My research focuses on the philosophy of language and foundations of cognitive science. My main focus is the nature of linguistic meaning, the operations that produce complex meanings and thoughts, and their interaction with context, memory and general reasoning. Those topics overlap with central topics in philosophy of mind, including questions about the structure of concepts and their role in judgment and decision-making. I also work on the foundations of cognitive science and neuroscience, focusing on reductionism, multiple-realizability, and the nature of scientific inference and inter-level explanation. I briefly describe my current and future work in each of these topics below, and include some selected papers.
language, meaning, and Natural logic
Most linguists and philosophers agree that the language system includes a syntax and a compositional semantics. Recently, several influential semanticists have argued that the language system also includes an automatic inferential system---sometimes called a `natural logic'---that can determine when expressions are informationally useless, and mark them as unacceptable. Building on this work, I have advanced three claims. First, I argue that the natural logic used by the language system is largely classical. This suggests that we need not posit a logic that is idiosyncratic and domain specific for language, as various extant accounts have done. Second, I argue that the logical forms of natural language expressions are representations which can support general reasoning. This contrasts with several recent accounts which postulate various levels of representation between linguistic logical form and the representations which support general cognition and inference. Third, I argue that this view works best when combined with the hypothesis that the lexicon encodes, and the language system has access to, rich sets of encyclopedic and other information. Taken together, this claims support a view of the language system as, ultimately, a very powerful inferential device, i.e., a system that does a lot of useful yet unconscious/automatic thinking for us.
- The Logicality of Language: a new take on Triviality, 'Ungrammaticality', and Logical Form. Noûs (online first). DOI: 10.1111/nous.12235 [p. draft here]
- The Structure of Semantic Competence: Compositionality as an Innate Constraint of The Faculty of Language. Mind & Language, (2015), Vol 30(4): pp. 375-413. DOI: 10.1111/mila.12084 . [p. draft here]
MEANING, CONTEXT, and multidimensional semantics
Traditionally, work in semantics and philosophy of language models the meaning of expressions as that which determines their extension. The meaning of complex expressions is compositionally determined from that of their parts. Recently, I have developed a multidimensional theory according to which meaning includes more than just extension-determining components. These additional dimensions include information about the extension of terms, but unlike Fregean senses they do not directly determine their extension. These additional dimensions are still part of linguistic meaning because they can determine the compositional contribution of terms to complex expressions of which they are parts. I argue that my multidimensional semantics can help us understand how linguistic meaning systematically interacts with context. I also argue that it helps us understand how linguistic meaning interacts with perception and inference. I show that this approach can be implemented in fully compositional and type-driven formal semantic theories, and give accounts of some puzzling kinds of complex expressions.
- Dual content semantics, privative adjectives, and dynamic compositionality. Semantics and Pragmatics, (2015) Vol, 8, DOI: http://dx.doi.org/10.3765/sp.8.7. (open access)
- Meaning, Modulation, and Context: A Multidimensional Semantics for Truth-conditional Pragmatics Linguistics and Philosophy, (2018), 41(2): 165-207. DOI: http://dx.doi.org/10.1007/s10988-017-9221-z.
CONCEPTS and SOCIAL COGNITION
Multidimensional theories can directly inform the study of concepts and how they affect social cognition. For example, research on implicit bias assumes that concepts encode salient or statistical associations between features and categories, e.g., stripped is encoded as salient and typical of tigers. Similar structures are thought underlie stereotypes, such as when the negative feature lazy is used to represent entire social groups. However, concepts also encode additional information, esp., how conceptual features ‘depend on each other’, which determines their degree of centrality. In a series of collaborative papers, we have argued that these non-associative, dependency networks can encode socially significant biases. These biases are undetectable by measures of saliency/typicality, and have been largely ignored by research in social psychology and the philosophical accounts of implicit bias that rest on them.
- Dual character concepts in social cognition: Commitments and the normative dimension of conceptual representations. (with Reuter, K.). Cognitive Science, (2016), vol. 41(S3): 477-501. DOI: 10.1111/cogs.12456.
- Conceptual centrality and implicit bias (with Spaulding, S.). Mind & Language, (2018). 33(1): 95-111. DOI: 10.1111/mila.12166 [p. draft here]
FOUNDATIONS OF COGNITIVE NEUROSCIENCE
The scientific study of cognition---incl., language, memory and reasoning---is being increasingly pursued with neuroscientific tools and sophisticated inferential techniques (e.g., neuroimaging + machine learning). Some argue that this approach rests on objectionable forms of mind-brain reductionism. Critics even labeled the approach a `new Phrenology’, arguing that it assumes that most cognitive functions are implemented in particular locations of the brain. Clearly, we cannot yet reduce most complex cognitive processes to their neural implementations. Under what conditions can neuroscience advance our understanding of cognition? Most of my research in this area focuses on this question, arguing that the kinds of inferences currently used to draw conclusions about the mind from data about the brain do not depend on objectionable forms of reductionism or functional locationism. Indeed, recent techniques—esp., machine learning algorithms---can extract information about cognitive function from widely distributed patterns of activation. In forthcoming work, I explore the limits and prospects of the increasingly influential use of machine learning tools to draw inferences from neural data to cognitive functions.
- There and Up Again: On the Uses and Misuses of Neuroimaging for Psychology (with Marco Nathan). Cognitive Neuropsychology, (2013), Vol 30, Issue 4: pp. 233-252. DOI: 10.1080/02643294.2013.846254.
Mapping the mind: Bridge-laws at the Psycho-Neural Interface (with Marco Nathan). Synthese, (2015), Vol. 193(2): pp. 637-657. DOI: 10.1007/s11229-015-0769-2.