Categorization response time with multidimensional stimuli

The representational dynamics of perceived voice emotions evolve from categories to dimensions

Long-standing affective science theories conceive the perception of emotional stimuli either as discrete categories (for example, an angry voice) or continuous dimensional attributes (for example, an intense and negative vocal emotion). Which position provides a better account is still widely debated. Here we contrast the positions to account for acoustics-independent perceptual and cerebral representational geometry of perceived voice emotions. We combined multimodal imaging of the cerebral response to heard vocal stimuli (using functional magnetic resonance imaging and magneto-encephalography) with post-scanning behavioural assessment of voice emotion perception. By using representational similarity analysis, we find that categories prevail in perceptual and early (less than 200 ms) frontotemporal cerebral representational geometries and that dimensions impinge predominantly on a later limbic-temporal network (at 240 ms and after 500 ms). These results reconcile the two opposing views by reframing the perception of emotions as the interplay of cerebral networks with different representational dynamics that emphasize either categories or dimensions.

From convolutional neural networks to models of higher-level cognition (and back again)

The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might also provide a basis for modeling higher-level cognition, focusing on the core phenomena of similarity and categorization. The most important advance comes from the ability of CNNs to learn high-dimensional representations of complex naturalistic images, substantially extending the scope of traditional cognitive models that were previously only evaluated with simple artificial stimuli. In all cases, the most successful combinations arise when CNN representations are used with cognitive models that have the capacity to transform them to better fit human behavior. One consequence of these insights is a toolkit for the integration of cognitively motivated constraints back into CNN training paradigms in computer vision and machine learning, and we review cases where this leads to improved performance. A second consequence is a roadmap for how CNNs and cognitive models can be more fully integrated in the future, allowing for flexible end-to-end algorithms that can learn representations from data while still retaining the structured behavior characteristic of human cognition.

Relating categorization to set summary statistics perception

Two cognitive processes have been explored that compensate for the limited information that can be perceived and remembered at any given moment. The first parsimonious cognitive process is object categorization. We naturally relate objects to their category, assume they share relevant category properties, often disregarding irrelevant characteristics. Another scene organizing mechanism is representing aspects of the visual world in terms of summary statistics. Spreading attention over a group of objects with some similarity, one perceives an ensemble representation of the group. Without encoding detailed information of individuals, observers process summary data concerning the group, including set mean for various features (from circle size to face expression). Just as categorization may include/depend on prototype and intercategory boundaries, so set perception includes property mean and range. We now explore common features of these processes. We previously investigated summary perception of low-level features with a rapid serial visual presentation (RSVP) paradigm and found that participants perceive both the mean and range extremes of stimulus sets, automatically, implicitly, and on-the-fly, for each RSVP sequence, independently. We now use the same experimental paradigm to test category representation of high-level objects. We find participants perceive categorical characteristics better than they code individual elements. We relate category prototype to set mean and same/different category to in/out-of-range elements, defining a direct parallel between low-level set perception and high-level categorization. The implicit effects of mean or prototype and set or category boundaries are very similar. We suggest that object categorization may share perceptual-computational mechanisms with set summary statistics perception.

Combining error-driven models of associative learning with evidence accumulation models of decision-making

As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings.

Evidence that within-dimension features are generally processed coactively

In this paper, we examine whether information about an item's category, provided by the same dimension type presented across multiple spatial locations (which we term within-dimension features), is processed independently or pooled into a common representation. We use Systems Factorial Technology (SFT; Townsend & Nozawa, Journal of Mathematical Psychology, 39, 321-340, 1995) and fit parametric logical rule-based models to diagnose whether information processing is serial, parallel, or coactive. The present work focuses on expanding the scope of categorization response time (RT) models by synthesizing recent work in perceptual categorization with theories of visual attention. Our results show that for the majority of participants, processing occurs coactively (i.e., is pooled into a single decision process). For the remainder, other processing strategies were found (e.g., parallel processing). This finding provides new insight into decision-making using within-dimension features presented in multiple locations. It also highlights the importance of both featural information and spatial attention in categorization decision-making.

Neural basis for categorical boundaries in the primate pre-SMA during relative categorization of time intervals

Perceptual categorization depends on the assignment of different stimuli to specific groups based, in principle, on the notion of flexible categorical boundaries. To determine the neural basis of categorical boundaries, we record the activity of pre-SMA neurons of monkeys executing an interval categorization task in which the limit between short and long categories changes between blocks of trials within a session. A large population of cells encodes this boundary by reaching a constant peak of activity close to the corresponding subjective limit. Notably, the time at which this peak is reached changes according to the categorical boundary of the current block, predicting the monkeys’ categorical decision on a trial-by-trial basis. In addition, pre-SMA cells also represent the category selected by the monkeys and the outcome of the decision. These results suggest that the pre-SMA adaptively encodes subjective duration boundaries between short and long durations and contains crucial neural information to categorize intervals and evaluate the outcome of such perceptual decisions.

Grouping stimuli into categories often depends on a subjective determination of category boundaries. Here the authors report a neuronal population in pre-supplementary motor area whose peak activity predicts the categorical decision boundary between long and short time intervals on a trial-by-trial basis.

Generalization in Human Category Learning: A Connectionist Account of Differences in Gradient after Discriminative and Non discriminative Training

Two experiments are reported that investigate the difference in gradient of generalization observed between one-category (non-discriminative) and two-category (discriminative) training. Extrapolating from the results of a number of animal learning studies, it was predicted that the gradient should be steeper under discriminative training. The first experiment confirms this basic prediction for the stimuli used, which were novel, prototype-structured, and constructed from 12 symbols positioned on a grid. An explanation for the effect, based on the Rescorla-Wagner theory of Pavlovian conditioning (Rescorla & Wagner, 1972), is that under non-discriminative training “incidental stimuli” have significant control over responding, whereas under discriminative training they do not. Incidental stimuli are those aspects of the stimulus, or the surrounding context, that are not differentially reinforced under discriminative training. This explanation leads to the prediction that a comparable effect of blocked versus intermixed discriminative training should also be found. This prediction is disconfirmed by the second experiment. An alternative model, still based on the Rescorla Wagner theory but with the addition of a decision mechanism comprising a threshold unit and a competitive network system, is proposed, and its ability to predict both the choice probabilities and the pattern of response times found is evaluated via simulation.

Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application

It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar- and rule-based processes. In contrast, Hahn et al. (2010) have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT-R cognitive architecture (by Anderson and Betz, 2001) can account for the Hahn et al. dataset. This model implements Nosofsky and Palmeri's (1997) exemplar-based random walk model as its exemplar route, and combines it with an implementation of Nosofsky et al. (1994) rule-based model RULEX. A thorough search of the model's parameter space showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.

Categorizing Fetal Heart Rate Variability with and without Visual Aids

Objective This study examined the ability of clinicians to correctly categorize images of fetal heart rate (FHR) variability with and without the use of exemplars. Study Design A sample of 33 labor and delivery clinicians inspected static FHR images and categorized them into one of four categories defined by the National Institute of Child Health and Human Development (NICHD) based on the amount of variability within absent, minimal, moderate, or marked ranges. Participants took part in three conditions: two in which they used exemplars representing FHR variability near the center or near the boundaries of each range, and a third control condition with no exemplars. The data gathered from clinicians were compared with those from a previous study using novices. Results Clinicians correctly categorized more images when the FHR variability fell near the center rather than the boundaries of each range, F (1,32) = 71.69, p < 0.001, partial η2 = 0.69. They also correctly categorized more images when exemplars were available, F (2,64) = 5.44, p = 0.007, partial η2 = 0.15. Compared with the novices, the clinicians were more accurate and quicker in their category judgments, but this difference was limited to the condition without exemplars. Conclusion The results suggest that categorizing FHR variability is more difficult when the examples fall near the boundaries of each NICHD-defined range. Thus, clinicians could benefit from training with visual aids to improve judgments about FHR variability and potentially enhance safety in labor and delivery.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so.

Erasing the engram: the unlearning of procedural skills

Huge amounts of money are spent every year on unlearning programs--in drug-treatment facilities, prisons, psychotherapy clinics, and schools. Yet almost all of these programs fail, since recidivism rates are high in each of these fields. Progress on this problem requires a better understanding of the mechanisms that make unlearning so difficult. Much cognitive neuroscience evidence suggests that an important component of these mechanisms also dictates success on categorization tasks that recruit procedural learning and depend on synaptic plasticity within the striatum. A biologically detailed computational model of this striatal-dependent learning is described (based on Ashby & Crossley, 2011). The model assumes that a key component of striatal-dependent learning is provided by interneurons in the striatum called the tonically active neurons (TANs), which act as a gate for the learning and expression of striatal-dependent behaviors. In their tonically active state, the TANs prevent the expression of any striatal-dependent behavior. However, they learn to pause in rewarding environments and thereby permit the learning and expression of striatal-dependent behaviors. The model predicts that when rewards are no longer contingent on behavior, the TANs cease to pause, which protects striatal learning from decay and prevents unlearning. In addition, the model predicts that when rewards are partially contingent on behavior, the TANs remain partially paused, leaving the striatum available for unlearning. The results from 3 human behavioral studies support the model predictions and suggest a novel unlearning protocol that shows promising initial signs of success.

Humans deploy diverse strategies in learning same-different discrimination tasks

Prior research suggests that variability discrimination is basic to same-different conceptualization (Young and Wasserman, 2001). In that research, people were trained with 16-item arrays; this training might have encouraged people to use perceptual variability to solve the task. Here, two groups of participants were trained with either 2- or 16-item Same and Different arrays (Groups 2 and 16, respectively). Participants had to learn which of two arbitrary responses was correct for the arrays without being told about the "sameness" or "differentness" of the stimuli. Surprisingly, 52% of participants in Group 2 did not learn the discrimination compared to only 21% of participants in Group 16; also, learners in Group 16 reached higher accuracy levels sooner and their choice responding was faster than learners in Group 2. A large disparity in the variability (measured by entropy) between the Same and Different arrays evidently helped participants to learn the same-different task. As well, in Group 16, we found the same two patterns of performance-Categorical and Continuous-as in prior research (Castro et al., 2006; Young and Wasserman, 2001). In Group 2, we again found the Categorical cluster, but we lost the genuine Continuous cluster and we observed a novel strategy: some participants developed a highly inclusive notion of "sameness" that applied to any array containing at least two identical icons. These findings indicate that individuals may deploy a multiplicity of possible strategies when learning a seemingly simple same-different discrimination.

No-loss gambling shows the speed of the unconscious

This paper investigates the time it takes unconscious vs. conscious knowledge to form by using an improved "no-loss gambling" method to measure awareness of knowing. Subjects could either bet on a transparently random process or on their grammaticality judgment in an artificial grammar learning task. A conflict in the literature is resolved concerning whether unconscious rather than conscious knowledge is especially fast or slow to form. When guessing (betting on a random process), accuracy was above chance and RTs were longer than when feeling confident (betting on the grammaticality decision). In a second experiment, short response deadlines only interfered with the quality of confident decisions (betting on grammaticality). When people are unaware of their knowledge, externally enforced decisions can be made rapidly with little decline in quality; but if given ample time, they await a metacognitive process to complete. The dissociation validates no-loss gambling as a measure of conscious awareness.

Cortical and striatal contributions to automaticity in information-integration categorization

In information-integration categorization, accuracy is maximized only if information from two or more stimulus components is integrated at some pre-decisional stage. In many cases the optimal strategy is difficult or impossible to describe verbally. Evidence suggests that success in information-integration tasks depends on procedural learning that is mediated largely within the striatum. Although many studies have examined initial information-integration learning, little is known about how automaticity develops in information-integration tasks. To address this issue, each of ten human participants received feedback training on the same information-integration categories for more than 11,000 trials spread over 20 different training sessions. Sessions 2, 4, 10, and 20 were performed inside an MRI scanner. The following results stood out. 1) Automaticity developed between sessions 10 and 20. 2) Pre-automatic performance depended on the putamen, but not on the body and tail of the caudate nucleus. 3) Automatic performance depended only on cortical regions, particularly the supplementary and pre-supplementary motor areas. 4) Feedback processing was mainly associated with deactivations in motor and premotor regions of cortex, and in the ventral lateral prefrontal cortex. 5) The overall effects of practice were consistent with the existing literature on the development of automaticity.

Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT-distribution data associated with individual stimuli in tasks of speeded perceptual classification.

Graded motor responses in the time course of categorizing atypical exemplars

The time course of categorization was investigated in four experiments, which revealed graded competitive effects in a categorization task. Participants clicked one of two categories (e.g., mammal or fish) in response to atypical or typical exemplars (e.g., whale or cat) in the form of words (Experiments 1 and 2) or pictures (Experiments 3 and 4). Streaming x, y coordinates of mouse movement trajectories were recorded. Normalized mean trajectories revealed a graded competitive process: Atypical exemplars produced trajectories with greater curvature toward the competing category than did typical exemplars. The experiments contribute to recent examination of the time course of categorization and carry implications for theories of representation in cognitive science.

Effects of number of items and visual display variability onsame-different discrimination behavior

We explored college students' discrimination of complex visual stimuli that involvedmultiple-item displays. The items in each of the displays could be all the same, all different, or diverse mixtures of some same and some different items. The participants had to learn which of two arbitrary responses was correct for each of the displays without being told about the sameness or differentness of the stimuli. We observed a general improvement in discrimination performance--a rise in choice accuracy and a fall in reaction time-as the number of icons in the display was increased, even when the participants had been trained from the outset with displays containing different numbers of items and when smaller numbers of items were not randomly distributed but grouped in the center of the display. The participants' discrimination behavior also depended on the mixture of same and different items in the displays. Striking individual differences in the participants' discrimination behavior disclosed that people sometimes respond as do pigeons and baboons trained with a similar task. This and previous related research suggest that variability discrimination may lie at the root of same-different categorization behavior.

Response times seen as decompression times in Boolean concept use

This paper reports a study of a multi-agent model of working memory (WM) in the context of Boolean concept learning. The model aims to assess the compressibility of information processed in WM. Concept complexity is described as a function of communication resources (i.e., the number of agents and the structure of communication between agents) required in WM to learn a target concept. This model has been successfully applied in measuring learning times for three-dimensional (3D) concepts (Mathy and Bradmetz in Curr Psychol Cognit 22(1):41-82, 2004). In this previous study, learning time was found to be a function of compression time. To assess the effect of decompression time, this paper presents an extended intra-conceptual study of response times for two- and 3D concepts. Response times are measured in recognition phases. The model explains why the time required to compress a sample of examples into a rule is directly linked to the time to decompress this rule when categorizing examples. Three experiments were conducted with 65, 49, and 84 undergraduate students who were given Boolean concept learning tasks in two and three dimensions (also called rule-based classification tasks). The results corroborate the metric of decompression given by the multi-agent model, especially when the model is parameterized following static serial processing of information. Also, this static serial model better fits the patterns of response times than an exemplar-based model.

Classification of faces in man and machine

We attempt to shed light on the algorithms humans use to classify images of human faces according to their gender. For this, a novel methodology combining human psychophysics and machine learning is introduced. We proceed as follows. First, we apply principal component analysis (PCA) on the pixel information of the face stimuli. We then obtain a data set composed of these PCA eigenvectors combined with the subjects' gender estimates of the corresponding stimuli. Second, we model the gender classification process on this data set using a separating hyperplane (SH) between both classes. This SH is computed using algorithms from machine learning: the support vector machine (SVM), the relevance vector machine, the prototype classifier, and the K-means classifier. The classification behavior of humans and machines is then analyzed in three steps. First, the classification errors of humans and machines are compared for the various classifiers, and we also assess how well machines can recreate the subjects' internal decision boundary by studying the training errors of the machines. Second, we study the correlations between the rank-order of the subjects' responses to each stimulus-the gender estimate with its reaction time and confidence rating-and the rank-order of the distance of these stimuli to the SH. Finally, we attempt to compare the metric of the representations used by humans and machines for classification by relating the subjects' gender estimate of each stimulus and the distance of this stimulus to the SH. While we show that the classification error alone is not a sufficient selection criterion between the different algorithms humans might use to classify face stimuli, the distance of these stimuli to the SH is shown to capture essentials of the internal decision space of humans. Furthermore, algorithms such as the prototype classifier using stimuli in the center of the classes are shown to be less adapted to model human classification behavior than algorithms such as the SVM based on stimuli close to the boundary between the classes.

Modeling unidimensional categorization in monkeys

The categorization performance of monkeys on a unidimensional perceptual categorization task was examined with reference to decision bound and exemplar theories of categorization. Three rhesus monkeys were presented with stimuli varying along a single dimension, the displacement of a target light from a fixation point. Left or right saccade responses were probabilistically reinforced according to one of three functions, two of which were nonmonotonic at one end of the stimulus space. The monkeys all showed a monotonic increase in response probability as a function of target light displacement in this region, consistent with decision bound theory. Fits of a single-boundary model (GRT, Ashby & Gott, 1988) and two exemplar models--one using a probabilistic response function (GCM; Nosofsky, 1986), the other using a deterministic response function (DEM; Ashby & Maddox, 1993)--revealed overall support for the decision bound model. The results suggest that monkeys used a perceptual decision boundary to perform the task.

Speeded classification in a probabilistic category structure: contrasting exemplar-retrieval, decision-boundary, and prototype models

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models.

Decision-bound theory and the influence of familiarity

In this article, we derive a nonparametric prediction from decision-bound theory (DBT). The crucial aspect that is tested is whether or not familiarity of a stimulus affects response time in categorization. We show that, for our design, DBT, extended with some reasonable and testable assumptions, predicts no familiarity effect. Our prediction is nonparametric in that, rather than fit a specific instantiation of general DBT, we posit only some general assumptions of this theory and derive the prediction from these assumptions. It is found that familiarity did have a strong impact on response time for at least half of our participants. We suggest that DBT is in itself incomplete and should be extended to account for the full range of available data.

Feature sampling in categorization and recognition of objects

This article presents an overview of some recent work on the time course of perceptual categorization and recognition memory. First, The EGCM-RT, which is a feature-sampling model of the time course of categorization, is described. It is shown that the model explains a wide range of categorization data. Second, an overview is given of a feature-sampling model of the time course of recognition that is derived from the EGCM-RT. This model explains results that have been interpreted in the past as evidence for dual-process models of recognition, and it provides a single-process alternative to dual-process accounts.

Responding under time pressure: testing two animal learning models and a model of visual categorization

Two experiments are reported, which employed a Pavlovian eyelid conditioning procedure with human participants. The experiments tested the predictions of three models of the time-course of processing under time pressure. These were the extended generalized context model (Lamberts, 1998), and two variants of the Rescorla-Wagner model (Rescorla & Wagner, 1972), which were activated in cascade mode. Reinforcement schedules in the experiments were equivalent either to an AND rule or to an XOR rule. The time available for processing the conditioned stimulus and initiating a conditioned response was manipulated by varying the interval from the onset of the conditioned stimulus to the onset of the unconditioned stimulus. The results were in accord with the predictions of one of the two variants of the Rescorla-Wagner model.

Perceptual interactions of facial dimensions in speeded classification and identification

The representation underlying the identification and classification of semirealistic line drawings taken from a computer model of the face was investigated by using a speeded classification task and an identification task. These data were analyzed by using a multidimensional extension of signal detection theory, within which varieties of perceptual interactions between dimensions within and across stimuli can be characterized. The dimensions of interest here were eye separation, nose length, and mouth width. The response time and accuracy data from the speeded classification task suggest that processing of a given feature did depend on whether other features were present or absent, but given that other features were present, the results strongly support separability (a macrolevel, across-stimulus form of invariance) for all pairs of facial dimensions used. This separability was confirmed by the subsequent identification task. Owing to its greater resolution, the identification task can reveal interactions that might exist at more microlevels of processing. In fact, the identification data did indicate the presence of perceptual dependence between facial dimensions within a stimulus when the dimensions that were varied were close in spatial proximity (i.e., eye separation and nose length). Within the theoretical framework, perceptual dependence can be interpreted as correlated noise between otherwise separate channels (and hence, is logically distinct from separability). This dependence was greatly reduced for dimensions that were more distant (eyes and mouth). The relation between these results and the configural effects that have been observed with faces as stimuli in other studies is discussed.

A Stochastic Version of General Recognition Theory

General recognition theory (GRT) is a multivariate generalization of signal detection theory. Past versions of GRT were static and lacked a process interpretation. This article presents a stochastic version of GRT that models moment-by-moment fluctuations in the output of perceptual channels via a multivariate diffusion process. A decision stage then computes a linear or quadratic function of the outputs from the perceptual channels, which drives a univariate diffusion process that determines the subject's response. Conditions are established under which the stochastic and static versions of GRT make identical accuracy predictions. These equivalence relations show that traditional estimates of perceptual noise may often be corrupted by decisional influences. Copyright 2000 Academic Press.

Concepts and transformational knowledge

The effect of exposure to principled change in concept formation was investigated in four experiments. In Experiment 1, participants were trained on either patterns that transformed systematically or control patterns that were distorted randomly. Training on transformational patterns produced concepts that were more resistant to false intrusions and decay. Experiment 2 separated the relative influences of transformational knowledge and pairwise similarity. Participants were able to identify the next pattern in a transformational sequence even though the foils were closer to the training patterns. Experiment 3 investigated whether participants use transformational information in a speeded categorization task. Participants were faster at classifying patterns that continued a transformational path than patterns that fell off the path, only if they had trained on the transformational patterns in a systematic order. Experiment 4 used multidimensional scaling to explore the psychological structure of transformational knowledge following training. Analyses revealed clear evidence of a transformational path with systematic training. Implications for theories of similarity and categorization are discussed.

Effects of similarity and practice on speeded classification response times and accuracies: further tests of an exemplar-retrieval model

Observers were tested in a perceptual category-learning experiment in which they were instructed to make classification decisions as rapidly as possible without making errors. Nosofsky and Palmeri's (1997b) exemplar-based random walk (EBRW) model of speeded classification was tested for its ability to fit the classification response times and accuracies. The authors demonstrated that the EBRW model provided good quantitative fits to the mean response times and accuracies associated with individual objects as a function of their locations in a multidimensional similarity space and as a function of practice in the task. Preliminary evidence was also obtained that stimulus-specific adjustments in the random walk response criteria may have occurred during the course of learning.

Response time distributions in multidimensional perceptual categorization

Three speeded categorization experiments were conducted using separable dimension stimuli. The form of the category boundary was manipulated across experiments, and the distance from category exemplars to the category boundary was manipulated within each experiment. Observers completed several sessions in each experiment, yielding 300-400 repetitions of each stimulus. The large sample sizes permitted accurate estimates of the response time (RT) distributions and RT hazard functions. Analyses of these data indicated: (1) RT was faster for stimuli farther from the category boundary, and this stochastic dominance held at the level of the RT distributions; (2) RT was invariant for all stimuli the same distance from the category boundary; (3) when task difficulty was high, errors were slower than correct responses, whereas this difference disappeared when difficulty was low; (4) small, consistent response biases appeared to have a large effect on the relation between correct and error RT; (5) the shape of the RT hazard function was qualitatively affected by distance to the category boundary. These data establish a rich set of empirical constraints for testing developing models of categorization RT.

Comparing exemplar-retrieval and decision-bound models of speeded perceptual classification

The authors compared the exemplar-based random-walk (EBRW) model of Nosofsky and Palmeri (1997) and the decision-bound model (DBM) of Ashby and Maddox (1994; Maddox & Ashby, 1996) on their ability to predict performance in Garner's (1974) speeded classification tasks. A key question was the extent to which the models could predict facilitation in the correlated task and interference in the filtering task, in situations involving integral-dimension stimuli. To obtain rigorous constraints for model evaluation, the goal was to fit the detailed structure of the response time (RT) distribution data associated with each individual stimulus in each task. Both models yielded reasonably good global quantitative fits to the RT distribution and accuracy data. However, the DBM failed to properly characterize the interference effects in the filtering task. Apparently, a fundamental limitation of the DBM is that it predicts that the fastest RTs in the filtering task should be faster than the fastest RTs in the control task, whereas the opposite pattern was observed in our data.