Whichever way you choose to categorize,working memory helps you learn

There has been growing interest in the relationship between the capacity of a person's working memory and their ability to learn to categorize stimuli. While there is evidence that working memory capacity (WMC) is related to the speed of category learning, it is unknown whether WMC predicts which strategies people use when there are multiple possible solutions to a categorization problem. To explore the relationship between WMC, category learning, and categorization strategy use, 173 participants completed two categorization tasks and a battery of WMC tasks. WMC predicted the speed of category learning, but it did not predict which strategies participants chose to perform categorization. Thus, WMC does not predict which categorization strategies people use but it predicts how well they will use the strategy they select.     

Whichever way you choose to categorize, working memory helps you learn

There has been growing interest in the relationship between the capacity of a person's working memory and their ability to learn to categorize stimuli. While there is evidence that working memory capacity (WMC) is related to the speed of category learning, it is unknown whether WMC predicts which strategies people use when there are multiple possible solutions to a categorization problem. To explore the relationship between WMC, category learning, and categorization strategy use, 173 participants completed two categorization tasks and a battery of WMC tasks. WMC predicted the speed of category learning, but it did not predict which strategies participants chose to perform categorization. Thus, WMC does not predict which categorization strategies people use but it predicts how well they will use the strategy they select.     

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement.     

An investigation of the role of some person and situation variables in multiple cue probability learning

Making decisions using judgements of multiple non-deterministic indicators is an important task, both in everyday and professional life. Learning of such decision making has often been studied as the mapping of stimuli (cues) to an environmental variable (criterion); however, little attention has been paid to the effects of situation-by-person interactions on this learning. Accordingly, we manipulated cue and feedback presentation mode (graphic or numeric) and task difficulty, and measured individual differences in working memory capacity (WMC). We predicted that graphic presentation, fewer cues, and elevated WMC would facilitate learning, and that person and task characteristics would interact such that presentation mode compatible with the decision maker's cognitive capability (enhanced visual or verbal WMC) would assist learning, particularly for more difficult tasks. We found our predicted main effects, but no significant interactions, except that those with greater WMC benefited to a larger extent with graphic than with numeric presentation, regardless of which type of working memory was enhanced or number of cues. Our findings suggest that the conclusions of past research based predominantly on tasks using numeric presentation need to be reevaluated and cast light on how working memory helps us learn multiple cue–criterion relationships, with implications for dual-process theories of cognition.     

Learning about the internal structure of categories through classification and feature inference

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a “partial inference” condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.     

Heterogeneity in generalized reinforcement learning and its relation to cognitive ability

In this paper, we study the connections between working memory capacity (WMC) and learning in the context of economic guessing games. We apply a generalized version of reinforcement learning, popularly known as the experience-weighted attraction (EWA) learning model, which has a connection to specific cognitive constructs, such as memory decay, the depreciation of past experience, counterfactual thinking, and choice intensity. Through the estimates of the model, we examine behavioral differences among individuals due to different levels of WMC. In accordance with ‘Miller’s magic number’, which is the constraint of working memory capacity, we consider two different sizes (granularities) of strategy space: one is larger (finer) and one is smaller (coarser). We find that constraining the EWA models by using levels (granules) within the limits of working memory allows for a better characterization of the data based on individual differences in WMC. Using this level-reinforcement version of EWA learning, also referred to as the EWA rule learning model, we find that working memory capacity can significantly affect learning behavior. Our likelihood ratio test rejects the null that subjects with high WMC and subjects with low WMC follow the same EWA learning model. In addition, the parameter corresponding to ‘counterfactual thinking ability’ is found to be reduced when working memory capacity is low.     

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement.     

Working memory does not dissociate between different perceptual categorization tasks

Working memory is crucial for many higher level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization. This article reports 2 studies that related people's working memory capacity (WMC) to their learning performance on multiple rule-based and information-integration perceptual categorization tasks. In both studies, structural equation modeling revealed a strong relationship between WMC and category learning irrespective of the requirement to integrate information across multiple perceptual dimensions. WMC was also uniformly related to people's ability to focus on the most task-appropriate strategy, regardless of whether or not that strategy involved information integration. Contrary to the predictions of the multiple systems view of categorization, working memory thus appears to underpin performance in both major classes of perceptual category-learning tasks.     

工作记忆容量与内容相关性对类别学习的影响

类别学习是通过不断地分类练习,学会如何将类别刺激进行归类的过程。采用2(工作记忆容量:高、低)×4(内容相关性:方向、宽度、亮度、控制组)被试间实验设计,通过两个实验探讨工作记忆容量与内容相关性对基于规则类别学习和信息整合类别学习的影响。结果显示:(1)对基于规则类别学习来说,在高工作记忆容量条件下,当关注相关维度时,类别学习的成绩更好;(2)对基于信息整合类别学习来说,不管工作记忆容量如何,只要关注相关维度类别学习的成绩更好。     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information-integration category learning

DeCaro et al. [DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). Individual differences in category learning: Sometimes less working memory capacity is better than more. Cognition, 107(1), 284-294] explored how individual differences in working memory capacity differentially mediate the learning of distinct category structures. Specifically, their results showed that greater working memory capacity facilitates the learning of novel category structures that are verbalisable and discoverable through logical reasoning processes. Conversely, however, greater working memory was shown to impede the learning of novel category structures thought to be non-verbalisable, inaccessible to conscious reasoning and discoverable only through implicit (procedural) learning of appropriate stimulus-category responses. The present paper calls into question the specific nature of the category learning tasks used, in particular their ability to discriminate between different modes of category learning.     

Attention and working memory capacity: insights from blocking, highlighting, and knowledge restructuring

The concept of attention is central to theorizing in learning as well as in working memory. However, research to date has yet to establish how attention as construed in one domain maps onto the other. We investigate two manifestations of attention in category- and cue-learning to examine whether they might provide common ground between learning and working memory. Experiment 1 examined blocking and highlighting effects in an associative learning paradigm, which are widely thought to be attentionally mediated. No relationship between attentional performance indicators and working memory capacity (WMC) was observed, despite the fact that WMC was strongly associated with overall learning performance. Experiment 2 used a knowledge restructuring paradigm, which is known to require recoordination of partial category knowledge using representational attention. We found that the extent to which people successfully recoordinated their knowledge was related to WMC. The results illustrate a link between WMC and representational-but not dimensional-attention in category learning.     

Working memory capacity and categorization: individual differences and modeling

Working memory is crucial for many higher-level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization, and modeling in category learning has thus far been largely uninformed by knowledge about people's memory processes. This article reports a large study (N = 113) that related people's working memory capacity (WMC) to their category-learning performance using the 6 problem types of Shepard, Hovland, and Jenkins (1961). Structural equation modeling revealed a strong relationship between WMC and category learning, with a single latent variable accommodating performance on all 6 problems. A model of categorization (the Attention Learning COVEring map, ALCOVE; Kruschke, 1992) was fit to the individual data and a single latent variable was sufficient to capture the variation among associative learning parameters across all problems. The data and modeling suggest that working memory mediates category learning across a broad range of tasks.     

Classification versus inference learning contrasted with real-world categories

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories—what each category is like—while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.     

两种学习模式下类别学习的结果：原型和样例

利用“学习－迁移”的任务范式和单一特征类别判断技术,探讨了分类和推理两种类别学习模式的结果,比较了两种学习模式的效果和策略。研究表明：两种学习模式产生了不同的结果,分类学习的结果是样例,推理学习的结果是原型;在学习效果方面,分类学习比推理学习在达标比例上更高,但在进度上差异不显著;在策略运用方面,分类学习比推理学习更快地使用单维度策略,而在高水平策略的运用上,两者差异不显著     

Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning, classification and inference learning, in terms of sensitivity to diagnostic and prototypical information. Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic, but prototypical, features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic, prototypical, features. Discussion focuses on aspects of the 2 learning tasks that might lead to this differential sensitivity and the implications for learning real-world categories.     

Individual differences in category learning: sometimes less working memory capacity is better than more

We examined whether individual differences in working memory influence the facility with which individuals learn new categories. Participants learned two different types of category structures: rule-based and information-integration. Successful learning of the former category structure is thought to be based on explicit hypothesis testing that relies heavily on working memory. Successful learning of the latter category structure is believed to be driven by procedural learning processes that operate largely outside of conscious control. Consistent with a widespread literature touting the positive benefits of working memory and attentional control, the higher one’s working memory, the fewer trials one took to learn rule-based categories. The opposite occurred for information-integration categories - the lower one’s working memory, the fewer trials one took to learn this category structure. Thus, the positive relation commonly seen between individual differences in working memory and performance can not only be absent, but reversed. As such, a comprehensive understanding of skill learning - and category learning in particular - requires considering the demands of the tasks being performed and the cognitive abilities of the performer.     

任务数量与工作记忆容量对交错学习的影响

研究以分子结构式为材料,让被试在单任务或双任务条件下以集中呈现和交错呈现的方式进行学习,并通过操作广度、旋转广度和对称广度任务测量工作记忆容量（working memory capacity, WMC）,探究在基于规则类别学习中交错呈现优势的稳定性。结果显示,交错呈现优势仅体现在文科生中;而对于理科生,集中呈现效果较佳。另外,任务数量和WMC均不影响交错呈现优势,表明交错呈现优势稳定存在于单任务和双任务条件中,且不同WMC的个体均能从交错学习获益。     

Executive attention and task switching in category learning: Evidence for stimulus-dependent representation

One class of multiple-system models of category learning posits that within a single category-learning task people can learn to utilize different systems with different category representations to classify different stimuli. This is referred to as stimulus-dependent representation (SDR). The use of SDR implies that learners switch from subtask to subtask as trials demand. Thus, the use of SDR can be assessed via slowed response times, following a representation switch. Additionally, the use of SDR requires control of executive attention to keep inactive representations from interfering with the current response. Subjects were given a category learning task composed of one- and two-dimensional substructures. Control of executive attention was measured using a working memory capacity (WMC) task. Subjects most likely to be using SDR showed greater slowing of responses following a substructure switch and a greater correlation between learning performance and WMC. These results provide support for the principle of SDR in category learning and the reliance of SDR on executive attention.     

类别学习中的分类和推理

该文介绍了类别学习中的分类和推理两种任务,并从学习的条件、过程、结果和发展等方面的归纳了当前研究的最新进展。表明了类别的分类学习和推理学习有相同的形式,但学习的信息处理过程和学习的结果不同。分类学习关注类别间的区分性信息,更可能是样例学习结果;推理学习更为关注单个类别内部的共同性信息,更可能是原型学习结果。这方面的结论强化了基于解释的观点。