When and how less is more: reply to Tharp and Pickering

In 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, 284-294] we demonstrated that sometimes less working memory (WM) has its advantages. The lower individuals’ WM, the faster they achieved success on an information-integration (II) category learning task adopted from Waldron and Ashby [Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8, 168-176]. We attributed this success to the inability of lower WM individuals to employ explicit learning strategies heavily reliant on executive control. This in turn, we hypothesized, might push lower WM individuals to readily adopt procedural-based strategies thought to lead to success on the II task. Tharp and Pickering [Tharp, I. J., & Pickering, A. D. (2009). A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information-integration category learning. Cognition] recently questioned whether the II category learning task DeCaro et al. used really reflects procedural learning. In an effort to investigate Tharp and Pickering’s assertions with respect to individual differences in WM, we replicate and extend our previous work, in part by modeling participants’ response strategies during learning. We once again reveal that lower WM individuals demonstrate earlier II learning than their higher WM counterparts. However, we also show that low WM individuals’ initial success is not because of procedural-based responding. Instead, individuals lower in WM capacity perseverate in using simple rule-based strategies that circumvent heavy demands on WM while producing above-chance accuracy.     

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.     

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.     

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。     

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.     

The relationship between working memory capacity and cortical activity during performance of a novel motor task

ObjectivesThis study assessed whether individual differences in working memory capacity influenced verbal-analytical processes when performing a novel motor skill.DesignParticipants performed a tennis-hitting task in two conditions: no pressure and high-pressure.MethodsEighteen young adults participated in the study. EEG coherence between the T3-F3 and T4-F4 regions in the Beta1 and Alpha2 frequencies was recorded during performance in each condition. Verbal and visuo-spatial working memory capacity were assessed using the Automated Working Memory Assessment.ResultsNo differences were found between the two conditions for hitting performance and EEG activity. However, across both conditions, verbal and visuo-spatial working memory were significant predictors of EEG coherence between the T3-F3 and T4-F4 regions in the Beta1 and Alpha2 frequencies. Larger verbal working memory capacity was associated with greater coherence while the opposite trend was observed for visuo-spatial working memory capacity.ConclusionsThese results indicate that larger verbal working memory capacity is associated with a greater tendency to use explicit processes during motor performance, whereas larger visuo-spatial working memory capacity is associated more with implicit processes. The findings are discussed with relevance to the theory of implicit motor learning.     

嵌套范式下视空工作记忆对基于规则类别学习

基于COVIS模型与认知加工阶段假设,通过2个实验探讨嵌套范式下, 视空工作记忆对基于规则类别学习的影响。实验1采用类别学习中嵌套视空工作记忆的范式,结果发现视空工作记忆削弱基于规则类别学习成绩,与COVIS模型预测相一致。实验2则采用视空工作记忆中嵌套类别学习任务的范式,结果却发现视空工作记忆对基于规则类别学习的影响消失。实验结果表明嵌套范式下视空工作记忆的位置影响基于规则类别学习,初步验证了类别学习存在多个认知加工阶段的假设,视空工作记忆主要影响基于规则类别学习中规则的发现和检验阶段。     

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.     

工作记忆对知觉类别学习的影响:问题与构想

人们学习将感知到的客体按照不同的标准或规则分类存储,即类别学习。而工作记忆作为多种认知加工的基础,对类别学习具有重要作用。基于已有研究,分别梳理了言语工作记忆和视空工作记忆对基于规则和信息整合类别结构的影响。目前,执行功能是否影响内隐类别学习系统、工作记忆负荷对内隐外显类别学习系统的影响是否一致还存在争议。此外,工作记忆影响知觉类别学习的认知加工阶段尚不清晰。未来的研究还需明确工作记忆负荷对内隐和外显类别学习的影响,进一步验证工作记忆影响类别学习的认知加工阶段假设的合理性。     

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.     

Neurocognitive Contributions to Motor Skill Learning: The Role of Working Memory

ABSTRACT

Researchers have begun to delineate the precise nature and neural correlates of the cognitive processes that contribute to motor skill learning. The authors review recent work from their laboratory designed to further understand the neurocognitive mechanisms of skill acquisition. The authors have demonstrated an important role for spatial working memory in 2 different types of motor skill learning, sensorimotor adaptation and motor sequence learning. They have shown that individual differences in spatial working memory capacity predict the rate of motor learning for sensorimotor adaptation and motor sequence learning, and have also reported neural overlap between a spatial working memory task and the early, but not late, stages of adaptation, particularly in the right dorsolateral prefrontal cortex and bilateral inferior parietal lobules. The authors propose that spatial working memory is relied on for processing motor error information to update motor control for subsequent actions. Further, they suggest that working memory is relied on during learning new action sequences for chunking individual action elements together.     

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.     

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.     

Individual differences in working memory capacity and resistance to belief bias in syllogistic reasoning

In two experiments, we investigated the possibility that individual differences in working memory capacity (WMC) would provide resistance to belief bias in syllogistic reasoning. In Experiment 1 (N?=?157), participants showed a belief bias effect in that they had longer response times and decreased accuracy on syllogisms with conflict between the validity and believability of the conclusion than on syllogisms with no such conflict. However, this effect did not differ as a function of individual differences in WMC. Experiment 2 (N?=?122) replicated this effect with the addition of decontextualized (i.e., nonsense) syllogisms as a different means of measuring the magnitude of the belief bias effect. Although individual differences in WMC and fluid intelligence were related to better reasoning overall, the magnitude of the belief bias effect was not smaller for participants with greater WMC. The present study offers two novel findings: (a) The belief bias effect is independent of individual differences in WMC and fluid intelligence, and (b) resolving conflict in verbal reasoning is not a type of conflict resolution that correlates with individual differences in WMC, further establishing boundary conditions for the role of WMC in human cognitive processes.     

Monitoring of learning at the category level when learning a natural concept: Will task experience improve its resolution?

Researchers have recently begun to investigate people's ability to monitor their learning of natural categories. For concept learning tasks, a learner seeks to accurately monitor learning at the category level — i.e., to accurately judge whether exemplars will be correctly classified into the appropriate category on an upcoming test. Our interest was in whether monitoring resolution at the category level would improve as participants gain task experience across multiple study-test blocks, as well as within each block. In four experiments, exemplar birds (e.g., American Goldfinch, Cassin's Finch) paired with each family name (e.g., Finch) were studied, and participants made a judgment of learning (JOL) for each exemplar. Of most interest, before and after studying the exemplars, participants made category learning judgments (CLJs), which involved predicting the likelihood of correctly classifying novel birds into each family. Tests included exemplars that had been studied or exemplars that had not been studied (novel). This procedure was repeated for either one or two additional blocks. The relative accuracy of CLJs did not improve across blocks even when explicit feedback was provided, whereas item-by-item JOL accuracy improved across blocks. Category level resolution did improve from pre-study to post-study on an initial block, but it did not consistently increase within later blocks. The stable accuracy of CLJs across blocks poses a theoretical and empirical challenge for identifying techniques to improve people's ability to judge their learning of natural categories.     

Mind–culture interactions: How writing molds mental fluidity in early development

This study investigated intellectual development in 4–7 years old Greek and Chinese children. They were examined on speeded performance, working memory, reasoning, and self-awareness tasks in order to investigate possible effects of learning the Chinese logographic system on possible differences in intellectual development between these ethnic groups. Speeded performance was examined with commonly familiar objects and tasks related to reading (i.e., Latin, Arabic, and Chinese characters). Chinese outperformed Greeks in (1) reading-related processing efficiency tasks but not in common objects (2) spatial but not verbal WM, (3) cognitive, and (4) the self-awareness tasks. Structural equation modeling showed that performance is organized in four systems (i.e., domain-specific problem solving, representational capacity, inference, and consciousness) integrated by g, in both ethnic groups. There were differences between the two ethnicities in the strength of relations between constructs, attributed to Chinese logographic experience. That is, the massive practice in visuo/spatial processing and memory seemed to provide an advantage in the communication between systems of the mind causing increased general cognitive fluidity, expressed in higher intellectual performance among the Chinese.     

Information processing and reasoning with premises that are empirically false: Interference,working memory,and processing speed

In this study, we looked at the contributions of individual differences in susceptibility to interference and working memory to logical reasoning with premises that were empirically false (i.e., not necessarily true). A total of 97 university students were given a sentence completion task for which a subset of stimuli was designed to generate inappropriate semantic activation that interfered with the correct response, a measure of working memory capacity, and a series of logical reasoning tasks with premises that were not always true. The results indicate that susceptibility to interference, as measured by the error rate on the relevant subset of the sentence completion task, and working memory independently account for variation in reasoning performance. The participants who made more errors in the relevant portion of the sentence completion task also showed more empirical intrusions in the deductive reasoning task, even when the effects of working memory were partialed out. Working memory capacity was more clearly related to processes involved in generating uncertainty responses to inferences for which there was no certain conclusion. A comparison of the results of this study with studies of children's reasoning suggests that adults are capable of more selective executive processes than are children. An analysis of latency measures on the sentence completion task indicated that high working memory participants who made no errors on the sentence completion task used a strategy that involved slower processing speed, as compared with participants with similar levels of working memory who did make errors. In contrast, low working memory participants who made no errors on the sentence completion task had relatively shorter reaction times than did comparable participants who did make errors.     

Visual variability affects early verb learning

Research demonstrates that within‐category visual variability facilitates noun learning; however, the effect of visual variability on verb learning is unknown. We habituated 24‐month‐old children to a novel verb paired with an animated star‐shaped actor. Across multiple trials, children saw either a single action from an action category (identical actions condition, for example, travelling while repeatedly changing into a circle shape) or multiple actions from that action category (variable actions condition, for example, travelling while changing into a circle shape, then a square shape, then a triangle shape). Four test trials followed habituation. One paired the habituated verb with a new action from the habituated category (e.g., ‘dacking’ + pentagon shape) and one with a completely novel action (e.g., ‘dacking’ + leg movement). The others paired a new verb with a new same‐category action (e.g., ‘keefing’ + pentagon shape), or a completely novel category action (e.g., ‘keefing’ + leg movement). Although all children discriminated novel verb/action pairs, children in the identical actions condition discriminated trials that included the completely novel verb, while children in the variable actions condition discriminated the out‐of‐category action. These data suggest that – as in noun learning – visual variability affects verb learning and children's ability to form action categories.     

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.