Modeling distracted performance
DOI: 10.1016/j.cogpsych.2019.05.002
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Summary
This paper addresses the lack of quantitative cognitive process models capable of explaining behavioral performance during mind wandering. While qualitative theories such as executive resource depletion or perceptual decoupling exist, they fail to generate precise predictions for observed behavior. The authors argue that developing a model of task performance in the presence of distraction is a prerequisite for discriminating between these latent theories. The study focuses on the Sustained Attention to Response Task (SART), a go-nogo paradigm widely used to study mind wandering, where participants must withhold responses to rare target stimuli. Previous models, including ACT-R strategies and standard evidence accumulation models, failed to account for the specific response time distributions and error patterns observed in SART data, particularly the speed-accuracy tradeoffs and variability associated with off-task thoughts. To resolve this, the authors developed and tested the "rhythmic race model," an integrated cognitive process model. The model posits that performance is generated by a competitive race between two processes: a stimulus-related decision process (evidence accumulation) and a stimulus-unrelated rhythmic response process. The rhythmic process is proposed to entrain to timing regularities in the task environment, acting as an unconditional habit or "insurance policy" to maintain performance despite mind wandering. The model was tested using data from two SART experiments. Experiment 1 involved 19 participants performing 720 trials, with self-reported mind wandering indexed via thought probes. The authors analyzed choice data, response times, and self-reports using hierarchical Bayesian analysis to identify specific response time trends incompatible with existing models. The results demonstrated that the rhythmic race model provided a quantitatively precise account of stationary features of SART performance, including choice, response time, and self-reported mind wandering data. Crucially, the model accounted for three previously unidentified features of response time distributions that constrain cognitive models of distraction. The model’s parameters were meaningfully associated with participants’ self-reported distraction levels, despite the model not being directly informed by these self-reports during fitting. In a validation test, the authors manipulated inter-trial-interval variability to disrupt the latent rhythmic component, confirming the model’s architecture and its counter-intuitive effects on performance. The significance of this work lies in providing the first integrated cognitive process model that quantitatively explains performance in the presence of mind wandering. By framing distracted performance as a competition between latent decision and rhythmic response processes, the model offers a mechanism for understanding how individuals maintain task performance via autopilot behavior. The authors conclude that this framework is not limited to distraction research but is applicable to any domain requiring repetitive responding where evidence accumulation is an underlying principle, thereby enabling more decisive arbitration among competing qualitative theories of mind wandering.
Key finding
The rhythmic race model quantitatively explains SART performance by modeling it as a competition between a stimulus-related decision process and a stimulus-unrelated rhythmic response process, accurately accounting for choice, response time, and self-reported mind wandering data.
Methodology
modeling
Sample size: 19
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | unpaywall | — | — | 2 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | semantic_scholar | — | — | 3 | 2026-06-15 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model, theory or model