The Psychological and Neural Basis of Loss Aversion

Sokol‐Hessner, Peter; Rutledge, Robb B.

doi:10.1177/0963721418806510

Cited by 105 publications

(84 citation statements)

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“…The results presented here provide strong behavioral evidence that these pathways have a dissociated influences on prefrontal or fronto-parietal top-down representations (Womelsdorf and Everling, 2015;Bisley and Mirpour, 2019). It will be important to understand in future studies how dopaminergic, noradrenergic and cholinergic modulation mediate these distinct processes that determine attentional efficacy (Collins and Frank, 2014;Berridge and Robinson, 2016;Hassani et al, 2017;Azimi et al, 2019;Sokol-Hessner and Rutledge, 2019).…”

Section: Expecting Gains Enhances Attentional Efficacy But Can't Compmentioning

confidence: 68%

Gains and Losses affect Learning Differentially at Low and High Attentional Load

Boroujeni

Watson

Womelsdorf

2020

Preprint

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Visual attention involves both enhancing the processing of stimuli that may lead to reward and avoiding the processing of stimuli that may lead to loss. But compared to reward expectancy little is known how loss-avoidance mediates attention. One hypothesis holds that attention is more efficiently deployed when expecting larger gains and larger losses as both increase motivational saliency. Alternatively, expecting larger penalties might reduce attentional efficacy and ‘loom’ larger than gains. Here, we tested these opposing views in four monkeys with a feature-token learning task that quantifies attentional efficacy by increasing distractor load from multidimensional objects. During learning the number of token rewards gained for correct choices and lost for incorrect choices were varied. We found that expecting larger gains improves attentional efficacy and learning speed as long as distractor load was low. In contrast, expecting larger losses impairs attentional efficacy and this impairment increases with distractor load and uncertainty about the relevance of visual features. These findings functionally dissociate the contributions of expecting gains and losses on attentional learning, suggesting that they operate via separate control pathways. One pathway is linked to avoiding loss by slowing down learning and disrupting attention non-selectively, while the second pathway enhances learning selectively for higher valued target features up to a limited distractor load. These results illustrate the strength and the limitation of motivational regulation of attentional efficacy during learning.Significance statementVisual attention is biased to objects promising higher expected gains, but it should likewise be biased to avoid processing objects associated with incurring a loss. Clarifying the roles of gains and losses to modulate attention is critical for understanding the possible neuronal processes that determine how efficient attention is deployed in real-world learning scenarios. This study clarifies that gains and losses have surprisingly strong, opposite effects on the efficacy of attentional deployment in environments with low and high distractor load. Attentional efficacy is enhanced with increased gains primarily when there are few distractors, while attentional efficacy is impaired with increased loss expectancy particularly when there are many distractors. These results clarify that avoiding loss and striving for gains affect attentional efficacy via partly distinct mechanisms.

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Section: Expecting Gains Enhances Attentional Efficacy But Can't Compmentioning

confidence: 68%

Gains and Losses affect Learning Differentially at Low and High Attentional Load

Boroujeni

Watson

Womelsdorf

2020

Preprint

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“…Finally, we examined whether the previous outcome effect differed as a function of outcome type (gain, loss, or guaranteed). Differences on the basis of outcome type could arise from differences between gains (risky and safe) and losses due to loss aversion 27,28 , or differences between risky outcomes (gain and loss) and guaranteed outcomes due to possible expectation-based learning processes (for example, processes reflecting prediction errors [29][30][31] ) as well as more complex interactions between valence and uncertainty. In model 7, we thus regressed binary choice onto three separate regressors, one each for previous risky gain outcome amount, previous risky loss outcome amount, and previous guaranteed outcome amount.…”

Section: Resultsmentioning

confidence: 99%

Quantifying the immediate computational effects of preceding outcomes on subsequent risky choices

Brooks

Sokol‐Hessner

2020

Sci Rep

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Forty years ago, prospect theory introduced the notion that risky options are evaluated relative to their recent context, causing a significant shift in the study of risky monetary decision-making in psychology, economics, and neuroscience. Despite the central role of past experiences, it remains unclear whether, how, and how much past experiences quantitatively influence risky monetary choices moment-tomoment in a nominally learning-free setting. We analyzed a large dataset of risky monetary choices with trial-by-trial feedback to quantify how past experiences, or recent events, influence risky choice behavior and the underlying processes. We found larger recent outcomes both negatively influence subsequent risk-taking and positively influence the weight put on potential losses. Using a hierarchical Bayesian framework to fit a modified version of prospect theory, we demonstrated that the same risks will be evaluated differently given different past experiences. The computations underlying risky decision-making are fundamentally dynamic, even if the environment is not. Prospect theory is conceivably the most successful recent theory in risky decision-making 1. The theory is founded on the idea that people evaluate options relative to "the past and present context of experience" (p. 277) 1. While this central insight led to two of the most successful components of prospect theory, the reflection effect in risk aversion (diminishing marginal utility of values expressed as concavity for gains and convexity for losses) and loss aversion (overweighting losses compared to gains of the same magnitude), it is unclear how recent events quantitatively influence risky monetary decision-making, moment-to-moment. We sought to quantify how recent events (e.g. previous outcomes) influence choices by analyzing a large dataset of risky choices in the presence of feedback compiled from four studies 2-5. Laboratory and field studies have suggested that recent events shape the subjective value of risky choice options 6-9 and actions 10. The directionality and mechanism supporting these effects are unclear as some studies of risky decision-making in the presence of feedback have found that previous gains, relative to losses, lead to less risk-taking 6,10 or a mixture of less and more risk-taking 7-9,11 , discrepancies that may be related to how and when outcomes are realized, or paid 12. Theories that have sought to specify the directionality of the effect of feedback on risky choice have not described the size of the effect, its characteristics (e.g. how far it extends in time) or the precise decision processes that would be altered by feedback 13. Interpreting these previous findings is complicated by the fact that almost all of these studies included design features intended to elicit temporal context effects (e.g. explicitly-signaled contexts, displays with cumulative elements) and thus may have strengthened, or even created, dynamic context effects. While such an approach identifies an upper bound on the possible presence and ma...

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“…While the above studies clearly demonstrate that social factors influence risk-taking, they do not dissociate or quantify how and which specific computational mechanisms supporting risky decisionmaking are altered when our choices affect another person, and this matters: depending on the mechanism(s) affected, we might expect to subsequently find differences in risktaking in some social settings but not in others, thus reconciling seemingly contradictory findings (Ogawa et al, 2018;Zhang et al, 2019). Furthermore, identifying socially dependent changes in dissociable psychological mechanisms underlying risk-taking can indicate targets for cognitive or pharmacological intervention when such choices are maladaptive (Sokol-Hessner & Rutledge, 2019).…”

Section: Introductionmentioning

confidence: 94%

“…Healthy individuals tend to be risk averse for gains, favoring smaller but guaranteed positive options relative to larger but uncertain or risky options (Kahneman & Tversky, 1979). Healthy individuals also tend to be loss averse, requiring a larger potential reward to accept the risk of incurring a loss (Kahneman & Tversky, 1979;Sokol-Hessner & Rutledge, 2019). While individual differences in risk attitudes are relatively stable, loss aversion has been shown to be malleable as a function of decision context (Harrison et al, 2005;Sokol-Hessner et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Choosing for others changes dissociable computations underlying risky decision-making

Ds¹,

Stasiak²,

Sokol‐Hessner³

2020

Preprint

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Choices under conditions of risk often have consequences not just for ourselves, but for others. Yet, it is unclear how the other’s identity (stranger, close friend, etc.) influences risky choices made on their behalf. Here, two groups of undergraduates made a series of risky economic decisions for themselves, for another person, or for both themselves and another person (i.e., shared outcomes); one group of participants made choices involving a same-sex stranger (n = 29), the other made choices involving a same-sex close friend (n = 28). Hierarchical Bayesian Estimation of computations underlying risky decision-making revealed that relative to choosing for themselves, people were more risk averse, more loss averse, and more consistent when choices involved another person. Interestingly, partner identity differentially modulated decision computations. People became risk neutral and more consistent when choosing for friends relative to strangers. In sum, these findings suggest that the complexity of the social world is mirrored in its nuanced consequences for our choices.

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The Psychological and Neural Basis of Loss Aversion

Cited by 105 publications

References 50 publications

Gains and Losses affect Learning Differentially at Low and High Attentional Load

Gains and Losses affect Learning Differentially at Low and High Attentional Load

Quantifying the immediate computational effects of preceding outcomes on subsequent risky choices

Choosing for others changes dissociable computations underlying risky decision-making

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