Research

2023 (Peer-Reviewed Journal Articles):

Afactivism about understanding cognition (Open access, Publisher link)

Abstract: 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. 

Tasks in cognitive science: mechanistic and nonmechanistic perspectives (Preprint) (Publisher link)

Abstract: 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. 

2022 (Peer-Reviewed Journal Articles):

Evidence and Cognition (Open Access, Publisher link)

Abstract: 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. 

Concepts as a Working Hypothesis (Open Access, Publisher link)

Abstract: According to philosophers such as Bloch-Mullins (2017), Machery (2009), Rice (2016) 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.

2021 (Peer-Reviewed Journal Articles):

Cognitive Instrumentalism about Mental Representations (Publisher Link)

Abstract: 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 argue that this dispute cannot (yet) be resolved, because the two camps disagree about whether mental content should be naturalised in terms of physical entities or in terms of the entities that play a role in our best science. To circumvent the gridlock, I develop a third way to think about mental representations based on a philosophical description of (cognitive) science inspired by cognitive instrumentalism (Rowbottom, 2011). On this view, acceptance of a theory positing mental representations involves a belief in mental representations only if mental representations can be defined in terms of observable properties. I conclude that this perspective is of value because it recommends that our beliefs in (something like) mental representations need not depend on the naturalisation of content.

Two Kinds of Explanatory Integration in Cognitive Science (Preprint) (Publisher link)

Abstract: Kaplan and Craver (2011) argue that integrations of mechanistic explanations will deliver a complete explanation of cognition. Here I compare integrations of mechanistic explanations with another, putative kind of integration in cognitive science: cross-explanatory integrations of mechanistic, dynamicist, and psychological explanations. I first consider an example of an integration of mechanistic explanations and identify two theoretical virtues of such integrations: unification and greater qualitative parsimony. Then, I introduce dynamicist and psychological explanations to show that no cross-explanatory integration could have the theoretical virtues of unification and greater qualitative parsimony. 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 has at least two theoretical virtues to a greater extent than do 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.

The Explanatory Role of Concepts (Open Access, Publisher link)

Abstract:  Machery (2009) and Weiskopf (2009) 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.

A Frame-Theoretic Model of Bayesian Category Learning (Preprint)

Abstract: Bayesian models of category learning typically assume that the most probable categories are those that group input stimuli together around a maximally optimal number of shared features. One potential weakness of such feature list approaches, however, is that it is unclear how to weight observed features to be more or less diagnostic for a given category. In this theoretically oriented paper, we develop a frame-theoretic model of Bayesian category learning that weights the diagnosticity of observed attribute values in terms of their position within the structure of a frame (formalised as distance from the frame's central node). We argue that there are good grounds to further develop and empirically test frame-based learning models, because they have theoretical advantages over unweighted feature list models, and because frame structures provide a principled means of assigning weights to attribute values without appealing to supervised training data.

Causation and Cognition: An Epistemic Approach (Preprint) (View-Only Full-Text) (Publisher Link)

Abstract Kaplan and Craver (2011) and Piccinini and Craver (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 causalnonmechanistic 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.

2021 (Book):

Concepts and the Appeal to Cognitive Science (Book) (Waterstones Link)

Abstract: This book evaluates whether or not we can decide on the best theory of concepts by appealing to the explanatory results of cognitive science. It undertakes an in-depth analysis of different theories of concepts and of the explanations formulated in cognitive science. As a result, two reasons are provided for thinking that an appeal to cognitive science cannot help to decide on the best theory of concepts.

• Reviews the state of the art with respect to theories of concepts.

• Analyses the relation between theories of concepts and cognitive science.

• Problematises appeals to cognitive science to determine the best theory of concepts.

2017 (Peer-Reviewed Journal Articles):

Mastering as an inferentialist alternative to the acquisition and participation metaphors for learning (Publisher link)

Abstract: 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. 

Inferentialism as an alternative to socioconstructivism in mathematics education (Publisher link)

Abstract: The purpose of this article is to draw the attention of mathematics education researchers to a relatively new semantic theory called inferentialism, as developed by the philosopher Robert Brandom. 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. The article argues that inferentialism can help to overcome certain problems that have plagued the various forms of constructivism, and socioconstructivism in particular. Despite the range of socioconstructivist positions on offer, there is reason to think that versions of these problems will continue to haunt socioconstructivism. The problems are that socioconstructivists (i) have not come to a satisfactory resolution of the social-individual dichotomy, (ii) are still threatened by relativism, and (iii) have been vague in their characterization of what construction is. We first present these problems; then we introduce inferentialism, and finally we show how inferentialism can help to overcome the problems. We argue that inferentialism (i) contains a powerful conception of norms that can overcome the social-individual dichotomy, (ii) draws attention to the reality that constrains our inferences, and (iii) develops a clearer conception of learning in terms of the mastering of webs of reasons. Inferentialism therefore represents a powerful alternative theoretical framework to socioconstructivism. 

2014 (Conference Proceedings):

An inferentialist alternative to constructivism in mathematics education (Preprint)

Abstract:  The purpose of this paper is to draw attention to a relatively new semantic theory called inferentialism as developed by the philosopher Robert Brandom. We argue that it offers a better alternative to the still present representational view of mind than does (socio)constructivism. After a discussion of the shortcomings of (socio)constructivism, we summarize the key features of inferentialism that make it worth thinking through more carefully in mathematics education research.