Costs and benefits of automatization in category learning of ill-defined rules

Individual and age differences in block-by-block dynamics of category learning strategies

The present experiment examined individual and age differences in the dynamics of category learning strategies. Participants learned categories determined by a disjunctive rule with relational features through a feedback training procedure. During training, participants responded to strategy probes following each block to provide online assessment of the extent to which rule- and exemplar-based strategies were used throughout the training period. We introduced this measure as an alternative to model-based approaches to assessing individual differences in strategy use during training. Following training, participants classified ambiguous transfer objects that were assumed to distinguish between earlier use of rule- and exemplar-based learning strategies. We included this measure to obtain a relatively objective index of strategy use during training. Next, participants provided global ratings of their use of rule- and exemplar-based strategies during training. Results showed that strategy preferences expressed on the final training block predicted categorisation of ambiguous transfer objects better than global strategy reports. In addition, we utilised the block-by-block strategy reports to investigate the dynamics of learners’ strategy preferences over the course of training. The findings revealed greater fluidity in strategy preferences for both younger and older adults than has been previously documented in the category learning literature. The novel block-by-block strategy reports in conjunction with the transfer-based approach allowed for a more nuanced examination of individual and age differences in strategy use and categorisation performance.

Stimulus modality influences the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning

This study aimed to investigate whether stimulus modality influenced the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning and whether the use of the two strategies was supported by shared or separate neural substrates. To address these issues, we combined behavioral and fNIRS methods in a modified prototype distortion task in which each category member has one rule feature and ten similarity features, and each type of feature can be presented in either the visual modality or the auditory modality. The results in Experiment 1 revealed that the learning effect in the "auditory rule-visual similarity" condition was the highest among all four conditions; further analysis revealed that in the "auditory rule-visual similarity" condition, the number of participants who used the rule-based strategy was more than the number of participants who used the similarity-based strategy, and the learning effect was always much higher for the rule-based strategy than for the similarity-based strategy. The behavioral results in Experiment 2 replicated the main findings in Experiment 1, and the fNIRS results showed that the use of the visual rule-based strategy was mediated by the dorsolateral prefrontal cortex, whereas the use of the auditory similarity-based strategy mainly engaged in the superior temporal gyrus, and the use of the visual similarity-based strategy mainly engaged in the inferior temporal gyrus. The results in Experiment 3 revealed that when the stimuli had only one type of feature, the visual rule rather than the auditory rule was learned more easily. The results provide new evidence that the stimulus modality can influence the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning and that the use of the two types of strategies is supported by separate neural substrates both in the auditory modality and the visual modality.

Occipitotemporal representations reflect individual differences in conceptual knowledge

Through selective attention, decision-makers can learn to ignore behaviorally irrelevant stimulus dimensions. This can improve learning and increase the perceptual discriminability of relevant stimulus information. Across cognitive models of categorization, this is typically accomplished through the inclusion of attentional parameters, which provide information about the importance assigned to each stimulus dimension by each participant. The effect of these parameters on psychological representation is often described geometrically, such that perceptual differences over relevant psychological dimensions are accentuated (or stretched), and differences over irrelevant dimensions are down-weighted (or compressed). In sensory and association cortex, representations of stimulus features are known to covary with their behavioral relevance. Although this implies that neural representational space might closely resemble that hypothesized by formal categorization theory, to date, attentional effects in the brain have been demonstrated through powerful experimental manipulations (e.g., contrasts between relevant and irrelevant features). This approach sidesteps the role of idiosyncratic conceptual knowledge in guiding attention to useful information sources. To bridge this divide, we used formal categorization models, which were fit to behavioral data, to make inferences about the concepts and strategies used by individual participants during decision-making. We found that when greater attentional weight was devoted to a particular visual feature (e.g., "color"), its value (e.g., "red") was more accurately decoded from occipitotemporal cortex. We also found that this effect was sufficiently sensitive to reflect individual differences in conceptual knowledge, indicating that occipitotemporal stimulus representations are embedded within a space closely resembling that formalized by classic categorization theory. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Does practice in category learning increase rule use or exemplar use-or both?

Categorization research has demonstrated the use of both rules and remembered exemplars in classification, although there is disagreement over whether learners shift from one to the other or use both strategies simultaneously. Theoretical arguments can motivate predictions for both rule use and exemplar use increasing with more practice. We describe a single large experiment (n = 190) that manipulated the number of training items (category size), the number of presentations of each training item, and the similarity between the training and the transfer stimuli in order to discover when rules and exemplars are most likely to be used. Results showed that rules and exemplars both influenced classification and that exemplars were used more often with smaller categories, with more training on items, and when test items were similar to training items. There was no consistent evidence of a shift from rule-based to exemplar-based categorization with more learning. Importantly, we found a number of conditions in which rules and exemplars were both used, even within individual participants. We discuss our results in terms of hybrid models of classification.

Learning from observation, feedback, and intervention in linear and non-linear task environments

This multiple-cue judgment study investigates whether we can manipulate the judgment strategy and increase accuracy in linear and non-linear cue-criterion environments just by changing the training mode. Three experiments show that accuracy in simple linear additive task environments are improved with feedback training and intervention training, while accuracy in complex multiplicative tasks are improved with observational training. The observed interaction effect suggests that the training mode invites different strategies that are adjusted as a function of experience to the demands from the underlying cue-criterion structure. Thus, feedback and the intervention training modes invite cue abstraction, an effortful but successful strategy in combination with simple linear task structures, and observational training invites exemplar memory processes, a simple but successful strategy in combination with complex non-linear task structures. The study discusses adaptive cognition and the implication of the different training modes across a life span and for clinical populations.

Modelling individual difference in visual categorization

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

The Influence of Presentation Order on Category Transfer

This study of supervised categorization shows how different kinds of category representations are influenced by the order in which training examples are presented. We used the well-studied 5-4 category structure of Medin and Schaffer (1978) , which allows transfer of category learning to new stimuli to be discriminated as a function of rule-based or similarity-based category knowledge. In the rule-based training condition (thought to facilitate the learning of abstract logical rules and hypothesized to produce rule-based classification), items were grouped by subcategories and randomized within each subcategory. In the similarity-based training condition (thought to facilitate associative learning and hypothesized to produce exemplar classification), transitions between items within the same category were determined by their featural similarity and subcategories were ignored. We found that transfer patterns depended on whether the presentation order was similarity-based, or rule-based, with the participants particularly capitalizing on the rule-based order.

Understanding individual differences in representational abstraction: The role of working memory capacity

Several studies have reported differences in categorization strategies among participants: some learn a category by making abstraction across the category members while others use a memorization strategy. Despite the prevalence of these differences, little attention has been paid to investigating what influences some to use an abstraction strategy and others a memorization strategy. The current study had two goals: in a first experiment we investigated whether these differences were stable across time, using the parallel form method often used in psychometric research, and in a second experiment we investigated whether the individual differences in categorization strategy were related to working memory capacity. We used a modelling strategy, in which we not only focused on full abstraction and memorization strategies, but also on intermediate strategies in which some category members are abstracted and others are not. The first study revealed that the individual abstraction strategy of individual participants in two different experiments, performed at different times, correlate significantly, and second study showed that these individual differences were related to the working memory capacity of the participants.

The contribution of temporary storage and executive processes to category learning

Three distinctly different working memory processes, temporary storage, mental shifting and inhibition, were proposed to account for individual differences in category learning. A sample of 213 participants completed a classic category learning task and two working memory tasks that were experimentally manipulated for tapping specific working memory processes. Fixed-links models were used to decompose data of the category learning task into two independent components representing basic performance and improvement in performance in category learning. Processes of working memory were also represented by fixed-links models. In a next step the three working memory processes were linked to components of category learning. Results from modeling analyses indicated that temporary storage had a significant effect on basic performance and shifting had a moderate effect on improvement in performance. In contrast, inhibition showed no effect on any component of the category learning task. These results suggest that temporary storage and the shifting process play different roles in the course of acquiring new categories.