Contributions of self-report and performance-based individual differences measures of social cognitive ability to large-scale neural network functioning

2016 ◽  
Vol 11 (3) ◽  
pp. 685-697 ◽  
Author(s):  
Ryan Smith ◽  
Anna Alkozei ◽  
William D. S. Killgore
Keyword(s):  
Neural Network ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Large Scale ◽  
Social Cognitive ◽  
Self Report ◽  
And Performance

scholarly journals Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

2020 ◽  
Vol 117 (32) ◽  
pp. 19061-19071 ◽  
Author(s):  
Samantha Joel ◽  
Paul W. Eastwick ◽  
Colleen J. Allison ◽  
Ximena B. Arriaga ◽  
Zachary G. Baker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Relationship Quality ◽  
Individual Difference ◽  
Large Scale ◽  
Well Being ◽  
Attachment Anxiety ◽  
Self Report ◽  
Individual Difference Variables ◽  
Partner Satisfaction

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

scholarly journals Large-scale analysis of test–retest reliabilities of self-regulation measures

2019 ◽  
Vol 116 (12) ◽  
pp. 5472-5477 ◽  
Author(s):  
A. Zeynep Enkavi ◽  
Ian W. Eisenberg ◽  
Patrick G. Bissett ◽  
Gina L. Mazza ◽  
David P. MacKinnon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Large Scale ◽  
Response Times ◽  
Self Regulation ◽  
Self Report ◽  
Model Parameters ◽  
Retest Reliability ◽  
Large Scale Analysis ◽  
Behavioral Tasks ◽  
Test Retest Reliability

The ability to regulate behavior in service of long-term goals is a widely studied psychological construct known as self-regulation. This wide interest is in part due to the putative relations between self-regulation and a range of real-world behaviors. Self-regulation is generally viewed as a trait, and individual differences are quantified using a diverse set of measures, including self-report surveys and behavioral tasks. Accurate characterization of individual differences requires measurement reliability, a property frequently characterized in self-report surveys, but rarely assessed in behavioral tasks. We remedy this gap by (i) providing a comprehensive literature review on an extensive set of self-regulation measures and (ii) empirically evaluating test–retest reliability of this battery in a new sample. We find that dependent variables (DVs) from self-report surveys of self-regulation have high test–retest reliability, while DVs derived from behavioral tasks do not. This holds both in the literature and in our sample, although the test–retest reliability estimates in the literature are highly variable. We confirm that this is due to differences in between-subject variability. We also compare different types of task DVs (e.g., model parameters vs. raw response times) in their suitability as individual difference DVs, finding that certain model parameters are as stable as raw DVs. Our results provide greater psychometric footing for the study of self-regulation and provide guidance for future studies of individual differences in this domain.

scholarly journals Is baseline pupil size related to cognitive ability? Yes (under proper lighting conditions).

2020 ◽  
Author(s):  
Jason S. Tsukahara ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Pupil Size ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Functional Organization ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Lighting Conditions

We found that individual differences in baseline pupil size correlated with fluid intelligence and working memory capacity. Larger pupil size was associated with higher cognitive ability. However, other researchers have not been able to replicate our 2016 finding – though they only measured working memory capacity and not fluid intelligence. In a reanalysis of Tsukahara et al. (2016) we show that reduced variability on baseline pupil size will result in a higher probability of obtaining smaller and non-significant correlations with working memory capacity. In two large-scale studies, we demonstrated that reduced variability in baseline pupil size values was due to the monitor being too bright. Additionally, fluid intelligence and working memory capacity did correlate with baseline pupil size except in the brightest lighting conditions. Overall, our findings demonstrated that the baseline pupil size – working memory capacity relationship was not as strong or robust as that with fluid intelligence. Our findings have strong methodological implications for researchers investigating individual differences in task-free or task-evoked pupil size. We conclude that fluid intelligence does correlate with baseline pupil size and that this is related to the functional organization of the resting-state brain through the locus coeruleus-norepinephrine system.

scholarly journals More than meets the ear: Individual differences in trait and state willingness to communicate as predictors of language learning performance in a Chinese EFL context

2020 ◽  
pp. 136216882095193
Author(s):  
Jiayi Zhang ◽  
Nadin Beckmann ◽  
Jens F. Beckmann
Keyword(s):  
Individual Differences ◽  
Language Learning ◽  
Willingness To Communicate ◽  
Self Report ◽  
Learning Performance ◽  
Communication Behaviour ◽  
English Classrooms ◽  
Report Data ◽  
Self Report Data ◽  
And Performance

Chinese students are frequently seen as passive learners because of their apparent reluctance to speak, particularly in English classrooms. However, this impression seems to reflect a stereotype which is likely to confound willingness to communicate (WTC) and communication behaviour. In this article we argue for more attention to be paid to individual differences to complement culture-related explanations of differences in WTC. Self-report data on WTC at both trait and state levels and personality characteristics were analysed in relation to L2 language learning performance in a sample of 103 university students. Individual differences in WTCL1 were found to be strongly related to extraversion; whilst individual differences in WTCL2 were associated with openness to experience, conscientiousness, and agreeableness, rather than extraversion. Moreover, this study differentiates state WTCL2 from communication behaviour, and provides evidence for both trait and state WTCL2 being important predictors of L2 learning performance despite being differently related to personality. Our results overall suggest that exclusively relying on observable communication behaviour is likely to overlook effective antecedences of learning and performance. This study pleads for a more differentiated perspective on WTC and its personality correlates at both trait and state levels. It provides further evidence that WTC is a useful construct in working towards a better understanding of language learning processes.

scholarly journals Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing

2021 ◽  
Vol 3 (4) ◽  
pp. 365-374
Author(s):  
J. Samuel Manoharan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Language Processing ◽  
Large Scale ◽  
Learning Technology ◽  
Emotion Classification ◽  
Sound Event ◽  
Current Scenario ◽  
And Performance

Sound event detection, speech emotion classification, music classification, acoustic scene classification, audio tagging and several other audio pattern recognition applications are largely dependent on the growing machine learning technology. The audio pattern recognition issues are also addressed by neural networks in recent days. The existing systems operate within limited durations on specific datasets. Pretrained systems with large datasets in natural language processing and computer vision applications over the recent years perform well in several tasks. However, audio pattern recognition research with large-scale datasets is limited in the current scenario. In this paper, a large-scale audio dataset is used for training a pre-trained audio neural network. Several audio related tasks are performed by transferring this audio neural network. Several convolution neural networks are used for modeling the proposed audio neural network. The computational complexity and performance of this system are analyzed. The waveform and leg-mel spectrogram are used as input features in this architecture. During audio tagging, the proposed system outperforms the existing systems with a mean average of 0.45. The performance of the proposed model is demonstrated by applying the audio neural network to five specific audio pattern recognition tasks.

Idiographically Desirable Responding: Individual Differences in Perceived Trait Desirability Predict Overclaiming

2013 ◽  
Vol 27 (6) ◽  
pp. 580-592 ◽  
Author(s):  
Steven G. Ludeke ◽  
Yanna J. Weisberg ◽  
Colin G. Deyoung
Keyword(s):  
Individual Differences ◽  
Self Esteem ◽  
Self Report ◽  
Community Members ◽  
Peer Ratings ◽  
Personality Psychology ◽  
Self Reports ◽  
Performance Based Assessment ◽  
And Performance ◽  
Peer Reports

Objective Conventional measures of self–report bias implicitly assume consistent patterns of overclaiming across individuals. We contrast this with the effects of individual differences in views of trait desirability on overclaiming, which we label idiographically desirable responding (IDR). Method We obtained self–reports and peer reports of trait levels on mixed–sex samples of undergraduates (N = 352) and middle–aged community members (N = 541), with an additional performance–based assessment in the latter sample. Results Compared to conventional measures of bias, individual differences in trait desirability ratings identified an independent and comparatively large amount of the variance in overclaiming for personality and physical attractiveness. The importance of IDR was confirmed by the replication of these results for intelligence, for which both peer–ratings and performance data were available. Individuals differed in the extent to which they rely on IDR, with these differences indexed by the correlation between views of the desirability of a given trait and the extent to which one overclaimed that trait. Individuals who were more prone to overclaim in this fashion exhibited higher self–esteem as well as higher scores on questionnaire measures of socially desirable responding. Conclusion Overclaiming of traits resulted both from the patterns of biases identified by conventional overclaiming measures and from individual differences in perceptions of what traits are most desirable. Copyright © 2013 European Association of Personality Psychology

scholarly journals Predicting individual differences in conflict detection and bias susceptibility during reasoning

2019 ◽  
Author(s):  
Jakub Šrol ◽  
Wim De Neys
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Large Scale ◽  
Cognitive Biases ◽  
Detection Efficiency ◽  
Conflict Detection ◽  
Process Models ◽  
Thinking Dispositions ◽  
Cognitive Reflection

One of the key components of the susceptibility to cognitive biases is the ability to monitor for conflict that may arise between intuitively cued “heuristic” answers and logical principles. While there is evidence that people differ in their ability to detect such conflicts, it is not clear which individual factors are driving these differences. In the present large-scale study (N = 399) we explored the role of cognitive ability, thinking dispositions, numeracy, cognitive reflection, and mindware instantiation (i.e. knowledge of logical principles) as potential predictors of individual differences in conflict detection ability and overall accuracy on a battery of reasoning problems. Results showed that mindware instantiation was the single best predictor of both conflict detection efficiency and reasoning accuracy. Cognitive reflection, thinking dispositions, numeracy, and cognitive ability played a significant but smaller role. The full regression model accounted for 40% of the variance in overall reasoning accuracy, but only 7% of the variance in conflict detection efficiency. We discuss the implications of these findings for popular process models of bias susceptibility.

Individual Differences in Asymmetric Resting-State Frontal Cortical Activity Modulate ERPs and Performance in a Global-Local Attention Task

2012 ◽  
Vol 26 (2) ◽  
pp. 51-62 ◽  
Author(s):  
Maarten A. S. Boksem ◽  
Evelien Kostermans ◽  
Mattie Tops ◽  
David De Cremer
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Visual Information ◽  
Event Related Potentials ◽  
Self Report ◽  
Approach Motivation ◽  
Attention Task ◽  
State Measurement ◽  
Related Potentials ◽  
And Performance

Recent research has demonstrated that individual differences in approach motivation modulate attentional scope. In turn, approach and inhibition have been related to different neural systems that are associated with asymmetries in relative frontal activity (RFA). Here, we investigated whether such individual differences in asymmetric hemispheric activity during rest, and self-report measures of approach motivation (as measured by the behavioral inhibition system, BIS/behavioral activation system, BAS scales) would be predictive of the efficiency of attentional processing of global and local visual information, as indexed by event-related potentials (ERPs) and performance measures. In the reported experiment, participants performed a visual attention task in which they were required to either attend to the global shape or the local components of presented stimuli. Electroencephalogram was recorded during task performance and during an initial “resting state” measurement. The results showed that only the BAS-Reward Responsiveness subscale was associated with left RFA during rest, while BIS, BAS-Drive, and BAS-Fun Seeking were associated with more right-lateralized RFA. Importantly, left RFA during the “resting state” measurement was associated with increased P3 (right-lateralized) amplitudes and decreased P3 latencies on trials requiring a global focus. In turn, these ERPs were associated with enhanced performance on trials requiring a global focus. These results provide the first evidence for a positive association between left RFA during rest and increased efficiency of right-lateralized brain mechanisms that are involved in processing global information.

scholarly journals Addressing inactivity after stroke: The Collaborative Rehabilitation in Acute Stroke (CREATE) study

2020 ◽  
pp. 174749302096936
Author(s):  
Fiona Jones ◽  
Karolina Gombert- ◽  
Stephanie Honey ◽  
Geoffrey Cloud ◽  
Ruth Harris ◽  
...  
Keyword(s):  
Large Scale ◽  
Self Report ◽  
Space Environment ◽  
Activity Levels ◽  
National Guidelines ◽  
Stroke Units ◽  
Case Comparison ◽  
And Performance ◽  
Survey Responses ◽  
Increased Activity

Background Stroke patients are often inactive outside of structured therapy sessions – an enduring international challenge despite large scale organizational changes, national guidelines and performance targets. We examined whether experienced-based co-design (EBCD) – an improvement methodology – could address inactivity in stroke units. Aims To evaluate the feasibility and impact of patients, carers, and staff co-designing and implementing improvements to increase supervised and independent therapeutic patient activity in stroke units and to compare use of full and accelerated EBCD cycles. Methods Mixed-methods case comparison in four stroke units in England. Results Interviews were held with 156 patients, staff, and carers in total; ethnographic observations for 364 hours, behavioral mapping of 68 patients, and self-report surveys from 179 patients, pre- and post-implementation of EBCD improvement cycles. Three priority areas emerged: (1) ‘Space’ (environment); (2) ‘Activity opportunities’ and (3) ‘Communication’. More than 40 improvements were co-designed and implemented to address these priorities across participating units. Post-implementation interview and ethnographic observational data confirmed use of new social spaces and increased activity opportunities. However, staff interactions remained largely task-driven with limited focus on enabling patient activity. Behavioral mapping indicated some increases in social, cognitive, and physical activity post-implementation, but was variable across sites. Survey responses rates were low at 12–38% and inconclusive. Conclusion It was feasible to implement EBCD in stroke units. This resulted in multiple improvements in stroke unit environments and increased activity opportunities but minimal change in recorded activity levels. There was no discernible difference in experience or outcome between full and accelerated EBCD; this methodology could be used across hospital stroke units to assist staff and other stakeholders to co-design and implement improvement plans.

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