A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation. The adequacy of this theory is assessed by applying a formalization of the theory, known as the Rasch model (Rasch, 1960; Thissen & Steinberg, 1986), to categorization data for eight natural language categories and subjecting it to a formal test. In validating the model special care is given to its ability to account for inter- and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Rasch model that can be used to uncover the nature of category representations and the sources of categorization differences are discussed.     

Modelling individual difference in visual categorization

Recent years has seen growing interest in understanding, characterizing, and explaining individual differences in visual cognition. We focus here on individual differences in visual categorization. Categorization is the fundamental visual ability to group different objects together as the same kind of thing. Research on visual categorization and category learning has been significantly informed by computational modelling, so our review will focus both on how formal models of visual categorization have captured individual differences and how individual difference have informed the development of formal models. We first examine the potential sources of individual differences in leading models of visual categorization, providing a brief review of a range of different models. We then describe several examples of how computational models have captured individual differences in visual categorization. This review also provides a bit of an historical perspective, starting with models that predicted no individual differences, to those that captured group differences, to those that predict true individual differences, and to more recent hierarchical approaches that can simultaneously capture both group and individual differences in visual categorization. Via this selective review, we see how considerations of individual differences can lead to important theoretical insights into how people visually categorize objects in the world around them. We also consider new directions for work examining individual differences in visual categorization.     

Sequence effects in categorization of simple perceptual stimuli

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of the opposite category than by a distant member of the same category. It is argued that this category contrast effect cannot be accounted for by extant exemplar or decision-bound models of categorization. The effect suggests the use of relative magnitude information in categorization. A memory and contrast model illustrates how relative magnitude information may be used in categorization.     

Model-guided search for optimal natural-science-category training exemplars: A work in progress

Psychonomic Bulletin & Review - Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in...     

A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes

When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for binary or ordered-categorical outcomes, however, comparisons between such models are impeded by the implicit rescaling of the model estimates that takes place with the inclusion of new predictors and/or random effects. This paper presents an approach for putting the estimates on a common scale to facilitate relative comparisons between models fit to binary or ordinal outcomes. The approach is developed for both population-average and unit-specific models.     

What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations

Most theories of categorization emphasize how continuous perceptual information is mapped to categories. However, equally important are the informational assumptions of a model, the type of information subserving this mapping. This is crucial in speech perception where the signal is variable and context dependent. This study assessed the informational assumptions of several models of speech categorization, in particular, the number of cues that are the basis of categorization and whether these cues represent the input veridically or have undergone compensation. We collected a corpus of 2,880 fricative productions (Jongman, Wayland, & Wong, 2000) spanning many talker and vowel contexts and measured 24 cues for each. A subset was also presented to listeners in an 8AFC phoneme categorization task. We then trained a common classification model based on logistic regression to categorize the fricative from the cue values and manipulated the information in the training set to contrast (a) models based on a small number of invariant cues, (b) models using all cues without compensation, and (c) models in which cues underwent compensation for contextual factors. Compensation was modeled by computing cues relative to expectations (C-CuRE), a new approach to compensation that preserves fine-grained detail in the signal. Only the compensation model achieved a similar accuracy to listeners and showed the same effects of context. Thus, even simple categorization metrics can overcome the variability in speech when sufficient information is available and compensation schemes like C-CuRE are employed.     

Factors Affecting the Adequacy and Preferability of Semiparametric Groups-Based Approximations of Continuous Growth Trajectories

Psychologists have long been interested in characterizing individual differences in change over time. It is often plausible to assume that the distribution of these individual differences is continuous in nature, yet theory is seldom so specific as to designate its parametric form (e.g., normal). Semiparametric groups-based trajectory models (SPGMs) were thus developed to provide a discrete approximation for continuously distributed growth of unknown form. Previous research has demonstrated the adequacy of the approximation provided by SPGM but only under relatively narrow, theoretically optimal conditions. Under alternative conditions, which may be more common in practice (e.g., higher dimension random effects, smaller sample sizes), this study shows that approximation adequacy can suffer. Furthermore, this study also evaluates whether SPGM's discrete approximation is preferable to a parametric trajectory model that assumes normally distributed random effects when in fact the distribution is modestly nonnormal. The answer is shown to depend on distributional characteristics of both repeated measures (binary or continuous) and random effects (bimodal or skewed). Implications for practice are discussed in light of empirical examples on externalizing behavior.     

Geometric and featural representations in semantic concepts

We explore the adequacy of two types of similarity representation in the context of semantic concepts. To this end, we evaluate different categorization models, assuming either a geometric or a featural representation, using categorization decisions involving familiar and unfamiliar foods and animals. The study aims to assess the optimal stimulus representation as a function of the familiarity of the stimuli. For the unfamiliar stimuli, the geometric categorization models provide the best account of the categorization data, whereas for the familiar stimuli, the featural categorization models provide the best account. This pattern of results suggests that people rely on perceptual information to assign an unfamiliar stimulus to a category but rely on more elaborate conceptual knowledge when assigning a familiar stimulus.     

Categorization of briefly presented objects

Two experiments investigated binary categorization of artificial, multidimensional objects that were presented for intervals between 33 and 200 ms. The experiments consisted of two stages. In the first stage, participants learned to categorize nine objects into two categories. In the second stage, the same objects were presented for categorization again, and the presentation duration of the objects was manipulated. The results show systematic effects of exposure duration on category choices. Three formal process models of perceptual categorization were applied to the choice data. The extended generalized context model, which is a dimension-sampling model of perceptual categorization, provided the best account of the data. Received: 31 October 1997 / Accepted: 1 June 1998     

Formation of category representations

Many formal models of categorization assume, implicitly or explicitly, that categorization results in the formation of direct associations from representations of the presented stimuli to representations of the experimentally provided category labels. In three categorization experiments employing a polymorphous classification structure (Dennis, Hampton, & Lea, 1973) and a partial reversal,optional shift procedure (Kendler, Kendler, & Wells, 1960), we provide evidence consistent with the hypothesis that learning a new classification problem results in the creation of category representations that mediate between representations of the stimulus and the label. This hypothesis can be instantiated through the AMBRY model (Kruschke, 1996).     

Incorporating prior biases innetwork models of conceptual rule learning

A series of simulations is reported in which extant formal categorization models are applied to human rule-learning data (Salatas & Bourne, 1974). These data show that there are clear differences in the ease with which humans learn rules, with the conjunctive the easiest and the biconditional the hardest. The original ALCOVE model (an exemplar-based model), a configuralcue model, and two-layer backpropagation models did not fit the rule-learning data. ALCOVE successfully fit the data, however, when prior biases observed in human rule learning were implemented into weights of the network. Thus, current empirical learning models may not fare well in situations in which learners enter the concept-formation situation with preconceived biases regarding the kinds of concepts that are possible, but such biases might nevertheless be captured within these models. By incorporating preexperimental biases, ALCOVE may hold promise as a comprehensive category-learning model.     

Measuring category intuitiveness in unconstrained categorization tasks

What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models.     

Spain is Not Greece: How Metaphors are Understood

Metaphoric expressions are pervasive and powerful communication tools. The present report sheds light on two questions: how do we extract meaning from metaphors and similes, and are these two tropes interchangeable? Existing models propose different mechanisms for metaphor comprehension: comparison, categorization, and a shift from comparison to categorization as metaphors become conventionalized. While the categorization model allows for the possibility that metaphors and similes are not always interchangeable, all the variants of the comparison model assume equivalence of meaning between the two tropes. The findings reported here rule out this assumption: they show that metaphors and similes may express different, and even incompatible meanings. Aptness, rather than conventionalization, seems to determine processing mechanisms: apt metaphors, both novel and conventionalized, are understood as categorizations, while similes and inapt metaphors are understood as comparisons.     

Multiple attention systems in perceptual categorization

Five observers categorized inverted L-shaped stimuli according to the length of the horizontal line segment. A centrally located spatial cue preceded the stimulus on each trial. On 80% of the trials, the (relevant) horizontal line segment fell within the cued location, and on 20% of the trials the (irrelevant) vertical line segment fell within the cued location. The empirical results provide support for the hypothesis that perceptual attention can focus on the stimulus attribute inside the spatially cued location at the same time that decisional attention is focused on the (relevant) horizontal attribute--that is, the results suggest that perceptual and decisional attention can function independently during categorization. Decision bound models and extended generalized context models that assume separate perceptual and decisional attention systems were fitted to the data. Versions of the models that assume that the spatial cue affected perceptual attention were superior to versions that assume no effect on perceptual attention. These theoretical analyses support the functional independence hypothesis and suggest that formal theories of categorization should model the effects of perceptual and decisional attention separately.     

The flankers task and response competition: A useful tool for investigating a variety of cognitive problems

Abstract

Research has shown that the reaction-time interference produced by the flankers task arises, at least in large part, from the incipient activation of competing responses. The response competition paradigm has made valuable contributions to evaluating continuous flow versus discrete stage models of information processing as well as understanding cortical evoked potentials. The paradigm has been used to map the visual attentional field as a function of task demands and has also been found useful in the study of memory. It offers promise in studies of cognitive categorization and has provided insights into the “fast same effect” on same-different judgements on comparison tasks. It is currently being applied to the study of disjunctive comparisons.     

Crossed categorization and stereotyping: Structural analyses, effect patterns, and dissociative effects of context relevance

Based on the “Who said what?” paradigm, a new method is proposed for investigating social categorization and non-evaluative stereotype application in crossed categorization. The method is applied in 5 studies that manipulated relative context relevance of crossed age and gender categories. Social categorization is characterized by 2 indices: Relative subgroup memory assesses the amount of subgroup formation represented in memory, and relative category dominance the relative weight of each dimension of categorization. Both indices were affected by context relevance. There was strong evidence for social categorization at the subgroup level, whereas stereotype application followed a simple pattern of category dominance, in which only the context-relevant dimension exerted an effect. The results bear on current models of category-based impression formation and intergroup perception, and on category-activation cum stereotype-inhibition models.     

Rule-based extrapolation: A continuing challenge for exemplar models

Erickson and Kruschke (1998, 2002) demonstrated that in rule-plus-exception categorization, people generalize category knowledge by extrapolating in a rule-like fashion, even when they are presented with a novel stimulus that is most similar to a known exception. Although exemplar models have been found to be deficient in explaining rule-based extrapolation, Rodrigues and Murre (2007) offered a variation of an exemplar model that was better able to account for such performance. Here, we present the results of a new rule-plus-exception experiment that yields rule-like extrapolation similar to that of previous experiments, and yet the data are not accounted for by Rodrigues and Murre's augmented exemplar model. Further, a hybrid rule-and-exemplar model is shown to better describe the data. Thus, we maintain that rule-plus-exception categorization continues to be a challenge for exemplar-only models.     

群体交叉分类效应的代数模型及其潜在加工过程

交叉分类被认为是一种能够有效减少群际偏见的方法。交叉分类效应的代数模型主要包括平均模型、相加模型和等价模型,各个模型都有自己成立的证据,其中支持相加模型的研究证据最多。尽管如此,交叉分类效应的代数模型有很明显的局限,即要求所有进行交叉的类别维度权重相等,这与真实社会情境是相矛盾的。交叉分类的潜在加工过程主要有基于认知角度的类别差异模型和基于动机角度的社会认同两种比较成熟的理论,前者又分为类别差异消除模型和类别差异减少模型。