The current meta-analysis accumulates empirical findings on the phenomenon of mind-wandering, integrating and interpreting findings in light of psychological theories of cognitive resource allocation. Cognitive resource theory emphasizes both individual differences in attentional resources and task demands together to predict variance in task performance. This theory motivated our conceptual and meta-analysis framework by introducing moderators indicative of task-demand to predict who is more likely to mind-wander under what conditions, and to predict when mind-wandering and task-related thought are more (or less) predictive of task performance. Predictions were tested via a random-effects meta-analysis of correlations obtained from normal adult samples (k = 88) based on measurement of specified episodes of off-task and/or on-task thought frequency and task performance. Results demonstrated that people with fewer cognitive resources tend to engage in more mind-wandering, whereas those with more cognitive resources are more likely to engage in task-related thought. Addressing predictions of resource theory, we found that greater time-on-task-although not greater task complexity-tended to strengthen the negative relation between cognitive resources and mind-wandering. Additionally, increases in mind-wandering were generally associated with decreases in task performance, whereas increases in task-related thought were associated with increased performance. Further supporting resource theory, the negative relation between mind-wandering and performance was more pronounced for more complex tasks, though not longer tasks. Complementarily, the positive association between task-related thought and performance was stronger for more complex tasks and for longer tasks. We conclude by discussing implications and future research directions for mind-wandering as a construct of interest in psychological research.
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http://dx.doi.org/10.1037/a0037428 | DOI Listing |
J Vis
January 2025
Department of Psychology, New York University, New York, NY, USA.
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational models struggle to incorporate time.
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Department of Computer Science, Hunan University, Changsha 410008, China.
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback.
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