Literature DB >> 26427961

Toward a model-based cognitive neuroscience of mind wandering.

G E Hawkins1, M Mittner2, W Boekel3, A Heathcote4, B U Forstmann3.   

Abstract

People often "mind wander" during everyday tasks, temporarily losing track of time, place, or current task goals. In laboratory-based tasks, mind wandering is often associated with performance decrements in behavioral variables and changes in neural recordings. Such empirical associations provide descriptive accounts of mind wandering - how it affects ongoing task performance - but fail to provide true explanatory accounts - why it affects task performance. In this perspectives paper, we consider mind wandering as a neural state or process that affects the parameters of quantitative cognitive process models, which in turn affect observed behavioral performance. Our approach thus uses cognitive process models to bridge the explanatory divide between neural and behavioral data. We provide an overview of two general frameworks for developing a model-based cognitive neuroscience of mind wandering. The first approach uses neural data to segment observed performance into a discrete mixture of latent task-related and task-unrelated states, and the second regresses single-trial measures of neural activity onto structured trial-by-trial variation in the parameters of cognitive process models. We discuss the relative merits of the two approaches, and the research questions they can answer, and highlight that both approaches allow neural data to provide additional constraint on the parameters of cognitive models, which will lead to a more precise account of the effect of mind wandering on brain and behavior. We conclude by summarizing prospects for mind wandering as conceived within a model-based cognitive neuroscience framework, highlighting the opportunities for its continued study and the benefits that arise from using well-developed quantitative techniques to study abstract theoretical constructs.
Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  mind wandering; mixture model; model-based cognitive neuroscience; sequential sampling model; single-trial regression; task-unrelated thoughts

Mesh:

Year:  2015        PMID: 26427961     DOI: 10.1016/j.neuroscience.2015.09.053

Source DB:  PubMed          Journal:  Neuroscience        ISSN: 0306-4522            Impact factor:   3.590


  9 in total

1.  The validity of the online thought-probing procedure of mind wandering is not threatened by variations of probe rate and probe framing.

Authors:  Anna-Lena Schubert; Gidon T Frischkorn; Jan Rummel
Journal:  Psychol Res       Date:  2019-05-02

2.  A cognitive model-based approach to testing mechanistic explanations for neuropsychological decrements during tobacco abstinence.

Authors:  Alexander Weigard; Cynthia Huang-Pollock; Andrew Heathcote; Larry Hawk; Nicolas J Schlienz
Journal:  Psychopharmacology (Berl)       Date:  2018-09-04       Impact factor: 4.530

3.  Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

Authors:  Ping Tan; Guan-Zheng Tan; Zi-Xing Cai; Wei-Ping Sa; Yi-Qun Zou
Journal:  Med Biol Eng Comput       Date:  2016-04-21       Impact factor: 2.602

4.  Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience.

Authors:  Alexander Weigard; Chandra Sripada
Journal:  Biol Psychiatry Glob Open Sci       Date:  2021-03-13

5.  Testing formal predictions of neuroscientific theories of ADHD with a cognitive model-based approach.

Authors:  Alexander Weigard; Cynthia Huang-Pollock; Scott Brown; Andrew Heathcote
Journal:  J Abnorm Psychol       Date:  2018-07

6.  Detection of mind wandering using EEG: Within and across individuals.

Authors:  Henry W Dong; Caitlin Mills; Robert T Knight; Julia W Y Kam
Journal:  PLoS One       Date:  2021-05-12       Impact factor: 3.240

7.  Real-time prediction of short-timescale fluctuations in cognitive workload.

Authors:  Udo Boehm; Dora Matzke; Matthew Gretton; Spencer Castro; Joel Cooper; Michael Skinner; David Strayer; Andrew Heathcote
Journal:  Cogn Res Princ Implic       Date:  2021-04-09

8.  An Attention-Based Diffusion Model for Psychometric Analyses.

Authors:  Udo Boehm; Maarten Marsman; Han L J van der Maas; Gunter Maris
Journal:  Psychometrika       Date:  2021-07-13       Impact factor: 2.290

9.  The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults.

Authors:  Patrycja Dzianok; Ingrida Antonova; Jakub Wojciechowski; Joanna Dreszer; Ewa Kublik
Journal:  Gigascience       Date:  2022-03-07       Impact factor: 6.524

  9 in total

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