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).
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’.
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.
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.
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