Theoria, Volume 88, Issue 3, Page 494-528, June 2022.

What are the semantic representations that underlie language production? We use structural priming to distinguish between two competing theories. Thematic roles define semantic structure in terms of atomic units that specify event participants and are ordered with respect to each other through a hierarchy of roles. Event structures instead instantiate semantic structure as embedded sub‐predicates that impose an order on verbal arguments based on their relative positioning in these embeddings. Across two experiments, we found that priming for (...) datives depended on the degree of overlap in event structures. Specifically, while all dative structures showed priming, due to common syntax, there was a boost for compositional datives priming other compositional datives. Here, the two syntactic forms have distinct event structures. In contrast, there was no boost in priming for dative light verbs, where the two forms map onto a single event representation. On the thematic roles hypothesis, we would have expected a similar degree of priming for the two cases. Thus, our results support event structural approaches to semantic representation and not thematic roles. (shrink)

I distinguish among three senses in which meaning may be said to be normative, one trivial, the other two more robust. According to the trivial sense, meaningful expressions have conditions of correct application. According to the first robust sense, these conditions are determined by norms. According to the second robust sense, statements about these conditions have normative implications. Normativity in one or the other of the robust senses, but not in the trivial sense, is commonly thought to pose a threat (...) to naturalism. I argue that, given its trivial normativity, meaning cannot be normative in the first robust sense but it is normative in the sense that statements about the meaning of terms have hypothetical normative implications that are essential to meaning. I further argue that this normativity itself poses no threat to naturalism. Rather, this normativity follows from the fact that the trivial normativity of meaning precludes its naturalization. (shrink)

This paper argues that Locke has a representative theory of sensitive knowledge. Perceivers are immediately aware of nothing but sensory ideas in the mind; yet perceivers think of real external substances that correspond to and cause those ideas, and they are warranted in believing that those substances exist (at that time). The theory poses two questions: what warrants the truth of such beliefs? What is it in virtue of which sensory ideas represent external objects and how do they make perceivers (...) think of those objects? Both the epistemic and semantic issues need to be addressed. This paper urges that Locke's basic account of warrant is roughly reliabilist. The causal origin of sensory ideas assures that, in general, sense based beliefs are true. Locke defines the limit of this warrant by the theoretical point that we cannot discuss skeptical doubt without assuming the truthfulness of our perceptual faculty. Turning to the semantic question, the paper argues that ideas are mental modifications or entities. They are not intrinsically representative (satisfiable), but rather represent only by virtue of their causal origin. They merely “track” the presence of substances and their qualities. Ideas nevertheless prompt perceivers to think of their causes. This is roughly because sensory ideas have a specific mental role, namely, to serve as marks for distinguishing substances and their respective qualities for purposes of action. The paper suggests that, for Locke, the challenge posed by the semantic veil of ideas is to explain this externally directed marking function within bounds of his anti‐innatism. But it concludes that his answers to the twin questions fit together reasonably well. (shrink)

Semantic dispositionalism is roughly the view that meaning a certain thing by a word, or possessing a certain concept, consists in being disposed to do something, e.g., infer a certain way. Its main problem is that it seems to have so many and disparate exceptions. People can fail to infer as required due to lack of logical acumen, intoxication, confusion, deviant theories, neural malfunctioning, and so on. I present a theory stating possession conditions of concepts that are counterfactuals, rather (...) than disposition attributions, but which is otherwise similar to inferentialist versions of dispositionalism. I argue that it can handle all the exceptions discussed in the literature without recourse to ceteris paribus clauses. Psychological exceptions are handled by suitably undemanding requirements (unlike that of giving the sum of any two numbers) and by setting the following two preconditions upon someone’s making the inference: that she considers the inference and has no motivating reason against it. The non-psychological exceptions, i.e., cases of neural malfunctioning, are handled by requiring that the counterfactuals be true sufficiently often during the relevant interval. I argue that this accommodates some important intuitions about concept possession, in particular, the intuition that concept possession is vague along a certain dimension. (shrink)

Semantic Structures is a large-scale study of conceptual structure and its lexical and syntactic expression in English that builds on the theory of Conceptual...

One of the great successes of the application of generalized quantifiers to natural language has been the ability to formulate robust semantic universals. When such a universal is attested, the question arises as to the source of the universal. In this paper, we explore the hypothesis that many semantic universals arise because expressions satisfying the universal are easier to learn than those that do not. While the idea that learnability explains universals is not new, explicit accounts of learning (...) that can make good on this hypothesis are few and far between. We propose a model of learning — back-propagation through a recurrent neural network — which can make good on this promise. In particular, we discuss the universals of monotonicity, quantity, and conservativity and perform computational experiments of training such a network to learn to verify quantifiers. Our results are able to explain monotonicity and quantity quite well. We suggest that conservativity may have a different source than the other universals. (shrink)