RESUMO
Interruptions are an inevitable, and often negative, part of everyday life that increase both errors and the time needed to complete even menial tasks. However, existing research suggests that being given time to prepare for a pending interruption-a lag time-can mitigate some of the interruption costs. To understand better why interruption lags are effective, we present a series of three experiments in which we develop and test a novel sequential decision-making paradigm, the mazing race. We find that interruption lags were only beneficial when participants had a clear strategy for how to complete the task, allowing them to avoid specific errors. In the final experiment, we attempted to use what we learned about the kinds of errors introduced by interruptions to develop a feedback-based intervention, aimed at dealing with situations in which interruption lags are not possible. We found that feedback was, only in certain situations, an effective replacement for an interruption lag. Overall, however, because the usefulness of interruption lags depend on the specific strategy a participant adopts, developing generic interventions to replace interruption lags is likely to be difficult. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Assuntos
Atenção , Análise e Desempenho de Tarefas , Humanos , Fatores de Tempo , AprendizagemRESUMO
In three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly enumerate the algorithm's accuracy while also providing summary feedback and training that allowed participants to assess their own skills. Our results highlight that such direct performance comparisons between the algorithm and the individual encourages a strategy of selective reliance on the decision aid; individuals ignored the algorithm when the task was easier and relied on the algorithm when the task was harder. Our systematic investigation of summary feedback, training experience, and strategy hint manipulations shows that further opportunities to learn about the algorithm encourage not only increased reliance on the algorithm but also engagement in experimentation and verification of its recommendations. Together, our findings emphasize the decision-maker's capacity to learn about the algorithm providing insights for how we can improve the use of decision aids.
Assuntos
Algoritmos , Aprendizagem , Técnicas de Apoio para a Decisão , HumanosRESUMO
Across two experiments, Newell, Rakow, Yechiam, and Sambur (Nature Climate Change, 6(2), 158-161, 2016) demonstrated that providing rare disaster information increased people's tolerance for risk-taking. These results motivated a series of as yet-unpublished follow-up experiments involving new manipulations. However, the failure to replicate the original finding in these follow-ups has led our confidence in the original effect to wane. The aim of this registered report was to reconsider the evidence, published and unpublished, for the rare disaster information effect in light of new data. We conducted a large scale replication (N= 242) in which we failed to find evidence for the effect reported in Newell et al. thus further reducing our confidence. This registered report format provides a transparent framework by which to address the discrepancy between the published and previously-unpublished findings.
Assuntos
Desastres , Assunção de Riscos , Adulto , Tomada de Decisões , Feminino , Humanos , Masculino , Adulto JovemRESUMO
We investigated previous findings suggesting a paradoxical inconsistency of people's beliefs and choices: When making decisions under uncertainty, people seem to both overestimate the probability of rare events in their judgments and underweight the probability of the same rare events in their choices. In our reexamination, we found that people's beliefs are consistent with their decisions, but they do not necessarily correspond with the environment. Both overestimation and underweighting of the rare event seemed to result from (most, but not all) participants' mistaken belief that they can infer and exploit sequential patterns in a static environment. In addition, we found that such inaccurate representations can be improved through incentives. Finally, detailed analysis suggested a mixture of individual-level response patterns, which can give rise to an erroneous interpretation of group-level patterns. Our results offer an explanation for why beliefs and decisions can appear contradictory and present challenges to some current models of decisions under uncertainty. (PsycINFO Database Record (c) 2019 APA, all rights reserved).