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.

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 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.

Understanding the Relationship Between Rationality and Intelligence: A Latent-Variable Approach

2021 ◽  
Author(s):  
Alexander P. Burgoyne ◽  
Cody Mashburn ◽  
Jason S. Tsukahara ◽  
Zach Hambrick ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Latent Variable ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Equation Modeling ◽  
General Intelligence ◽  
Variable Approach ◽  
The Relationship

A hallmark of intelligent behavior is rationality—the disposition and ability to think analytically to make decisions that maximize expected utility or follow the laws of probability, and therefore align with normative principles of decision making. However, the question remains as to whether rationality and intelligence are empirically distinct, as does the question of what cognitive mechanisms underlie individual differences in rationality. In a large sample of participants (N = 331), we used latent variable analyses to assess the relationship between rationality and intelligence. The results indicated that there was a common ability underpinning performance on some, but not all, rationality tests. Latent factors representing rationality and general intelligence were strongly correlated (r = .54), but their correlation fell well short of unity. Indeed, after accounting for variance in performance attributable to general intelligence, rationality measures still cohered on a latent factor. Confirmatory factor analysis indicated that rationality correlated significantly with fluid intelligence (r = .56), working memory capacity (r = .44), and attention control (r = .49). Structural equation modeling revealed that attention control fully accounted for the relationship between working memory capacity and rationality, and partially accounted for the relationship between fluid intelligence and rationality. Results are interpreted in light of the executive attention framework, which holds that attention control supports information maintenance and disengagement in service of complex cognition. We conclude by speculating about factors rationality tests may tap that other cognitive ability tests miss, and outline directions for further research.

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.

scholarly journals How do Individual Differences in Attention Control and Working Memory Capacity Relate to Monitoring Automation?

2019 ◽  
Vol 63 (1) ◽  
pp. 1409-1413
Author(s):  
Cyrus K. Foroughi ◽  
Ericka Rovira ◽  
Kaley Rose ◽  
DaShawn Davis ◽  
Jaritzel J. Jurado ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Positive Relationship ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Automated System ◽  
Significant Positive Relationship ◽  
Operation Span ◽  
Saccade Task

With the proliferation of automated tasks, software, and systems, humans are moving from an active participant in the function of a task to a passive monitor of an automated system that is completing that task. Unfortunately, humans are not well-suited for monitoring roles and there is a need to better understand the factors involved when humans successfully identify when an automated system fails. The goal for this research was to determine whether individual differences in attention control (as measured by the anti-saccade task) and working memory capacity (as measured by the shortened operation span) related to an individual’s ability to detect automation failures. In experiment 1, there was a significant positive relationship ( r = .31) between scores on the anti-saccade task and the number of automation failures that participants detected. In experiment 2, there was a significant positive relationship ( r = .32) between scores on the shortened operation span and the number of automation failures that participants’ detected. The results suggest that certain individuals are better suited for detecting automation failures. Selecting for these individuals may be a fruitful endeavor as automated systems continue to grow across society.

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.

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