective, however, is what Dewey calls a “restoration of integration” in which the situation changes as our situation within it changes (e.g. as we change) (106).

Underlying Dewey’s proposals, then, is a kind of cognitive mechanism, which he does not label outright, but which, likewise, rests on prediction, and on which the stages of inquiry itself appear to rest. For Dewey (107-108), it is possible to remain in the doubtful situation forever, particularly should you find an effective means of “withdrawing from reality.” The next stage in the process of inquiry will only occur through a change in “cognitive operations,” specifically what Dewey labels “the institution of the problem … The first result of evocation of inquiry is that the situation is taken, adjudged, to be problematic. To see that a situation requires inquiry is the initial step in inquiry” (107). But to take this step, as Dewey implies, requires a change in the manner of prediction, and in a not dissimilar sense as a roughly equivalent mechanism identified by predictive processing.

If the indeterminate situation does not allow for perceptions as “ends-in-view,” then in the problematic situation the actor (e.g. “the interpretant”) changes because, in the situation, she is now characterized by an explicit representation: “without a problem, there is blind grasping in the dark.” This representation is needed as a change in cognition, but only as a mediating and not a permanent state. But the constant in this process, that allows representation to appear now explicitly and then only to disappear later on, can only be successive forms of prediction that, in Dewey’s terms, is trying to obtain an end-in-view. In other words, the explicit representation of “problem” itself presupposes a prediction about error. More generally, we are part of a problematic situation because we predict that it should go one way and it does not, and then we anticipate what would be required to minimize that error, which then forms the basis for future action. In almost a directly analogous sense, predictive processing refers to this as “active inference.”

Hence, what follows this (“the determination of a problem-situation”) is subsequently characterized by the generation of “ideas” as part of the inherently progressive nature of inquiry along the lines of continuous prediction or forward-searching (e.g. guessing): “The statement of a problematic situation in terms of a problem has no meaning save as the problem instituted has, in the very terms of its statement, reference to a possible solution” (108). Put differently, the one (problem) never occurs without the other (solution); we actively infer solutions because we have problems. Dewey (110-111) uses this to critique all prior conceptions of “ideas” in a western philosophical tradition (empiricists, rationalists and Kantians) for not seeing how perceptions and ideas function correlatively rather than separately:

Observations of facts and suggested meanings or ideas arise and develop in correspondence with each other. The more the facts of the case come to light in consequence of their being subjected to observation, the clearer and more pertinent become the conceptions of the way the problem constituted by these facts is to be dealt with. On the other side, the clearer the idea, the more definite … become the operations of observation and of execution that must be performed in order to resolve the situation (109).

Ideas are not removed from the situation, or entirely defined by the situation. Rather, the most important thing about them is that they have a direction in relation to the situation. But this only works if they suggest a forward-facing (temporally speaking) cognitive mechanism, which again seems perfectly analogous to a predictive function that is trying (slowly) to minimize error. Dewey seeks to redeem the role of “suggestions” (which have “received scant courtesy in logical theory”) by giving them not the diminished importance of half-completed ideas, but elevating them to “the primary stuff of logical ideas.” In this sense, suggestions demonstrate how “perceptual and conceptual materials are instituted in functional correlativity to each other in such a manner that the former locates and describes the problem which the latter represents a method of solution” (111; emphasis added).

To “reason,” then, means to examine the meaning of ideas according to their simultaneous statement of problem and solution (e.g. “relationally”). For Dewey, this process involves “operating with symbols (constituting propositions)… in the sense of ratiocination and rational discourse.” If a suggested meaning is “immediately accepted,” then the inquiry will end prematurely. Full reasoning consists of a kind of “check upon immediate acceptance [as] the examination of … the meaning in question” according to what it “implies in relation to other meanings in the system of which it is a member” (111). By “meaning” Dewey refers to symbols in a semiotic sense or the connection of sign and object in a non-problematic or habitual way. This therefore opens those habitual associations up to transformation as the situation becomes more determinate. Dewey also emphasizes how symbols perform the semiotic function of “fact-meanings.” The process of inquiry subjects these connections to “ideas [as] operational in that they instigate and direct further operations of observation; they are proposals and plans for acting upon existing conditions to bring new facts to light and to organize all the selected facts into a coherent whole” (112-113). The process remains forward-facing, which means that there can be “trial facts” that can be taken on-board with a certain provisionality: “they are tested and ‘proved’ with respect to their evidential function.” Ideas and facts, then, become “operative” in the process of inquiry (problem-solving) “to the degree in which they are connected with experiment” (114). Again, all of this presupposes that forward momentum, or searching, appears to be fueled by advancing and constant prediction.

Thus, for Dewey, the transformation of the situation into “determinate” involves a change of “symbols” in the form of habitual associations (sign to object) which themselves always remain provisional and never fully determinate (114-115). This is what alters our “self” (interpretant) within the situation as no longer in a doubtful state, and replaces this with what we might call a “confident” state as signifying a kind of assurance of action in relation to the situation.

Thus, having passed through the stages of inquiry, and with new habitual associations, we are now predicting it well within the continuous flow of action. In Dewey’s terms, problem and solution effectively merge at the end of inquiry, and the forward-facing search ends. But we can translate the folk terms that Dewey uses here almost directly into the more technical terms that form the basis of predictive processing: the problem or trial-situation ends with the erasure of prediction error by a change in the generative model, such that the tiered coding of sensory input will generate the perceptions that the generative model expects. X is now Y in a non-problematic way, which for Dewey means that it becomes a “symbol” as a connection that is now habitual (see also Peirce CP 2: 234). Inquiry in “common sense” and inquiry in science are not different, according to Dewey, they simply involve differences in problems. For common sense, problems appear from symbols as the habitual culture of groups (115-116).

This can lead us to make an even more radical claim: prediction in action and prediction in sociology are also not different; they simply involve differences in problems between those that occur in the continuous course of action, and those that are deliberately manufactured for the purposes of staging trials and leveraging them in order to make knowledge claims. Shared generative models also appear among actually-existing groups that make similar predictions, perceive similar things based on similar error, make similar active inferences, and therefore “solve problems” in ways that have a family resemblance.

It seems then, without too much presumptuousness, we can take Dewey’s original definition of inquiry and retranslate it into its implied cognitive terms:

The controlled or directed transformation of an indeterminate situation into one that is so determinate in its constituent distinctions and relations as to convert the elements of the original situation into a unified whole (Dewey 1938: 104-105).

We can translate this into a general statement about problem-solving as follows

The higher order transformation of a situation with lots of prediction error into a generative model that is able to convert the elements of the original situation into a predictable whole.

A follow-up post will discuss the broader significance of this translation in relation to pragmatist theories of action.

References

Clark, Andy. 2013. “Whatever next? Predictive Brains, Situated Agents, and the Future of Cognitive Science.” The Behavioral and Brain Sciences 36(3):181–204.

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