Value computations underlying human proposer behavior in the ultimatum game.

Pulcu, Erdem; Haruno, Masahiko

doi:10.1037/xge0000621

Cited by 12 publications

(42 citation statements)

References 53 publications

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“…The present results complement the findings of a recent work in which we described a computational model accounting for human proposer behaviour in the UG 7 . In these two studies, focussing on value computations underlying the proposer’s and the responder’s behaviour separately allow us to restrict the vast model space and identify suitable models accounting for human social interactive decision-making.…”

Section: Discussionsupporting

confidence: 89%

“…proposer’s facial emotions modulated by the nonlinear weighting model, Eq.1) and decision-values generated under the best-fitting model for each participant is shown in Supplementary Figure 7A (correlation coefficient r (mean±SD) =.058±.26). In a similar manner to our previous study 7 , we also asked our participants a number of questions related to how they felt about their opponents to make sure that the computerised strategy was perceived as “human-enough” (full set of questions given in Supplementary Figure 7B legends). On average, participants were able to identify more than one person from their social circles who would make offers and display affective reactions in a similar manner to the computerised opponent (response to Q3; mean (±SD) =3.82(±.40); t (43) =7.12, p<.001), reassuring that our experimental manipulation was successful in terms of the computerised opponent adequately mimicking human behaviour while keeping the correlations between perceived faces and offer amounts within an acceptable range.…”

Section: Resultsmentioning

confidence: 99%

“…Those individuals were explicitly told that they would be playing against a computerised strategy which was developed based on the behaviour of a previous participant. We used this manipulation in our previous work to investigate proposer behaviour in the UG 51 . In fact, all participants played against the same computerised strategy which was developed to sample offers and facial emotions probabilistically from two independent sliding windows.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, the responder’s rejections in the UG violate the assumptions of the Rational Actor Model, which posits that any monetary gain is better than no gain 6 and all offers should be accepted. In a recent study, we showed evidence to suggest that proposers use a risky decision-making model to navigate around violating responders’ fairness threshold, and try maximise their gains by choosing between Ultimatums based on their expected values 7 . Despite a breadth of studies describing decision-making processes in the UG 8–11 , the value computations underlying human responder behaviour are elusive and summary statistics describing average acceptance probabilities at different offer amounts remains as the only the primary outcome measure commonly reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Affective biases encoded by the central arousal systems dynamically modulate inequality aversion in human interpersonal negotiations

Murphy

Harmer

Browning

et al. 2019

Preprint

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AbstractNegotiating with others about how finite resources should be distributed is an important aspect of human social life. However, little is known about mechanisms underlying human social-interactive decision-making. Here, we report results from a novel iterative Ultimatum Game (UG) task, in which the proposer’s facial emotions and offer amounts were sampled probabilistically based on the participant’s decisions, creating a gradually evolving social-interactive decision-making environment. Our model-free results confirm the prediction that both the proposer’s facial emotions and the offer amount influence human choice behaviour. These main effects demonstrate that biases in facial emotion recognition also contribute to violations of the Rational Actor model (i.e. all offers should be accepted). Model-based analyses extend these findings, indicating that participants’ decisions are guided by an aversion to inequality in the UG. We highlight that the proposer’s facial responses to participant decisions dynamically modulate how human decision-makers perceive self–other inequality, relaxing its otherwise negative influence on decision values. In iterative games, this cognitive model underlies how offers initially rejected can gradually become more acceptable under increasing affective load, and accurately predicts 86% of participant decisions. Activity of the central arousal systems, assessed by measuring pupil size, encode a key element of this model: proposer’s affective reactions in response to participant decisions. Taken together, our results demonstrate that, under affective load, participants’ aversion to inequality is a malleable cognitive process which is modulated by the activity of the pupil-linked central arousal systems.

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Section: Discussionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Affective biases encoded by the central arousal systems dynamically modulate inequality aversion in human interpersonal negotiations

Murphy

Harmer

Browning

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a recent study, we showed novel evidence to suggest that in iterative games proposers use a risky decisionmaking model to navigate around violating responders' fairness thresholds. Proposers were shown to maximise their gains by choosing between Ultimatums based on their expected returns, while taking the probability of rejection into account 3 . Rejecting an unequal proposal in the UG is also framed as a form of altruistic punishment [4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Dynamic modulation of inequality aversion in human interpersonal negotiations

Murphy

Harmer

Browning

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Negotiating with others about how finite resources should be distributed is an important aspect of human social life. However, little is known about mechanisms underlying human social-interactive decision-making in gradually evolving environments. Here, we report results from a novel iterative Ultimatum Game (UG) task, in which the proposer’s facial emotions and offer amounts were sampled probabilistically based on the participant’s decisions. Our model-free results confirm the prediction that both the proposer’s facial emotions and the offer amount should influence human choice behaviour. Model-based analyses extend these findings, indicating that participants’ decisions in the UG are guided by aversion to inequality. We highlight that the proposer’s facial affective reactions to participant decisions dynamically modulate how human decision-makers perceive self–other inequality, relaxing its otherwise negative influence on decision values. In iterative games, this cognitive model underlies how offers initially rejected can gradually become more acceptable under increasing affective load, and accurately predicts ~ 86% of participant decisions. Furthermore, modelling human choice behaviour isolated the role of the central arousal systems, assessed by measuring pupil size, during interpersonal negotiations. We demonstrate that pupil-linked central arousal systems selectively encode a key component of subjective decision values that relate to the magnitude of self–other inequality. Taken together, our results demonstrate that, under affective influence, aversion to inequality is a malleable cognitive process.

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Population Modelling in Affective Disorders

Pulcu

2021

Curr Behav Neurosci Rep

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Purpose of Review The prevalence of affective disorders is on the rise. This upward trajectory leads to a substantial personal and societal cost. There is growing body of literature demonstrating decision-making impairments associated with affective disorders, and more studies are using computational modelling methods to infer underlying mechanisms of these impairments from participant choice behaviour. However, lack of population modelling suggests that data resources may still be underutilised. Recent Findings A number of recent studies associated major depression with abnormal risky decision-making as well as impairments in temporal discounting and social decision-making. These domains capture relevant aspects of real-life decision-making. Consequently, data from these studies can be used to define behavioural phenotypes for major depression. Summary The manuscript describes a detailed proposal for population modelling to capture changes in the prevalence rate of major depression. The population modelling approach can also identify which decision-making domains can account for a larger part of impairments in psychosocial functioning and how behavioural interventions built on computational principles can target these to improve real-life psychosocial functioning in patient groups.

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Value computations underlying human proposer behavior in the ultimatum game.

Cited by 12 publications

References 53 publications

Affective biases encoded by the central arousal systems dynamically modulate inequality aversion in human interpersonal negotiations

Affective biases encoded by the central arousal systems dynamically modulate inequality aversion in human interpersonal negotiations

Dynamic modulation of inequality aversion in human interpersonal negotiations

Population Modelling in Affective Disorders

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