Too Good to Be True: Rhesus Monkeys React Negatively to Better-than-Expected Offers

Knight, Emily; Klepac, Kristen M.; Kralik, Jerald D.

doi:10.1371/journal.pone.0075768

Cited by 7 publications

(7 citation statements)

References 96 publications

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“…To better understand the influence of attention and memory on decision-making dynamics, we examined free-choice sequences of multi-option, multi-attribute decisions in rhesus monkeys as they compared different food items in view. Rhesus monkeys are a representative catarrhine primate (comprising Old World monkeys, apes, and humans) whose findings contribute to our understanding of the evolutionary origins of decision-making processes; they are also well-established as a non-human primate model of human decision making (Glimcher et al, 2008; Platt and Ghazanfar, 2010; Xu et al, 2011; Kralik et al, 2012; Jung and Kralik, 2013; Knight et al, 2013). In particular, we addressed the question of how selective attention and memory relate to the two key components of sequential decision making: preference bias, i.e., the overall choice distribution, and choice persistence, i.e., the tendency to repeat choices.…”

Section: Resultsmentioning

confidence: 99%

A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making

Jung

Jeong

Kralik

2019

Front. Comput. Neurosci.

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Real-life decisions often require a comparison of multi-attribute options with various benefits and costs, and the evaluation of each option depends partly on the others in the choice set (i.e., the choice context). Although reinforcement learning models have successfully described choice behavior, how to account for multi-attribute information when making a context-dependent decision remains unclear. Here we develop a computational model of attention control that includes context effects on multi-attribute decisions, linking a context-dependent choice model with a reinforcement learning model. The overall model suggests that the distinctiveness of attributes guides an individual's preferences among multi-attribute options via an attention-control mechanism that determines whether choices are selectively biased toward the most distinctive attribute (selective attention) or proportionally distributed based on the relative distinctiveness of attributes (divided attention). To test the model, we conducted a behavioral experiment in rhesus monkeys, in which they made simple multi-attribute decisions over three conditions that manipulated the degree of distinctiveness between alternatives: (1) four foods of different size and calorie; (2) four pieces of the same food in different colors; and (3) four identical pieces of food. The model simulation of the choice behavior captured the preference bias (i.e., overall preference structure) and the choice persistence (repeated choices) in the empirical data, providing evidence for the respective influences of attention and memory on preference bias and choice persistence. Our study provides insights into computations underlying multi-attribute decisions, linking attentional control to decision-making processes.

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Section: Resultsmentioning

confidence: 99%

A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making

Jung

Jeong

Kralik

2019

Front. Comput. Neurosci.

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“…Recent experimental human and monkey studies have shown that surprising events or outcomes harm individuals, even when the error is positive (Knight et al, 2013;Topolinski and Strack, 2015). Topolinski and Strack (2015) used facial electromyography to measure people's responses immediately after surprising information; they observed that participants flexed their corrugator muscles, which indicated negative valence.…”

Section: Introductionmentioning

confidence: 99%

“…Topolinski and Strack (2015) used facial electromyography to measure people's responses immediately after surprising information; they observed that participants flexed their corrugator muscles, which indicated negative valence. Knight et al (2013) showed that in a predictable food-taking paradigm, rhesus monkeys were slower to accept unexpected offers and exhibited aversive reactions, such as repeatedly turning their heads and looking away before accepting the food item, especially in response to better-than-expected offers. These studies indicate that surprise decreases, at least initially, the value of an event for individuals and decreases the value of the offer associated with the event.…”

Section: Introductionmentioning

confidence: 99%

Surprise Acts as a Reducer of Outcome Value in Human Reinforcement Learning

Sumiya

Katahira

2020

Front. Neurosci.

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Surprise occurs because of differences between a decision outcome and its predicted outcome (prediction error), regardless of whether the error is positive or negative. It has recently been postulated that surprise affects the reward value of the action outcome; studies have indicated that increasing surprise as an absolute value of prediction error decreases the value of the outcome. However, how surprise affects the value of the outcome and subsequent decision making is unclear. We suggest that, on the assumption that surprise decreases the outcome value, agents will increase their risk-averse choices when an outcome is often surprising. Here, we propose the surprisesensitive utility model, a reinforcement learning model that states that surprise decreases the outcome value, to explain how surprise affects subsequent decision making. To investigate the properties of the proposed model, we compare the model with previous reinforcement learning models on two probabilistic learning tasks by simulations. As a result, the proposed model explains the risk-averse choices like the previous models, and the risk-averse choices increase as the surprise-based modulation parameter of outcome value increases. We also performed statistical model selection by using two experimental datasets with different tasks. The proposed model fits these datasets better than the other models with the same number of free parameters, indicating that the model can better capture the trial-by-trial dynamics of choice behavior.

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“…Recent human and monkey experimental studies have shown that surprisal events or outcomes have a negative impact on individuals, even when the error is positive [4,5]. Topolinski and Strack [5] employed facial electromyography to measure people's responses immediately following surprising information; they observed that participants flexed their corrugator muscles, corresponding to a furrowing of the eyebrows, which indicated negative valence.…”

Section: Introductionmentioning

confidence: 99%

“…Topolinski and Strack [5] employed facial electromyography to measure people's responses immediately following surprising information; they observed that participants flexed their corrugator muscles, corresponding to a furrowing of the eyebrows, which indicated negative valence. Knight et al [4] showed that, in a predictable food taking paradigm, rhesus monkeys were slower to accept unexpected offers and exhibited aversive reactions such as repeatedly turning their heads and looking away before accepting the food item, especially in response to better-than-expected offers. These studies indicate that surprise decreases, at least initially, the value of an event for individuals, and decreases the value of the offer associated with the event.…”

Section: Introductionmentioning

confidence: 99%

Surprise acts as a reducer of outcome value in human reinforcement learning

Sumiya¹,

Katahira²

2019

Preprint

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Surprise occurs because of differences between a decision outcome and its predicted outcome (prediction error), regardless of whether the error is positive or negative. It has recently been postulated that surprise affects the reward value of the action outcome itself; studies have indicated that increasing surprise, as absolute value of prediction error, decreases the value of the outcome. However, how surprise affects the value of the outcome and subsequent decision making is unclear. We suggested that, on the assumption that surprise decreases the outcome value, agents will increase their risk averse choices when an outcome is often surprisal. Here, we propose the surprise-sensitive utility model, a reinforcement learning model that states that surprise decreases the outcome value, to explain how surprise affects subsequent decision-making. To investigate the assumption, we compared this model with previous reinforcement learning models on a risky probabilistic learning task with simulation analysis, and model selection with two experimental datasets with different tasks and population. We further simulated a simple decision-making task to investigate how parameters within the proposed model modulate the choice preference. As a result, we found the proposed model explains the risk averse choices in a manner similar to the previous models, and risk averse choices increased as the surprise-based modulation parameter of outcome value increased. The model fits these datasets better than the other models, with same free parameters, thus providing a more parsimonious and robust account for risk averse choices. These findings indicate that surprise acts as a reducer of outcome value and decreases the action value for risky choices in which prediction error often occurs.

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Too Good to Be True: Rhesus Monkeys React Negatively to Better-than-Expected Offers

Cited by 7 publications

References 96 publications

A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making

A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making

Surprise Acts as a Reducer of Outcome Value in Human Reinforcement Learning

Surprise acts as a reducer of outcome value in human reinforcement learning

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