Neural Mechanisms of Individual Differences in Working Memory Capacity: Observations From Functional Neuroimaging Studies

2017 ◽  
Vol 26 (4) ◽  
pp. 335-345 ◽  
Author(s):  
Takehiro Minamoto ◽  
Hiroyuki Tsubomi ◽  
Naoyuki Osaka
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Fluid Intelligence ◽  
Functional Neuroimaging ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Individual Variability ◽  
Neural Mechanisms ◽  
Related Activity ◽  
Intrinsic Connectivity

Working memory capacity (WMC) indicates an individual’s capability of executive attentional control, which is thought to be critical for general fluid intelligence. Individual variability in WMC has been attributed to the function of the lateral prefrontal cortex (lPFC); however, it is still less clear how the lPFC contributes to individual differences in WMC. Referring to functional neuroimaging studies, we consider three possible neural mechanisms. First, greater task-related activity of the lPFC predicts higher WMC across tasks. Second, a specific task-related functional connectivity also predicts higher WMC. The lPFC consistently forms a part of the connectivity while the coupled region varies depending on tasks. Thus, WMC is reflected by not a fixed but flexible connectivity regulated by the lPFC. Third, distinctive intrinsic connectivity even during resting state is also responsible for individual differences in WMC, with the lPFC seated at a critical hub within the network. These three neural mechanisms differentially contribute to WMC, and therefore, complementarily explain individual differences in WMC.

scholarly journals Is baseline pupil size related to cognitive ability? Yes (under proper lighting conditions).

2020 ◽  
Author(s):  
Jason S. Tsukahara ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Pupil Size ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Functional Organization ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Lighting Conditions

We found that individual differences in baseline pupil size correlated with fluid intelligence and working memory capacity. Larger pupil size was associated with higher cognitive ability. However, other researchers have not been able to replicate our 2016 finding – though they only measured working memory capacity and not fluid intelligence. In a reanalysis of Tsukahara et al. (2016) we show that reduced variability on baseline pupil size will result in a higher probability of obtaining smaller and non-significant correlations with working memory capacity. In two large-scale studies, we demonstrated that reduced variability in baseline pupil size values was due to the monitor being too bright. Additionally, fluid intelligence and working memory capacity did correlate with baseline pupil size except in the brightest lighting conditions. Overall, our findings demonstrated that the baseline pupil size – working memory capacity relationship was not as strong or robust as that with fluid intelligence. Our findings have strong methodological implications for researchers investigating individual differences in task-free or task-evoked pupil size. We conclude that fluid intelligence does correlate with baseline pupil size and that this is related to the functional organization of the resting-state brain through the locus coeruleus-norepinephrine system.

Individual Differences in Attention Control

2020 ◽  
pp. 175-211
Author(s):  
Cody A. Mashburn ◽  
Jason S. Tsukahara ◽  
Randall W. Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Executive Attention ◽  
Attention Theory ◽  
Attentional Resources ◽  
The Relationship

This chapter outlines the executive attention theory of higher-order cognition, which argues that individual differences in the ability to maintain information in working memory and disengage from irrelevant information is inextricably linked to variation in the ability to deploy domain-free attentional resources in a goal-directed fashion. It also summarizes recent addendums to the theory, particularly regarding the relationship between attention control, working memory capacity, and fluid intelligence. Specifically, the chapter argues that working memory capacity and fluid intelligence measures require different allocations of the same attentional resources, a fact which accounts for their strong correlation. At various points, it addresses theoretical alternatives to the executive attention theory of working memory capacity and empirical complications of the study of attention control, including difficulties deriving coherent attention control latent factors.

scholarly journals Binding Costs in Processing Efficiency as Determinants of Cognitive Ability

2021 ◽  
Vol 9 (2) ◽  
pp. 18
Author(s):  
Benjamin Goecke ◽  
Florian Schmitz ◽  
Oliver Wilhelm
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Reaction Time ◽  
Fluid Intelligence ◽  
Task Complexity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Tasks ◽  
General Factor ◽  
Elementary Cognitive Tasks

Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a person’s ability to establish and maintain temporary bindings (e.g., relations between items or relations between items and their context), we manipulated binding requirements (i.e., 2, 4, and 6 relations) in three choice reaction time paradigms (i.e., two comparison tasks, two change detection tasks, and two substitution tasks) measuring mental speed. Response time distributions of 115 participants were analyzed with the diffusion model. Higher binding requirements resulted in generally reduced efficiency of information processing, as indicated by lower drift rates. Additionally, we fitted bi-factor confirmatory factor analysis to the elementary cognitive tasks to separate basal speed and binding requirements of the employed tasks to quantify their specific contributions to working memory capacity, as measured by Recall−1-Back tasks. A latent factor capturing individual differences in binding was incrementally predictive of working memory capacity, over and above a general factor capturing speed. These results indicate that the relation between reaction time tasks and working memory capacity hinges on the complexity of the reaction time tasks. We conclude that binding requirements and, therefore, demands on working memory capacity offer a satisfactory account of task complexity that accounts for a large portion of individual differences in ability.

scholarly journals Individual differences in working memory capacity, attention control, fluid intelligence, and pupillary measures of arousal

2021 ◽  
Author(s):  
Matthew Kyle Robison ◽  
Gene Arnold Brewer
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Abilities ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Future Research ◽  
Control Fluid ◽  
Thought Probes

The present study examined individual differences in three cognitive abilities: attention control (AC), working memory capacity (WMC), and fluid intelligence (gF) as they relate the tendency to experience task-unrelated thoughts (TUTs) and the regulation of arousal. Cognitive abilities were measured with a battery of nine laboratory tasks, TUTs were measured via thought probes inserted into two tasks, and arousal regulation was measured via pupillometry. Recent theorizing (Robison & Unsworth, 2017a) suggests that one reason why some people experience relatively frequent TUTs and relatively poor cognitive performance - especially AC and WMC - is that they exhibit dysregulated arousal. Here, we examined how arousal regulation might predict both AC and WMC, but also higher-order cognitive abilities like gF. Further, we examine direct and indirect associations with these abilities via a mediating influence of TUT. Participants who reported more TUTs also tended to exhibit poorer AC, lower WMC, and lower gF. Arousal dysregulation correlated with more TUTs and lower AC. However there was no direct correlation between arousal regulation and WMC, nor between arousal regulation and gF. Rather, the associations between arousal regulation, WMC, and gF were indirect via TUT. We discuss the implications of the results in light of the arousal regulation theory of individual differences and directions for future research.

scholarly journals Emotion-related impulsivity moderates the cognitive interference effect of smartphone availability on working memory

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Natale Canale ◽  
Alessio Vieno ◽  
Mattia Doro ◽  
Erika Rosa Mineo ◽  
Claudia Marino ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Task Performance ◽  
Interference Effect ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Negative Urgency ◽  
Cognitive Interference ◽  
Silent Mode

AbstractAlthough recent studies suggest that the mere presence of a smartphone might negatively impact on working memory capacity, fluid intelligence, and attentional processes, less is known about the individual differences that are liable to moderate this cognitive interference effect. This study tested whether individual differences in emotion-related impulsivity traits (positive urgency and negative urgency) moderate the effect of smartphone availability on cognitive performance. We designed an experiment in which 132 college students (age 18–25 years) completed a laboratory task that assessed visual working memory capacity in three different conditions: two conditions differing in terms of smartphone availability (smartphone turned off and visible, smartphone in silent mode and visible) and a condition in which the smartphone was not available and was replaced by a calculator (control condition). Participants also completed self-reports that assessed their thoughts after the task performance, positive/negative urgency, and problematic smartphone use. The results showed that participants with higher positive urgency presented increased cognitive interference (reflected by poorer task performance) in the “silent-mode smartphone” condition compared with participants in the “turned-off smartphone” condition. The present study provides new insights into the psychological factors that explain how smartphone availability is liable to interfere with high-level cognitive processes.

scholarly journals A toolbox approach to improving the measurement of attention control

2019 ◽  
Author(s):  
Chris Draheim ◽  
Jason S. Tsukahara ◽  
Jessie Martin ◽  
Cody Mashburn ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Reaction Time ◽  
Fluid Intelligence ◽  
Empirical Test ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
The Individual ◽  
Unitary Concept

Cognitive tasks that produce reliable and robust effects at the group level often fail to yield reliable and valid individual differences. An ongoing debate among attention researchers is whether conflict resolution mechanisms are task-specific or domain-general, and the lack of correlation between most attention measures seems to favor the view that attention control is not a unitary concept. We have argued that the use of difference scores, particularly in reaction time, is the primary cause of null and conflicting results at the individual differences level, and that methodological issues with existing tasks preclude making strong theoretical conclusions. The present article is an empirical test of this view in which we used a toolbox approach to develop and validate new tasks hypothesized to reflect attention processes. Here, we administered existing, modified, and new attention tasks to over 400 subjects (final N = 396). Compared to the traditional Stroop and flanker tasks, performance on the accuracy-based measures was more reliable, had stronger intercorrelations, formed a more coherent latent factor, and had stronger associations to measures of working memory capacity and fluid intelligence. Further, attention control fully accounted for the relationship between working memory capacity and fluid intelligence. These results show that accuracy-based tasks can be better suited to individual differences investigations than traditional reaction time tasks, particularly when the goal is to maximize prediction. We conclude that attention control is a unitary concept.

scholarly journals Binding Costs in Processing Efficiency as Determinants of Cognitive Ability

2020 ◽  
Author(s):  
Benjamin Goecke ◽  
Florian Schmitz ◽  
Oliver Wilhelm
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Fluid Intelligence ◽  
Task Complexity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Tasks ◽  
General Factor ◽  
Elementary Cognitive Tasks

Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a person’s ability to establish and maintain temporary bindings (e.g., relations between items or relations between items and their context), we manipulated binding requirements (i.e., 2, 4, and 6 relations) in three choice reaction time paradigms (i.e., two comparison tasks, two change-detection tasks, and two substitution tasks) measuring mental speed. Response time distributions of N = 115 participants were analyzed with the diffusion model. Higher binding requirements resulted in generally reduced efficiency of information processing, as indicated by lower drift rates. Additionally, we fitted bi-factor confirmatory factor analysis to the elementary cognitive tasks to separate basal speed and binding requirements of the employed tasks to quantify their specific contributions to working memory capacity, as measured by Recall-1-Back tasks. A latent factor capturing individual differences in binding was incrementally predictive of working memory capacity, over and above a general factor capturing speed. These results indicate that the theory-driven task complexity manipulation in terms of binding requirements moderated the relation of mental speed tasks with cognitive ability in the predicted way. We conclude that binding requirements and, therefore, demands on working memory capacity offer a satisfactory account of task complexity that accounts for a large portion of individual differences in ability.

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