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Nonlinear Probability Weighting Can Reflect Attentional Biases in Sequential Sampling| title | Nonlinear Probability Weighting Can Reflect Attentional Biases in Sequential Sampling |
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| start_date | 2022/05/20 |
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| schedule | 14h30 |
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| online | no |
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| summary | Nonlinear probability weighting allows cumulative prospect theory (CPT) to account for key violations of utility maximization in decision making under risk (e.g., certainty effect, fourfold pattern of risk attitudes). It describes the impact of risky outcomes on preferences in terms of a rank-dependent nonlinear transformation of their objective probabilities. However, it is unclear how specific shapes of the probability weighting function come about on the level of cognitive processing. The attentional Drift Diffusion Model (aDDM) formalizes the finding that attentional biases toward an option can shape preferences within a sequential sampling process. Here I link these two influential frameworks. The aDDM is used to simulate choices between two options while systematically varying the strength of attentional biases to either option. The resulting choices were modeled with CPT. Changes in preference due to attentional biases in the aDDM were reflected in highly systematic signatures in the parameters of CPT’s weighting function (curvature, elevation). These results advance the integration of two prominent computational frameworks for decision making. Moreover, I demonstrate that attentional biases are also empirically linked to patterns in probability weighting as suggested by the simulations, and test whether these effects of attention allocation on probability weighting are causal. Overall, the findings highlight that distortions in probability weighting can arise from simple option-specific attentional biases in information search, and suggest an alternative to common interpretations of weighting-function parameters in terms of probability sensitivity and optimism. They also point to novel, attention-based explanations for empirical phenomena associated with characteristic shapes of CPT’s probability-weighting function (e.g., certainty effect, description–experience gap), and to possible interventions to alleviate common biases in decision making under risk. |
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| responsibles | Palminteri |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2022/06/30 08:26 UTC |
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