We study the role of attention and working memory in choices where options are presented sequentially rather than simultaneously. We build a model where a costly attention effort is chosen, which can vary over time. Evidence is accumulated proportionally to this effort and the utility of the reward. Crucially, the evidence accumulated decays over time. Optimal attention allocation maximizes expected utility from final choice; the optimal solution takes the decay into account, so attention is preferentially devoted to later times; but convexity of the flow attention cost prevents it from being concentrated near the end. We test this model with a choice experiment where participants observe sequentially two options. In our data the option presented first is, everything else being equal, significantly less likely to be chosen. This recency effect has a natural explanation with appropriate parameter values in our model of leaky evidence accumulation, where the decline is stronger for the option observed first. Analysis of choice, response time and brain imaging data provide support for the model. Working memory plays an essential role. The recency bias is stronger for participants with weaker performance in working memory tasks. Also activity in parietal areas, coding the stored value in working, declines over time as predicted.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566694 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284127 | PLOS |
Pilot Feasibility Stud
January 2025
Department of Health Service & Population Research, David Goldberg Centre, King's College London, Denmark Hill, London, UK.
Background: Mental health disorders are one of the leading causes of illness globally. The importance of psychosocial skills acquired in early childhood, such as executive functions, inhibitory control, emotional regulation, and social problem-solving, in preventing mental disorders has been reported. Furthermore, mental health care delivery is evolving, and mobile technology is becoming the medium for assessment and intervention.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany.
A substantial proportion of patients suffer from Post-COVID Syndrome (PCS) with fatigue and impairment of memory and concentration being the most important symptoms. We here set out to perform in-depth neuropsychological assessment of PCS patients referred to the Neurologic PCS clinic compared to patients without sequelae after COVID-19 (non-PCS) and healthy controls (HC) to decipher the most prevalent cognitive deficits. We included n = 60 PCS patients with neurologic symptoms, n = 15 non-PCS patients and n = 15 healthy controls.
View Article and Find Full Text PDFComput Biol Med
January 2025
Thai Nguyen University of Information and Communication Technology, Thai Nguyen City, Viet Nam. Electronic address:
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
Accurately predicting carbon prices is crucial for effective government decision-making and maintenance the stable operation of carbon markets. However, the instability and nonlinearity of carbon prices, driven by the complex interaction between economic, environmental, and political factors, often result in inaccurate predictions. To confront this challenge, this paper proposed a carbon price prediction model that integrates dual decomposition integration and error correction.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!