scholarly journals Age-Related Differences in Advantageous Decision Making Are Associated with Distinct Differences in Functional Community Structure

2014 ◽  
Vol 4 (3) ◽  
pp. 193-202 ◽  
Author(s):  
Malaak Nasser Moussa ◽  
Michael J. Wesley ◽  
Linda J. Porrino ◽  
Satoru Hayasaka ◽  
Antoine Bechara ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Structure ◽  
Functional Community ◽  
Age Related

Age-Related Differences in Motivational Integration and Cognitive Control

2019 ◽  
Author(s):  
Debbie Marianne Yee ◽  
Sarah L Adams ◽  
Asad Beck ◽  
Todd Samuel Braver
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Cognitive Control ◽  
Task Performance ◽  
Cognitive Task ◽  
Monetary Reward ◽  
Potential Candidate ◽  
Younger Adults ◽  
Age Related ◽  
Cognitive Task Performance

Motivational incentives play an influential role in value-based decision-making and cognitive control. A compelling hypothesis in the literature suggests that the brain integrates the motivational value of diverse incentives (e.g., motivational integration) into a common currency value signal that influences decision-making and behavior. To investigate whether motivational integration processes change during healthy aging, we tested older (N=44) and younger (N=54) adults in an innovative incentive integration task paradigm that establishes dissociable and additive effects of liquid (e.g., juice, neutral, saltwater) and monetary incentives on cognitive task performance. The results reveal that motivational incentives improve cognitive task performance in both older and younger adults, providing novel evidence demonstrating that age-related cognitive control deficits can be ameliorated with sufficient incentive motivation. Additional analyses revealed clear age-related differences in motivational integration. Younger adult task performance was modulated by both monetary and liquid incentives, whereas monetary reward effects were more gradual in older adults and more strongly impacted by trial-by-trial performance feedback. A surprising discovery was that older adults shifted attention from liquid valence toward monetary reward throughout task performance, but younger adults shifted attention from monetary reward toward integrating both monetary reward and liquid valence by the end of the task, suggesting differential strategic utilization of incentives. Together these data suggest that older adults may have impairments in incentive integration, and employ different motivational strategies to improve cognitive task performance. The findings suggest potential candidate neural mechanisms that may serve as the locus of age-related change, providing targets for future cognitive neuroscience investigations.

The Age-related Neural Strategy Alterations in Decision Making Under Risk

Neuroscience ◽  
2020 ◽  
Vol 440 ◽  
pp. 30-38
Author(s):  
Xue-rui Peng ◽  
Xu Lei ◽  
Peng Xu ◽  
Jing Yu
Keyword(s):  
Decision Making ◽  
Decision Making Under Risk ◽  
Age Related

scholarly journals Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making

2018 ◽  
Vol 2 (12) ◽  
pp. 955-966 ◽  
Author(s):  
David P. McGovern ◽  
Aoife Hayes ◽  
Simon P. Kelly ◽  
Redmond G. O’Connell
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Age Related ◽  
Age Related Changes

scholarly journals Perceptual decision-making in children: Age-related differences and EEG correlates

2020 ◽  
Author(s):  
Catherine Manning ◽  
Eric-Jan Wagenmakers ◽  
Anthony Norcia ◽  
Gaia Scerif ◽  
Udo Boehm
Keyword(s):  
Decision Making ◽  
Drift Rate ◽  
Decision Time ◽  
Diffusion Models ◽  
Perceptual Task ◽  
Model Parameters ◽  
Older Children ◽  
Perceptual Decision Making ◽  
Boundary Separation ◽  
Age Related

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.

scholarly journals Dynamics of taxonomic and functional community structure in macrozoobenthos and the underlying driving factors in Lake Dongting during 1988 and 2016

2021 ◽  
Vol 33 (1) ◽  
pp. 218-231
Author(s):  
Wang Chouming ◽  
◽  
Zhang Yi ◽  
Tian Qi ◽  
Huang Daizhong ◽  
...  
Keyword(s):  
Community Structure ◽  
Driving Factors ◽  
Functional Community

scholarly journals A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjun Cheng ◽  
Wenbo Zhang ◽  
Haijuan Yang ◽  
Xing Su ◽  
Tao Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Structure ◽  
Community Detection ◽  
Centrality Measures ◽  
Ideal Solution ◽  
Centrality Measure ◽  
Detection Algorithms ◽  
Pros And Cons ◽  
Topsis Technique

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

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