A call for precision in the study of behaviour and decision
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No evidence for decision fatigue using large-scale field data from healthcare
Decision fatigue is the idea that making decisions is mentally demanding and eventually leads to deteriorated decision quality. Many studies report results that appear consistent with decision fatigue. However, most of this evidence comes from observed sequential patterns using retrospective designs, without preregistration or external validation and with low precision in how decision fatigue is operationalized. Here we conducted an empirical test of decision fatigue using large-scale, high-resolution data on healthcare professionals’ medical judgments at a national telephone triage and medical advice service. This is a suitable setting for testing decision fatigue because the work is both hard and repetitive, yet qualified, and the variation in scheduling produced a setting where level of fatigue could be regarded as near random for some segments of the data. We hypothesized increased use of heuristics, more specifically convergence toward personal defaults in case judgments, and higher assigned urgency ratings with fatigue. We tested these hypotheses using one-sided Bayes Factors computed from underlying Bayesian generalized mixed models with random intercepts. The results consistently showed relative support for the statistical null hypothesis of no difference in decision-making depending on fatigue (BF0+ > 22 for all main tests). We thus found no evidence for decision fatigue. Whereas these results don’t preclude the existence of a weaker or more nuanced version of decision fatigue or more context-specific effects, they cast serious doubt on the empirical relevance of decision fatigue as a domain general effect for sequential decisions in healthcare and elsewhere.
The evolution of preferred male traits, female preference and the G matrix: “Toto, I’ve a feeling we’re not in Kansas anymore”
Female preference exerts selection on male traits. How such preferences affect male traits, how female preferences change and the genetic correlation between male traits and female preference were examined by an experiment in which females were either mated to males they preferred (S lines) or to males chosen at random from the population (R lines). Female preference was predicted to increase the time spent calling by males. Thirteen other song components were measured. Preference for individual traits was greatest for time spent calling(CALL), volume(VOL) and chirp rate(CHIRP) but the major contributors in the multivariate function were CALL and CHIRP, the univariate influence of VOL arising from correlations to these traits. Estimation of β, the standardized selection differential, for CALL resulting from female preference showed that it was under strong direct selection. However, contrary to prediction, CALL did not change over the course of the experiment whereas VOL, CHIRP and other song components did. Simulation of the experiment using the estimated G matrix showed that lack of change in CALL resulted from indirect genetic effects negating direct effects. Changes in song components were largely due to indirect effects. This experiment showed that female preference may exert strong selection on traits but how they respond to such selection will depend greatly upon the G matrix. As predicted, female preference declined in the R lines. The genetic correlations between preference and preferred traits did not decline significantly more in the R lines, suggesting correlations resulted from both linkage disequilibrium and pleiotropy.
Affective integration in experience, judgment, and decision-making
The role of affect in value-based judgment and decision-making has attracted increasing interest in recent decades. Most previous approaches neglect the temporal dependence of mental states leading to mapping a relatively well-defined, but largely static, feeling state to a behavioral tendency. In contrast, we posit that expected and experienced consequences of actions are integrated over time into a unified overall affective experience reflecting current resources under current demands. This affective integration is shaped by context and continually modulates judgments and decisions. Changes in affective states modulate evaluation of new information (affect-as-information), signal changes in the environment (affect-as-a-spotlight) and influence behavioral tendencies in relation to goals (affect-as-motivation). We advocate for an approach that integrates affective dynamics into decision-making paradigms. This dynamical account identifies the key variables explaining how changes in affect influence information processing may provide us with new insights into the role of affect in value-based judgment and decision-making.
To choose or not to choose? A study on decision-making for virtual reality intervention in children with ADHD
Digital health interventions (DHI) using virtual reality (VR) technologies have been developed to treat attention deficit hyperactivity disorder (ADHD). While previous studies have mainly evaluated the feasibility of VR as an ADHD intervention, there is a dearth of research examining the decision-making psychology and influencing factors among parents of ADHD patients regarding the adoption of such emerging VR intervention techniques, which carry inherent risks. Building on the principles of Prospect Theory, this study highlights preference structures, belief characteristics, and community participation. The study selected 23 explanatory variables, including parents’ comprehension of VR treatment, level of trust, information sources, time and financial costs. A self-designed questionnaire was used to collect data on the willingness of parents of ADHD children to opt for VR treatment. By constructing a binary logistic regression model, we examine the preference structure, belief characteristics and decision readiness of parents of children with ADHD when choosing a virtual reality intervention policy. Parents’ choices of VR interventions for their children are complex. While parents consider the therapeutic benefits of VR, the time investment required for children’s treatment, and knowledge on VR interventions from online communities, their decisions are not always made objectively like an agent would. Instead, they frequently make choices based on a willingness to take risks, placing greater emphasis on relative rather than absolute values. Their decision-making is often swayed by online community information, resulting in choices that may not optimize benefits and sometimes disregarding financial and time costs related to their children’s health. Overall, parents of children with ADHD have demonstrated acceptance of the innovative VR intervention technique. Through examining the factors that impact preference selection, the implementation and promotion of VR intervention in ADHD treatments can be facilitated, thereby advancing the development of Digital Health Interventions (DHI). This can provide valuable insights for developing effective ADHD intervention strategies.
Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data
Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.
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