Samuel D. Taylor

The epistemic theory of causality views causality as a tool that helps us to predict, explain and control our world, rather than as a relation that exists independently of our epistemic practices. In this chapter, we first provide an introduction to the epistemic theory of causality. We then outline four considerations that motivate the epistemic theory: the failure of standard theories of causality; parsimony; the epistemology of causality; and neutrality. We illustrate these four considerations in the contexts of the social sciences and the cognitive sciences. We argue that the epistemic theory provides a very natural account of causality across these contexts.

Here, I take alethic views of understanding to be all views that hold that whether an explanation is true or false matters for whether that explanation provides understanding. I then argue that there is (as yet) no naturalistic defence of alethic views of understanding in cognitive science, because there is no agreement about the correct descriptions of the content of cognitive scientific explanations. I use this claim to argue for the provisional acceptance of afactivism in cognitive science, which is the view that the truth or falsity of an explanation of cognition is irrelevant to whether that explanation provides understanding. I conclude by discussing the relation between understanding in cognitive science and understanding in other domains.

A tension exists between those who do—e.g. Meyer (The British Journal for the Philosophy of Science 71:959–985, 2020 ) and Chemero ( 2011 )—and those who do not—e.g. Kaplan and Craver (Philosophy of Science 78:601–627, 2011 ) Piccinini and Craver (Synthese 183:283–311, 2011 )—afford nonmechanistic explanations a role in cognitive science. Here, I argue that one’s perspective on this matter will cohere with one’s interpretation of the tasks of cognitive science; that is, of the actions for which cognitive scientists are specially fitted. To make this point concrete, I consider two tasks of cognitive science—establishing causal claims and unifying—and show how these tasks are interpreted differently by mechanists and nonmechanists respectively. I then argue that, as a result of these different interpretations, we cannot expect to resolve the tension between mechanists and nonmechanists by appeal to cognitive scientific practice. I conclude that we should give up on the debate between mechanists and nonmechanists, and search for progress elsewhere.

Cognitive theorists routinely disagree about the evidence supporting claims in cognitive science. Here, we first argue that some disagreements about evidence in cognitive science are about the evidence available to be drawn upon by cognitive theorists. Then, we show that one’s explanation of why this first kind of disagreement obtains will cohere with one’s theory of evidence. We argue that the best explanation for why cognitive theorists disagree in this way is because their evidence is what they rationally grant. Finally, we explain why our view does not lead to a pernicious kind of relativism in cognitive science.

Some philosophers argue that all concepts cannot have the same representational structure, because no single kind of representation has been successful in accounting for the phenomena related to the formation and application of concepts. Here, I argue against this “appeal to cognitive science” by demonstrating that different theories of the kind concept cohere with different interpretations of the argument. To circumvent the threat of relativism, I argue that theories of concept should be understood as working hypotheses, which are provisionally accepted to facilitate the investigation and explanation of the mind/brain. From this perspective, theories of concept are to be evaluated as part of a comparative analysis of the different kinds of cognitive science they inspire.

Machery and Weiskopf argue that the kind concept is a natural kind if and only if it plays an explanatory role in cognitive scientific explanations. In this paper, we argue against this explanationist approach to determining the natural kind-hood of concept. We first demonstrate that hybrid, pluralist, and eliminativist theories of concepts afford the kind concept different explanatory roles. Then, we argue that we cannot decide between hybrid, pluralist, and eliminativist theories of concepts, because each endorses a different, but equally viable, specification of the explananda of cognitive science. It follows that an explanationist approach to determining the natural kind-hood of concept fails, because there is no consensus about whether or not concept should be afforded an explanatory role in our best cognitive scientific explanations. We conclude by considering what our critique of explanationism could imply for further discussions about the explanatory role of concepts in cognitive science.

Representationalists and anti-representationalists disagree about whether a naturalisation of mental content is possible and, hence, whether positing mental representations in cognitive science is justified. Here, I develop a novel way to think about mental representations based on a philosophical description of (cognitive) science inspired by cognitive instrumentalism. On this view, our acceptance of theories positing mental representations and our beliefs in (something like) mental representations do not depend on the naturalisation of content. Thus, I conclude that if we endorse cognitive instrumentalism about mental representations, then we can finally leave the dispute between representationalism and anti-representationalism behind.

Kaplan and Craver :601–627, 2011) and Piccinini and Craver :283–311, 2011) argue that only mechanistic explanations of cognition are genuine causal explanations, because only evidence of mechanisms reveals the causal structure of cognition. I first argue that this claim is grounded in a commitment to the mechanistic account of causality, which cannot be endorsed by a defender of causal-nonmechanistic explanations. Then, I defend the epistemic theory of causality, which holds that causal explanations are not genuine to the extent that they reveal mechanistic causal structure, but, rather, to the extent that they have evidential support and yield successful prediction, explanation, and control inferences. Finally, I enact an epistemic unification of causal explanation in cognitive science, according to which both mechanistic and nonmechanistic explanations of cognition can be genuine causal explanations.

Some philosophers argue that we should eschew cross-explanatory integrations of mechanistic, dynamicist, and psychological explanations in cognitive science, because, unlike integrations of mechanistic explanations, they do not deliver genuine, cognitive scientific explanations. Here I challenge this claim by comparing the theoretical virtues of both kinds of explanatory integrations. I first identify two theoretical virtues of integrations of mechanistic explanations—unification and greater qualitative parsimony—and argue that no cross-explanatory integration could have such virtues. However, I go on to argue that this is only a problem for those who think that cognitive science aims to specify one fundamental structure responsible for cognition. For those who do not, cross-explanatory integration will have at least two theoretical virtues to a greater extent than integrations of mechanistic explanations: explanatory depth and applicability. I conclude that one’s views about explanatory integration in cognitive science cannot be segregated from one’s views about the explanatory task of cognitive science.

A tension has been identified between the acquisition and participation metaphors for learning, and it is generally agreed that this tension has still not been adequately resolved. In this paper, we offer an alternative to the acquisition and participation metaphors for learning: the metaphor of mastering. Our claim is that the mastering metaphor, as grounded in inferentialism, allows one to treat both the acquisition and participation dimensions of learning as complementary and mutually constitutive. Inferentialism is a semantic theory which explains concept formation in terms of the inferences individuals make in the context of an intersubjective practice of acknowledging, attributing, and challenging one another's commitments. We first introduce the key concepts of inferentialism and consider the perspective on learning that inferentialism inspires. Then, we condense the lessons of the inferentialist concepts into a single mastering metaphor for learning and argue that learning consists in the process by which learners come to master concepts and practices. We conclude by discussing how the mastering metaphor could be put to work in a theoretical reconciliation of the cognitive and sociocultural dimensions of learning.