Toward a model-based cognitive neuroscience of mind wandering

Hawkins, Guy E.; Mittner, Matthias; Boekel, Wouter; Heathcote, Andrew; Forstmann, Birte U. · 2015 · Neuroscience

DOI: 10.1016/j.neuroscience.2015.09.053

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Summary

This perspective paper addresses the lack of explanatory power in current research on mind wandering, which typically relies on descriptive associations between behavioral performance decrements and neural activity. The authors argue that existing qualitative theories, such as resource depletion or perceptual decoupling, fail to provide mechanistic accounts of *why* mind wandering affects task performance. To bridge this gap, the paper proposes a model-based cognitive neuroscience framework that treats mind wandering as a neural state influencing the parameters of quantitative cognitive process models, specifically sequential sampling models like the diffusion model. This approach aims to decompose observed behavioral variables, such as response times and accuracy, into latent components like processing efficiency and response caution, thereby offering a more precise understanding of the cognitive mechanisms underlying mind wandering. The authors outline two primary methodological frameworks for implementing this approach. The first utilizes latent mixture models, such as hidden Markov models, to segment observed performance into discrete task-related and task-unrelated states using neural data. The second framework employs regression techniques to dynamically track transitions between cognitive states by regressing single-trial neural measures onto the trial-by-trial variation in cognitive model parameters. The paper emphasizes the superiority of quantitative modeling over qualitative theorizing, illustrating how precise mathematical constraints allow for rigorous model selection and discrimination between competing theories, a capability lacking in verbal models. The authors also critique the reliance on introspective thought probes, suggesting they should be treated as outcome measures rather than primary identifiers of mind wandering due to potential biases and limited metacognitive insight. While the paper is a theoretical review rather than an empirical study, it synthesizes existing literature to demonstrate the utility of these frameworks. It highlights that sequential sampling models can distinguish between changes in processing efficiency and response caution, which are often conflated in raw behavioral data. The authors note that these methods are applicable to common tasks used in mind wandering research, such as the sustained attention to respond task. By using neural data to constrain cognitive model parameters, researchers can achieve a more coherent account of how mind wandering alters information processing. The significance of this work lies in its potential to advance the field from descriptive correlations to mechanistic explanations, offering tools to identify the specific cognitive processes disrupted by mind wandering and providing principled methods for removing contaminant trials from datasets in broader experimental psychology.

Key finding

Quantitative cognitive process models provide a superior framework for explaining the mechanistic effects of mind wandering on behavior and neural activity compared to traditional qualitative theories.

Methodology

review

Provenance

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Topics

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• mind wandering