Individual Differences in Reward‐Based Learning Predict Fluid Reasoning Abilities

2021 ◽  
Vol 45 (2) ◽  
Author(s):  
Andrea Stocco ◽  
Chantel S. Prat ◽  
Lauren K. Graham
Keyword(s):  
Individual Differences ◽  
Reasoning Abilities ◽  
Fluid Reasoning

Individual differences in fluid reasoning on a complex recent probes task

2009 ◽  
Author(s):  
Eric G. Freedman ◽  
Michael D. McManaman ◽  
Nezar Khatib
Keyword(s):  
Individual Differences ◽  
Fluid Reasoning

Brief Reports: The Interaction of Spatial Visualization and General Reasoning Abilities with Instructional Treatment on Absolute Value Equations

1978 ◽  
Vol 9 (2) ◽  
pp. 152-154
Author(s):  
Phillip M. Eastman ◽  
Mohammed Salhab
Keyword(s):  
Individual Differences ◽  
Spatial Visualization ◽  
Absolute Value Equations ◽  
Absolute Value ◽  
Reasoning Abilities ◽  
Instructional Treatment ◽  
Treatment Interaction ◽  
Aptitude Treatment Interaction ◽  
Method Of Instruction

Aptitude treatment interaction (ATI) studies essentially deal with two related questions: Can we adapt instruction to patterns of individual differences among students? If so, for which students is a particular method of instruction most effective? The general ATI problem as advanced by Cronbach (1957) is well known (e.g., Becker, 1970), and no further discussion of it is presented here.

scholarly journals Making Cognitive Latent Variables Manifest: Distinct Neural Networks for Fluid Reasoning and Processing Speed

2015 ◽  
Vol 27 (6) ◽  
pp. 1249-1258 ◽  
Author(s):  
Christian Habeck ◽  
Jason Steffener ◽  
Daniel Barulli ◽  
Yunglin Gazes ◽  
Qolamreza Razlighi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variables ◽  
Latent Variable ◽  
Cognitive Tasks ◽  
Neural Activation ◽  
Strongly Correlated ◽  
Activation Patterns ◽  
Fluid Reasoning

Cognitive psychologists posit several specific cognitive abilities that are measured with sets of cognitive tasks. Tasks that purportedly tap a specific underlying cognitive ability are strongly correlated with one another, whereas performances on tasks that tap different cognitive abilities are less strongly correlated. For these reasons, latent variables are often considered optimal for describing individual differences in cognitive abilities. Although latent variables cannot be directly observed, all cognitive tasks representing a specific latent ability should have a common neural underpinning. Here, we show that cognitive tasks representing one ability (i.e., either perceptual speed or fluid reasoning) had a neural activation pattern distinct from that of tasks in the other ability. One hundred six participants between the ages of 20 and 77 years were imaged in an fMRI scanner while performing six cognitive tasks, three representing each cognitive ability. Consistent with prior research, behavioral performance on these six tasks clustered into the two abilities based on their patterns of individual differences and tasks postulated to represent one ability showed higher similarity across individuals than tasks postulated to represent a different ability. This finding was extended in the current report to the spatial resemblance of the task-related activation patterns: The topographic similarity of the mean activation maps for tasks postulated to reflect the same reference ability was higher than for tasks postulated to reflect a different reference ability. Furthermore, for any task pairing, behavioral and topographic similarities of underlying activation patterns are strongly linked. These findings suggest that differences in the strengths of correlations between various cognitive tasks may be because of the degree of overlap in the neural structures that are active when the tasks are being performed. Thus, the latent variable postulated to account for correlations at a behavioral level may reflect topographic similarities in the neural activation across different brain regions.

Cortical Thickness in Inferior Parietal Cortex is Associated with Individual Differences in Fluid Reasoning Ability

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S110
Author(s):  
E.D. O'Hare ◽  
K.J. Whitaker ◽  
Z.A. Op de Macks ◽  
B.D. Johnson ◽  
E. Ferrer ◽  
...  
Keyword(s):  
Individual Differences ◽  
Parietal Cortex ◽  
Cortical Thickness ◽  
Reasoning Ability ◽  
Inferior Parietal Cortex ◽  
Fluid Reasoning

scholarly journals Inter- and intra-individual differences in fluid reasoning show distinct cortical responses

2016 ◽  
Author(s):  
Rogier Kievit ◽  
H. Steven Scholte ◽  
Lourens J. Waldorp ◽  
Denny Borsboom
Keyword(s):  
Individual Differences ◽  
Fluid Intelligence ◽  
Two Dimensions ◽  
Specific Knowledge ◽  
Cortical Responses ◽  
Visual Areas ◽  
Fluid Reasoning ◽  
Partial Overlap ◽  
Higher Visual Areas ◽  
Increased Activity

Fluid intelligence is a general cognitive ability associated with problem solving in the absence of task-specific knowledge. Neuroscientific studies of fluid intelligence have studied both fluid intelligence tasks of varying difficulty and individual differences in fluid intelligence ability, but have failed to appropriately distinguish the two dimensions. Here we use task-based fMRI (N=34) to show that within and between subject dimensions show both partial overlap and widespread differences. Individuals with higher ability showed widespread increased activity including bilateral frontoparietal systems, whereas more difficult items were associated with more focal activity increases in middle frontal gyri, frontal poles and superior frontal poles. Finally, we show that when difficulty is equated across individuals, those with higher ability tend to show more fronto-parietal activity, whereas low fluid intelligence individuals tend to show greater activity in higher visual areas. The fMRI and behavioural data for our paper are freely available in online repositories.

The Role of Individual Differences in Determining Interaction Strategies and Performance on Computer Based Tasks

1995 ◽  
Vol 39 (4) ◽  
pp. 248-252
Author(s):  
Jennifer A. Bohan ◽  
Rafael Marshall ◽  
Deborah A. Boehm-Davis ◽  
Astrid Schmidt-Nielsen
Keyword(s):  
Individual Differences ◽  
Reasoning Ability ◽  
Noun Pair ◽  
Field Dependency ◽  
Reasoning Abilities ◽  
Cognitive Reasoning ◽  
Computer Based ◽  
And Performance ◽  
Computer Tasks

In completing any given task, whether it be driving or a computer task, indviduals have a wide array of strategies available to them. Investigations of computer tasks have shown that individual differences of cognitive styles and abilites are related to the types of strategies individuals use to complete the task (Schmidt-Nielsen and Ackerman, 1993). Typically, those who have higher reasoning abilities use more sophisticated strategies for performing the task than those with a lower level of ability. Further, it has been demonstrated that these strategies tend to hold over a variety of tasks. For example, performance on a computer graphing task was shown to be correlated to cognitive reasoning ability. The current study extended the work of Schmidt-Nielsen and Ackerman and found that there were a wide variety of performance strategies and these strategies were correlated with reasoning ability, field dependency, and performance on the noun pair task.

What Does the Wonderlic Personnel Test Measure?

2007 ◽  
Vol 100 (3) ◽  
pp. 707-712 ◽  
Author(s):  
T. Darin Matthews ◽  
Kerry S. Lassiter
Keyword(s):  
Cognitive Ability ◽  
Correlation Coefficients ◽  
Knowledge Score ◽  
Validity Evidence ◽  
Reasoning Abilities ◽  
Comprehensive Knowledge ◽  
Fluid Reasoning ◽  
Convergent And Divergent Validity ◽  
Personnel Test ◽  
Woodcock Johnson

The present investigation examined the concurrent validity of the Wonderlic Personnel Test and Woodcock-Johnson–Revised Tests of Cognitive Ability which were administered to 37 college students, 27 women and 10 men, who ranged in age from 18 to 54 years ( M = 27.1, SD = 8.7). Analysis yielded significant correlation coefficients between the Wonderlic Total score and the score for the WJ–R Broad Cognitive Ability Standard Battery ( r = .55) and the Comprehensive Knowledge score ( r = .34). Performance on the Wonderlic was not significantly correlated with fluid reasoning skills ( r = .26) but was most strongly associated with overall intellectual functioning, as measured by the Woodcock-Johnson Standard Battery IQ score. While scores on the Wonderlic were more strongly associated with crystallized than fluid reasoning abilities, the Wonderlic test scores did not clearly show convergent and divergent validity evidence across these two broad domains of cognitive ability.

scholarly journals Neural Correlates of human cognitive abilities during sleep

2017 ◽  
Author(s):  
Zhuo Fang ◽  
Laura B. Ray ◽  
Adrian M. Owen ◽  
Stuart M. Fogel
Keyword(s):  
Individual Differences ◽  
Cognitive Abilities ◽  
Short Term Memory ◽  
Neural Activation ◽  
Activation Patterns ◽  
Problem Solving Skills ◽  
Verbal Abilities ◽  
Reasoning Abilities ◽  
Highly Correlated ◽  
Function Of Sleep

ABSTRACTInter-individual differences in sleep spindles are highly correlated with “Reasoning” abilities (problem solving skills; i.e., the ability to employ logic, identify complex patterns), but not Short Term Memory or Verbal abilities. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have revealed brain activations time-locked to spindles (e.g., thalamic, paralimbic, and motor cortical areas)–yet the functional significance of inter-individual differences in spindle-related brain activation remains to be investigated. Using EEG-fMRI during sleep, we identified, for the first time, the neural activation patterns time-locked to spindles that are correlated with cognitive abilities. Similar to previous studies, activations time-locked to spindles were observed in thalamocortical circuitry and basal ganglia regions. Importantly, spindle-related activation in a subset of these regions were specifically related to inter-individual differences in Reasoning, but not STM or Verbal abilities. These results may help elucidate the physiological mechanisms which support the function of sleep for the capacity for reasoning.

scholarly journals Individual Differences in Resting-state Brain Rhythms Uniquely Predict Second Language Learning Rate and Willingness to Communicate in Adults

2019 ◽  
Vol 31 (1) ◽  
pp. 78-94 ◽  
Author(s):  
Chantel S. Prat ◽  
Brianna L. Yamasaki ◽  
Erica R. Peterson
Keyword(s):  
Second Language ◽  
Individual Differences ◽  
Language Proficiency ◽  
Resting State ◽  
First Language ◽  
Learning Rate ◽  
Fluid Reasoning ◽  
L2 Learning ◽  
First Language Proficiency ◽  
Lh Rh

The current study used quantitative electroencephalography (qEEG) to characterize individual differences in neural rhythms at rest and to relate them to fluid reasoning ability, to first language proficiency, and to subsequent second language (L2) learning ability, with the goal of obtaining a better understanding of the neurocognitive bases of L2 aptitude. Mean spectral power, laterality, and coherence metrics were extracted across theta, alpha, beta, and gamma frequency bands obtained from eyes-closed resting-state qEEG data from 41 adults aged 18–34 years. Participants then completed 8 weeks of French training using a virtual language and cultural immersion software. Results replicate and extend previous studies showing that faster learners have higher beta power recorded over right hemisphere (RH) electrode sites, greater laterality (RH − LH/RH + LH) of alpha and beta bands, and greater coherence between RH frontotemporal sites across all frequencies, although only coherence measures survived multiple comparisons. Increased coherence within and between RH networks was also associated with greater posttest declarative memory scores and with more accurate speech during learning. Total speech attempts, in contrast, correlated with bilaterally distributed small-world network configurations, as indexed by lower power and coherence over high-frequency (beta and gamma) bands recorded over frontotemporal networks in both hemispheres. Results from partial correlations and regression analyses suggest that the neural predictors of L2 learning rate, posttest proficiency, and total speech attempts varied in their degree of overlap with qEEG correlates of first language proficiency and fluid reasoning abilities, but that neural predictors alone explained 26–60% of the variance in L2 outcomes.

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