scholarly journals People Adjust Recency Adaptively to Environment Structure

2021 ◽  
Author(s):  
Mahi Luthra ◽  
Peter M. Todd
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Real World ◽  
Recency Effects ◽  
Deliberate Strategy ◽  
Adaptive Decision Making ◽  
Decision Tasks ◽  
External Environments

Recency effects—giving exaggerated importance to recent outcomes—are a common aspect of decision tasks. In the current study, we explore two explanations of recency-based decision making, that it is (1) a deliberate strategy for adaptive decision making in real-world environments which tend to be dynamic and autocorrelated, and/or (2) a product of processing limitations of working memory. Supporting explanation 1, we found that participants strategically adjusted their recency levels across trials to achieve optimal levels in a range of tasks. Furthermore, they started with default recency values that had high aggregate performance across environments. However, only some correlations between recency values and WM scores were significant, providing no clear conclusion regarding explanation 2. Ultimately, we propose that recency involves a combination of the two—people can strategically change recency within the limits of WM capacities to adapt to external environments.

scholarly journals Geometry of neural computation unifies working memory and planning

2021 ◽  
Author(s):  
Daniel B. Ehrlich ◽  
John D. Murray
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Working Memory ◽  
Prefrontal Cortex ◽  
Human Behavior ◽  
Real World ◽  
Recurrent Neural Networks ◽  
Neural Computation ◽  
Neural Data ◽  
The Brain

Real-world tasks require coordination of working memory, decision making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. Our experiments revealed that human behavior is consistent with contingency representations, and not with traditional sensory models of working memory. In task-optimized recurrent neural networks we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from prefrontal cortex during working memory tasks. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision making.

Taxing Executive Processes Does Not Necessarily Increase Impulsive Decision Making

2010 ◽  
Vol 57 (3) ◽  
pp. 193-201 ◽  
Author(s):  
Ana M. Franco-Watkins ◽  
Timothy C. Rickard ◽  
Hal Pashler
Keyword(s):  
Decision Making ◽  
Substance Abuse ◽  
Working Memory ◽  
Cognitive Load ◽  
Delay Discounting ◽  
Real World ◽  
Load Condition ◽  
Normal Sample ◽  
Executive Processes ◽  
Generation Task

A link has been established between impulsivity in real-world situations and impulsive decision making in laboratory tasks in brain-damaged patients and individuals with substance abuse. Whether or not this link exists for all individuals is less clear. We conducted an experiment to determine whether taxing central executive processes with a demanding cognitive load task results in impulsive decision making in a normal sample. Participants (n = 53) completed a delay discounting task under the presence (load condition) and absence (control condition) of a demanding generation task. Results indicated that taxing working memory is neither necessary nor sufficient to produce impulsive decision making; instead, the demanding generation task resulted in an increase in the number of inconsistent choices.

Training Situation Awareness and Adaptive Decision-Making Skills Using a Desktop Computer Simulation

2009 ◽  
Author(s):  
Robert J. Pleban ◽  
Jennifer S. Tucker ◽  
Vanessa Johnson Katie /Gunther ◽  
Thomas R. Graves
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Situation Awareness ◽  
Desktop Computer ◽  
Adaptive Decision Making

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence
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