Peter Blouw

The term “Gettier Case” is a technical term frequently applied to a wide array of thought experiments in contemporary epistemology. What do these cases have in common? It is said that they all involve a justified true belief which, intuitively, is not knowledge, due to a form of luck called “Gettiering.” While this very broad characterization suffices for some purposes, it masks radical diversity. We argue that the extent of this diversity merits abandoning the notion of a “Gettier case” in a favour of more finely grained terminology. We propose such terminology, and use it to effectively sort the myriad Gettier cases from the theoretical literature in a way that charts deep fault lines in ordinary judgments about knowledge.

Can judging that an agent blamelessly broke a rule lead us to claim, paradoxically, that no rule was broken at all? Surprisingly, it can. Across seven experiments, we document and explain the phenomenon of excuse validation. We found when an agent blamelessly breaks a rule, it significantly distorts people’s description of the agent’s conduct. Roughly half of people deny that a rule was broken. The results suggest that people engage in excuse validation in order to avoid indirectly blaming others for blameless transgressions. Excuse validation has implications for recent debates in normative ethics, epistemology and the philosophy of language. These debates have featured thought experiments perfectly designed to trigger excuse validation, inhibiting progress in these areas

The most general goal of semantic theory is to explain facts about language use. In keeping with this goal, I introduce a framework for thinking about linguistic expressions in terms of the inferences they license, the behavioral predictions that their uses thereby sustain, and the affordances that they provide to language users in virtue of these inferential and predictive involvements. Within this framework, linguistic expressions acquire meanings by regulating social practices that involve “intentional interpretation,” wherein people explain and predict one another’s behavior through linguistically specified mental state attributions. Developing a theory of meaning therefore requires formalizing the inferential roles that determine how linguistic expressions license predictions in the context intentional interpretation. Accordingly, the view I develop is an inferential role semantics for natural language. To describe this semantics, I take advantage of recently developed techniques in the field of natural language processing. I introduce a model that assigns inferential roles to arbitrary linguistic expressions by learning from examples of how sentences are distributed as premises and conclusions in a space of possible inferences. I then empirically evaluate the model’s ability to generate accurate entailments for novel sentences not used as training examples. I argue that this model takes a small but important step towards codifying the meanings of the expressions it manipulates. Next, I examine the theoretical implications of this work with respect to debates about the compositionality of language, the relationship between language and cognition, and the relationship between language and the world. With respect to compositionality, I argue that the debate is really about generalization in language use, and that the required sort of generalization can be achieved by “interpolating” between familiar examples of correct inferential transitions. With respect to the relationship between thought and language, I argue that it is a mistake to try to derive a theory of natural language semantics from a prior theory of mental representation because theories of mental representation invoke the sort of intentional interpretation at play in language use from the get-go. With respect to the relationship between language and the world, I argue that questions about truth conditions and reference relations are best thought of in terms of questions about the norms governing language use. These norms, in turn, are best characterized in primarily inferential terms. I conclude with an all-things-considered evaluation of my theory that demonstrates how it overcomes a number of challenges associated with semantic theories that take inference, rather than reference, as their starting point.

Concepts are widely agreed to be the basic constituents of thought. Amongst philosophers and psychologists, however, the question of how concepts are structured has been a longstanding problem and a locus of disagreement. I draw on recent work describing how representational content is ascribed to populations of neurons to develop a novel solution to this problem. Because disputes over the structure of concepts often reflect divergent explanatory goals, I begin by arguing for a set of six criteria that a good theory ought to accommodate. These criteria address philosophical concerns related to content, reference, scope, publicity, and compositionality, and psychological concerns related to categorization phenomena and neural plausibility. Next, I evaluate a number of existing theoretical approaches in relation to these six criteria. I consider classical views that identify concepts with definitions, similarity-based views that identify concepts with prototypes or exemplars, theory-based views that identify concepts with explanatory schemas, and atomistic views that identify concepts with unstructured mental symbols that enter into law-like relations with their referents. I conclude that none of these accounts can satisfactorily accommodate all of the criteria. I then describe the theory of representational content that I employ to motivate a novel account of concept structure. I briefly defend this theory against competitors, and I describe how it can be scaled from the level of basic perceptual representations to the level of highly complex conceptual representations. On the basis of this description, I contend that concepts are structured dynamically through sets of transformations of single source representation, and that the content of a given concept specifies the set of potential transformations it can enter into. I conclude by demonstrating that the ability of this account to meet all of the criteria introduced beforehand. I consider objections to my views throughout.

The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol-like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts.