Don’t bet on it! Wagering as a measure of awareness in decision making under uncertainty.

Abstract

Can our decisions be guided by unconscious or implicit influences? According to the somatic marker hypothesis, emotion-based signals can guide our decisions in uncertain environments outside awareness. Postdecision wagering, in which participants make wagers on the outcomes of their decisions, has been recently proposed as an objective and sensitive measure of conscious content. In 5 experiments we employed variations of a classic decision-making assessment, the Iowa Gambling Task, in combination with wagering in order to investigate the role played by unconscious influences. We examined the validity of postdecision wagering by comparing it with alternative measures of conscious knowledge, specifically confidence ratings and quantitative questions. Consistent with a putative role for unconscious influences, in Experiments 2 and 3 we observed a lag between choice accuracy and the onset of advantageous wagering. However, the lag was eliminated by a change in the wagering payoff matrix (Experiment 2) and by a switch from a binary wager response to either a binary or a 4-point confidence response (Experiment 3), and wagering underestimated awareness compared to explicit quantitative questions (Experiments 1 and 4). Our results demonstrate the insensitivity of postdecision wagering as a direct measure of conscious knowledge and challenge the claim that implicit processes influence decision making under uncertainty.

Choice in experiential learning: True preferences or experimental artifacts?

“…In addition, in Study 2 we found that the majority of participants knew which option was better and had confidence in this assessment (Konstantinidis & Shanks, 2014;Maia & McClelland, 2004). Nevertheless, participantsÕ confidence in their ability to correctly identify the best option varied and this variation was predictive of the degree of choice variance they showed in the DF and policy setting tasks.…”

Confidence and certainty: distinct probabilistic quantities for different goals

“…It is particularly important in sequential decisions, when subsequent choices depend on previous decisions 21,28–30 . One example of such a task is a post-decision wager, in which subjects are asked to place a bet on whether their decision was correct 28,31,32 . The optimal size of the wager, the investment, depends on the degree of belief that the initial choice was correct, with a higher wager when confidence is high 32 .…”

Approaches to Learning to Control Dynamic Uncertainty

“…The reasons for this may be because decisions in the IOWA gambling task are predominately based on a variety of models of the environment and emotional factors (e.g., [22]), which we discuss in Section 1.3. For instance, one view is that card selection strategies are likely to change over time because people construe the IOWA gambling task as a non-stationary environment, despite the fact that it is a stationary one [27,29,30]. However, there has been considerable debate recently concerning the validity of the task and the reliability of the findings and whether much can actually be made of the learning profiles of participants in the task [27,29,30].…”