Relationships among processing speed, working memory, and fluid intelligence in children

2000 ◽  
Vol 54 (1-3) ◽  
pp. 1-34 ◽  
Author(s):  
Astrid F. Fry ◽  
Sandra Hale
Keyword(s):  
Working Memory ◽  
Processing Speed ◽  
Fluid Intelligence

scholarly journals Complexity and compositionality in fluid intelligence

2017 ◽  
Vol 114 (20) ◽  
pp. 5295-5299 ◽  
Author(s):  
John Duncan ◽  
Daphne Chylinski ◽  
Daniel J. Mitchell ◽  
Apoorva Bhandari
Keyword(s):  
Working Memory ◽  
Processing Speed ◽  
Information Integration ◽  
Fluid Intelligence ◽  
Human Mind ◽  
Critical Function ◽  
Complex Reasoning ◽  
Matrix Reasoning ◽  
Broad Role

Compositionality, or the ability to build complex cognitive structures from simple parts, is fundamental to the power of the human mind. Here we relate this principle to the psychometric concept of fluid intelligence, traditionally measured with tests of complex reasoning. Following the principle of compositionality, we propose that the critical function in fluid intelligence is splitting a complex whole into simple, separately attended parts. To test this proposal, we modify traditional matrix reasoning problems to minimize requirements on information integration, working memory, and processing speed, creating problems that are trivial once effectively divided into parts. Performance remains poor in participants with low fluid intelligence, but is radically improved by problem layout that aids cognitive segmentation. In line with the principle of compositionality, we suggest that effective cognitive segmentation is important in all organized behavior, explaining the broad role of fluid intelligence in successful cognition.

What's So Special About Working Memory?

2008 ◽  
Vol 19 (11) ◽  
pp. 1071-1077 ◽  
Author(s):  
Jacqueline A. Mogle ◽  
Benjamin J. Lovett ◽  
Robert S. Stawski ◽  
Martin J. Sliwinski
Keyword(s):  
Working Memory ◽  
Processing Speed ◽  
Fluid Intelligence ◽  
Working Memory Capacity ◽  
Cognitive Tasks ◽  
Secondary Memory ◽  
The Face ◽  
Study Participants ◽  
The Relationship ◽  
Search And Retrieval

Working memory capacity (WMC) has received attention across many areas of psychology, in part because of its relationship with intelligence. The mechanism underlying the relationship is unknown, but the nature of typical WMC tasks has led to two hypothesized mechanisms: secondary-memory processes (e.g., search and retrieval) and the maintenance of information in the face of distraction. In the present study, participants ( N = 383) completed a battery of cognitive tasks assessing processing speed, primary memory, working memory, secondary memory, and fluid intelligence. Secondary memory was the strongest predictor of fluid intelligence and added unique predictive value in models that accounted for working memory. In contrast, after accounting for the variance in fluid intelligence associated with the secondary-memory construct, the working memory construct did not significantly predict variability in fluid intelligence. Therefore, the secondary-memory requirements shared by many memory tasks may be responsible for the relationship between WMC and fluid intelligence, making the relationship less unique than is often supposed.

scholarly journals Predicting Academic Achievement with Cognitive Abilities: Cross-Sectional Study across School Education

2020 ◽  
Vol 10 (10) ◽  
pp. 158
Author(s):  
Tatiana Tikhomirova ◽  
Artem Malykh ◽  
Sergey Malykh
Keyword(s):  
Academic Achievement ◽  
Working Memory ◽  
Information Processing ◽  
Processing Speed ◽  
Cognitive Abilities ◽  
Fluid Intelligence ◽  
Number Sense ◽  
Information Processing Speed ◽  
Cross Sectional ◽  
The Relationship

The relationship between cognitive abilities and academic achievement across schooling from the first to the eleventh grade was analyzed. Information processing speed, visuospatial working memory, number sense, and fluid intelligence were considered predictors of general academic achievement, which was derived from grades in mathematics, language, and biology. This cross-sectional study involved 1560 pupils who were in grades 1–11 at general education schools and were aged from 6.8 to 19.1 years (50.4% were boys). Information processing speed, visuospatial working memory, and number sense were measured using the Choice Reaction Time, Corsi Block-Tapping, and Number Sense computerized tests, respectively. Fluid intelligence was measured using the paper-and-pencil version of the Standard Progressive Matrices test. Correlation analysis and structural equation modeling were carried out. It was shown that it is possible to describe the structure of the relationship between cognitive abilities and academic achievement for all levels of schooling with a single model. In this model, information processing speed is the key predictor of fluid intelligence, working memory, and number sense, which in turn contribute to individual differences in academic success. Additionally, the specificity of the relationship between individual indicators of cognitive abilities and academic achievement at each level of schooling was revealed.

A randomized controlled trial of working memory and processing speed training in schizophrenia

2018 ◽  
Vol 49 (12) ◽  
pp. 2009-2019 ◽  
Author(s):  
B.D. Cassetta ◽  
L.M. Tomfohr-Madsen ◽  
V.M. Goghari
Keyword(s):  
Working Memory ◽  
Cognitive Training ◽  
Real World ◽  
Processing Speed ◽  
Fluid Intelligence ◽  
Controlled Trial ◽  
Daily Functioning ◽  
Control Group ◽  
Far Transfer ◽  
Transfer Tasks

AbstractBackgroundAlthough prior research has shown that cognitive training may improve cognition for schizophrenia patients, it is currently unclear which domains of cognition should be targeted in training. One suggestion is to target low- or mid-level cognitive processes. In particular, working memory (WM) and processing speed (PS) have been named as two key areas of impairment in schizophrenia, and two domains of cognition that are linked to higher-order cognition and daily functioning. This study aimed to investigate the near-transfer (transfer of gains to related contexts), far-transfer (transfer of gains to unrelated contexts), and real-world gains associated with WM and PS training in schizophrenia.MethodsEighty-three participants with schizophrenia were recruited and randomly assigned to computerized WM training, PS training, or a no-training control group. Outcome measures included WM, PS, fluid intelligence, executive functioning, social cognition, and daily functioning and symptoms.ResultsPS training led to significant gains in untrained PS tasks, as well as gains in far-transfer tasks that required speed of processing. WM training did not lead to gains in untrained WM tasks and showed inconsistent effects on some far-transfer tasks.ConclusionsThese results suggest some benefit of domain-specific cognitive training, specifically PS training, in schizophrenia. Far-transfer of gains to other cognitive domains and to real-world functioning may not occur after targeted WM or PS training, though non-specific effects (e.g. through behavioral activation, increased motivation) may lead to improvements in some tasks. Future studies should continue to investigate the mechanisms by which cognitive training may enhance cognition and functioning in schizophrenia.

Basic numerical processing, calculation, and working memory in children with dyscalculia and/or ADHD symptoms

2016 ◽  
Vol 44 (5) ◽  
pp. 365-375 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Elena Ise ◽  
Julia Raddatz ◽  
Christin Schwenk ◽  
Christian Dobel
Keyword(s):  
Working Memory ◽  
Processing Speed ◽  
Verbal Working Memory ◽  
Typically Developing ◽  
Cognitive Profiles ◽  
Adhd Symptoms ◽  
Memory Processing ◽  
Numerical Processing ◽  
Hyperactivity Disorder ◽  
Neurocognitive Measures

Abstract. Objective: Deficits in basic numerical skills, calculation, and working memory have been found in children with developmental dyscalculia (DD) as well as children with attention-deficit/hyperactivity disorder (ADHD). This paper investigates cognitive profiles of children with DD and/or ADHD symptoms (AS) in a double dissociation design to obtain a better understanding of the comorbidity of DD and ADHD. Method: Children with DD-only (N = 33), AS-only (N = 16), comorbid DD+AS (N = 20), and typically developing controls (TD, N = 40) were assessed on measures of basic numerical processing, calculation, working memory, processing speed, and neurocognitive measures of attention. Results: Children with DD (DD, DD+AS) showed deficits in all basic numerical skills, calculation, working memory, and sustained attention. Children with AS (AS, DD+AS) displayed more selective difficulties in dot enumeration, subtraction, verbal working memory, and processing speed. Also, they generally performed more poorly in neurocognitive measures of attention, especially alertness. Children with DD+AS mostly showed an additive combination of the deficits associated with DD-only and A_Sonly, except for subtraction tasks, in which they were less impaired than expected. Conclusions: DD and AS appear to be related to largely distinct patterns of cognitive deficits, which are present in combination in children with DD+AS.


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