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.     

Working memory supports inference learning just like classification learning

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.     

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.     

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.     

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.     

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.     

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.     

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.     

Category induction in autism: Slower,perhaps different,but certainly possible

Available studies on categorization in autism indicate possibly intact category formation, performed through atypical processes. Category learning was investigated in 16 high-functioning autistic and 16 IQ-matched nonautistic participants, using a category structure that could generate a conflict between the application of a rule and exemplar memory. Same–different and matching-to-sample tasks allowed us to verify discrimination abilities for the stimuli to be used in category learning. Participants were then trained to distinguish between two categories of imaginary animals, using categorization tests early in the training and at the end (160 trials). A recognition test followed, in order to evaluate explicit exemplar memory. Similar discrimination performance was found in control tasks for both groups. For the categorization task, autistic participants did not use any identifiable strategy early in the training, but used strategies similar to those of the nonautistic participants by the end, with the same level of accuracy. Memory for the exemplars was poor in both groups. Our findings confirm that categorization may be successfully performed by autistics, but may necessitate longer exposure to material, as the top-down use of rules may be only secondary to a guessing strategy in autistics.     

Improvement in working memory is not related to increased intelligence scores

The acknowledged high relationship between working memory and intelligence suggests common underlying cognitive mechanisms and, perhaps, shared biological substrates. If this is the case, improvement in working memory by repeated exposure to challenging span tasks might be reflected in increased intelligence scores. Here we report a study in which 288 university undergraduates completed the odd numbered items of four intelligence tests on time 1 and the even numbered items of the same tests one month later (time 2). In between, 173 participants completed three sessions, separated by exactly one week, comprising verbal, numerical, and spatial short-term memory (STM) and working memory (WMC) tasks imposing high processing demands (STM–WMC group). 115 participants also completed three sessions, separated by exactly one week, but comprising verbal, numerical, and spatial simple speed tasks (processing speed, PS, and attention, ATT) with very low processing demands (PS-ATT group). The main finding reveals increased scores from the pre-test to the post-test intelligence session (more than half a standard deviation on average). However, there was no differential improvement on intelligence between the STM-WMC and PS-ATT groups.     

Predictors of multitasking performance in a synthetic work paradigm

This study investigated determinants of success in a ‘synthetic work’ task designed to reflect the requirement for multitasking that is common to many occupations. Participants were administered tests of working memory capacity (WMC) and processing speed (PS), and they reported experience with videogames, a type of activity presumed to involve multitasking. Results revealed that WMC was a strong predictor of multitasking in a ‘non‐emergency’ condition when the pace of the tasks was relatively slow, whereas PS was a weaker predictor. Additionally, there was evidence for the incremental validity of videogame experience (VGE), consistent with the possibility that multitasking is supported by a general, trainable skill. Finally, individual differences in strategy use accounted for a large proportion of the variance in multitasking, above and beyond other predictor variables, and WMC predicted use of an effective strategy. Copyright © 2009 John Wiley & Sons, Ltd.     

Diversion of attention in everyday concept learning: Identification in the service of use

Many people tend to believe that natural categories have perfectly predictive defining features. They do not easily accept the family resemblance view that the features characteristic of a category are not individually sufficient to predict the category. However, common category-learning tasks do not produce this simpler-than-it-is belief. If there is no simple classification principle in a task, the participants know that fact and can report it. We argue that most category-learning tasks in which family resemblance categories are used fail to produce the everyday simpler-than-it-is belief because they encourage analysis of identification criteria during training. To simulate the learning occurring in many natural circumstances, we developed a procedure in which participants' analytic activity is diverted from the way in which the stimuli are identified to the use to which the stimuli will be put. Finally, we discuss the prevalence of this diverted analysis in everyday categorization.     

工作记忆容量对创造性思维的预测

创造性思维(CT)是人类高级认知的重要组成部分,而工作记忆容量(WMC)是个体认知功能的重要指标,对于WMC能否预测CT存在不同的观点。本文先从正向预测、负向预测和不能预测的角度分类总结了相关研究证据,然后用游乐园模型(APT)、双通道模型(DPCM)和注意控制理论(CAT)对不同结果进行解释,分析了以往研究可能存在的问题,包括测量WMC和测量CT的任务类型不匹配、测量CT的领域特殊性问题、忽略其他认知能力差异以及未考虑智力、人格等因素对二者关系的影响等。最后,结合已有问题与当前的研究重点对未来研究进行了相应的展望。     

High working memory capacity does not always attenuate distraction: Bayesian evidence in support of the null hypothesis

Individual differences in working memory capacity (WMC) predict individual differences in basically all tasks that demand some form of cognitive labor, especially if the persons conducting the task are exposed to distraction. As such, tasks that measure WMC are very useful tools in individual-differences research. However, the predictive power of those tasks, combined with conventional statistical tools that cannot support the null hypothesis, also makes it difficult to study the limits of that power. In this article, we review studies that have failed to find a relationship between WMC and effects of auditory distraction on visual–verbal cognitive performance, and use meta-analytic Bayesian statistics to test the null hypothesis. The results favor the assumption that individual differences in WMC are, in fact, not (always) related to the magnitude of distraction. Implications for the nature of WMC are discussed.     

When high working memory capacity is and is not beneficial for predicting nonlinear processes

Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted.     

Strategies in probabilistic categorization: results from a new way of analyzing performance

The "Weather Prediction" task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. Prior studies showed that subjects can improve their performance with training, and that there is considerable individual variation in the strategies subjects use to approach this task. Here, we discuss a recently introduced analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Moreover, learning is shown to be highly nonlinear in probabilistic categorization. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.     

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.     

Knowledge partitioning in categorization: constraints on exemplar models

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified.     

Working memory capacity and mind-wandering during low-demand cognitive tasks

Individual differences in working memory capacity (WMC) typically predict reduced rates of mind-wandering during laboratory tasks (Randall, Oswald, & Beier, 2014). However, some studies have shown a positive relationship between WMC and mind-wandering during particularly low-demand tasks (Levinson, Smallwood, & Davidson, 2012; Rummel & Boywitt, 2014; Zavagnin, Borella, & De Beni, 2014). More specifically, Baird, Smallwood, and Schooler (2011) found that when individuals with greater WMC do mind-wander, they tend entertain more future-oriented thoughts. This piece of evidence is frequently used to support the context-regulation hypothesis, which states that using spare capacity to think productively (e.g. plan) during relatively simple tasks is indicative of a cognitive system that is functioning in an adaptive manner (Smallwood & Andrews-Hanna, 2013). The present investigation failed to replicate the finding that WMC is positively related to future-oriented off-task thought, which has implications for several theoretical viewpoints.     

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。