Inferences about target variables can be achieved by deliberate integration of probabilistic cues or by retrieving similar cue-patterns (exemplars) from memory. In tasks with cue information presented in on-screen displays, rule-based strategies tend to dominate unless the abstraction of cue-target relations is unfeasible. This dominance has also been demonstrated — surprisingly — in experiments that demanded the retrieval of cue values from memory (M. Persson & J. Rieskamp, 2009). In three modified replications involving a fictitious disease, binary cue values were represented either by alternative symptoms (e.g., fever vs. hypothermia) or by symptom presence vs. absence (e.g., fever vs. no fever). The former representation might hinder cue abstraction. The cues were predictive of the severity of the disease, and participants had to infer in each trial who of two patients was sicker. Both experiments replicated the rule-dominance with present-absent cues but yielded higher percentages of exemplar-based strategies with alternative cues. The experiments demonstrate that a change in cue representation may induce a dramatic shift from rule-based to exemplar-based reasoning in formally identical tasks.

Research has shown that risk tolerance increases when multiple decisions and associated outcomes are presented together in a broader “bracket” rather than one at a time. The present studies disentangle the influence of problem bracketing (presenting multiple investment options together) from that of outcome bracketing (presenting the aggregated outcomes of multiple decisions), factors which have been deliberately confounded in previous research. In the standard version of the bracketing task, in which participants decide how much of an initial endowment to invest into each in a series of repeated, identical gambles, we find a problem bracketing effect but not an outcome bracketing effect. However, this pattern of results does not generalize to the cases of non-identical gambles nor discrete choice, where we fail to find the standard bracketing effect.

This paper examines conditions that do or do not lead to accurate judgments of frequency (JOF) and judgments of duration (JOD). In three experiments, duration and frequency of visually presented stimuli are varied orthogonally in a within-subjects design. Experiment 1 reveals an asymmetric judgment pattern. JOFs reflected actual presentation frequency quite accurately and were unbiased by exposure duration. Conversely, JODs were almost insensitive to actual exposure duration and were systematically biased by presentation frequency. We show, however, that a tendency towards a symmetric judgment pattern can be obtained by manipulating encoding conditions. Sustaining attention during encoding (Experiment 2) or enhancing richness of the encoded stimuli (Experiment 3) increases judgment sensitivity in JOD and yields biases in both directions (JOF biased by exposure duration, JOD biased by presentation frequency). The implications of these findings for underlying memory mechanisms are discussed.

Often to the detriment of human decision making, people are prone to an impact bias when making affective forecasts, overestimating the emotional consequences of future events. The cognitive processes underlying the impact bias, and methods for correcting it, have been debated and warrant further exploration. In the present investigation, we examined both individual differences and contextual variables associated with cognitive processing in affective forecasting for an election. Results showed that the perceived importance of the event and working memory capacity were both associated with an increased impact bias for some participants, whereas retrieval interference had no relationship with bias. Additionally, an experimental manipulation effectively reduced biased forecasts, particularly among participants who were most distracted thinking about peripheral life events. These findings have theoretical implications for understanding the impact bias, highlight the importance of individual differences in affective forecasting, and have ramifications for future decision making research. The possible functional role of the impact bias is discussed within the context of evolutionary psychology.

Unbiased lotteries seem the least unfair and simplest procedures to allocate scarce indivisible resources to those with equal claims. But, when lotteries are repeated, it is not immediately obvious whether prior winners should be included or excluded. As in design questions surrounding single-shot lotteries, considerations of self-interest and distributive social preferences may interact. We investigate preferences for allowing participation of earlier winners in sequential lotteries. We found a strong preference for exclusion, both in settings where subjects were involved, and those where they were not. Subjects who answered questions about both settings did not differ in their tendency to prefer exclusion. Stated rationales significantly predicted choice but did not predict switching of choices between the two settings.

We compared South Koreans with Australians in order to characterize cultural differences in attitudes and choices regarding risk, at both the individual and group levels. Our results showed that Australians, when assessed individually, consistently self-reported higher preference for risk than South Koreans, regardless of gender. The data revealed that South Koreans, regardless of gender composition, were willing to take greater risks when making decisions in group decision-making situations than when they were alone. This is a different pattern from that seen in the Australian sample, in which a risky shift was noted only among males. This difference was attributed to the influence of various cultural orientations (independent vs. interdependent relationship styles). This study also provides a discussion of the implications of these results in terms of cultural differences in attitudes and decisions regarding risk.

Schelling (1969, 1971a,b, 1978) observed that macro-level patterns do not necessarily reflect micro-level intentions, desires or goals. In his classic model on neighborhood segregation which initiated a large and influential literature, individuals with no desire to be segregated from those who belong to other social groups nevertheless wind up clustering with their own type. Most extensions of Schelling’s model have replicated this result. There is an important mismatch, however, between theory and observation, which has received relatively little attention. Whereas Schelling-inspired models typically predict large degrees of segregation starting from virtually any initial condition, the empirical literature documents considerable heterogeneity in measured levels of segregation. This paper introduces a mechanism that can produce significantly higher levels of integration and, therefore, brings predicted distributions of segregation more in line with real-world observation. As in the classic Schelling model, agents in a simulated world want to stay or move to a new location depending on the proportion of neighbors they judge to be acceptable. In contrast to the classic model, agents’ classifications of their neighbors as acceptable or not depend lexicographically on recognition first and group type (e.g., ethnic stereotyping) second. The FACE-recognition model nests classic Schelling: When agents have no recognition memory, judgments about the acceptability of a prospective neighbor rely solely on his or her group type (as in the Schelling model). A very small amount of recognition memory, however, eventually leads to different classifications that, in turn, produce dramatic macro-level effects resulting in significantly higher levels of integration. A novel implication of the FACE-recognition model concerns the large potential impact of policy interventions that generate modest numbers of face-to-face encounters with members of other social groups.

Although Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool.