What is an unconscious representation?

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

Explaining social phenomena by multilevel mechanisms

Four questions about multilevel mechanisms

In our previous post, we discussed mechanistic philosophy of science and its contribution to the cognitive social sciences. In this blog post, we will discuss three case studies of research programs at the interface of the cognitive sciences and the social sciences. In our cases, we apply mechanistic philosophy of science to make sense of the epistemological, ontological, and methodological aspects of the cognitive social sciences. Our case studies deal with the phenomena of social coordination, transactive memory, and ethnicity.

In our work, we have drawn on Stuart Glennan’s minimal account of mechanisms, according to which 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). We understand entities and activities liberally so as to accommodate the highly diverse sets of entities that are studied in the cognitive social sciences, from physically grounded mental representations to material artifacts and entire social systems. In our article, we make use of the following four questions drawn from William Bechtel’s (2009) work to assess the adequacy and comprehensiveness of mechanistic explanations:

What is the phenomenon to be explained (‘looking at’)?
What are the relevant entities and their activities (‘looking down’)?
What are the organization and interactions of these entities and activities through which they contribute to the phenomenon (‘looking around’)?
What is the environment in which the mechanism is situated, and how does it affect its functioning (‘looking up’)?

The visual metaphors of looking at the phenomenon to be explained, looking down at the entities and activities that underlie the phenomenon, looking around at the ways in which these entities and activities are organized, and looking up at the environment in which the mechanism operates, are intended to emphasize that mechanistic explanations are not strongly reductive or “bottom-up” explanations. Rather, multilevel mechanistic explanations can bring together more “bottom-up” perspectives from the cognitive sciences with more “top-down” perspectives from the social sciences in order to provide integrated explanations of complex social phenomena. In the following, we will illustrate how we have used mechanistic philosophy of science in our case studies and what we have learned from them.

Social Coordination

Interpersonal social coordination has been studied during recent decades in many different scientific disciplines, from developmental psychology (e.g., Carpenter&Svetlova 2016) to evolutionary anthropology (e.g., Tomasello et al. 2005) and cognitive science (e.g., Knoblich et al. 2011). However, despite their shared interests, there has so far been relatively limited interaction between different disciplinary research programs studying social coordination. In this case study, we argued that mechanistic philosophy of science can ground a feasible division of labor between researchers in different scientific disciplines studying social coordination.

In evolutionary anthropology and developmental psychology, one of the most important ideas that has gained considerable empirical support during recent decades is that human agents and our nearest primate relatives differ fundamentally in our dispositions to social coordination and cooperation: for example, chimpanzees rarely act together instrumentally in natural settings, and they are not motivated to engage in the types of social games and joint attention that human infants find intrinsically rewarding already at an early age (Warneken et al. 2006). Importantly, this does not seem to be due to a deficit in general intelligence since chimpanzees score as well as young human infants on tests of quantitative, spatial, and causal cognition (Herrmann et al. 2007). According to the shared intentionality -hypothesis of evolutionary anthropologist Michael Tomasello, this is because “human beings, and only human beings, are biologically adapted for participating in collaborative activities involving shared goals and socially coordinated action plans (joint intentions)” (Tomasello et al. 2005).

Given a basic capacity to engage in social coordination, one can raise the question of what types of cognitive mechanisms enable individuals to share mental states and act together with other individuals. To answer this question, we made use of the distinction between emergent and planned forms of coordination put forth by cognitive scientist Günther Knoblich and his collaborators. According to Knoblich et al. (2011: 62), in emergent coordination, “coordinated behavior occurs due to perception-action couplings that make multiple individuals act in similar ways… independent of any joint plans or common knowledge”. In planned coordination, ”agents’ behavior is driven by representations that specify the desired outcomes of joint action and the agent’s own part in achieving these outcomes.” Knoblich et al. (2011) discuss four different mechanisms for emergent coordination: entrainment, common object affordances, action simulation, and perception-action matching. While emergent coordination is explained primarily by sub-intentional mechanisms of action control (which space does not allow us to discuss in more detail here), planned coordination is explained by reference to explicit mental representations of a common goal, the other individuals in joint action, and/or the division of tasks between the participants.

In our article, we argued that cognitive scientists and social scientists answer different questions (see above) about mechanisms that bring about and sustain social coordination in different environments and over time. Thus they are in a position to make mutually interlocking yet irreducible contributions to a unified mechanistic theory of social coordination, although they may also sometimes reach results that challenge assumptions that are deeply ingrained in the other group of disciplines. For a more detailed discussion of how cognitive and social scientists can collaborate in explaining social coordination, we refer the reader to our article (Sarkia et al. 2020: 8-11).

Transactive Memory

Our second case study concerned the phenomenon of transactive memory, which has been studied in the fields of cognitive, organizational, and social psychology as well as in communication studies, information science, and management. The social psychologist Daniel Wegner and his colleagues (Wegner et al. 1985: 256) define transactive memory in terms of the following two components:

An organized store of knowledge that is contained entirely in the individual memory systems of the group members
A set of knowledge-relevant transactive processes that occur among group members.

They attribute transactive memory systems to organized groups insofar as these groups perform functionally equivalent roles in group-level information processing as individual memory mechanisms perform in individual cognition, i.e. (transactive) encoding, (transactive) storing, and (transactive) retrieving of information. For example, Wegner et al. (1985) found that close romantic couples responded to factual and opinion questions by using integrative strategies, such as interactive cueing in memory retrieval. Subsequent research on transactive memory systems has addressed small interaction groups, work teams, and organizations in addition to intimate couples (e.g., Ren & Argote 2011; Peltokorpi 2008). What is crucial for the development of a transactive memory system is that the group members have at least partially different domains of expertise and that the group members have learned about each other’s domains of expertise. If these two conditions are met, each group member can utilize the other group members’ domain-specific information in group-related cognitive tasks and transcend the limitations of their own internal memories.

In our article, we made use of the theory of transactive memory systems to argue that some cognitive mechanisms transcend the brains and bodies of individuals to the social and material environments that they inhabit. For example, in addition to brain-based memories, individual group members may also utilize material artifacts, such as notebooks, archives, and data files, as their memory stores. In addition, other members’ internal and external memory storages may in an extended sense be understood as part of the focal member’s external memory storages as long as she knows their domains of expertise and can communicate with them. Thus the theory of transactive memory can be understood as describing a socially distributed and extended cognitive system that goes beyond intra-cranial cognition (Hutchins 1995; Sutton et al. 2010). For a more detailed discussion of this thesis and its implications for interdisciplinary memory studies, we refer the reader to our article (Sarkia et al. 2011, 11-15).

Ethnicity

The sociologist Rogers Brubaker and his collaborators (Brubaker et al. 2004) has made use of theories in cognitive psychology and anthropology to challenge traditional approaches to ethnicity, nationhood, and race that view them as substantial groups or entities with clear boundaries, interests, and agency. Rather, he treats them as different ways of seeing the world, based on universal cognitive mechanisms, such as categorizing the world into ‘us’ and ‘them.’ Brubaker et al. (2004) also make use of the notions of cognitive schema and stereotype, defining stereotypes as “cognitive structures that contain knowledge, beliefs, and expectations about social groups” and schemas as “representations of knowledge and information-processing mechanisms” (DiMaggio 1997). For example, Brubaker et al. (2004, 44) discuss the process of ethnicization, where ”ethnic schemas become hyper-accessible and… crowd out other interpretive schemas.”

In our article, we made use of Brubaker’s approach to ethnicity to illustrate how cognitive accounts of social phenomena need to be supplemented by traditional social scientific research methods, such as ethnographic and survey methods when we seek to understand the broader social and cultural environment in which cognitive mechanisms operate. For example, in their case study of Cluj, a Romanian town with a significant Hungarian minority, Brubaker et al. (2006) found that while public discourse was filled with ethnic rhetoric, ethnic tension was surprisingly scarce in everyday life. By collecting data with interviews, participant observation, and group discussions, they were able to identify cues in various situations that turned a unique person into a representative of an ethnic group. Importantly, this result could not be achieved simply by studying the universal cognitive mechanisms of stereotypes, schemas, and categorization, since these mechanisms serve merely as the vehicles of ethnic representations, and they do not teach us about the culture-specific contents that these vehicles carry. We refer the reader to our article for more discussion of the complementarity of social scientific and cognitive approaches to ethnicity (Sarkia et al. 2020, 15-17).

References

Bechtel W (2009) “Looking down, around, and up: mechanistic explanation in psychology.” Philosophical Psychology 22(5): 543–564.

Brubaker R, Loveman M and Stamatov P (2004) “Ethnicity as cognition.” Theory and Society 33(1): 31–64.

Brubaker R, Feischmidt M, Fox J, Grancea L (2006) Nationalist Politics and Everyday Ethnicity in a Transylvanian Town. Princeton: Princeton University Press.

Carpenter M, Svetlova M (2016) “Social development.” In: Hopkins B, Geangu E, Linkenauer S (eds) Cambridge Encyclopedia of Child Development. Cambridge: Cambridge University Press, 415–423.

DiMaggio P (1997) “Culture and cognition.” Annual Review of Sociology 23: 263-287.

Herrmann E, Call J, Hernandez-Loreda, M, Hare B, and Tomasello, M (2007). “Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis.” Science 317: 1360-1366.

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

Peltokorpi V (2008). Transactive memory systems. Review of General Psychology 12(4): 378–394.

Ren Y and Argote L (2011) “Transactive memory systems 1985–2010: An integrative framework of key dimensions, antecedents, and consequences.” The Academy of Management Annals 5(1): 189–229.

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

Sutton J, Harris C.B., Keil P.G. and Barnier A.J. 2010. “The psychology of memory, extended cognition and socially distributed remembering.” Phenomenology and the Cognitive Sciences 9(4), pp. 521-560.

Tomasello M, Carpenter M, Call J, et al. (2005) “Understanding and sharing intentions: The origins of cultural cognition.” Behavioral and Brain Sciences 28: 675–691.

Warneken F, Chen F, Tomasello M (2006) “Cooperative activities in young children and chimpanzees.” Child Development 77(3): 640–663.

Wegner DM, Giuliano T and Hertel P (1985) “Cognitive interdependence in close relationships.” In: Ickes WJ (ed) Compatible and Incompatible Relationships. New York: Springer, pp. 253–276.

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.

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