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Self-reported mind wandering reflects executive control and selective attention. Psychon Bull Rev 2022; 29:2167-2180. [PMID: 35672655 DOI: 10.3758/s13423-022-02110-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2022] [Indexed: 11/08/2022]
Abstract
Mind wandering is ubiquitous in everyday life and has a pervasive and profound impact on task-related performance. A range of psychological processes have been proposed to underlie these performance-related decrements, including failures of executive control, volatile information processing, and shortcomings in selective attention to critical task-relevant stimuli. Despite progress in the development of such theories, existing descriptive analyses have limited capacity to discriminate between the theories. We propose a cognitive-model based analysis that simultaneously explains self-reported mind wandering and task performance. We quantitatively compare six explanations of poor performance in the presence of mind wandering. The competing theories are distinguished by whether there is an impact on executive control and, if so, how executive control acts on information processing, and whether there is an impact on volatility of information processing. Across two experiments using the sustained attention to response task, we find quantitative evidence that mind wandering is associated with two latent factors. Our strongest conclusion is that executive control is impaired: increased mind wandering is associated with reduced ability to inhibit habitual response tendencies. Our nuanced conclusion is that executive control deficits manifest in reduced ability to selectively attend to the information value of rare but task-critical events.
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Weigard A, Sripada C. Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 1:5-15. [PMID: 35317408 DOI: 10.1016/j.bpsgos.2021.02.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., "inhibition", "updating") through a combination of experimental manipulation and factor analysis. However, the putative constructs obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.
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Dzianok P, Antonova I, Wojciechowski J, Dreszer J, Kublik E. The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults. Gigascience 2022; 11:6543635. [PMID: 35254424 PMCID: PMC8900497 DOI: 10.1093/gigascience/giac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/22/2021] [Accepted: 01/27/2022] [Indexed: 12/28/2022] Open
Abstract
Background One of the goals of neuropsychology is to understand the brain mechanisms underlying aspects of attention and cognitive control. Several tasks have been developed as a part of this body of research, however their results are not always consistent. A reliable comparison of the data and a synthesis of study conclusions has been precluded by multiple methodological differences. Here, we describe a publicly available, high-density electroencephalography (EEG) dataset obtained from 42 healthy young adults while they performed 3 cognitive tasks: (i) an extended multi-source interference task; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task; and (iv) a resting-state protocol. Demographic and psychometric information are included within the dataset. Dataset Validation First, data validation confirmed acceptable quality of the obtained EEG signals. Typical event-related potential (ERP) waveforms were obtained, as expected for attention and cognitive control tasks (i.e., N200, P300, N450). Behavioral results showed the expected progression of reaction times and error rates, which confirmed the effectiveness of the applied paradigms. Conclusions This dataset is well suited for neuropsychological research regarding common and distinct mechanisms involved in different cognitive tasks. Using this dataset, researchers can compare a wide range of classical EEG/ERP features across tasks for any selected subset of electrodes. At the same time, 128-channel EEG recording allows for source localization and detailed connectivity studies. Neurophysiological measures can be correlated with additional psychometric data obtained from the same participants. This dataset can also be used to develop and verify novel analytical and classification approaches that can advance the field of deep/machine learning algorithms, recognition of single-trial ERP responses to different task conditions, and detection of EEG/ERP features for use in brain-computer interface applications.
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Affiliation(s)
- Patrycja Dzianok
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland
| | - Ingrida Antonova
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland
| | - Jakub Wojciechowski
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland.,Bioimaging Research Center, Institute of Physiology and Pathology of Hearing, 02-042, Warsaw, Poland
| | - Joanna Dreszer
- Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Toruń, 87-100, Toruń, Poland
| | - Ewa Kublik
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland
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Boehm U, Marsman M, van der Maas HLJ, Maris G. An Attention-Based Diffusion Model for Psychometric Analyses. PSYCHOMETRIKA 2021; 86:938-972. [PMID: 34258714 PMCID: PMC8636464 DOI: 10.1007/s11336-021-09783-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 04/11/2021] [Indexed: 06/13/2023]
Abstract
The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers' latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. However, existing substantive theories such as the diffusion model have been slow to gain traction due to their unwieldy functional form and regular violations of model assumptions in psychometric contexts. In the present work, we develop an attention-based diffusion model based on process assumptions that are appropriate for psychometric applications. This model is straightforward to analyse using Gibbs sampling and can be readily extended. We demonstrate our model's good computational and statistical properties in a comparison with two well-established psychometric models.
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Affiliation(s)
- Udo Boehm
- Department of Psychology, University of Amsterdam, Nieuwe Prinsengracht 129B, 1018 WS Amsterdam, The Netherlands
| | - Maarten Marsman
- Department of Psychology, University of Amsterdam, Nieuwe Prinsengracht 129B, 1018 WS Amsterdam, The Netherlands
| | - Han L. J. van der Maas
- Department of Psychology, University of Amsterdam, Nieuwe Prinsengracht 129B, 1018 WS Amsterdam, The Netherlands
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Dong HW, Mills C, Knight RT, Kam JWY. Detection of mind wandering using EEG: Within and across individuals. PLoS One 2021; 16:e0251490. [PMID: 33979407 PMCID: PMC8115801 DOI: 10.1371/journal.pone.0251490] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 04/27/2021] [Indexed: 11/19/2022] Open
Abstract
Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual's attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to "never-seen-before" individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.
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Affiliation(s)
- Henry W. Dong
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Caitlin Mills
- Department of Psychology, University of New Hampshire, Durham, New Hampshire, United States of America
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Julia W. Y. Kam
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
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Boehm U, Matzke D, Gretton M, Castro S, Cooper J, Skinner M, Strayer D, Heathcote A. Real-time prediction of short-timescale fluctuations in cognitive workload. Cogn Res Princ Implic 2021; 6:30. [PMID: 33835271 PMCID: PMC8035388 DOI: 10.1186/s41235-021-00289-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 03/10/2021] [Indexed: 11/23/2022] Open
Abstract
Human operators often experience large fluctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators' spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators' situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators' cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.
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Affiliation(s)
- Udo Boehm
- Department of Psychology, University of Amsterdam, PO Box 15906, 1001 NK Amsterdam, The Netherlands
| | - Dora Matzke
- Department of Psychology, University of Amsterdam, PO Box 15906, 1001 NK Amsterdam, The Netherlands
| | - Matthew Gretton
- Department of Psychology, University of Tasmania, Sandy Bay, Australia
| | | | - Joel Cooper
- Department of Psychology, University of Utah, Utah, USA
| | - Michael Skinner
- Aerospace Division, Defence Science and Technology Group, Melbourne, Australia
| | - David Strayer
- Department of Psychology, University of Utah, Utah, USA
| | - Andrew Heathcote
- Department of Psychology, University of Tasmania, Sandy Bay, Australia
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Nunez MD, Gosai A, Vandekerckhove J, Srinivasan R. The latency of a visual evoked potential tracks the onset of decision making. Neuroimage 2019; 197:93-108. [DOI: 10.1016/j.neuroimage.2019.04.052] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 01/23/2019] [Accepted: 04/18/2019] [Indexed: 12/30/2022] Open
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Modeling distracted performance. Cogn Psychol 2019; 112:48-80. [DOI: 10.1016/j.cogpsych.2019.05.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 04/11/2019] [Accepted: 05/10/2019] [Indexed: 11/21/2022]
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Schubert AL, Frischkorn GT, Rummel J. The validity of the online thought-probing procedure of mind wandering is not threatened by variations of probe rate and probe framing. PSYCHOLOGICAL RESEARCH 2019; 84:1846-1856. [DOI: 10.1007/s00426-019-01194-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 04/23/2019] [Indexed: 11/25/2022]
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Weigard A, Huang-Pollock C, Heathcote A, Hawk L, Schlienz NJ. A cognitive model-based approach to testing mechanistic explanations for neuropsychological decrements during tobacco abstinence. Psychopharmacology (Berl) 2018; 235:3115-3124. [PMID: 30182252 DOI: 10.1007/s00213-018-5008-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 08/20/2018] [Indexed: 10/28/2022]
Abstract
RATIONALE Cigarette smokers often experience cognitive decrements during abstinence from tobacco, and these decrements may have clinical relevance in the context of smoking cessation interventions. However, limitations of the behavioral summary statistics used to measure cognitive effects of abstinence, response times (RT) and accuracy rates, may restrict the field's ability to identify robust abstinence effects on task performance and test mechanistic hypotheses about the etiology of these cognitive changes. OBJECTIVES The current study explored whether a measurement approach based on mathematical models of cognition, which make the cognitive mechanisms necessary to perform choice RT tasks explicit, would be able to address these limitations. METHODS The linear ballistic accumulator model (LBA: Brown and Heathcote, Cogn Psychol 57(3):153-178, 2008) was fit to an existing data set from a study that evaluated the impact of overnight abstinence on flanker task performance. RESULTS The model-based analysis provided evidence that smokers' rates of mind wandering increased during abstinence, and was able to index this effect while controlling for participants' strategy changes that were related to the specific experimental paradigm used. CONCLUSION Mind wandering is a putative explanation for cognitive withdrawal symptoms during smoking cessation and may be indexed using the LBA. More broadly, the use of formal model-based analyses in future research on this topic has the potential to allow for strong and specific tests of mechanistic explanations for these symptoms.
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Affiliation(s)
- Alexander Weigard
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, Ann Arbor, MI, 48109, USA.
| | - Cynthia Huang-Pollock
- Department of Psychology, Penn State University, University Park, Hobart, PA, 16801, USA
| | - Andrew Heathcote
- Department of Psychology, Penn State University, University Park, Hobart, PA, 16801, USA
| | - Larry Hawk
- The State University of New York at Buffalo, New York, USA
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Abstract
Mind wandering is a ubiquitous phenomenon in which attention shifts from task-related to task-unrelated thoughts. The last decade has witnessed an explosion of interest in mind wandering, but research has been stymied by a lack of objective measures, leading to a near-exclusive reliance on self-reports. We addressed this issue by developing an eye-gaze-based, machine-learned model of mind wandering during computerized reading. Data were collected in a study in which 132 participants reported self-caught mind wandering while reading excerpts from a book on a computer screen. A remote Tobii TX300 or T60 eyetracker recorded their gaze during reading. The data were used to train supervised classification models to discriminate between mind wandering and normal reading in a manner that would generalize to new participants. We found that at the point of maximal agreement between the model-based and self-reported mind-wandering means (smallest difference between the group-level means: M model = .310, M self = .319), the participant-level mind-wandering proportional distributions were similar and were significantly correlated (r = .400). The model-based estimates were internally consistent (r = .751) and predicted text comprehension more strongly than did self-reported mind wandering (r model = -.374, r self = -.208). Our results also indicate that a robust strategy of probabilistically predicting mind wandering in cases with poor or missing gaze data led to improved performance on all metrics, as compared to simply discarding these data. Our findings demonstrate that an automated objective measure might be available for laboratory studies of mind wandering during reading, providing an appealing alternative or complement to self-reports.
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Weigard A, Huang-Pollock C, Brown S, Heathcote A. Testing formal predictions of neuroscientific theories of ADHD with a cognitive model-based approach. JOURNAL OF ABNORMAL PSYCHOLOGY 2018; 127:529-539. [PMID: 30010369 PMCID: PMC6091877 DOI: 10.1037/abn0000357] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Neuroscientific theories of attention-deficit/hyperactivity disorder (ADHD) alternately posit that cognitive aberrations in the disorder are due to acute attentional lapses, slowed neural processing, or reduced signal-to-noise ratios. However, they make similar predictions about behavioral summary statistics (response times [RTs] and accuracy), hindering the field's ability to produce strong and specific tests of these theories. The current study uses the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), a mathematical model of choice RT tasks, to distinguish between competing theory predictions. Children with ADHD (n = 80) and age-matched controls (n = 32) completed a numerosity discrimination paradigm at 2 levels of difficulty, and RT data were fit to the LBA model to test theoretical predictions. Individuals with ADHD displayed slowed processing of evidence for correct responses (signal) relative to their peers but comparable processing of evidence for error responses (noise) and between-trial variability in processing (performance lapses). The findings are inconsistent with accounts that posit an increased incidence of attentional lapses in the disorder and provide partial support for those that posit slowed neural processing and lower signal-to-noise ratios. Results also highlight the utility of well-developed cognitive models for distinguishing between the predictions of etiological theories of psychopathology. (PsycINFO Database Record
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Affiliation(s)
| | | | - Scott Brown
- School of Psychology, University of Newcastle
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Mittner M, Hawkins GE, Boekel W, Forstmann BU. A Neural Model of Mind Wandering. Trends Cogn Sci 2016; 20:570-578. [DOI: 10.1016/j.tics.2016.06.004] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 06/07/2016] [Accepted: 06/07/2016] [Indexed: 10/21/2022]
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Tan P, Tan GZ, Cai ZX, Sa WP, Zou YQ. Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI. Med Biol Eng Comput 2016; 55:33-43. [PMID: 27099159 DOI: 10.1007/s11517-016-1493-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 03/24/2016] [Indexed: 11/30/2022]
Abstract
Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.
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Affiliation(s)
- Ping Tan
- School of Information Science and Engineering, Central South University, Changsha, 410083, China
| | - Guan-Zheng Tan
- School of Information Science and Engineering, Central South University, Changsha, 410083, China
| | - Zi-Xing Cai
- School of Information Science and Engineering, Central South University, Changsha, 410083, China
| | - Wei-Ping Sa
- College of Network Education, Central South University, Changsha, 410083, China
| | - Yi-Qun Zou
- School of Information Science and Engineering, Central South University, Changsha, 410083, China.
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de Hollander G, Forstmann BU, Brown SD. Different Ways of Linking Behavioral and Neural Data via Computational Cognitive Models. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2015; 1:101-109. [PMID: 29560872 DOI: 10.1016/j.bpsc.2015.11.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/13/2015] [Accepted: 11/14/2015] [Indexed: 11/17/2022]
Abstract
Cognitive neuroscientists sometimes apply formal models to investigate how the brain implements cognitive processes. These models describe behavioral data in terms of underlying, latent variables linked to hypothesized cognitive processes. A goal of model-based cognitive neuroscience is to link these variables to brain measurements, which can advance progress in both cognitive and neuroscientific research. However, the details and the philosophical approach for this linking problem can vary greatly. We propose a continuum of approaches that differ in the degree of tight, quantitative, and explicit hypothesizing. We describe this continuum using four points along it, which we dub qualitative structural, qualitative predictive, quantitative predictive, and single model linking approaches. We further illustrate by providing examples from three research fields (decision making, reinforcement learning, and symbolic reasoning) for the different linking approaches.
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Affiliation(s)
- Gilles de Hollander
- Amsterdam Brain & Cognition Center, University of Amsterdam, Amsterdam, The Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
| | - Birte U Forstmann
- Amsterdam Brain & Cognition Center, University of Amsterdam, Amsterdam, The Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Scott D Brown
- School of Psychology, University of Newcastle, Callaghan, New South Wales, Australia
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