Affect, Risk and Uncertainty in Decision-Marking an Integrated Computational-Empirical Approach
Abstract
We summarize a cross-disciplinary effort exploring affective biases in decision-making. The work consisted of an empirical and a computational modeling study, within the same synthetic task: a search-and-rescue task. The empirical study assessed effects of anxiety on decision-making (route selection). Participants were more sensitive to probabilities of costs and benefits, than to their quantitative values. Both threat and anxious mood induction (under low threat) appeared to increase sensitivity to loss. With a neutral emotion-induction, trait anxiety was associated with a classic selective attention basis. Anxious individuals sampled information on potential costs more frequently than information on potential gains. This bias was eliminated in the anxious emotion-induction condition. In the neutral condition, anxious subjects may frame decisions as requiring vigilance to threat (i.e., elevated attention and analysis), whereas in the anxious condition, the frame is one of escape (requiring less analysis). Computational modeling studies used the MAMID cognitive-affective architecture to construct a process model of anxiety effects: attentional threat and self-bias, and interpretive threat bias. Different levels of anxiety intensities were encoded in different values of architecture parameters, which controlled processing within the architecture modules, yielding results consistent with existing empirical data. The model was also used to construct alternative mechanisms capable of explaining the observed effects, thereby providing a means of generating candidate hypotheses regarding the nature of the processes mediating the biases. Findings make a methodological contribution in demonstrating how experimental emotion-induction can be successfully employed in a task that is longer, more complex and more demanding than those typically used in affective bias research. The data support the validity of the empirical-computational approach of this project.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 26, 2009
- Accession Number
- ADA505192
Entities
People
- Eva Hudlicka
- Gerald Matthews