Concepts as Pluralistic Hybrids

In contrast to earlier views that argued for a particular kind of concept (e.g. prototypes), several recent accounts have proposed that there are multiple distinct kinds of concepts, or that there is a plurality of concepts for each category. In this paper, I argue for a novel account of concepts as pluralistic hybrids. According to this view, concepts are pluralistic because there are several concepts for the same category whose use is heavily determined by context. In addition, concepts are hybrids because they typically link together several different kinds of information that are used in the same cognitive processes. This alternative view accounts for the available empirical data, allows for greater cognitive flexibility than Machery’s recent account, and overcomes several objections to traditional hybrid views

Concepts as Pluralistic Hybrids

In contrast to earlier views that argued for a particular kind of concept (e.g. prototypes), several recent accounts have proposed that there are multiple distinct kinds of concepts, or that there is a plurality of concepts for each category. In this paper, I argue for a novel account of concepts as pluralistic hybrids. According to this view, concepts are pluralistic because there are several concepts for the same category whose use is heavily determined by context. In addition, concepts are hybrids because they typically link together several different kinds of information that are used in the same cognitive processes. This alternative view accounts for the available empirical data, allows for greater cognitive flexibility than Machery’s recent account, and overcomes several objections to traditional hybrid views

What does the mind learn? A comparison of human and machine learning representations

We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contain an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation. We suggest that continued applications will allow cognitive researchers the ability to model the complex real-world problems where machine learning has recently been successful, providing more complete behavioural descriptions. This will however also require careful consideration of appropriate algorithmic constraints alongside these methods in order to find a combination which captures both the strengths and weaknesses of human cognition

Predicting Category Intuitiveness With the Rational Model, the Simplicity Model, and the Generalized Context Model

Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM—the authors refer to this way of applying the GCM as “unsupervised GCM.” The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness

Bridging the Gap between Similarity and Causality: An Integrated Approach to Concepts

A growing consensus in the philosophy and psychology of concepts is that while theories such as the prototype, exemplar, and theory theories successfully account for some instances of concept formation and application, none of them successfully accounts for all such instances. I argue against this ‘new consensus’ and show that the problem is, in fact, more severe: the explanatory force of each of these theories is limited even with respect to the phenomena often cited to support it, as each fails to satisfy an important explanatory desideratum with respect to these phenomena. I argue that these explanatory shortcomings arise from a shared assumption on the part of these theories, namely, they take similarity judgements and application of causal knowledge to be discrete elements in a theory of concepts. I further propose that the same assumption carries over into alternative theories offered by proponents of the new consensus: pluralism, eliminativism, and hybrid theories. I put forth a sketch of an integrated model of concept formation and application, which rejects this shared assumption and satisfies the explanatory desiderata I discuss. I suggest that this model undermines the motivation for hybrid, pluralist, and eliminativist accounts of concepts

Psychological challenges for the analysis of style.

This article remains the copyright of Cambridge University Press. The definitive version of this article can be found at: http://dx.doi.org/10.1017/S089006040606015XAnalyses of styles in design have paid little attention to how people see style, and how designers use perceptions of style to guide designing. While formal and computational methods for analysing styles and generating designs provide impressively parsimonious accounts of what some styles are, they do not address many of the factors that influence how humans understand styles. The subtlety of human style judgements raises challenges for computational approaches to style. This paper differentiates between a range of distinct meanings of 'style', and explores how designers and ordinary people learn and apply perceptual similarity classes and style concepts in different situations to interpret and create designed artefacts. A range of psychological evidence indicates that style perception is dependent on knowledge, and involves the interaction of perceptual recognition of style features and explanatory inference processes that create a coherent understanding of an object as an exemplar of a style. This paper concludes by outlining how formal style analyses can be used in combination with psychological research to develop a fuller understanding of style perception and creative design

The Relation Between Categorization and Recognition in Ill-Defined Categories.

This study investigated the relation between recognition and categorization and examined learning processes associated with categorization of ill-defined concepts. Three experiments were conducted, in which recognition and categorization data were simultaneously collected (Estes, 1986b). Stimuli were bar charts and letter strings that simulated symptom patterns. Category structures were defined by independent features in Experiment 1 and correlated features in Experiments 2 and 3. Three different models of categorization, exemplar models, rule models, and dual process models, were contrasted in predicting recognition and categorization performances and the learning processes used to categorize. Across the three experiments, recognition and categorization were often affected differently by experimental variables. Stimulus types in Experiment 1 and salience in Experiment 3 had significant effects on categorization, but had no effect on recognition. The main effect of duration was significant in recognition, but not in categorization in Experiment 3. Finally, in all three experiments, block effects suggested that recognition performance decreased across blocks of practice, whereas categorization tended to increase across blocks. As noted by Metcalfe & Fisher (1986), recognition is based on exemplar memories, whereas categorization depends on abstract rules but is also influenced by exemplars in some conditions. Categorization rules were related to relative feature frequency in the learning of categories based on independent features. In categorization of categories based on correlated features, biconditional or symmetry rules were used when the correlations were salient or non-salient, respectively

Localist representation can improve efficiency for detection and counting

Almost all representations have both distributed and localist aspects, depending upon what properties of the data are being considered. With noisy data, features represented in a localist way can be detected very efficiently, and in binary representations they can be counted more efficiently than those represented in a distributed way. Brains operate in noisy environments, so the localist representation of behaviourally important events is advantageous, and fits what has been found experimentally. Distributed representations require more neurons to perform as efficiently, but they do have greater versatility

On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)

Mind in Action: Action Representation and the Perception of Biological Motion

The ability to understand and communicate about the actions of others is a fundamental aspect of our daily activity. How can we talk about what others are doing? What qualities do different actions have such that they cause us to see them as being different or similar? What is the connection between what we see and the development of concepts and words or expressions for the things that we see? To what extent can two different people see and talk about the same things? Is there a common basis for our perception, and is there then a common basis for the concepts we form and the way in which the concepts become lexicalized in language? The broad purpose of this thesis is to relate aspects of perception, categorization and language to action recognition and conceptualization. This is achieved by empirically demonstrating a prototype structure for action categories and by revealing the effect this structure has on language via the semantic organization of verbs for natural actions. The results also show that implicit access to categorical information can affect the perceptual processing of basic actions. These findings indicate that our understanding of human actions is guided by the activation of high level information in the form of dynamic action templates or prototypes. More specifically, the first two empirical studies investigate the relation between perception and the hierarchical structure of action categories, i.e., subordinate, basic, and superordinate level action categories. Subjects generated lists of verbs based on perceptual criteria. Analyses based on multidimensional scaling showed a significant correlation for the semantic organization of a subset of the verbs for English and Swedish speaking subjects. Two additional experiments were performed in order to further determine the extent to which action categories exhibit graded structure, which would indicate the existence of prototypes for action categories. The results from typicality ratings and category verification showed that typicality judgments reliably predict category verification times for instances of different actions. Finally, the results from a repetition (short-term) priming paradigm suggest that high level information about the categorical differences between actions can be implicitly activated and facilitates the later visual processing of displays of biological motion. This facilitation occurs for upright displays, but appears to be lacking for displays that are shown upside down. These results show that the implicit activation of information about action categories can play a critical role in the perception of human actions