Showing posts with label attentional control. Show all posts
Showing posts with label attentional control. Show all posts

Thursday, January 30, 2025

Research Byte: Individual differences in #workingmemory (Gwm) and #attentionalcontrol (#AC) continue to predict memory #Gl) performance despite extensive learning—#CHC #schoolpsychology


Individual differences in working memory and attentional control continue to predict memory performance despite extensive learning.

Zhao, C., & Vogel, E. K. (2025). Individual differences in working memory and attentional control continue to predict memory performance despite extensive learning. Journal of Experimental Psychology: General. Advance online publication. https://doi.org/10.1037/xge0001728


Abstract

Individual differences in working memory predict a wide range of cognitive abilities. However, little research has been done on whether working memory continues to predict task performance after repetitive learning. Here, we tested whether working memory ability continued to predict long-term memory (LTM) performance for picture sequences even after participants showed massive learning. In Experiments 1–3, subjects performed a source memory task in which they were presented a sequence of 30 objects shown in one of four quadrants and then were tested on each item’s position. We repeated this procedure for five times in Experiment 1 and 12 times in Experiments 2 and 3. Interestingly, we discovered that individual differences in working memory continually predicted LTM accuracy across all repetitions. In Experiment 4, we replicated the stable working memory demands with word pairs. In Experiment 5, we generalized the stable working memory demands model to attentional control abilities. Together, these results suggest that people, instead of relying less on working memory, optimized their working memory and attentional control throughout learning. 
Impact Statement

Working memory ability predicts various cognitive abilities. However, whether its predictive power remains after participants repetitively study the test materials remains unknown. Here, in five experiments with visual and verbal materials, we found that individual differences in working memory and attentional control (WMAC) constantly predicted people’s memory performance even after extensive training of the same materials. Our results provided a new understanding of WMAC, in that learning may better tune participants’ attention and working memory toward task demands, instead of eliminating the reliance on attentional control in performing tasks.

Wednesday, November 13, 2024

Research Byte: Examining #WorkingMemory Training for Healthy Adults—A Second-Order #MetaAnalysis—-#CHC #WJV #Gwm

This meta-analytic review suggests some promise for working memory training programs, although for every slightly positive research synthesis there are multiple other syntheses (and position papers) that suggest that working memory training does not transfer to real world settings or is not effective.  I, being an optimist, am not ready to give up on the idea of working memory interventions to improve intellectual performance, given the central role working memory plays in cognition.  There is probably some kind of individual differences X type of treatment effect interaction.  See McGrew et. al. (2023) for recent psychometric network analysis paper that identifies the working memory-attentional control complex (Gwm-AC) as the most likely “target system” for effective intellectual ability interventions.

Click on image to enlarge for easier reading



Saturday, November 07, 2020

More support for the Gs—>Gwm—>—Gf/ Gc developmental cascade model as per CHC taxonomy

 More support for the developmental cascade model


Speed of processing, control of processing, working memory and crystallized and fluid intelligence: Evidence for a developmental cascade 

Anna Tourva, George Spanoudis
 
Keywords: Fluid intelligence Crystallized intelligence Working memory Speed of processing Executive attention Developmental-cascade model 

A B S T R A C T  

The present study investigated the causal relations among age, speed of processing, control of processing, working memory and intelligence, fluid and crystallized. 158 participants aged from 7 to 18 years old completed a large battery of tests measuring latent factors of speed, control of processing and working memory. Intelligence was assessed using the Wechsler Abbreviated Scale of Intelligence. Structural equation modeling was performed to determine whether there is a cognitive-developmental cascade in which age-related increases in processing speed lead to improvements in control of processing that leads to increases in working memory, and whether improved working memory, in turn, leads to increases in both fluid and crystallized intelligence. Several alternative models of a different cascade order of the above factors were also tested. The results of the present study provide evidence of a cognitive-developmental cascade, confirming that this model describes cognitive development during childhood and adolescence.  

Click images to enlarge.








Sunday, May 10, 2020

Attentional control has indirect effect on Gf via working memory (Gwm)


Another study  supporting attentional control (AC) as having an indirect causal effect on Gf mediated via working memory (Gwm).





Abstract

Human fluid intelligence emerges from the interactions of various cognitive processes. Although some classic models characterize intelligence as a unitary “general ability,” many distinct lines of research have suggested that it is possible to at least partially decompose intelligence into a set of subsidiary cognitive functions. Much of this work has focused on the relationship between intelligence and working memory, and more specifically between intelligence and the capacity-loading aspects of working memory. These theories focus on domain-general processing capacity limitations, rather than limitations specifically linked to working memory tasks. Performance on other capacity-constrained tasks, even those that have typically been given the label of “attention tasks,” may thus also be related to fluid intelligence. We tested a wide range of attention and working memory tasks in 7- to 9-year-old children and adults, and we used the results of these cognitive measures to predict intelligence scores. In a set of 13 measures we did not observe a single “positive manifold” that would indicate a general-ability understanding of intelligence. Instead, we found that a small number of measures were related to intelligence scores. More specifically, we found two tasks that are typically labeled as “attentional measures”, Multiple Object Tracking and
Enumeration, and two tasks that are typically labeled as “working memory” measures, N-back and Spatial Span, were reliably related to intelligence. However, the links between attention and intelligence scores were fully mediated by working memory measures. In contrast, attention scores did not mediate the relations between working memory and intelligence. Furthermore, these patterns were indistinguishable across age groups, indicating ahierarchical cognitive basis of intelligence that is stable from childhood into adulthood.
study

Thursday, November 07, 2019

Individual differences in learning efficiency


Kathleen B. McDermott and Christopher L. Zerr

Abstract 

Most research on long-term memory uses an experimental approach whereby participants are assigned to different conditions, and condition means are the measures of interest. This approach has demonstrated repeatedly that conditions that slow the rate of learning tend to improve later retention. A neglected question is whether aggregate findings at the level of the group (i.e., slower learning tends to improve retention) translate to the level of individual people. We identify a discrepancy whereby—across people—slower learning tends to coincide with poorer memory. The positive relation between learning rate (speed of learning) and retention (amount remembered after a delay) across people is referred to as learning efficiency. A more efficient learner can acquire information faster and remember more of it over time. We discuss potential characteristics of efficient learners and consider future directions for research.

Keywords learning efficiency, individual differences, memory, learning rate, retention

A few select quotes below.  Dr. Joel Schneider and I have written elsewhere that we believe that attentional control (AC; a key mechanism of working memory or Gwm) is a key cognitive mechanism in learning and cognitive functioning.

Learning strategy differences:  Faster learners generate more mediators while learning, and these mediators tend to be both implemented earlier in the learning process and more effective in aiding memory 

Prior knowledge:  However, prior knowledge (or crystallized intelligence) may still promote integration of new information into existing knowledge and improve the efficacy of learning strategies—the more knowledge someone possesses, the richer the set of potential mediators.

Attentional control:  People who are better able to focus their attention are less susceptible to interfering information; further, they more quickly search long-term memory when retrieving information (Unsworth & Spillers, 2010). Neuroimaging evidence suggests that the ability to control one's attention is a potential driver of efficient learning. 


Sunday, July 15, 2018

Excellent conceptual suggestion for organizing mind wandering research



Trends in Cognitive Sciences, June 2018, Vol. 22, No. 6

ABSTRACT

As empirical research on mind-wandering accelerates, we draw attention to an emerging trend in how mind-wandering is conceptualized. Previously articulated definitions of mind-wandering differ from each other in important ways, yet they also maintain overlapping characteristics. This conceptual structure suggests that mind-wandering is best considered from a family-resemblances perspective, which entails treating it as a graded, heterogeneous construct and clearly measuring and describing the specific aspect(s) of mind-wandering that researchers are investigating. We believe that adopting this family-resemblances approach will increase conceptual and methodological connections among related phenomena in the mind-wandering family and encourage a more nuanced and precise understanding of the many varieties of mind-wandering.

Click on image to enlarge



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Friday, November 10, 2017

Research Byte: Is General Intelligence Little More Than the Speed of Higher-Order Processing?

Although a small sample, this is still and interesting study. The results are consistent with the continued nexus of the g, Gf, Gwm, attentional control and speed of higher order processing (especially P300 in ERP’s), white matter tract integrity and the PFIT model of intelligence as well as the recent process overlap theory (POT) of g.

Click on images to enlarge









Article link.

Anna-Lena Schubert, Dirk Hagemann, and Gidon T. Frischkorn Heidelberg University

ABSTRACT

Individual differences in the speed of information processing have been hypothesized to give rise to individual differences in general intelligence. Consistent with this hypothesis, reaction times (RTs) and latencies of event-related potential have been shown to be moderately associated with intelligence. These associations have been explained either in terms of individual differences in some brain-wide property such as myelination, the speed of neural oscillations, or white-matter tract integrity, or in terms of individual differences in specific processes such as the signal-to-noise ratio in evidence accumulation, executive control, or the cholinergic system. Here we show in a sample of 122 participants, who completed a battery of RT tasks at 2 laboratory sessions while an EEG was recorded, that more intelligent individuals have a higher speed of higher-order information processing that explains about 80% of the variance in general intelligence. Our results do not support the notion that individuals with higher levels of general intelligence show advantages in some brain-wide property. Instead, they suggest that more intelligent individuals benefit from a more efficient transmission of information from frontal attention and working memory processes to temporal-parietal processes of memory storage.

Keywords: ERP latencies, event-related potentials, intelligence, processing speed, reaction times



- Posted using BlogPress from my iPad

Thursday, December 29, 2016

Research Byte: A closer look at who "chokes under pressure" - importance of attentional control (AC)

Volume 5, Issue 4, December 2016, Pages 470–477
Working Memory in the Wild: Applied Research in Working Memory

A Closer Look at Who “Chokes Under Pressure”



Highlights

High pressure settings compromise working memory and decrease cognitive performance.
Those with higher working memory show greatest pressure-induced cognitive deficits.
Attentional control alters relation of working memory to performance under pressure.

Previous research has shown that the higher one's working memory capacity, the more likely his/her performance is to be negatively impacted by performance pressure. In the current research we examined potential explanations for this finding by assessing the relation between pressure-induced performance deficits (i.e. “choking under pressure”) in math-based problem solving and individual differences in both working memory (as assessed via complex span tasks) and attentional control (as assessed via two measures from an Eriksen Flanker task). We find higher working memory only relates to “choking under pressure” when individuals were low in attentional control. These results further elucidate the mechanism by which high-pressure scenarios can lead to errors in performance and carry implications for developing effective intervention strategies to prevent poor performance in high-stakes situations.

Tuesday, June 14, 2016

Research byte: The role of attentional control (AC) and working memory in sports performance: A review of recent literature

 
The ever growing body of research re the importance of the construct of attentional control (AC) in all kinds of human performance is, IMHO, one of the most important findings in cognitive psychology during the past few years.  In my opinion, improving AC may be one of the key's to effective brain training/fitness programs.  Also, differences in the AC of individuals has important implications for understanding differences in cognitive functioning.
 
 
Available online 10 June 2016
Target Article

Working Memory, Attentional Control, and Expertise in Sports: A Review of Current Literature and Directions for Future Research

Choose an option to locate/access this article:

The aim of the present review was to investigate the theoretical framework of working memory as it relates to the control of attention in sport and thereby apply cognitive psychological theory to sports, but also use the sports domain to advance cognitive theory. We first introduce dual-process theories as an overarching framework for attention-related research in sports. Then a central mechanism is highlighted how working memory is involved in the control of attention in sports by reviewing research demonstrating that the activated contents in working memory control the focus of attention. The second part of the paper reviews literature showing that working memory capacity is an important individual difference variable that is predictive of controlling attention in a goal-directed manner and avoiding distraction and interference in sports. Finally, we address the question whether differences in working memory capacity contribute to sport expertise.

Keywords

  • Dual-process;
  • Working memory;
  • Attention;
  • Sport;
  • Individual differences

Research byte: Multi-domain training may improve attentional control (AC) in older adults



Multi-domain training enhances attentional control.
Psychology and Aging, Vol 31(4), Jun 2016, 390-408. http://dx.doi.org.ezp1.lib.umn.edu/10.1037/pag0000081

Abstract

Multi-domain training potentially increases the likelihood of overlap in processing components with transfer tasks and everyday life, and hence is a promising training approach for older adults. To empirically test this, 84 healthy older adults aged 64 to 75 years were randomly assigned to one of three single-domain training conditions (inhibition, visuomotor function, spatial navigation) or to the simultaneous training of all three cognitive functions (multi-domain training condition). All participants trained on an iPad at home for 50 training sessions. Before and after the training, and at a 6-month follow-up measurement, cognitive functioning and training transfer were assessed with a neuropsychological test battery including tests targeting the trained functions (near transfer) and transfer to executive functions (far transfer: attentional control, working memory, speed). Participants in all four training groups showed a linear increase in training performance over the 50 training sessions. Using a latent difference score model, the multi-domain training group, compared with the single-domain training groups, showed more improvement on the far transfer attentional control composite. Individuals with initially lower baseline performance showed higher training-related improvements, indicating that training compensated for lower initial cognitive performance. At the 6-month follow-up, performance on the cognitive test battery remained stable. This is one of the first studies to investigate systematically multi-domain training including comparable single-domain training conditions. Our findings suggest that multi-domain training enhances attentional control involved in handling several different tasks at the same time, an aspect in everyday life that is particularly challenging for older people. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Friday, February 26, 2016

White matter matters! An oldie-but-goodie (OBG) post

White matter, in contrast to the grey squiggly mass (the cerebrum) that most people associate with the human brain, was for many years the research step-child to the cerebrum. That is no more. White matter, which has been called the brain's subway, super information system, or interstate highway communication system, now has a glass slipper. Research during the past decade has implicated white matter as performing the critical task of connecting and synchronizing different brain regions or networks so they can perform a wide variety of complex human cognitive or motor behaviors. The white matter system is considered the communication backbone system for the flow of information in the brain. Of particular interest (to me) is the parietal-frontal network, which is implicated as central to abstract human intelligence, fluid intelligence (Gf), working memory and attentional control (see prior posts re: the P-FIT model).

In a MindHub white paper I hypothesized that increasing white matter tract integrity may be a key mechanism behind the efficacy of the Interactive Metronome neuro-timing intervention (see figure below). I have gone as far as suggesting that the efficacy of many brain training/fitness programs may stem from a common domain-general effect--improving communication between and within various brain network(s) via more efficient white matter tract speed and communication. [Click on image to enlarge]
White matter integrity or dysfunction as been implicated in a wide variety of cognitive disorders or abilities, including cognitive control, math and intellectual giftedness, fluid intelligence or reasoning, processing speed, reading, decrease in cognitive functioning, meditation, working memory, vascular cognitive impairment, ADHD, autism, and cognitve and language maturation in infants. A sampling of recent white matter research article abstracts I have accumulated can be found by clicking here.
White matter matters!


Monday, January 11, 2016

Your brain is a time machine: An oldie-but-goodie (OBG) post

This is an OBG (oldie-but-goodie) post I originally made on the IM-HOME blog

Time and space are the two fundamental dimensions of our lives. All forms of human behavior require us to process and understand information we receive from our environment in either spatial or temporal patterns. Even though mental timing (temporal processing) research is in a stage of infancy (when compared to spatial processing) important insights regarding the human brain clock have emerged.

Below is a list (albeit incomplete) of some of the major conclusions regarding the human brain clock. The sources for these statements come from my review of the temporal processing and brain clock literature during the past five years. Most of this information has been disseminated at the Brain Clock blog or the Brain Clock Evolving Web of Knowledge (EWOK). The goal of this post is to provide a Readers Digest summary of the major conclusions. This material can serve as a set of "talking points" at your next social event where you can impress your friends and family as you explain why you use the high-tech IM "clapper" (with a cowbell tone no less) either as a provider or as client.

Our brains measure time constantly. It's hard to find any complex human behavior where mental timing is not involved. Timing is required to walk, talk, perform complex movements and coordinate information flow across the brain for complex human thought. Think about moving your arm and hand to grasp a coffee cup. The messages to perform this task originate in your brain, which is not directly connected to your arm, hands and fingers. The ability to perform the necessary motor movements is possible only because the mind and extremities are connected via timing. Precisely timed neural messages connect your brain and extremities. You are a time machine.


Humans are remarkably proficient at internally perceiving and monitoring time to produce precisely timed behaviors and thinking. “We are aware of how long we have been doing a particular thing, how long it has been since we last slept, and how long it will be until lunch or dinner. We are ready, at any moment, to make complex movements requiring muscle coordination with microsecond accuracy, or to decode temporally complex auditory signals in the form of speech or music. Our timing abilities are impressive…” (Lewis & Walsh, 2005, p. 389).

To deal with time, humans have developed multiple timing systems that are active over more than 10 orders of magnitude with various degrees of precision (see figure below from Buhusi & Meck, 2005). These different timing systems can be classified into three general classes (viz., circadian, interval, and millisecond timing), each associated with different behaviors and brain structures and mechanisms. The fastest timing system (millisecond or interval timing) is involved in a numerous human behaviors such as speech and language, music perception and production, coordinated motor behaviors, attention, and thinking. This fast interval timing system is the most important timing system for understanding and diagnosing clinical disorders and for developing and evaluating effective treatment interventions for educational and rehabilitation settings. It is this timing system, and the relevant research, that is relevant to understanding Interactive Metronome. (Note.  See my conflict of interest statement at this blog.  I have an ongoing consulting relationship with IM).



Although there is consensus that the human brain contains some kind of clock, the jury is still out on the exact brain mechanisms and locations. It is also not clear whether there is one functional master clock or a series of clocks deployed in different brain areas. The areas of the brain most consistently associated with milli-second interval mental timing are the cerebellum, anterior cingulate, basal ganglia, the dorsolateral prefrontal cortex, right parietal cortex, motor cortex, and the frontal-striatal loop. That is a mouthful of technical brain terms. But, if you can memorize them and have them roll of your tongue with ease you will “shock and awe” your family and friends. Most of these areas of the brain are illustrated below. Now, if you really want to demonstrate your expertise, get your own illustrated “brain-in-a-pocket”. These images were generated by the free 3D Brain app available for your iPhone or iPad. Even cooler is the fact that you can rotate the images with your finger! You can give neuroanatomy lessons anytime…anywhere!



Research suggests that mental interval timing is controlled by two sub-systems. The automatic timing system processes discrete-event (discontinuous) timing in milliseconds. The cognitively-controlled timing system deals with continuous-event timing (in seconds) that requires controlled attention and working memory. Both systems are likely involved in IM. For example, the synchronized clapping requires motor planning and execution, functions most associated with the automatic timing system. However, the cognitive aspects of IM (focus, controlled attention, executive functions) invoke the cognitively controlled timing system. Aren’t these brain images awesome?



The dominant model in the brain clock research literature is that of a centralized internal clock that functions as per the pacemaker–accumulator model. Briefly, this is a model where an oscillator beating at a fixed frequency generates tics that are detected by a counter. For now I am just going to tease you with an image of this model. You can read more about this model at the Brain Clock blog.


Research suggests that the brain mechanisms underlying mental timing can be fine-tuned (modified) via experience and environmental manipulation. Modifiability of mental interval timing and subsequent transfer suggest a domain-general timing mechanism that, if harnessed via appropriately designed timing-based interventions, may improve human performance in a number of important cognitive and motor domains.

Tuesday, December 01, 2015

Positive Cogmed working memory training study in math and reading

Front Psychol. 2015; 6: 1711.
Published online 2015 Nov 10. doi:  10.3389/fpsyg.2015.01711
PMCID: PMC4639603

Working Memory Training is Associated with Long Term Attainments in Math and Reading

Abstract

Training working memory (WM) using computerized programs has been shown to improve functions directly linked to WM such as following instructions and attention. These functions influence academic performance, which leads to the question of whether WM training can transfer to improved academic performance. We followed the academic performance of two age-matched groups during 2 years. As part of the curriculum in grade 4 (age 9–10), all students in one classroom (n = 20) completed Cogmed Working Memory Training (CWMT) whereas children in the other classroom (n = 22) received education as usual. Performance on nationally standardized tests in math and reading was used as outcome measures at baseline and two years later. At baseline both classes were normal/high performing according to national standards. At grade 6, reading had improved to a significantly greater extent for the training group compared to the control group (medium effect size, Cohen’s d = 0.66, p = 0.045). For math performance the same pattern was observed with a medium effect size (Cohen’s d = 0.58) reaching statistical trend levels (p = 0.091). Moreover, the academic attainments were found to correlate with the degree of improvements during training (p < 0.053). This is the first study of long-term (>1 year) effects of WM training on academic performance. We found performance on both reading and math to be positively impacted after completion of CWMT. Since there were no baseline differences between the groups, the results may reflect an influence on learning capacity, with improved WM leading to a boost in students’ capacity to learn. This study is also the first to investigate the effects of CWMT on academic performance in typical or high achieving students. The results suggest that WM training can help optimize the academic potential of high performers.
Keywords: working memory training, academic attainment, cognitive training, cogmed, educational psychology