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      Microsaccades as a long-term oculomotor correlate in visual perceptual learning

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      1 , , 1 , 2
      Psychonomic Bulletin & Review
      Springer US
      Perceptual learning, Eye movements, Visual attention

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          Abstract

          Human perceptual learning, experience-induced gains in sensory discrimination, typically yields long-term performance improvements. Recent research revealed long-lasting transfer at the untrained location enabled by feature-based attention (FBA), reminiscent of its global effect (Hung & Carrasco, Scientific Reports, 11(1), 13914, ( 2021)). Visual Perceptual Learning (VPL) is typically studied while observers maintain fixation, but the role of fixational eye movements is unknown. Microsaccades – the largest of fixational eye movements – provide a continuous, online, physiological measure from the oculomotor system that reveals dynamic processing, which is unavailable from behavioral measures alone. We investigated whether and how microsaccades change after training in an orientation discrimination task. For human observers trained with or without FBA, microsaccade rates were significantly reduced during the response window in both trained and untrained locations and orientations. Critically, consistent with long-term training benefits, this microsaccade-rate reduction persisted over a year. Furthermore, microsaccades were biased toward the target location prior to stimulus onset and were more suppressed for incorrect than correct trials after observers’ responses. These findings reveal that fixational eye movements and VPL are tightly coupled and that learning-induced microsaccade changes are long lasting. Thus, microsaccades reflect functional dynamics of the oculomotor system during information encoding, maintenance and readout, and may serve as a reliable long-term physiological correlate in VPL.

          Supplementary Information

          The online version contains supplementary material available at 10.3758/s13423-022-02151-8.

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          Most cited references54

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          The Psychophysics Toolbox

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            Nonparametric statistical testing of EEG- and MEG-data.

            In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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              The VideoToolbox software for visual psychophysics: transforming numbers into movies

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                Author and article information

                Contributors
                sch462@nyu.edu
                Journal
                Psychon Bull Rev
                Psychon Bull Rev
                Psychonomic Bulletin & Review
                Springer US (New York )
                1069-9384
                1531-5320
                1 August 2022
                : 1-15
                Affiliations
                [1 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Department of Psychology, , New York University, ; New York, NY USA
                [2 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Center for Neural Science, , New York University, ; New York, NY USA
                Author information
                http://orcid.org/0000-0001-7909-3526
                Article
                2151
                10.3758/s13423-022-02151-8
                9342836
                35915381
                9f7aa5dd-907d-47ca-8a4c-d6faac3821af
                © The Psychonomic Society, Inc. 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 10 July 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000053, National Eye Institute;
                Award ID: EY027401
                Categories
                Brief Report

                Clinical Psychology & Psychiatry
                perceptual learning,eye movements,visual attention
                Clinical Psychology & Psychiatry
                perceptual learning, eye movements, visual attention

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