The purpose of this paper is to explore the semantics of metaphoric language in scientific contexts. According to the theory of metaphor advanced below, the benchmark of a metaphoric expression is the implicit transfer of semantic features across incongruous semantic fields. This transfer results in a conceptual variation of "meaning" in the receiving semantic field. Thus, the theory of metaphor rests on semantic field theory. Existing semantic approaches to metaphor are evaluated in Section 1. In Sections 2 and 3 an (...) alternative theory is introduced through an analysis of the mechanics of the feature transfer process followed by a discussion of some methodological cues for deciphering the metaphor's "meaning." As I explore in Section 4, this theory of metaphor explains how all metaphoric expressions are potentially literal depending on the general compliance of the community of speakers. Consequently, concept formation in scientific contexts is, in large measure, metaphoric. Finally, I argue in Section 5 that metaphoric concept formation is an essential aspect of scientific reasoning for the purpose of solving conceptual problems. (shrink)

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, (...) as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure. (shrink)

Acquiring perceptual expertise is slow and effortful. However, untrained novices can accurately make difficult classification decisions by reformulating the task as similarity judgment. Given a query image and a set of reference images, individuals are asked to select the best matching reference. When references are suitably chosen, the procedure yields an implicit classification of the query image. To optimize reference selection, we develop and evaluate a predictive model of similarity-based choice. The model builds on existing psychological literature and accommodates stochastic, (...) dynamic shifts of attention among visual feature dimensions. We perform a series of human experiments with two stimulus types and nine classification tasks to validate the model and to demonstrate the model's potential to boost performance. Our system achieves high accuracy for participants who are naive as to the classification task, even when the classification task switches from trial to trial. (shrink)

This article considers how people judge the identity of objects (e.g., how people decide that a description of an object at one time, t₀, belongs to the same object as a description of it at another time, t₁). The authors propose a causal continuer model for these judgments, based on an earlier theory by Nozick (1981). According to this model, the 2 descriptions belong to the same object if (a) the object at t₁ is among those that are causally close (...) enough to be genuine continuers of the original and (b) it is the closest of these close-enough contenders. A quantitative version of the model makes accurate predictions about judgments of which a pair of objects is identical to an original (Experiments 1 and 2). The model makes correct qualitative predictions about identity across radical disassembly (Experiment 1) as well as more ordinary transformations (Experiments 2 and 3). (shrink)

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established (...) similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

Exposing the chimpanzee to language training appears to enhance the animal's ability to perform some kinds of tasks but not others. The abilities that are enhanced involve abstract judgment, as in analogical reasoning, matching proportions of physically unlike exemplars, and completing incomplete representations of action. The abilities that do not improve concern the location of items in space and the inferences one might make in attempting to obtain them. Representing items in space and making inferences about them could be done (...) with an imaginal code, but representing relations and judging the relations between them, as in analogies, requires a more abstract code. Language training cannot instill such an abstract code, but for species that have the code to start with, it may enhance the animal's ability to use it. (shrink)

Given a 4-tuple of Boolean variables (a, b, c, d), logical proportions are modeled by a pair of equivalences relating similarity indicators ( \({a \wedge b}\) and \({\overline{a} \wedge \overline{b}}\) ), or dissimilarity indicators ( \({a \wedge \overline{b}}\) and \({\overline{a} \wedge b}\) ) pertaining to the pair (a, b), to the ones associated with the pair (c, d). There are 120 semantically distinct logical proportions. One of them models the analogical proportion which corresponds to a statement of the form “a (...) is to b as c is to d”. The paper inventories the whole set of logical proportions by dividing it into five subfamilies according to what they express, and then identifies the proportions that satisfy noticeable properties such as full identity (the pair of equivalences defining the proportion hold as true for the 4-tuple (a, a, a, a)), symmetry (if the proportion holds for (a, b, c, d), it also holds for (c, d, a, b)), or code independency (if the proportion holds for (a, b, c, d), it also holds for their negations \({{(\overline{a},\overline{b}, \overline{c}, \overline{d})}}\) ). It appears that only four proportions (including analogical proportion) are homogeneous in the sense that they use only one type of indicator (either similarity or dissimilarity) in their definition. Due to their specific patterns, they have a particular cognitive appeal, and as such are studied in greater details. Finally, the paper provides a discussion of the other existing works on analogical proportions. (shrink)

The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be (...) described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity. Key Words: categorization; cognitive explanation; language; learning; reasoning; rules; similarity. (shrink)

Tenenbaum and Griffiths (Behavioral and Brain Sciences 24(4):629–640, 2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (Science 237(4820): 1317–1323, 1987) and Tversky’s (Psychological Review 84(4): 327–352, 1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that (...) generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity. (shrink)

The feature-matching approach pioneered by Amos Tversky remains a groundwork for psychological models of similarity and categorization but is rarely explicitly justified considering recent advances in thinking about cognition. While psychologists often view similarity as an unproblematic foundational concept that explains generalization and conceptual thought, long-standing philosophical problems challenging this assumption suggest that similarity derives from processes of higher-level cognition, including inference and conceptual thought. This paper addresses three specific challenges to Tversky’s approach: (i) the feature-selection problem, (ii) the problem (...) of cognitive implausibility, and (iii) the problem of unprincipled tweaking. It subsequently supports key insights from Tversky’s account based on recent developments in Bayesian modeling of cognition. A novel computational view of similarity as inference is proposed that addresses each challenge by considering the contrast class as constitutive of similarity and selecting for highly informative features. In so doing, this view illustrates the ongoing promise of the feature-matching approach in explaining perception, generalization and conceptual thought by grounding them in principles of probabilistic inference. (shrink)

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...) fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality. (shrink)

We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number (...) of categories sought or information about the distributional properties of the objects to be classified. These features of the simplicity model distinguish it from other models in unsupervised categorization (where, for example, the number of categories sought is determined via a free parameter), and we discuss how these computational differences are related to differences in modeling objectives. The predictions of the simplicity model are validated in four experiments. We also discuss the significance of simplicity in cognitive modeling more generally. (shrink)

A rational agent has beliefs reflecting the state of its environment, and likes or dislikes Its situation. When it finds the world not entirely to Its liking, it tries to change that. We can, accordingly, evaluate a system of cognition in terms of its probable success in bringing about situations that are to the agent's liking. In doing this we are viewing practical reasoning from “the design stance.” It is argued that a considerable amount of the structure of rationality can (...) be elicited as providing the only apporant solutions to various logical and feasibility problems that arise in the course of trying to design a rational agent that satisfies this design specification. (shrink)

We re-evaluate existing data that demonstrate a large amount of variability in the content of categories considering the fact that these data have been obtained in a specific task: the production of features of single isolated categories. We present new data that reveal a large consensus when participants have to judge whether or not a given feature is characteristic of a category and we show that classification tasks produce an intermediate level of consensus. We argue that the differences observed between (...) tasks are due to the extent of implied context and we propose a reinterpretation of typicality effects, demonstrating that they are compatible with the existence of a stable conceptual core. In order to explain how the existence of a conceptual core is consistent with variability due to context, we present a theory of categorisation based on a property tree organisation. Within a domain of description, we distinguish between semantic implications (flying → moving) and empirical implications (flying → having wings) as well as between properties used to describe objects. Semantic implications serve to build property lines and Galois lattices are used to reveal category structures according to empirical implications. We show that variability in the category content may be explained by the fact that some properties are emphasised while others are masked according to the context. (shrink)

Quantum probability (QP) provides a new perspective for cognitive science. However, one must be clear about the outcome the QP model is predicting. We discuss this concern in reference to modeling the subjective probabilities given by people as opposed to modeling the choice proportions of people. These two models would appear to have different cognitive assumptions.

Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real‐world stimuli that can potentially be leveraged to capture psychological representations. We find that state‐of‐the‐art object classification networks provide surprisingly accurate predictions of human similarity judgments for (...) natural images, but they fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets. (shrink)

I intend to show some of the limits of the decision-theoretic model in connection with the analysis of cognitive agency. Although the concept of maximum expected utility can be helpful for explaining the decision-making process, it is certainly not the primary motor that moves agents to action. Moreover, it has been noticed elsewhere that this model is inadequate to the analysis of single cases of practical reasoning. A theory is proposed that introduces a plan-structure as a basic idea. In order (...) to know its very conceptual scope, we need to accommodate that theory to a cognitive architecture whereby the rational and autonomous agent makes choices and deliberates between courses of action to achieve specific goals that are of its interest. The paper endeavours also to clarify the relationship between the different modules that should constitute the architecture of an agent to accomplish the requisites of efficiency and autonomy in a changeable and dynamic world. (shrink)

In Gärdenfors and Makinson :197–245, 1994) and Gärdenfors it was shown that it is possible to model nonmonotonic inference using a classical consequence relation plus an expectation-based ordering of formulas. In this article, we argue that this framework can be significantly enriched by adopting a conceptual spaces-based analysis of the role of expectations in reasoning. In particular, we show that this can solve various epistemological issues that surround nonmonotonic and default logics. We propose some formal criteria for constructing and updating (...) expectation orderings based on conceptual spaces, and we explain how to apply them to nonmonotonic reasoning about objects and properties. (shrink)

This paper starts out from the proposition that case-based decision theory is an appropriate tool to explain human decision behavior in situations of structural ignorance. Although the developers of CBDT suggest its reality adequacy, CBDT has not yet been tested empirically very often, especially not in repetitive decision situations. Therefore, our main objective is to analyse the decision behavior of subjects in a repeated-choice experiment by comparing the explanation power of CBDT to reinforcement learning and to classical decision criteria under (...) uncertainty namely maximin, maximax, and pessimism-optimism. Our findings substantiate a predominant significantly higher validity of CBDT compared to the classical criteria and to reinforcement learning. For this reason, the experimental results confirm the suggested reality adequacy of CBDT in repetitive decision situations of structural ignorance. (shrink)