ORCID Profile
0000-0003-0832-1565
Current Organisation
University of Tsukuba
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Science and Business Media LLC
Date: 04-10-2022
DOI: 10.1038/S41467-022-33579-0
Abstract: Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of in idual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of in idual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The erse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals’ choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 19-05-2023
Abstract: Research in the multidisciplinary field of neuroeconomics has mainly been driven by two influential theories regarding human economic choice: prospect theory, which describes decision-making under risk, and reinforcement learning theory, which describes learning for decision-making. We hypothesized that these two distinct theories guide decision-making in a comprehensive manner. Here, we propose and test a decision-making theory under uncertainty that combines these highly influential theories. Collecting many gambling decisions from laboratory monkeys allowed for reliable testing of our model and revealed a systematic violation of prospect theory’s assumption that probability weighting is static. Using the same experimental paradigm in humans, substantial similarities between these species were uncovered by various econometric analyses of our dynamic prospect theory model, which incorporates decision-by-decision learning dynamics of prediction errors into static prospect theory. Our model provides a unified theoretical framework for exploring a neurobiological model of economic choice in human and nonhuman primates.
Publisher: Cold Spring Harbor Laboratory
Date: 05-04-2021
DOI: 10.1101/2021.04.04.438415
Abstract: Research in behavioral economics and reinforcement learning has given rise to two influential theories describing human economic choice under uncertainty. The first, prospect theory, assumes that decision-makers use static mathematical functions, utility and probability weighting, to calculate the values of alternatives. The second, reinforcement learning theory, posits that dynamic mathematical functions update the values of alternatives based on experience through reward prediction error (RPE). To date, these theories have been examined in isolation without reference to one another. Therefore, it remains unclear whether RPE affects a decision-maker’s utility and/or probability weighting functions, or whether these functions are indeed static as in prospect theory. Here, we propose a dynamic prospect theory model that combines prospect theory and RPE, and test this combined model using choice data on gambling behavior of captive macaques. We found that under standard prospect theory, monkeys, like humans, had a concave utility function. Unlike humans, monkeys exhibited a concave, rather than inverse-S shaped, probability weighting function. Our dynamic prospect theory model revealed that probability distortions, not the utility of rewards, solely and systematically varied with RPE: after a positive RPE, the estimated probability weighting functions became more concave, suggesting more optimistic belief about receiving rewards and over-weighted subjective probabilities at all probability levels. Thus, the probability perceptions in laboratory monkeys are not static even after extensive training, and are governed by a dynamic function well captured by the algorithmic feature of reinforcement learning. This novel evidence supports combining these two major theories to capture choice behavior under uncertainty. We propose and test a new decision theory under uncertainty by combining pre-existing two influential theories in the neuroeconomics: prospect theory from economics and prediction error theory from reinforcement learning. Collecting a large dataset (over 60,000 gambling decisions) from laboratory monkeys enables us to test the hybrid model of these two core decision theories reliably. Our results showed over-weighted subjective probabilities at all probability levels after lucky win, indicating that positive prediction error systematically bias decision-makers more optimistically about receiving rewards. This trial-by-trial prediction-error dynamics in probability perception provides outperformed performance of the model compared to the standard static prospect theory. Thus, both static and dynamic elements coexist in monkey’s risky decision-making, an evidence contradicting the assumption of prospect theory.
Publisher: Research Square Platform LLC
Date: 09-09-2022
DOI: 10.21203/RS.3.RS-2017714/V1
Abstract: Research in the multidisciplinary field of neuroeconomics has been driven by two influential theories regarding human economic choice: prospect theory, which describes decision-making under risk, and reinforcement learning theory, which describes learning for decision-making. We hypothesized that these two distinct theories guide decision-making in a comprehensive manner. Here, we propose and test a new decision-making theory under uncertainty that combines these highly influential theories. Collecting many gambling decisions from laboratory monkeys allowed for reliable testing of our hybrid model and revealed a systematic violation of prospect theory’s assumption that probability weighting is static. Using the same experimental paradigm in humans, substantial similarities between monkey and human behavior were described by our hybrid model, which incorporates decision-by-decision learning dynamics of prediction errors into static prospect theory. Our new model provides a single unified theoretical framework for exploring the neurobiological model of economic choice in human and nonhuman primates.
No related grants have been discovered for Masayuki Matsumoto.