High-variability training does not enhance generalization in the prototype-distortion paradigm.

Classic studies of human categorization learning provided evidence that high-variability training in the prototype-distortion paradigm enhances subsequent generalization to novel test patterns from the learned categories. More recent work suggests, however, that when the number of training trials is equated across low-variability and high-variability training conditions, it is low-variability training that yields better generalization performance. Whereas the recent studies used cartoon-animal stimuli varying along binary-valued dimensions, in the present work we return to the use of prototype-distorted dot-pattern stimuli that had been used in the original classic studies. In accord with the recent findings, we observe that high-variability training does not enhance generalization in the dot-pattern prototype-distortion paradigm when the total number of training trials is equated across the conditions, even when training with very large numbers of distinct instances. A baseline version of an exemplar model captures the major qualitative pattern of results in the experiment, as do prototype models that make allowance for changes in parameter settings across the different training conditions. Based on the modeling results, we hypothesize that although high-variability training does not enhance generalization in the prototype-distortion paradigm, it may do so when participants learn more complex category structures.

Testing formal cognitive models of classification and old-new recognition in a real-world high-dimensional category domain.

Categorization and old-new recognition memory are closely linked topics in the cognitive-psychology literature and there have been extensive past efforts at developing unified formal modeling accounts of these fundamental psychological processes. However, the existing formal-modeling literature has almost exclusively used small sets of simplified stimuli and artificial category structures. The present work extends this literature by collecting both categorization and old-new recognition judgments on a large set of high-dimensional stimuli that form real-world category structures: namely, a set of 540 images of rocks belonging to the geologically-defined categories igneous, metamorphic and sedimentary. Participants first engaged in a learning phase in which they classified large sets of training instances into these real-world categories. This was followed by a test phase in which they classified both training and novel transfer items into the learned categories and also judged whether each item was old or new. We attempted to model both the classification and recognition test data at the level of individual items. Ultimately, the categorization data were well fit by both an exemplar and clustering model, but not by a prototype model. Only the exemplar model was able to provide a reasonable first-order account of the old-new recognition data; however, the standard version of the model failed to capture the variability in hit rates within the class of old-training items themselves. An extended hybrid-similarity version of the exemplar model that made allowance for boosts in self-similarity due to matching distinctive features yielded much improved accounts of the old-new recognition data. The study is among the first to test cognitive-process models on their ability to account quantitatively for old-new recognition of real-world, high-dimensional stimuli at the level of individual items.

Discrimination of paediatric acuity test optotypes by 6-year-old children.

PURPOSE: To compare the discrimination performance of 6-year-old children for optotypes from six paediatric visual acuity tests and to fit Luce's Biased Choice Model to the data to estimate the relative similarities and bias for each optotype. METHODS: Full data sets were collected from 20 typically developing 6-year-olds who had passed a vision screening. They were presented with single optotypes labelled 6/12 at a distance of 9 m and were asked to identify the optotype using a matching task containing all optotypes from the relevant test. The data were combined to form a confusion matrix for each test and a biased choice model was fitted to the data. RESULTS: Median correct performance varied from 40% to 100% across optotypes, with the HOTV test having the highest values. Estimates of the similarity of each pair of optotypes indicated equal values for all pairs in the Landolt C, HOTV, Lea numbers and Tumbling E tests. The values differed for the picture tests, that is Lea Symbols and Allen figures. The estimates of bias for each individual optotype also indicated different values with the picture tests. CONCLUSIONS: Previous studies of the threshold acuity of young children and adults have indicated differences in acuity estimates across paediatric tests. A recognition acuity task typically requires resolving the difference information between optotypes. The performance of the 6-year-olds here reveals variance in similarity and bias values for picture tests, particularly for the Allen figures when compared with the Lea Symbols. Ideally, this analysis should be performed when designing new tests, and these results motivate progression from the use of current picture tests to well calibrated letter or number tests at the earliest possible age.

Category structure and region-specific selective attention.

A fundamental component of human categorization involves learning to attend selectively to relevant dimensions and ignore irrelevant ones. Past research has shown that humans can learn flexible strategies in which the attended dimensions vary depending on the region of feature space in which classification takes place. However, region-specific selective attention (RSA) is often challenging to learn. Here, we test the hypothesis that RSA is facilitated when individual categories are embedded within single regions of stimulus space rather than dispersed across multiple regions. We conduct an experiment that varies across conditions whether categories are embedded within regions, but in which the same RSA strategy would benefit performance across the conditions. To evaluate the hypothesis, we use measures of overall performance accuracy as well as comparisons among formal computational models that do and do not make allowance for RSA. We find strong support for the hypothesis among the upper-median-performing participants in the tested groups. However, even in the condition that promotes the learning of RSA, performance is considerably worse than in comparison conditions in which a single set of dimensions can be attended throughout the entire stimulus space.

Item frequency in probe-recognition memory search: Converging evidence for a role of item-response learning.

In short-term probe-recognition tasks, observers make speeded old-new recognition judgments for items that are members of short lists. However, long-term memory (LTM) for items from previous lists influences current-list performance. The current experiment pursued the nature of these long-term influences-in particular, whether they emerged from item-familiarity or item-response-learning mechanisms. Subjects engaged in varied-mapping (VM) and consistent-mapping (CM) short-term probe-recognition tasks (e.g., Schneider & Shiffrin, Psychological Review, 84, 1-66, 1977). The key manipulation was to vary the frequency with which individual items were presented across trials. We observed a striking dissociation: Whereas increased presentation frequency led to benefits in performance for both old and new test probes in CM search, it resulted in interference effects for both old and new test probes in VM search. Formal modeling suggested that a form of item-response learning took place in both conditions: Each presentation of a test probe led to the storage of that test probe-along with its associated "old" or "new" response-as an exemplar in LTM. These item-response pairs were retrieved along with current-list items in driving observers' old-- recognition judgments. We conclude that item-response learning is a core component of the LTM mechanisms that influence CM and VM memory search.

Toward the development of a feature-space representation for a complex natural category domain.

This article reports data sets aimed at the development of a detailed feature-space representation for a complex natural category domain, namely 30 common subtypes of the categories of igneous, metamorphic, and sedimentary rocks. We conducted web searches to develop a library of 12 tokens each of the 30 subtypes, for a total of 360 rock pictures. In one study, subjects provided ratings along a set of 18 hypothesized primary dimensions involving visual characteristics of the rocks. In other studies, subjects provided similarity judgments among pairs of the rock tokens. Analyses are reported to validate the regularity and information value of the dimension ratings. In addition, analyses are reported that derive psychological scaling solutions from the similarity-ratings data and that interrelate the derived dimensions of the scaling solutions with the directly rated dimensions of the rocks. The stimulus set and various forms of ratings data, as well as the psychological scaling solutions, are made available on an online website (https://osf.io/w64fv/) associated with the article. The study provides a fundamental data set that should be of value for a wide variety of research purposes, including: (1) probing the statistical and psychological structure of a complex natural category domain, (2) testing models of similarity judgment, and (3) developing a feature-space representation that can be used in combination with formal models of category learning to predict classification performance in this complex natural category domain.

On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.

The general view in psychological science is that natural categories obey a coherent, family-resemblance principle. In this investigation, we documented an example of an important exception to this principle: Results of a multidimensional-scaling study of igneous, metamorphic, and sedimentary rocks (Experiment 1) suggested that the structure of these categories is disorganized and dispersed. This finding motivated us to explore what might be the optimal procedures for teaching dispersed categories, a goal that is likely critical to science education in general. Subjects in Experiment 2 learned to classify pictures of rocks into compact or dispersed high-level categories. One group learned the categories through focused high-level training, whereas a second group was required to simultaneously learn classifications at a subtype level. Although high-level training led to enhanced performance when the categories were compact, subtype training was better when the categories were dispersed. We provide an interpretation of the results in terms of an exemplar-memory model of category learning.

Response-time evidence for mixed memory states in a sequential-presentation change-detection task.

Response-time (RT) and choice-probability data were obtained in a rapid visual sequential-presentation change-detection task in which memory set size, study-test lag, and objective change probabilities were manipulated. False "change" judgments increased dramatically with increasing lag, consistent with the idea that study items with long lags were ejected from a discrete-slots buffer. Error RTs were nearly invariant with set size and lag, consistent with the idea that the errors were produced by a stimulus-independent guessing process. The patterns of error and RT data could not be explained in terms of encoding limitations, but were consistent with the hypothesis that long retention lags produced a zero-stimulus-information state that required guessing. Formal modeling of the change-detection RT and error data pointed toward a hybrid model of visual working memory. The hybrid model assumed mixed states involving a combination of memory and guessing, but with higher memory resolution for items with shorter retention lags. The work raises new questions concerning the nature of the memory representations that are produced across the closely related tasks of change detection and visual memory search.

Sequence-sensitive exemplar and decision-bound accounts of speeded-classification performance in a modified Garner-tasks paradigm.

Among the most fundamental results in the area of perceptual classification are the "correlated facilitation" and "filtering interference" effects observed in Garner's (1974) speeded categorization tasks: In the case of integral-dimension stimuli, relative to a control task, single-dimension classification is faster when there is correlated variation along a second dimension, but slower when there is orthogonal variation that cannot be filtered out (e.g., by attention). These fundamental effects may result from participants' use of a trial-by-trial bypass strategy in the control and correlated tasks: The observer changes the previous category response whenever the stimulus changes, and maintains responses if the stimulus repeats. Here we conduct modified versions of the Garner tasks that eliminate the availability of a pure bypass strategy. The fundamental facilitation and interference effects remain, but are still largely explainable in terms of pronounced sequential effects in all tasks. We develop sequence-sensitive versions of exemplar-retrieval and decision-bound models aimed at capturing the detailed, trial-by-trial response-time distribution data. The models combine assumptions involving: (i) strengthened perceptual/memory representations of stimuli that repeat across consecutive trials, and (ii) a bias to change category responses on trials in which the stimulus changes. These models can predict our observed effects and provide a more complete account of the underlying bases of performance in our modified Garner tasks.

Activation in the neural network responsible for categorization and recognition reflects parameter changes.

According to various influential formal models of cognition, perceptual categorization and old-new recognition recruit the same memory system. By contrast, the prevailing view in the cognitive neuroscience literature is that separate neural systems mediate perceptual categorization and recognition. A direct form of evidence is that separate brain regions are activated when observers engage in categorization and recognition tasks involving the same types of stimuli. However, even if the same memory-based comparison processes underlie categorization and recognition, one would not expect to see identical patterns of brain activity across the tasks; the reason is that observers would adjust parameter settings (e.g., vary criterion settings) across the tasks to satisfy the different task goals. In this fMRI study, we conducted categorization and recognition tasks in which stimulus conditions were held constant, and in which observers were induced to vary hypothesized parameter settings across conditions. A formal exemplar model was fitted to the data to track the changes in parameters to help interpret the fMRI results. We observed systematic effects of changes in parameters on patterns of brain activity, which were interpretable in terms of differing forms of evidence accumulation that resulted from the changed parameter settings. After controlling for stimulus and parameter-related differences, we found little evidence that categorization and recognition recruit separate memory systems.

Familiarity and categorization processes in memory search.

A fundamental distinction in tasks of memory search is whether items receive varied mappings (targets and distractors switch roles across trials) or consistent mappings (targets and distractors never switch roles). The type of mapping often produces markedly different performance patterns, but formal memory-based models that account quantitatively for detailed aspects of the results have not yet been developed and evaluated. Experiments were conducted to test a modern exemplar-retrieval model on its ability to account for memory-search performance involving a wide range of memory-set sizes in both varied-mapping (VM) and consistent-mapping (CM) probe-recognition tasks. The model formalized the idea that both familiarity-based and categorization-based processes operate. The model was required to fit detailed response-time (RT) distributions of individual, highly practiced subjects. A key manipulation involved the repetition of negative probes across trials. This manipulation produced a dramatic dissociation: False-alarm rates increased and correct-rejection RTs got longer in VM, but not in CM. The qualitative pattern of results and modeling analyses provided evidence for a strong form of categorization-based processing in CM, in which observers made use of the membership of negative probes in the "new" category to make old-new recognition decisions.

Integrating Categorization and Decision-Making.

Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the subsequent decision stage, while the quantum model produced interference effect by superposition, measurement, and quantum entanglement.

Modeling within-session dynamics of categorical and item-memory mechanisms in pigeons.

Past studies have shown that pigeons can learn complex categories and can also remember large numbers of individual objects. In recent work, Cook et al. Psychonomic Bulletin & Review, 28, 548-555, (2021) provided evidence that pigeons may use a dynamic combination of both category-based information and item-specific memorization to solve a categorical variation of the mid-session reversal (MSR) task, which is an influential task for exploring the nature of temporally organized behaviors in animals. To provide greater insight into these pigeons' behaviors, in this article we developed and investigated different computational models and their variations to account for these data. Of these, two models emerged as good candidates. One was a multinomial-processing-tree categorization/memory model, formalizing the two-process mechanism initially proposed by Cook et al. Psychonomic Bulletin & Review, 28, 548-555, (2021). The second was a new object/time-coding model, which posits the storage of object-specific memories with an additional within-session time code and assumes that a basic stimulus generalization process underlies the pigeons' choice behavior. Both provided high-quality fits to the published sets of training and transfer data collected in the categorical MSR task. These computational efforts give deeper insights into the theoretical mechanisms underlying the temporal and sequential structure of behavior in animals and stimulate future empirical research further revealing the organization of the pigeons' cognitive processes.

A power-law model of psychological memory strength in short- and long-term recognition.

A classic law of cognition is that forgetting curves are closely approximated by power functions. This law describes relations between different empirical dependent variables and the retention interval, and the precise form of the functional relation depends on the scale used to measure each variable. In the research reported here, we conducted a recognition task involving both short- and long-term probes. We discovered that formal memory-strength parameters from an exemplar-recognition model closely followed a power function of the lag between studied items and a test probe. The model accounted for rich sets of response time (RT) data at both individual-subject and individual-lag levels. Because memory strengths were derived from model fits to choices and RTs from individual trials, the psychological power law was independent of the scale used to summarize the forgetting functions. Alternative models that assumed different functional relations or posited a separate fixed-strength working memory store fared considerably worse than the power-law model did in predicting the data.

Exemplar-model account of categorization and recognition when training instances never repeat.

In a novel version of the classic dot-pattern prototype-distortion paradigm of category learning, Homa et al. (2019) tested a condition in which individual training instances never repeated, and observed results that they claimed severely challenged exemplar models of classification and recognition. Among the results was a dissociation in which participants classified transfer items with high accuracy in the no-repeat condition, yet in old-new recognition tests showed no ability to discriminate between old and new items of the same level of distortion from the prototype. In addition, speed of classification learning was no faster in a condition in which a small set of training instances was repeated continuously compared with the no-repeat condition. Here we show through computer-simulation modeling that exemplar models naturally capture the classification-recognition dissociation in the no-repeat condition, as well as a wide variety of other qualitative effects reported by Homa et al. (2019). We also conduct new conceptual-replication experiments to investigate their reported null effect of repeated versus nonrepeated training instances on speed of classification learning. In contrast to Homa et al. (2019) we find that speed of learning is substantially faster in the repeat condition than in the no-repeat condition, precisely as exemplar models predict. The exemplar model also captures a wide variety of transfer effects observed following the completion of category learning, including the classification-recognition dissociation observed across the repeat and no-repeat conditions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Contrasting exemplar and prototype models in a natural-science category domain.

A classic issue in the cognitive psychology of human category learning has involved the contrast between exemplar and prototype models. However, experimental tests to distinguish the models have relied almost solely on use of artificially-constructed categories composed of simplified stimuli. Here we contrast the predictions from the models in a real-world natural-science category domain-geologic rock types. Previous work in this domain used a set of complementary methods, including multidimensional scaling and direct dimension ratings, to derive a high-dimensional feature space in which the rock stimuli are embedded. The present work compares the category-learning predictions of exemplar and prototype models that make reference to this derived feature space. The experiments include conditions that should be favorable to prototype abstraction, including use of multiple large-size categories, delayed transfer testing, and real-world category structures. Nevertheless, the results of the qualitative and quantitative model comparisons point toward the exemplar model as providing a far better account of the observed results. Evidence is also provided that participants do not rely on all-or-none rote memories for the stored exemplars but rather use remembered exemplars as a basis for generalizing to novel transfer items from the learned categories. Limitations and directions of future work are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Effects of feature highlighting and causal explanations on category learning in a natural-science domain.

Teaching natural-science categories is highly challenging because the objects in such categories are composed of numerous complex dimensions that need to be perceived, evaluated, and integrated. Furthermore, the boundaries separating such categories are often fuzzy. A technique that has been proposed and investigated for enhancing the teaching of natural-science categories is feature highlighting, in which diagnostic features for identifying category members are explicitly described and illustrated. Using rock classification in geology as an example target domain, the present study further investigated the potential benefits of feature highlighting and also of providing causal explanations for the highlighted features. The authors found that feature highlighting did not always lead to improved generalization to novel members of the taught categories. However, robust beneficial effects were seen when the categories were relatively confusable ones and the stated diagnostic features were highly valid for distinguishing among the categories. Finally, at least under the present conditions, supplementing the highlighted features with causal explanations of the reasons for their occurrence did not further enhance the participants' rock-classification learning and generalization. Although the teaching of causal explanations is fundamental to science education, clear evidence that causal explanations enhance classification-learning per se in this domain remains to be demonstrated. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Re-evaluating dissociations between implicit and explicit category learning: an event-related fMRI study.

Recent fMRI studies have found that distinct neural systems may mediate perceptual category learning under implicit and explicit learning conditions. In these previous studies, however, different stimulus-encoding processes may have been associated with implicit versus explicit learning. The present design was aimed at decoupling the influence of these factors on the recruitment of alternate neural systems. Consistent with previous reports, following incidental learning in a dot-pattern classification task, participants showed decreased neural activity in occipital visual cortex (extrastriate region V3, BA 19) in response to novel exemplars of a studied category compared to members of a foil category, but did not show this decreased neural activity following explicit learning. Crucially, however, our results show that this pattern was primarily modulated by aspects of the stimulus-encoding instructions provided at the time of study. In particular, when participants in an implicit learning condition were encouraged to evaluate the overall shape and configuration of the stimuli during study, we failed to find the pattern of brain activity that has been taken to be a signature of implicit learning, suggesting that activity in this area does not uniquely reflect implicit memory for perceptual categories but instead may reflect aspects of processing or perceptual encoding strategies.

Modeling short- and long-term memory contributions to recent event recognition.

Participants gave recognition judgments for short lists of pictures of everyday objects. Pictures in a given list were an equal mixture of three types that varied according to the way they were used as targets and foils earlier in the same session. Under consistent-mapping (CM), targets and foils never switch roles; under varied-mapping (VM), targets and foils switch roles randomly across trials; whereas all-new (AN) items are novel on each trial of the experiment. Past research has shown that markedly enhanced performance occurs in CM conditions, leading to conclusions that item-response learning takes place in CM, perhaps automatically. However, almost all past research has compared CM, VM, and AN performance in between-blocks designs in which participants may adopt different cognitive strategies and criterion settings across the conditions. The present mixed-list design holds constant the strategy and criterion settings that are used for CM, VM, and AN items, and produced patterns of performance dramatically different than those observed in pure-list control conditions. We develop an extended version of an exemplar-based random-walk model of probe recognition to account for the major qualitative effects in the data. The data and the modeling provide evidence for strong item-response learning for CM foils but weak item-response learning for CM targets. We consider possible explanations for these effects in our General Discussion. (PsycInfo Database Record (c) 2021 APA, all rights reserved).