It has been argued that prototypes cannot compose, and that for this reason concepts cannot be prototypes (Osherson and Smith in Cognition 9:35–58, 1981; Fodor and Lepore in Cognition 58:253–270, 1996; Connolly et al. in Cognition 103:1–22, 2007). In this paper I examine the intensional and extensional approaches to prototype compositionality, arguing that neither succeeds in their present formulations. I then propose a hybrid extensional theory of prototype compositionality, according to which the extension of a complex concept is determined as (...) a function of what triggers its constituent prototypes. I argue that the theory escapes the problems traditionally raised against extensional theories of compositionality. (shrink)

Fodor’s theory of concepts holds that the psychological capacities, beliefs or intentions which determine how we use concepts do not determine reference. Instead, causal relations of a specific kind between properties and our dispositions to token a concept are claimed to do so. Fodor does admit that there needs to be some psychological mechanisms mediating the property–concept tokening relations, but argues that they are purely accidental for reference. In contrast, I argue that the actual mechanisms that sustain the reference determining (...) concept tokening relations are necessary for reference. Fodor’s atomism is thus undermined, since in order to refer with a concept it is necessary to possess some specific psychological capacities. (shrink)

While recognizing the theoretical importance of context, current research has treated naming as though semantic meaning were invariant and the same mapping of category exemplars and names should exist across experimental contexts. An assumed symmetry or bidirectionality in naming behavior has been implicit in the interchangeable use of tasks that ask subjects to match names to stimuli and tasks that ask subjects to match stimuli to names. Examples from the literature are discussed together with several studies of color naming and (...) basic emotion naming in which no such symmetry was found. A more complete model of naming is proposed to account for flexible mapping of names to items. Principles of naming are suggested to describe effects of stimulus sampling, differing access to terms, task demands, and other impacts on naming behavior. (shrink)

Our research focuses on the perception of difference in the evaluations of positive and negative options. The literature provides evidence for two opposite effects: on the one hand, negative objects are said to be more differentiated, on the other, people are shown to see greater differences between positive options. In our study, we investigated the perception of difference between fictitious political candidates, hypothesizing greater differences among the evaluations of favorable candidates. Additionally, we analyzed how positive and negative information affect candidate (...) evaluation, predicting further asymmetries. In three experiments, participants evaluated various candidate profiles presented in a numeric and narrative manner. The evaluation tasks were designed as individual or joint assessments. In all three studies, we found more differentiation between positive than negative options. Our research suggests that after exceeding a certain, relatively small level of negativity, people do not see any further increase in negativity. The increase in positivity, on the other hand, is more gradual, with greater differentiation among positive options. Our findings are discussed in light of cognitive-experiential self-theory and density hypothesis. (shrink)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating (...) embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically (...) identifying, organizing, and retrieving information, and for encoding episodic events by time and stimuli. Inspired by prominent human memory models, we propose associative networks as a preferred representation. Networks are ideal for their parsimony, flexibility, and ease of inspection. Networks also possess topological properties—such as clusters, hubs, and the small world—that aid analysis and navigation in an information space. Our cognitive perspective addresses fundamental problems faced by information management systems, in particular the retrieval of related items and the representation of context. We present evidence from neuroscience and memory research in support of this approach, and discuss the implications of systems design within the constraints of P-MAK’s principles, using text documents as an illustrative semantic domain. (shrink)

Theory‐of‐mind (ToM) is an integral part of social cognition, but how it develops remains a critical question. There is evidence that children can gain insight into ToM through experience, including language training and explanatory interactions. But this still leaves open the question of how children gain these insights—what processes drive this learning? We propose that analogical comparison is a key mechanism in the development of ToM. In Experiment 1, children were shown true‐ and false‐belief scenarios and prompted to engage in (...) multiple comparisons (e.g., belief vs. world). In Experiments 2a, 2b, and 3, children saw a series of true‐ and false‐belief events, varying in order and in their alignability. Across these experiments, we found that providing support for comparing true‐ and false‐belief scenarios led to increased performance on false‐belief tests. These findings show that analogical comparison can support ToM learning. (shrink)

The result model of precedent holds that a legal precedent controls a fortiori cases—those cases, that is, that are at least as strong for the winning side of the precedent as the precedent case itself. This paper defends the result model against some objections by Larry Alexander, drawing on ideas from the field of Artificial Intelligence and Law in order to define an appropriate strength ordering for cases.

The possibility of spectrum inversion has been debated since it was raised by Locke and is still discussed because of its implications for functionalist theories of conscious experience . This paper provides a mathematical formulation of the question of spectrum inversion and proves that such inversions, and indeed bijective scramblings of color in general, are logically possible. Symmetries in the structure of color space are, for purposes of the proof, irrelevant. The proof entails that conscious experiences are not identical with (...) functional relations. It leaves open the empirical possibility that functional relations might, at least in part, be causally responsible for generating conscious experiences. Functionalists can propose causal accounts that meet the normal standards for scientific theories, including numerical precision and novel prediction; they cannot, however, claim that, because functional relationships and conscious experiences are identical, any attempt to construct such causal theories entails a category error. (shrink)

Philosophers and critics alike often contend that metaphors cannot or should not be paraphrased, ever. Yet a simple and decisive empirical argument — The Horse’s Mouth Argument—suffices to show that many metaphors can be paraphrased without violating the spirit in which they were put forward in the first place. This argument leaves us with urgent unanswered questions about the role of paraphrase in a more inclusive division of exegetical labor, about the tension between its notorious openendedness and its claim to (...) restate something already stated, and about the relation between the content of a paraphrase and the content (or contents) of the metaphor the paraphrase purports to explain. But it leaves us in a position to state such questions more clearly and hopefully than we could before. (shrink)

This study presents original evidence that abstract and concrete concepts are organized and represented differently in the mind, based on analyses of thousands of concepts in publicly available data sets and computational resources. First, we show that abstract and concrete concepts have differing patterns of association with other concepts. Second, we test recent hypotheses that abstract concepts are organized according to association, whereas concrete concepts are organized according to (semantic) similarity. Third, we present evidence suggesting that concrete representations are more (...) strongly feature-based than abstract concepts. We argue that degree of feature-based structure may fundamentally determine concreteness, and we discuss implications for cognitive and computational models of meaning. (shrink)

We present a novel way of accounting for similarity judgments. Our approach posits that similarity stems from three main sources—familiarity, priming, and inherent perceptual likeness. Here, we explore each of these constructs and demonstrate their individual and combined effectiveness in explaining similarity judgments. Using these three measures, our account of similarity explains ratings of simple, color-based perceptual stimuli that display asymmetry effects, as well as more complicated perceptual stimuli with structural properties; more traditional approaches to similarity solve one or the (...) other and have difficulty accounting for both. Overall, our work demonstrates the importance of each of these components of similarity in explaining similarity judgments, both individually and together, and suggests important implications for other similarity approaches. (shrink)

One might expect that, in pure coordination games, coordination would become less frequent as the number of options increases. Contrary to this expectation, we report an experiment which found more frequent coordination when the option set was unrestricted than when it was restricted. To try to explain this result, we develop a method for eliciting the general rules that subjects use to identify salient options in restricted and unrestricted sets. We find that each such rule, if used by all subjects, (...) would generate greater coordination in restricted sets. However, subjects tend to apply different rules to restricted and unrestricted sets. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

Different similarity relations on sets are introduced, and their logical properties are investigated. Close relationships are shown to hold between similarity relations that are based on symmetrical difference and operators of belief contraction that are based on relational selection functions. Two new rationality criteria for minimal belief contraction, the maximizing property and the reducing property, are proposed.