How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

Comparison and contrast in perceptual categorization

People categorized pairs of perceptual stimuli that varied in both category membership and pairwise similarity. Experiments 1 and 2 showed categorization of 1 color of a pair to be reliably contrasted from that of the other. This similarity-based contrast effect occurred only when the context stimulus was relevant for the categorization of the target (Experiment 3). The effect was not simply owing to perceptual color contrast (Experiment 4), and it extended to pictures from common semantic categories (Experiment 5). Results were consistent with a sign-and-magnitude version of N. Stewart and G. D. A. Brown's (2005) similarity-dissimilarity generalized context model, in which categorization is affected by both similarity to and difference from target categories. The data are also modeled with criterion setting theory (M. Treisman & T. C. Williams, 1984), in which the decision criterion is systematically shifted toward the mean of the current stimuli

Perceptual categorization

The categorization of external stimuli lies at the heart of cognitive science. Existing models of perceptual categorization assume (a) information about the absolute magnitude of a stimulus is used in the categorization decision, and (b) the representation of a stimulus does not change with experience. The three experimental programs presented here challenge these two assumptions. The experiments in Chapter 2 demonstrate that existing models of categorization are unable to predict the classification of items intermediate between two categories. Chapter 3 provides empirical evidence that categorization responses are heavily influenced by the immediately preceding context, consistent with evidence from absolute identification showing people have very poor access to absolute magnitude information. A memory and contrast model is presented where each categorization decision is based on the perceived difference between the current stimulus and immediately preceding stimuli. This model is shown to account for the data from Chapters 2 and 3. Chapter 4 explores the claim that new features may be created on experience with novel stimuli, and that these features serve to alter the representation of stimuli to facilitate new categorization tasks. An alternative account is offered for existing feature creation evidence. However, experimental work re-establishes a feature creation effect. Consideration is given as to how feature creation and memory and contrast accounts of categorization may be integrated, together with extensive suggestions for the development of these ideas

The effect of category variability in perceptual categorization

Exemplar and distributional accounts of categorization make differing predictions for the classification of a critical exemplar precisely halfway between the nearest exemplars of 2 categories differing in variability. Under standard conditions of sequential presentation, the critical exemplar was classified into the most similar, least variable category, consistent with an exemplar account. However, if the difference in variability is made more salient, then the same exemplar is classified into the more variable, most likely category, consistent with a distributional account. This suggests that participants may be strategic in their use of either strategy. However, when the relative variability of 2 categories was manipulated, participants showed changes in the classification of intermediate exemplars that neither approach could account for

Similarity and dissimilarity as evidence in perceptual categorization

In exemplar models the similarities between a new stimulus and each category exemplar constitute positive evidence for category membership. In contrast, other models assume that, if the new stimulus is sufficiently dissimilar to a category member, then that dissimilarity constitutes evidence against category membership. We propose a new similarity–dissimilarity exemplar model that provides a framework for integrating these two types of accounts. The evidence for a category is assumed to be the summed similarity to members of that category plus the summed dissimilarity to members of competing categories. The similarity–dissimilarity exemplar model is shown to mimic the standard exemplar model very closely in the unidimensional domain

Computational models of the development of perceptual expertise

In a recent article, Palmeri, Wong and Gauthier have argued that computational models may help direct hypotheses about the development of perceptual expertise. They support their claim by an analysis of models from the object-recognition and perceptual-categorization literatures. Surprisingly, however, they do not consider any computational models from traditional research into expertise, essentially the research deriving from Chase and Simon’s chunking theory, which itself was influenced by De Groot’s study of chessplayers. This is unfortunate, as a series of computational models based on perceptual chunking have explained a substantial number of phenomena related to expert behaviour and provide mechanisms that directly address the question of perceptual expertise

Categories, concepts, and calls : auditory perceptual mechanisms and cognitive abilities across different types of birds.

Although involving different animals, preparations, and objectives, our laboratories (Sturdy's and Cook's) are mutually interested in category perception and concept formation. The Sturdy laboratory has a history of studying perceptual categories in songbirds, while Cook laboratory has a history of studying abstract concept formation in pigeons. Recently, we undertook a suite of collaborative projects to combine our investigations to examine abstract concept formation in songbirds, and perception of songbird vocalizations in pigeons. This talk will include our recent findings of songbird category perception, songbird abstract concept formation (same/different task), and early results from pigeons' processing of songbird vocalizations in a same/different task. Our findings indicate that (1) categorization in birds seems to be most heavily influenced by acoustic, rather than genetic or experiential factors (2) songbirds treat their vocalizations as perceptual categories, both at the level of the note and species/whole call, (3) chickadees, like pigeons, can perceive abstract, same-different relations, and (4) pigeons are not as good at discriminating chickadee vocalizations as songbirds (chickadees and finches). Our findings suggest that although there are commonalities in complex auditory processing among birds, there are potentially important comparative differences between songbirds and non-songbirds in their treatment of certain types of auditory objects.Publisher PD

Dissimilarity is used as evidence of category membership in multidimensional perceptual categorization: a test of the similarity-dissimilarity generalized context model

In exemplar models of categorization, the similarity between an exemplar and category members constitutes evidence that the exemplar belongs to the category. We test the possibility that the dissimilarity to members of competing categories also contributes to this evidence. Data were collected from two 2-dimensional perceptual categorization experiments, one with lines varying in orientation and length and the other with coloured patches varying in saturation and brightness. Model fits of the similarity-dissimilarity generalized context model were used to compare a model where only similarity was used with a model where both similarity and dissimilarity were used. For the majority of participants the similarity-dissimilarity model provided both a significantly better fit and better generalization, suggesting that people do also use dissimilarity as evidence

Conceptual coordination bridges information processing and neurophysiology

Information processing theories of memory and skills can be reformulated in terms of how categories are physically and temporally related, a process called conceptual coordination. Dreaming can then be understood as a story understanding process in which two mechanisms found in everyday comprehension are missing: conceiving sequences (chunking categories in time as a higher-order categorization) and coordinating across modalities (e.g., relating the sound of a word and the image of its meaning). On this basis, we can readily identify isomorphisms between dream phenomenology and neurophysiology, and explain the function of dreaming as facilitating future coordination of sequential, cross-modal categorization (i.e., REM sleep lowers activation thresholds, “unlearning”)