2022
DOI: 10.1093/cercor/bhac482
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Distinct neural activations correlate with maximization of reward magnitude versus frequency

Abstract: Choice selection strategies and decision-making are typically investigated using multiple-choice gambling paradigms that require participants to maximize expected value of rewards. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains such that users often choose smaller frequent gains over larger rarely occurring gains, also referred to as melioration. To understand the basis of this subjective tradeoff, we used a simple 2-choice reward task p… Show more

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Cited by 12 publications
(12 citation statements)
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“…We applied a uniform processing pipeline to all EEG data published in several of our studies [ 43 , 45 , 46 , 48 - 53 ]. This included (1) EEG channel data processing and (2) cortical source localization of the EEG data to estimate source-level neural activity.…”
Section: Methodsmentioning
confidence: 99%
“…We applied a uniform processing pipeline to all EEG data published in several of our studies [ 43 , 45 , 46 , 48 - 53 ]. This included (1) EEG channel data processing and (2) cortical source localization of the EEG data to estimate source-level neural activity.…”
Section: Methodsmentioning
confidence: 99%
“…Urgent climate action is the need of the hour, but as individual persons and organizations, we focus on immediate short-term benefits, and shy away from sacrifices that are needed for long-term sustainability. In one of our other recent studies of how humans process rewards [12], we gave people the choice of two decks. One deck led to frequent short-term small value Brain activity correlates of human performance when choosing long-term payoff show medial brain region activations, especially in memory processing areas (encircled in left column).…”
Section: Competing Interestsmentioning
confidence: 99%
“…Non-encircled colored brain activity is significant on the payoff task but doesn't directly correlate with performance. Different colors denote different oscillatory frequencies of brain signals; dark blue: 3-7 Hz theta band, brown: 8-12 Hz alpha band, cyan: 13-30 Hz beta band [12]. https://doi.org/10.1371/journal.pclm.0000233.g001…”
Section: Competing Interestsmentioning
confidence: 99%
“…We have implemented a scalable platform for measuring core aspects of cognition that delivers standard cognitive assessments synchronized with neural recordings using electroencephalography (EEG) 26,27 . We have validated the reliability of this BrainE© platform and shown its utility in measuring cognitive changes across the lifespan [28][29][30][31][32] . We also demonstrated its relevance to predict mental health [33][34][35][36] .…”
Section: Introductionmentioning
confidence: 96%