Prospect Theory and the Brain

Fox, Craig R.; Poldrack, Russell A.

doi:10.1016/b978-0-12-416008-8.00042-5

Cited by 57 publications

(59 citation statements)

References 195 publications

(182 reference statements)

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“…The independently estimated mean and median parameter values are shown in Tables 1 and 2. Overall, the results are comparable to previously obtained parameter estimates (see Fox &Poldrack, 2008, andBirnbaum, 2008, for CPT andTAX, respectively), and the independently estimated parameters also approximate those from Glöckner and Pachur's analysis based on maximum likelihood estimation. Both CPT and TAX show the typical pattern of reduced outcome sensitivity (with α and β both being <1) and probability sensitivity (with γ + /γ -<1 and γ <1).…”

Section: Parameter Estimatessupporting

confidence: 78%

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

2014

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To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choicecumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test-retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.

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Section: Parameter Estimatessupporting

confidence: 78%

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

2014

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“…First, we inspected the means of the posterior group-level distributions of the CPT parameters in the two experimental sessions (see Table 1). The values were consistent with the range of values obtained in previous studies (for an overview, see Fox & Poldrack, 2014 The magnitude of the correlations suggests that the individual parameter values were quite stable over time (see Glöckner & Pachur, 2012). Note: HDI = 95% highest density interval.…”

Section: Resultssupporting

confidence: 79%

“…The parameters + and -(both > 0) govern the elevation of the weighting function for gains and losses, respectively, and are often interpreted as indicating the degree of optimism or pessimism (e.g., Gonzalez & Wu, 1999). We estimated a single γ parameter across gains and losses (i.e., γ + = γ -), because probability sensitivity is typically found to be very similar across domains (Fox & Poldrack, 2014;Glöckner & Pachur, 2012;Tversky & Kahneman, 1992). In contrast, because, for instance, a high value on the elevation parameter implies opposite risk attitudes in the gain and loss domains (optimism To derive predicted choice probabilities from CPT, we used an exponential version of Luce's choice rule (also known as softmax or logit function), which defines the probability that a gamble A is chosen over a gamble B as:…”

Section: Experiments 1 Methodsmentioning

confidence: 99%

Prospect Theory Reflects Selective Allocation of Attention

Pachur¹,

Schulte‐Mecklenbeck²,

Hertwig³

2014

PsycEXTRA Dataset

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“…The feeling function that best related feelings to value was revealed to be concave for gains and convex for losses, much as the value function in prospect theory (Kahneman & Tversky, 1979;Tversky & Kahneman, 1992) and other nonlinear utility functions (Bernoulli, 1954;Fox & Poldrack, 2014;Stauffer, Lak, & Schultz, 2014 The right column shows correlations between the propensity to gamble and loss-gain weight difference for expected feelings (top) and experienced feelings (bottom). Best-fitting regression lines are shown for each set of data.…”

Section: Discussionmentioning

confidence: 99%

Models of Affective Decision-Making: How Do Feelings Predict Choice?

et al. 2016

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Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision.

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Prospect Theory and the Brain

Cited by 57 publications

References 195 publications

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

Prospect Theory Reflects Selective Allocation of Attention

Models of Affective Decision-Making: How Do Feelings Predict Choice?

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