The cognitive mechanism underlying decisions based on sequential samples has been found to be affected by whether multiple alternatives are evaluated together or whether each alternative is evaluated individually. In this experiment, we examined whether evaluation format can also lead to different preference orders among risky alternatives. We hypothesized that because of differences in computational demands posed by the 2 evaluation formats, there would be differences in the type of the cognitive mechanism deployed: a risk-return mechanism, that trades off the mean reward and risk of an alternative, or a selective-accumulator mechanism, that sums the rewards of each alternative, with a higher weight to more extreme payoffs. Each participant rated the same set of alternatives (sequences of payoffs from slot machines) in both a one-by-one and a grouped evaluation format. The mean and the variance of the payoff distributions of each alternative were varied orthogonally. As predicted, in the grouped (but not in the one-by-one) condition, the impact of the variance on participants’ ratings interacted with the mean payoff. Specifically, participants were risk averse for alternatives with a low mean payoff and risk seeking for alternatives with a high mean payoff. Computational modeling showed that the majority of participants were best described by a risk–return model in the one-by-one condition but by a selective-accumulator model in the grouped condition. Our results underline the importance of studying the cognitive foundations of risk attitudes in order to understand how they are shaped by the structure of a given decision task.
An enduring debate in decision-making and social cognition concerns the algorithm governing the formation of intuitive preferences and attitudes. Here we contrast 2 principles that are considered central to such judgments: averaging versus summation. Participants in 4 experiments were prompted to rely on their intuition when rating the Hall of Fame eligibility of basketball players, or their liking of athletes, lecturers or slot-machines, on the basis of rapid numerical sequences that represent performances, class feedback, or rewards. Experiment 1 showed that participants are sensitive to the sequences’ averages, and prefer alternatives with high averages over those with high sums. Experiment 2 replicated these findings, and further showed that in a comparison between several models such as averaging, summation and the Peak-End heuristic, averaging type models account best for participants’ preferences. Experiment 3 indicated that these evaluations are mediated by automatic/intuitive processes. Based on computational considerations we propose that the critical variable, determining whether preferences are more sensitive to sums or to averages, is the presentation and evaluation format: one by one versus grouped. This prediction is confirmed in Experiment 4.
We examine how bottom-up (or stimulus-driven) and top-down (or goal-driven) processes govern the distribution of attention in risky choice. In three experiments, participants chose between a certain payoff and the chance of receiving a payoff drawn randomly from an array of eight numbers. We tested the hypothesis that initial attention is driven by perceptual properties of the stimulus (e.g., font size of the numbers), but subsequent choice is goal-driven (e.g., win the best outcome). Two experiments in which task framing (goal driven) and font size (stimulus driven) were manipulated demonstrated that payoffs with the highest values and the largest font sizes had the greatest impact on choice. The third experiment added a number in large font to the array, which could not be an outcome of the gamble (i.e., a distractor). Eye movement and choice data indicated that although the distractor attracted attention, it had no influence on option selection. Together with computational modeling analyses, the results suggest that perceptual salience can induce bottom-up effects of overt selection but that the perceived value of information is the crucial arbiter of intentional control over risky choice.
We present a diffusion model analysis of the effect of aging on decision processes during driving. Our goal was to examine the changes in the underlying components as a function of age and both task and environment difficulty. Younger and older adults performed each of three decision-making tasks while operating a computer-based driving simulator in which the task required a driving action. The first task was a one-choice task in which the response to brake lights turning on was to drive around a lead car. The second and third tasks were two-choice brightness-discrimination tasks in which participants were asked to drive the car to the left/right if there were more black/white pixels in an array of black and white pixels. Results showed that older adults were slower in the one-choice task and made more errors in the two-choice tasks than younger adults. The behavioral data were fitted well by one- and two-choice diffusion models, showing lower evidence accumulation rates (drift rates) in older than younger adults. Moreover, in the two-choice tasks under higher environmental demands, older adults showed a lower decision criterion (boundary separation) to compensate for a slower decision process. Together, the differences we found in the decision components between age groups provided an example of a subtle interaction between speed and accuracy in older versus younger adults, and this demonstrates the utility of this modeling approach in studying age effects in driving.
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