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.

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.

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.

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.

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.

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).

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).

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).

Effects of specific-level versus broad-level training for broad-level category learning in a complex natural science domain.

Category learning is a core component of course curricula in science education. For instance, geology courses teach categorization of rock types. Using the educationally authentic rock categories, the current project examined whether category learning at a broad-level (BL; igneous, sedimentary, and metamorphic rocks) could be enhanced by learning category information at a more specific-level (SL; e.g., diorite under igneous, breccia under sedimentary, etc.). Experiments 1 and 2 showed that SL training was inferior to BL training when participants were required to respond at the BL regardless of whether BL and SL category labels were presented simultaneously during classification training or SL categories were learned initially followed by training on the specific-broad level name associations. However, Experiments 3 and 4 showed that SL training was as good as BL training when the training was more extensive and participants were allowed to respond at the trained level. By considering confusion matrices (i.e., probabilities that instances in a given category was erroneously classified as belonging to other categories), we conjectured that between-SL category similarity, specifically the degree to which similar-looking SL categories belong to the same BL category, is an important factor in determining the efficacy of SL training. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels.

BACKGROUND: Most science categories are hierarchically organized, with various high-level divisions comprising numerous subtypes. If we suppose that one's goal is to teach students to classify at the high level, past research has provided mixed evidence about whether an effective strategy is to require simultaneous classification learning of the subtypes. This past research was limited, however, either because authentic science categories were not tested, or because the procedures did not allow participants to form strong associations between subtype-level and high-level category names. Here we investigate a two-stage response-training procedure in which participants provide both a high-level and subtype-level response on most trials, with feedback provided at both levels. The procedure is tested in experiments in which participants learn to classify large sets of rocks that are representative of those taught in geoscience classes. RESULTS: The two-stage procedure yielded high-level classification performance that was as good as the performance of comparison groups who were trained solely at the high level. In addition, the two-stage group achieved far greater knowledge of the hierarchical structure of the categories than did the comparison controls. CONCLUSION: In settings in which students are tasked with learning high-level names for rock types that are commonly taught in geoscience classes, it is best for students to learn simultaneously at the high and subtype levels (using training techniques similar to the presently investigated one). Beyond providing insights into the nature of category learning and representation, these findings have practical significance for improving science education.

Feature highlighting enhances learning of a complex natural-science category.

Learning naturalistic categories, which tend to have fuzzy boundaries and vary on many dimensions, can often be harder than learning well defined categories. One method for facilitating the category learning of naturalistic stimuli may be to provide explicit feature descriptions that highlight the characteristic features of each category. Although this method is commonly used in textbooks and classrooms, theoretically it remains uncertain whether feature descriptions should advantage learning complex natural-science categories. In three experiments, participants were trained on 12 categories of rocks, either without or with a brief description highlighting key features of each category. After training, they were tested on their ability to categorize both old and new rocks from each of the categories. Providing feature descriptions as a caption under a rock image failed to improve category learning relative to providing only the rock image with its category label (Experiment 1). However, when these same feature descriptions were presented such that they were explicitly linked to the relevant parts of the rock image (feature highlighting), participants showed significantly higher performance on both immediate generalization to new rocks (Experiment 2) and generalization after a 2-day delay (Experiment 3). Theoretical and practical implications are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in which the nature of the training examples is manipulated. The specific domain of inquiry is rock classification in the geologic sciences; the goal is to use the model to search for optimal training examples for teaching the rock categories. On the positive side, the model makes a number of successful predictions: Most notably, compared with conditions involving focused training on small sets of training examples, generalization to novel transfer items is significantly enhanced in a condition in which learners experience a broad swath of training examples from each category. Nevertheless, systematic departures from the model predictions are also observed. Further analyses lead us to the hypothesis that the high-dimensional feature-space representation derived for the rock stimuli (to which the exemplar model makes reference) systematically underestimates within-category similarities. We suggest that this limitation is likely to arise in numerous situations in which investigators attempt to build detailed feature-space representations for naturalistic categories. A low-parameter extended version of the model that adjusts for this limitation provides dramatically improved accounts of performance across the four conditions. We outline future steps for enhancing the current feature-space representation and continuing our goal of using formal psychological models to guide the search for effective methods of teaching science categories.

Correction to: Organized simultaneous displays facilitate learning of complex natural science categories.

The affiliation for Dr. Paulo F. Carvalho is listed incorrectly in this paper, The correct affiliation is Carnegie Mellon University, Pittsburgh, PA, USA.

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.

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.

Tests of an exemplar-memory model of classification learning in a high-dimensional natural-science category domain.

Experiments were conducted in which novice participants learned to classify pictures of rocks into real-world, scientifically defined categories. The experiments manipulated the distribution of training instances during an initial study phase, and then tested for correct classification and generalization performance during a transfer phase. The similarity structure of the to-be-learned categories was also manipulated across the experiments. A low-parameter version of an exemplar-memory model, used in combination with a high-dimensional feature-space representation for the rock stimuli, provided good overall accounts of the categorization data. The successful accounts included (a) predicting how performance on individual item types within the categories varied with the distributions of training examples, (b) predicting the overall levels of classification accuracy across the different rock categories, and (c) predicting the patterns of between-category confusions that arose when classification errors were made. The work represents a promising initial step in scaling up the application of formal models of perceptual classification learning to complex natural-category domains. We discuss further steps for making use of the model and its associated feature-space representation to search for effective techniques of teaching categories in the science classroom. (PsycINFO Database Record

Biased guessing in a complete-identification visual-working-memory task: Further evidence for mixed-state models.

Research is reported that provides evidence for a significant role of mixed states and guessing processes in tasks of visual working memory (VWM). Subjects engaged in a complete-identification VWM task. The stimulus set consisted of 16 colors roughly equally spaced around a color circle. On each trial, a memory-set drawn from the colors was briefly presented, followed by a location probe. Subjects attempted to reproduce the color of the probed item by clicking on the appropriate response button of a discrete color wheel. The key manipulation was to vary payoffs for alternative correct responses across trials. Analysis of the resulting matrices of individual-subject identification-confusion data provided evidence for a systematic guessing process: On trials in which subjects had no memory for the probed stimulus, they guessed with high probability using the high-payoff response. Formal modeling corroborated this interpretation. Mixed-state models that assumed that performance involved a combination of memory-based responding and biased guessing yielded accurate and easy-to-interpret accounts of the identification data; by comparison, variable-resources (VR) models without a guessing state struggled to account for the data, including versions with bias parameters for the high-payoff response. The authors argue that the work adds to recent converging sources of evidence that point to a significant role of discrete, mixed states in VWM. The authors also suggest directions for development of extended VR models with sophisticated knowledge-rich decision rules for the complete-identification task. (PsycINFO Database Record