Samuli Pöyhönen

Looping Kinds and Social Mechanisms

Human behavior is not always independent of the ways in which humans are scientifically classified. That there are looping effects of human kinds has been used as an argument for the methodological separation of the natural and the human sciences and to justify social constructionist claims. We suggest that these arguments rely on false presuppositions and present a mechanisms-based account of looping that provides a better way to understand the phenomenon and its theoretical and philosophical implications.

Explanatory power of extended cognition

I argue that examining the explanatory power of the hypothesis of extended cognition (HEC) offers a fruitful approach to the problem of cognitive system demarcation. Although in the discussions on HEC it has become common to refer to considerations of explanatory power as a means for assessing the plausibility of the extended cognition approach, to date no satisfying account of explanatory power has been presented in the literature. I suggest that the currently most prominent theory of explanation in the special sciences, James Woodwards contrastive-counterfactual theory, and an account of explanatory virtues building on that theory can be used to develop a systematic picture of cognitive system demarcation in the psychological sciences. A major difference between my differential influence (DI) account and most other theories of cognitive extension is the cognitive systems pluralism implied by my approach. By examining the explanatory power of competing traditions in psychological memory research, I conclude that internalist and externalist classificatory strategies are characterized by different profiles of explanatory virtues and should often be considered as complementary rather than competing approaches. This suggests a deflationary interpretation of HEC.

Understanding Nonmodular Functionality: Lessons from Genetic Algorithms

Evolution is often characterized as a tinkerer creating efficient but messy solutions. We analyze the nature of the problems that arise when trying to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem—solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show, first, that evolutionary designs are often hard to understand because they exhibit nonmodular functionality and, second, that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation.

Value of cognitive diversity in science

When should a scientific community be cognitively diverse? This article presents a model for studying how the heterogeneity of learning heuristics used by scientist agents affects the epistemic efficiency of a scientific community. By extending the epistemic landscapes modeling approach introduced by Weisberg and Muldoon, the article casts light on the micro-mechanisms mediating cognitive diversity, coordination, and problem-solving efficiency. The results suggest that social learning and cognitive diversity produce epistemic benefits only when the epistemic community is faced with problems of sufficient difficulty.

Intentional concepts in cognitive neuroscience

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennetts intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations.

Natural Kinds and Concept Eliminativism

Recently in the philosophy of psychology it has been suggested that several putative phenomena such as emotions, memory, or concepts are not genuine natural kinds and should therefore be eliminated from the vocabulary of scientific psychology. In this paper I examine the perhaps most well known case of scientific eliminativism, Edouard Machery’s concept eliminativism. I argue that the split-lump-eliminate scheme of conceptual change underlying Machery’s eliminativist proposal assumes a simplistic view of the functioning of scientific concepts. Conceiving of scientific concepts as natural kind terms is an important reason for the impasse between Machery and anti-eliminativists, as both sides allude to properties of natural kinds in their contradicting arguments. As a solution I propose that, in order to develop a more satisfactory theory of conceptual change in science, one needs to distinguish between three different types of scientific concepts, hitherto conflated under the loaded notion of natural kind.

Limitations of Behaviorally Informed Policy Under Social Interaction

Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Proponents of both the approaches claim capacity to enhance social welfare through better individual decisions. We question the validity of this claim. First, individual rationality is neither sufficient nor necessary for improving collective outcomes. Second, collective outcomes of complex interactions among individuals are largely ignored by the focus of both nudge and boost on individual decisions. We suggest that the design of mechanisms and norms can sometimes lead to better collective outcomes than nudge and boost. More generally, we present conditions under which the three approaches enhance social welfare. Furthermore, we argue that to reliably improve collective outcomes that depend on the aggregation of many decisions, it is necessary to understand the interface between the psychology of reasoning and decision making on the one hand and economics and policy on the other.