Feature centrality and property induction

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature’s dependencies was manipulated by varying the similarity of category pairs. Participants’ ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures.     

Beyond prototypes and classical definitions: Evidence for a theory-based representation of emotion concepts

The question of how people represent emotions is eminently important for a number of different domains of psychological research. The present study tested the assumption that emotion concepts are represented similar to theories in that they are comprised of a set of causally interrelated features. Using emotional scenarios and investigating the emotion concepts of anger, anxiety, and sadness it was found that people's representations of emotion concepts essentially involved the representation of the causal relation of emotion features and that the causal status of a feature determined the feature's centrality in the concept. Moreover, people's representations of emotion concepts corresponded to basic assumptions of cognitive emotion theories, namely that cognitive appraisals cause other surface features of emotion, like expression and behaviour. By providing evidence for a theory-based representation, the present results show that the classical and the prototype approach are insufficient to model the representation of emotion concepts.     

Feature Centrality and Conceptual Coherence

Conceptual features differ in how mentally tranformable they are. A robin that does not eat is harder to imagine than a robin that does not chirp. We argue that features are immutable to the extent that they are central in a network of dependency relations. The immutability of a feature reflects how much the internal structure of a concept depends on that feature; i.e., how much the feature contributes to the concept's coherence. Complementarily, mutability reflects the aspects in which a concept is flexible. We show that features can be reliably ordered according to their mutability using tasks that require people to conceive of objects missing a feature, and that mutability (conceptual centrality) can be distinguished from category centrality and from diagnosticity and salience. We test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations. With no free parameters, the model provides reasonable fits to data. Qualitative tests of the model show that mutability judgments are unaffected by the type of dependency relation and that dependency structure influences judgments of variability.     

Conceptual Clarification and the Task of Improving Research on Academic Ethics

What does the term academic ethics mean? How does this term relate to others in the academic integrity literature, such as research misconduct? Does conceptual confusion in the study of academic ethics complicate development of valid analyses of ethical behavior in an academic setting? The intended goal of many empirical projects on academic ethics is to draw causal conclusions about the factors that lead to faculty or students possessing or disregarding academic integrity. Yet, it is not clear that scholars using the concept academic ethics are measuring the same phenomenon when they use associated concepts, such as responsible conduct of research. The purpose of this paper is to develop a taxonomy of concepts for the empirical study of academic ethics. Based in research on comparative analysis of democracy, another normatively preferable but multifaceted concept, I argue for a taxonomy of concepts for the study of academic integrity that reduces problems of “conceptual stretching” and challenges to the validity of empirical research in this field.     

Feature centrality: Naming versus imagining

Being white is central to whether we call an animal a "polar bear," but it is fairly peripheral to our concept of what a polar bear is. We propose that a feature is central to category naming in proportion to the feature's category validity--the probability of the feature, given the category. In contrast, a feature is conceptually central in a representation of the object to the extent that the feature is depended on by other features. Further, we propose that naming and conceptual centrality are more likely to disagree for features that hold at more specific levels (such as is white, which holds only for the specific category of polar bear) than for features that hold at intermediate levels of abstraction (such as has claws, which holds for all bears). In support of these hypotheses, we report evidence that increasing the abstractness of category features has a greater effect on judgments of conceptual centrality than on judgments of name centrality and that other category features depend more on intermediate-level category features than on specific ones.     

The externalist challenge to conceptual engineering

Unlike conceptual analysis, conceptual engineering does not aim to identify the content that our current concepts do have, but the content which these concepts should have. For this method to show the results that its practitioners typically aim for, being able to change meanings seems to be a crucial presupposition. However, certain branches of semantic externalism raise doubts about whether this presupposition can be met. To the extent that meanings are determined by external factors such as causal histories or microphysical structures, it seems that they cannot be changed intentionally. This paper gives an extended discussion of this ‘externalist challenge’. Pace Herman Cappelen’s recent take on this issue, it argues that the viability of conceptual engineering crucially depends on our ability to bring about meaning change. Furthermore, it argues that, contrary to first appearance, causal theories of reference do allow for a sufficient degree of meaning control. To this purpose, it argues that there is a sense of what is called ‘collective long-range control’, and that popular versions of the causal theory of reference imply that people have this kind of control over meanings.

     

Category learning with a goal: how goals constrain conceptual acquisition

A novel task was used to examine the roles that goals play in concept acquisition. In Experiment 1, we varied the type of interaction, and thus the task goal, of participants working in a novel domain. Following those interactions, participant responses showed that they had organized their knowledge in terms of goal-relevant features. Using a variation of the same methodology, Experiment 2 provided evidence that the goal relevance also played a role in how the participants structured their knowledge of the items, specifically what information about the items was associated with differentiating the categories encountered. The results suggest that the goals not only highlighted particular features, they determined the centrality of those features in the conceptual knowledge. We discuss the results in terms of the goal-framework hypothesis; the idea that goals structure information and provide coherence to the acquired concepts. We discuss how this approach informs category learning research.     

Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system.     

Concepts and conceptual change

This paper argues that questions concerning the nature of concepts that are central in cognitive psychology are also important to epistemology and that there is more to conceptual change than mere belief revision. Understanding of epistemic change requires appreciation of the complex ways in which concepts are structured and organized and of how this organization can affect belief revision. Following a brief summary of the psychological functions of concepts and a discussion of some recent accounts of what concepts are, I propose a view of concepts as complex computational structures. This account suggests that conceptual change can come in varying degrees, with the most extreme consisting of fundamental conceptual reorganizations. These degrees of conceptual change are illustrated by the development of the concept of an acid.I am grateful to Michael Ranney and Gilbert Harman for comments on an earlier version of this paper.     

Effect of causal structure on category construction

In four experiments, the question of how the causal structure of features affects the creation of new categories was examined. Features of exemplars to be sorted were related in a single causal chain (causal chain), were caused by the same factor (common cause), or caused the same effect (common effect). The results showed that people are more likely to rely on common-cause or common-effect background knowledge than on causal-chain background knowledge in category construction. Such preferences suggest that the common-cause or the common-effect structures are considered more natural conceptual structures.     

Models of causation and the semantics of causal verbs

This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation (; ), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.     

Semantic significance: a new measure of feature salience

According to the feature-based model of semantic memory, concepts are described by a set of semantic features that contribute, with different weights, to the meaning of a concept. Interestingly, this theoretical framework has introduced numerous dimensions to describe semantic features. Recently, we proposed a new parameter to measure the importance of a semantic feature for the conceptual representation—that is, semantic significance. Here, with speeded verification tasks, we tested the predictive value of our index and investigated the relative roles of conceptual and featural dimensions on the participants’ performance. The results showed that semantic significance is a good predictor of participants’ verification latencies and suggested that it efficiently captures the salience of a feature for the computation of the meaning of a given concept. Therefore, we suggest that semantic significance can be considered an effective index of the importance of a feature in a given conceptual representation. Moreover, we propose that it may have straightforward implications for feature-based models of semantic memory, as an important additional factor for understanding conceptual representation.     

Situation models and memory: the effects of temporal and causal information on recall sequence

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.     

The development of causal categorization

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults.     

Conceptual Role Semantics and the Reference of Moral Concepts

This paper examines the prospects for a conceptual or functional role theory of moral concepts. It is argued that such an account is well‐placed to explain both the irreducibility and practicality of moral concepts. Several versions of conceptual role semantics for moral concepts are distinguished, depending on whether the concept‐constitutive conceptual roles are (i) wide or narrow (ii) normative or non‐normative and (iii) purely doxastic or conative. It is argued that the most plausible version of conceptual role semantics for moral concepts involves only ‘narrow’ conceptual roles, where these include connections to motivational, desire‐like, states. In the penultimate section it is argued, contrary to what Wedgwood, Enoch and others have claimed, that such an account of moral concepts cannot plausibly be combined with the claim that moral concepts refer to robust properties.     

Situation models and memory: The effects of temporal and causal information on recall sequence

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.     

Causal status as a determinant of feature centrality

One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel category with three characteristic features that were causally related into a single causal chain and judged the likelihood that new objects belong to the category. Likelihood ratings for items missing the most fundamental cause were lower than those for items missing the intermediate cause, which in turn were lower than those for items missing the terminal effect. The causal status effect was also obtained in goodness-of-exemplar judgments (Experiment 2) and in free-sorting tasks (Experiment 3), but it was weaker in similarity judgments than in categorization judgments (Experiment 4). Experiment 5 shows that the size of the causal status effect is moderated by plausibility of causal relations, and Experiment 6 shows that effect features can be useful in retrieving information about unknown causes. We discuss the scope of the causal status effect and its implications for categorization research.     

感知运动信息在概念表征中的作用

研究概念的表征问题对理解概念的本质非常重要,传统认知和具身认知视角下的概念表征理论争议的焦点在于感觉运动信息在表征中的作用。传统认知视角下的离身认知认为感知觉运动信息会转化成抽象的符号,概念表征不包含感知觉运动信息。概念表征的具身观点认为感知觉运动信息是概念表征的基础。对感知运动信息在概念表征中起作用这一命题已经达成共识。未来研究应该关注感知运动信息起作用的机制,以及抽象概念表征等问题,进一步完善发展概念表征理论。     

概念组合的理论模型

概念组合是将两个或多个概念组合成一个新概念的过程,生成的新概念被称为组合概念。组合概念的解释策略及影响因素已成为概念研究中的热点问题。研究者相继提出了关系竞争理论、双重加工理论和约束理论等模型。但是,这些理论模型只能解释特定的实验现象,对某些稳定的实验现象（如涌现特征、范畴效应等）却缺乏解释力度。文章提出,未来的研究应该整合已有的实验现象和理论模型,关注概念组合的时间进程,深入考察语境的作用,并系统地研究组合概念的涌现特征和范畴效应。