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.

Investment Traits and Intelligence in Adulthood

2013 ◽  
Vol 34 (2) ◽  
pp. 82-89 ◽  
Author(s):  
Sophie von Stumm
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Fluid Intelligence ◽  
Need For Cognition ◽  
Openness To Experience ◽  
Intelligence Tests ◽  
Crystallized Intelligence ◽  
New Knowledge ◽  
The Relationship ◽  
Over Time

Intelligence-as-knowledge in adulthood is influenced by individual differences in intelligence-as-process (i.e., fluid intelligence) and in personality traits that determine when, where, and how people invest their intelligence over time. Here, the relationship between two investment traits (i.e., Openness to Experience and Need for Cognition), intelligence-as-process and intelligence-as-knowledge, as assessed by a battery of crystallized intelligence tests and a new knowledge measure, was examined. The results showed that (1) both investment traits were positively associated with intelligence-as-knowledge; (2) this effect was stronger for Openness to Experience than for Need for Cognition; and (3) associations between investment and intelligence-as-knowledge reduced when adjusting for intelligence-as-process but remained mostly significant.

The Prediction of Problem-Solving Assessed Via Microworlds

2016 ◽  
Vol 32 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Samuel Greiff ◽  
Katarina Krkovic ◽  
Jarkko Hautamäki
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Complex Problem ◽  
Two Dimensions ◽  
Assessment Instruments ◽  
Complex Problem Solving ◽  
Visual Spatial ◽  
Rule Knowledge ◽  
Rule Application ◽  
Fluid Reasoning

Abstract. In this study, we explored the network of relations between fluid reasoning, working memory, and the two dimensions of complex problem solving, rule knowledge and rule application. In doing so, we replicated the recent study by Bühner, Kröner, and Ziegler (2008) and the structural relations investigated therein [ Bühner, Kröner, & Ziegler, (2008) . Working memory, visual-spatial intelligence and their relationship to problem-solving. Intelligence, 36, 672–680]. However, in the present study, we used different assessment instruments by employing assessments of figural, numerical, and verbal fluid reasoning, an assessment of numerical working memory, and a complex problem solving assessment using the MicroDYN approach. In a sample of N = 2,029 Finnish sixth-grade students of which 328 students took the numerical working memory assessment, the findings diverged substantially from the results reported by Bühner et al. Importantly, in the present study, fluid reasoning was the main source of variation for rule knowledge and rule application, and working memory contributed only a little added value. Albeit generally in line with previously conducted research on the relation between complex problem solving and other cognitive abilities, these findings directly contrast the results of Bühner et al. (2008) who reported that only working memory was a source of variation in complex problem solving, whereas fluid reasoning was not. Explanations for the different patterns of results are sought, and implications for the use of assessment instruments and for research on interindividual differences in complex problem solving are discussed.

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

scholarly journals Individual differences in strategy use and performance during fault diagnosis

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Michael Shreeves ◽  
Leo Gugerty ◽  
DeWayne Moore
Keyword(s):  
Individual Differences ◽  
Fault Diagnosis ◽  
Diagnostic Tests ◽  
Causal Reasoning ◽  
Fluid Intelligence ◽  
Positive Thinking ◽  
Reasoning Task ◽  
Thinking Dispositions ◽  
Analytic Processing ◽  
Heuristic Processing

Abstract Background Research on causal reasoning often uses group-level data analyses that downplay individual differences and simple reasoning problems that are unrepresentative of everyday reasoning. In three empirical studies, we used an individual differences approach to investigate the cognitive processes people used in fault diagnosis, which is a complex diagnostic reasoning task. After first showing how high-level fault diagnosis strategies can be composed of simpler causal inferences, we discussed how two of these strategies—elimination and inference to the best explanation (IBE)—allow normative performance, which minimizes the number of diagnostic tests, whereas backtracking strategies are less efficient. We then investigated whether the use of normative strategies was infrequent and associated with greater fluid intelligence and positive thinking dispositions and whether normative strategies used slow, analytic processing while non-normative strategies used fast, heuristic processing. Results Across three studies and 279 participants, uses of elimination and IBE were infrequent, and most participants used inefficient backtracking strategies. Fluid intelligence positively predicted elimination and IBE use but not backtracking use. Positive thinking dispositions predicted avoidance of backtracking. After classifying participants into groups that consistently used elimination, IBE, and backtracking, we found that participants who used elimination and IBE made fewer, but slower, diagnostic tests compared to backtracking users. Conclusions Participants’ fault diagnosis performance showed wide individual differences. Use of normative strategies was predicted by greater fluid intelligence and more open-minded and engaged thinking dispositions. Elimination and IBE users made the slow, efficient responses typical of analytic processing. Backtracking users made the fast, inefficient responses suggestive of heuristic processing.

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

Spanning the Face Space

1998 ◽  
Vol 06 (03) ◽  
pp. 265-279 ◽  
Author(s):  
Shimon Edelman
Keyword(s):  
Computational Modeling ◽  
Object Representation ◽  
Receptive Fields ◽  
Computational Approach ◽  
Face Representation ◽  
Comprehensive Theory ◽  
Visual Areas ◽  
The Face ◽  
Higher Visual Areas ◽  
Perceptual Systems

The paper outlines a computational approach to face representation and recognition, inspired by two major features of biological perceptual systems: graded-profile overlapping receptive fields, and object-specific responses in the higher visual areas. This approach, according to which a face is ultimately represented by its similarities to a number of reference faces, led to the development of a comprehensive theory of object representation in biological vision, and to its subsequent psychophysical exploration and computational modeling.

scholarly journals Early intervention and the growth of children's fluid intelligence: A cognitive developmental perspective

2006 ◽  
Vol 29 (2) ◽  
pp. 133-134 ◽  
Author(s):  
Ruth M. Ford
Keyword(s):  
Executive Function ◽  
Individual Differences ◽  
Early Intervention ◽  
Fluid Intelligence ◽  
Developmental Perspective ◽  
Effective Intervention ◽  
General Development ◽  
Developmental Theories ◽  
The Mind

From the stance of cognitive developmental theories, claims that general g is an entity of the mind are compatible with notions about domain-general development and age-invariant individual differences. Whether executive function is equated with general g or fluid g, research into the mechanisms by which development occurs is essential to elucidate the kinds of environmental inputs that engender effective intervention.

scholarly journals Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits

2008 ◽  
Vol 20 (7) ◽  
pp. 1847-1872 ◽  
Author(s):  
Mark C. W. van Rossum ◽  
Matthijs A. A. van der Meer ◽  
Dengke Xiao ◽  
Mike W. Oram
Keyword(s):  
Visual Cortex ◽  
Physiological Data ◽  
High Contrast ◽  
Cortical Circuits ◽  
Response Latencies ◽  
Low Contrast ◽  
Visual Areas ◽  
Higher Visual Areas ◽  
Recurrent Connections

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

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