Wiley: Journal of Behavioral Decision Making: Table of Contents

Adjustment of Adolescents and Young Adults to the Level of Risk in Ambiguous Risk‐Taking Situation

Céline Moncel, Bruno Dauvier, Théo Guiller, Anaïs Osmont — Sun, 07 Jun 2026 21:19:30 -0700

ABSTRACT

Adolescent risk-taking generally occurs in ambiguous situations where decisions must rely on experiential learning. Although adolescence is characterized by heightened exploration in such situations, this does not systematically lead to optimal risk adjustment based on feedback learning. This study examines initial exploration and risk adjustment through feedback in ambiguous situations. The Risk Learning Card Task (RLCT), distinguishing three levels of risk (low, medium, and high probability of loss), was used to assess risk-taking in ambiguous situations by adolescents (N = 285, aged 10–17 years, M = 13.39, SD = 1.63, 45.96% female) and young adults (N = 162, aged 18–25 years, M = 19.10, SD = 1.29, 70.99% female). Generalized additive mixed models (GAMM), considering age as a continuous variable, revealed that (1) the youngest adolescents showed the highest level of exploration, (2) a majority of participants exhibited a correct explicit risk level estimation regardless of age (i.e., they were able to estimate risk levels at the end of the task), (3) the younger the participants were, the less risk they took and the more points they earned in the low-risk condition; the more risk they took, the fewer points they earned in the high-risk situation. Thus, adolescents' adjustment is more optimal in low-risk situations and less optimal in high-risk situations.

From Distance to Ambiguity Perception: A Dual‐Mechanism Account of the Date‐Delay Effect

Xu Gao, Hongjie Li, Qian Wang, Yifan Ping, Feng Zhang — Tue, 19 May 2026 01:15:01 -0700

ABSTRACT

The date-delay effect, a well-established anomaly in intertemporal choice, describes the tendency for individuals to prefer future rewards more when they are described using calendar dates (e.g., “July 1, 2026”) compared to delays (e.g., “100 days later”). Although prior research has primarily attributed this effect to reduced perceived temporal distance under the date frame, evidence for this mechanism comes almost exclusively from short delay distance. Through five experiments systematically manipulating delay distance (15, 105, and 450 days) and description frame, the present study suggests that the date-delay effect is robust across both short and long delays. Critically, however, the underlying cognitive mechanism shifts with delay distance. Under short delays (15 days), the effect is mediated by reduced perceived temporal distance, supporting the Temporal Distance Perception Hypothesis. Under long delays (450 days), where frame-induced differences in perceived temporal distance disappear, the effect is instead driven by increased perceived temporal ambiguity, validating the Temporal Ambiguity Perception Hypothesis. Study 5 further validated the robustness of the findings from Studies 3 and 4, demonstrating that the two mechanisms operate respectively across different delay distances. These findings support a context-dependent dual-process account aligned with the attribute-comparison model, revealing that the date-delay effect may be underpinned by distinct temporal perception mechanisms operating at different temporal distances. This refined theoretical framework advances our understanding of how temporal framing shapes intertemporal decisions.

Opportunity Versus Threat Appraisals of AI Aids: The Effect of Appraisal Type on Decision Makers' Effort and Compliance When Using Powerful AI Aids

Oneg Singer, Ilanit SimanTov‐Nachlieli, Ellen Bamberger — Wed, 13 May 2026 15:39:15 -0700

ABSTRACT

Organizations are increasingly using powerful AI aids to support decision-making. Yet, performance improvements are difficult to predict, in part because employees vary in how they use them. We propose that such behavioral variation may stem from differences in how employees appraise these aids. Applying an opportunity–threat framework to AI augmentation (positing two primary cognitive AI appraisals—as either an opportunity or a threat to employees), we examine the effect of appraisal type on professionals' decision-making when using powerful AI aids. Specifically, we examine the independent effort they invest in deliberate, vigilant information-seeking during their initial decisions and their behavioral compliance with AI recommendations that diverged from their initial judgments during final decision-making. Four simulation-based experiments, in which we manipulated appraisal type and used the Wizard of Oz method to simulate recommendations from powerful AI aids, yielded consistent findings: Study 1, conducted among employees participating in a weight estimation simulation, revealed that opportunity (vs. threat) appraisals reduced participants' individual effort in making their initial decisions and increased their compliance with fictitious AI recommendations that contradicted their initial judgments in their final decisions. Studies 2–4, conducted among HR professionals and medical students engaged in realistic tasks within their work domains, further revealed that these effects of opportunity appraisals on decision-making were driven by an increased preference for using the powerful AI aid rather than making decisions alone (an effect evident among those with higher domain experience; Studies 3–4). Our findings provide important implications for organizational decision-making in hybrid human–AI environments.

Issue Information

Sun, 03 May 2026 19:17:26 -0700

Journal of Behavioral Decision Making, Volume 39, Issue 3, July 2026.

Never … Often? Comparisons That Shape People's Likert‐Type Ratings of Behavior Frequencies

Jeremy D. Strueder, Jane E. Miller, Isaac T. Petersen, Paul D. Windschitl — Sun, 03 May 2026 19:10:35 -0700

ABSTRACT

Responses to Likert-type behavioral frequency (LBF) questions often do not consistently map onto objective numerical estimates. Prior research suggests that social and other comparisons may underlie this divergence, but the relative influence of different comparison standards—and the cognitive processes supporting them—remains unclear. Across two studies, we examined how comparisons to peers, averages, experts, past selves, and conceptually irrelevant standards shape LBF responses for common health behaviors (e.g., hand washing and flossing). Participants provided LBF judgments, absolute frequency estimates, and comparative judgments for each behavior. Study 1 showed that direct comparisons predicted LBF judgments above and beyond participants' own absolute frequency estimates, with comparisons to experts and average others being especially influential. Even when controlling for shared methodological variance, all comparison types explained unique variance in LBF responses. Study 2 replicated this pattern of results. Moreover, additional analyses in Study 2 suggest that participants were not making precise, pairwise comparisons between numeric estimates, but were instead relying on more abstract, gist-like impressions of how their behavior compared to others. Together, these findings underscore the importance of considering the comparative and interpretive nature of self-report measures, particularly in contexts where behavioral frequency carries social, normative, or evaluative meaning.