Showing posts with label neurocognitive. Show all posts
Showing posts with label neurocognitive. Show all posts

Wednesday, August 27, 2025

IQs Corner: Practice effects persist over two decades of cognitive testing: Implications for longitudinal research - #practiceeffect #cognitive #neurocognit #IQ #intelligence #schoolpsychology #schoolpsychologists

Click on image to enlarge for easy reading


MedRxiv preprint available at.  https://doi.org/10.1101/2025.06.16.25329587

Elman et al. (2025)


ABSTRACT 

Background: Repeated cognitive testing can boost scores due to practice effects (PEs), yet it remains unclear whether PEs persist across multiple follow-ups and long durations. We examined PEs across  multiple assessments from midlife to old age in a nonclinical sample.   

Method: Men (N=1,608) in the Vietnam Era Twin Study of Aging (VETSA) underwent 
neuropsychological assessment comprising 30 measures across 4 waves (~6-year testing intervals) spanning up to 20 years. We leveraged age-matched replacement participants to estimate PEs at each wave. We compared cognitive trajectories and MCI prevalence using unadjusted versus PE-adjusted scores. 

Results: Across follow-ups, a range of 7-12 tests (out of 30) demonstrated significant PEs, especially in episodic memory and visuospatial domains. Adjusting for PEs resulted in improved detection of cognitive decline and MCI, with up to 20% higher MCI prevalence.  

Conclusion: PEs persist across multiple assessments and decades underscoring the 
importance of accounting for PEs in longitudinal studies.
  
Keywords: practice effects; repeat testing; serial testing; longitudinal testing; mild cognitive impairment; cognitive change

Sunday, February 23, 2025

Research Byte: Intrinsic #Brain Mapping of #Cognitive Abilities (as per #CHC): A Multiple-Dataset Study on #Intelligence and its Components (journal pre-proof)

 Click on image to enlarge for easy reading


A journal pre-proof copy of this article is available for download here.

Abstract

This study investigates how functional brain network features contribute to general intelligence and its cognitive components by analyzing three independent cohorts of healthy participants. Cognitive scores were derived from 1) the Wechsler Adult Intelligence Scale (WAIS-IV), 2) the Raven Standard Progressive Matrices (RPM), and 3) the NIH and Penn cognitive batteries from the Human Connectome Project. Factor analysis on the NIH and Penn cognitive batteries yielded latent variables that closely resembled the content of the WAIS-IV indices and RPM. We employed graph theory and a multi-resolution network analysis by varying the modularity parameter (γ) to investigate hierarchical brain-behavior relationships across different scales of brain organization. Brain-behavior associations were quantified using multi-level robust regression analyses to accommodate variability and confounds at the subject-level, node-level, and resolution-level. Our findings reveal consistent brain-behavior relationships across the datasets. Nodal efficiency in fronto-parietal sensorimotor regions consistently played a pivotal role in fluid reasoning, whereas efficiency in visual networks was linked to executive functions and memory. A broad, low-resolution 'task-positive' network emerged as predictive of full-scale IQ scores, indicating a hierarchical brain-behavior coding. Conversely, increased cross-network connections involving default mode and subcortical-limbic networks were associated with reductions in both general and specific cognitive performance. These outcomes highlight the relevance of network efficiency and integration, as well as of the hierarchical organization in supporting specific aspects of intelligence, while recognizing the inherent complexity of these relationships. Our multi-resolution network approach offers new insights into the interplay between multilayer network properties and the structure of cognitive abilities, advancing the understanding of the neural substrates of the intelligence construct.

Monday, November 25, 2024

A massive #dataset of the #NeuroCognitive Performance Test, a web-based #cognitive assessment

A massive dataset of the NeuroCognitive Performance Test, a web-based cognitive assessment

Click here to download/read PDF


Paul I. Jaffe , Aaron  Kaluszka, Nicole  F.  Ng & Robert  J.  Schafer  

We present a dataset of approximately 5.5 million subtest scores from over 750,000 adults who
completed the NeuroCognitive Performance test (NCPt; Lumos Labs, Inc.), a validated, self- administered cognitive test accessed via web browser. the dataset includes assessment scores from eight test batteries consisting of 5–11 subtests that collectively span several cognitive domains including working memory, visual attention, and abstract reasoning. In addition to the raw scores and normative data from each subtest, the dataset includes basic demographic information from each participant (age, gender, and educational background). the scale and diversity of the dataset provides an unprecedented opportunity for researchers to investigate population-level variability in cognitive abilities and their relation to demographic factors. to facilitate reuse of this dataset by other researchers, we provide a Python module that supports several common preprocessing steps.

Saturday, June 09, 2018

Mental rotation and fluid intelligence: A brain potential analysis

File under Gv and Gf

Mental rotation and fluid intelligence: A brain potential analysis
Intelligence 69 (2018) 146–157. Article link.

Vincenzo Varrialea, Maurits W. van der Molenb, Vilfredo De Pascalis


ABSTRACT

The current study examined the relation between mental rotation and fluid intelligence using performance measures augmented with brain potential indices. Participants took a Raven's Progressive Matrices Test and performed on a mental rotation task presenting upright and rotated letter stimuli (60°, 120° or 180°) in normal and mirror image requiring a response execution or inhibition depending on instructions. The performance results showed that the linear slope relating performance accuracy, but not speed, to the angular rotation of the stimuli was related to individual differences in fluid intelligence. For upright stimuli, P3 amplitude recorded at frontal and central areas was positively associated with fluid intelligence scores. The mental rotation process was related to a negative shift of the brain potential recorded over the parietal cortex. The linear function relating the amplitude of the rotation-related negativity to rotation angle was associated with fluid intelligence. The slope was more pronounced for high- relative to low-ability participants suggesting that the former flexibly adjust their expenditure of mental effort to the mental rotation demands while the latter ones are less proficient in doing so.


- Posted using BlogPress from my iPad

Saturday, November 04, 2017

Mathematical (Gq) giftedness: Review of cognitive, conative and neural variables

Click on image to enlarge.

Article link.




ABSTRACT

Most mathematical cognition research has focused on understanding normal adult function and child development as well as mildly and moderately impaired mathematical skill, often labeled developmental dyscalculia and/or mathematical learning disability. In contrast, much less research is available on cognitive and neural correlates of gifted/excellent mathematical knowledge in adults and children. In order to facilitate further inquiry into this area, here we review 40 available studies, which examine the cognitive and neural basis of gifted mathematics. Studies associated a large number of cognitive factors with gifted mathematics, with spatial processing and working memory being the most frequently identified contributors. However, the current literature suffers
from low statistical power, which most probably contributes to variability across findings. Other major shortcomings include failing to establish domain and stimulus specificity of findings, suggesting causation without sufficient evidence and the frequent use of invalid backward inference in neuro-imaging studies. Future studies must increase statistical power and neuro-imaging studies must rely on supporting behavioral data when interpreting findings. Studies should investigate the factors shown to correlate with math giftedness in a more specific manner and determine exactly how individual factors may contribute to gifted math ability.


SELECTIVE SUMMARY CONCLUSION STATEMENTS

In line with the heterogeneous nature of mathematical disabilities (e.g., Rubinsten and Henik, 2009; Fias et al., 2013), mathematical giftedness also seems to correlate with numerous factors—(see Appendix A for which factors were found in each study). These factors roughly fall into social, motivational, and cognitive domains. Specifically, in the social and motivational domains, motivation, high drive, and interest to learn mathematics, practice time, lack of involvement in social interpersonal, or religious issues, authoritarian attitudes, and high socio-economic status have all been related to high levels of mathematical achievement. Speculatively, it is interesting to ask whether some of these factors may be related to the so-called Spontaneous Focusing on Numerosity (SFON) concept which appears early in life and means that some children have a high tendency to pay attention to numerical information (Hannula and Lehtinen, 2005). To clarify this question, longitudinal studies could investigate whether high SFON at an early age is associated with high levels of mathematical expertise in later life. Better assessment of individual variability is also important, for example, Albert Einstein (who was a gifted even if sometimes “lazy” mathematician; see e.g., Isaacson, 2008) was famously anti-authoritarian.

In terms of cognitive variables, we found that spatial processing, working memory, motivation/practice time, reasoning, general IQ, speed of information processing, short-term memory, efficient switching from working memory to episodic memory, pattern recognition, inhibition, fluid intelligence, associative memory, and motor functions were all associated with mathematical giftedness. As a caveat it is important to point out that mere “significance counting” (i.e., just considering studies with statistical significant results regarding a concept) can be very misleading especially in the typically underpowered context of psychology and neuro-imaging research (see e.g., Szucs and Ioannidis, 2017). However, considering the patchy research, this is the best we can do at the moment. In addition, even if meta-analyses were possible, these also typically only take into account published research, so they usually (highly) overestimate effect sizes especially from small scale studies (see Szucs and Ioannidis, 2017).


- Posted using BlogPress from my iPad