Electroencephalographic evidence for a reinforcement learning advantage during motor skill acquisition

Colino, Francisco L.; Heath, Matthew; Hassall, Cameron D.; Krigolson, Olave E.

doi:10.1016/j.biopsycho.2020.107849

Cited by 7 publications

(8 citation statements)

References 34 publications

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“…Low feedback complexity with a high feedback precision ambiguity (e.g., binary feedback) is assumed to induce higher FRN amplitudes, as compared to a high feedback complexity with low feedback precision ambiguity (e.g., quantitative feedback indicating direction and magnitude of the error). This has already been shown for cognitive tasks (e.g., time estimation task; Cockburn & Holroyd, 2018;Mars et al, 2004), as well as for a simple cursor pointing task (Colino et al, 2020). In experiments in the motor domain, the feedback delay varies between studies.…”

Section: Aim Of the Meta-analysismentioning

confidence: 75%

“…If studies had more than one experimental condition (i.e., frequent vs. infrequent/valid vs. invalid/quantitative vs. qualitative feedback), the average of both ERP curves was calculated and used for further analysis. There was one exception to this rule: In the moderator variable analysis, the experimental groups (quantitative vs. qualitative feedback) of Colino et al (2020) were used separately when analyzing the moderator feedback complexity . Data sets with deviating FRN latencies were not excluded because latencies vary a lot in motor studies (Colino et al, 2017; Grand et al, 2017; Maurer et al, 2021; Torrecillos et al, 2014), and it was of special interest to reflect this temporal variability of the FRN in motor tasks.…”

Section: Methodsmentioning

confidence: 99%

“…Foremost, it is assumed that response complexity is a relevant moderator, due to the higher range of different movement elements in motor tasks. Moreover, we scrutinize feedback complexity (cf., Cockburn & Holroyd, 2018; Colino et al, 2020; Mars et al, 2004) and feedback delay (cf., Arbel et al, 2017; Höltje & Mecklinger, 2020; Peterburs et al, 2016; Weinberg et al, 2012; Weismüller & Bellebaum, 2016) as moderators, as these have been shown to substantially affect feedback processing in the FRN interval in the cognitive domain. In motor tasks, feedback complexity can vary strongly, because here, feedback can focus on response selection and/or on action execution (Maurer et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Feedback processing in cognitive and motor tasks: A meta‐analysis on the feedback‐related negativity

Faßbender,

Krause,

Weigelt

2023

Psychophysiology

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For motor learning, the processing of behavioral outcomes is of high significance. The feedback‐related negativity (FRN) is an event‐related potential, which is often described as a correlate of the reward prediction error in reinforcement learning. The number of studies examining the FRN in motor tasks is increasing. This meta‐analysis summarizes the component in the motor domain and compares it to the cognitive domain. Therefore, a data set of a previous meta‐analysis in the cognitive domain that comprised 47 studies was reanalyzed and compared to additional 25 studies of the motor domain. Further, a moderator analysis for the studies in the motor domain was conducted. The FRN amplitude was higher in the motor domain than in the cognitive domain. This might be related to a higher task complexity and a higher feedback ambiguity of motor tasks. The FRN latency was shorter in the motor domain than in the cognitive domain. Given that sensory information can be used as an external feedback predictor prior to the presentation of the final feedback, reward processing in the motor domain may have been faster and reduced the FRN latency. The moderator variable analysis revealed that the feedback modality influenced the FRN latency, with shorter FRN latencies after bimodal than after visual feedback. Processing of outcome feedback seems to share basic principles in both domains; however, differences exist and should be considered in FRN studies. Future research is motivated to scrutinize the effects of bimodal feedback and other moderators within the motor domain.

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Section: Aim Of the Meta-analysismentioning

confidence: 75%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feedback processing in cognitive and motor tasks: A meta‐analysis on the feedback‐related negativity

Faßbender,

Krause,

Weigelt

2023

Psychophysiology

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“…On the other hand, in agreement with Graybiel and Grafton [15], after more than a century of work on the motor functions of BG, in the last decades, researchers have focused on the functions in relations to learning habits and acquiring motor skills. Much evidence [15, 16, 17, 18, 19] has been collected and reviewed suggesting a major role of BG in learning behaviors by refining action selection according to an optimization process and shaping skills as a modulator of motor repertoires; and, this learning mechanism supported by striatal circuitry generalize to other domains, including cognitive skills. There is also evidence linking PD to degradation of motor learning [20, 2, 21, 22].…”

Section: Model Descriptionmentioning

confidence: 99%

A neurocomputational model of the basal ganglia for the analysis of motor deficits after dopaminergic loss in Parkinson’s disease

Gigi

Senatore

Marcelli

2021

Preprint

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The basal ganglia (BG) are part of a basic feedback circuit, regulating cortical function, such as voluntary movement control, via their influence on thalamocortical projections. BG disorders, namely Parkinson's disease (PD), characterized by the loss of neurons in the substantia nigra (SN), involve the progressive loss of motor functions. The process that leads to these neural alterations is still unknown. At the present, PD cannot be cured, but an early diagnosis (ED) could allow to better manage its symptoms and evolution. A branch of neuroscience research is currently investigating the possibility of using motor alterations, e.g. handwriting, caused by the disease as diagnostic signs in the early stage of the disease, expression of small entity of SN lesion. In the present work, we propose a neurocomputational model to investigate the behaviour of the simulated neural system after several degrees of lesion, with the aim of evaluating, if possible, which is the smallest lesion compromising motor learning. The performance of the network in learning a novel motor task has been analyzed, in physiological and pathological conditions. The proposed neural network proves that there may exist abnormalities of motor learning process, due to alterations in the BG, which do not yet involve the presence of symptoms typical of the confirmed diagnosis, since the network shows having some difficulties in motor learning already with 20% DA depletion.

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“…In spite of that limitation, the fTCD approach used here provided a valid measure for examining 'fast' changes in the neural activation supporting oculomotor inhibition. Further, our group's current work is combining fTCD with electroencephalography (see Colino et al, 2020;Heath et al, 2018) to evaluate source localized inhibitory control for MD saccades.…”

Section: Study Limitationsmentioning

confidence: 99%

‘Delaying’ a saccade: Preparatory phase cortical hemodynamics evince the neural cost of response inhibition

Tari

Shirzad

Badcock

et al. 2021

Brain and Cognition

Self Cite

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Electroencephalographic evidence for a reinforcement learning advantage during motor skill acquisition

Cited by 7 publications

References 34 publications

Feedback processing in cognitive and motor tasks: A meta‐analysis on the feedback‐related negativity

Feedback processing in cognitive and motor tasks: A meta‐analysis on the feedback‐related negativity

A neurocomputational model of the basal ganglia for the analysis of motor deficits after dopaminergic loss in Parkinson’s disease

‘Delaying’ a saccade: Preparatory phase cortical hemodynamics evince the neural cost of response inhibition

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