Visual search performance is predicted by both prestimulus and poststimulus electrical brain activity

An individual’s performance on cognitive and perceptual tasks varies considerably across time and circumstances. We investigated neural mechanisms underlying such performance variability using regression-based analyses to examine trial-by-trial relationships between response times (RTs) and different facets of electrical brain activity. Thirteen participants trained five days on a color-popout visual-search task, with EEG recorded on days one and five. The task was to find a color-popout target ellipse in a briefly presented array of ellipses and discriminate its orientation. Later within a session, better preparatory attention (reflected by less prestimulus Alpha-band oscillatory activity) and better poststimulus early visual responses (reflected by larger sensory N1 waves) correlated with faster RTs. However, N1 amplitudes decreased by half throughout each session, suggesting adoption of a more efficient search strategy within a session. Additionally, fast RTs were preceded by earlier and larger lateralized N2pc waves, reflecting faster and stronger attentional orienting to the targets. Finally, SPCN waves associated with target-orientation discrimination were smaller for fast RTs in the first but not the fifth session, suggesting optimization with practice. Collectively, these results delineate variations in visual search processes that change over an experimental session, while also pointing to cortical mechanisms underlying performance in visual search.

[1]  O. Jensen,et al.  Modulation of Gamma and Alpha Activity during a Working Memory Task Engaging the Dorsal or Ventral Stream , 2007, The Journal of Neuroscience.

[2]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[3]  Geert J. M. van Boxtel,et al.  Negative Slow Waves as Indices of Anticipation: The Bereitschaftspotential, the Contingent Negative Variation, and the Stimulus-Preceding Negativity , 2011 .

[4]  C. Gilbert,et al.  Learning to find a shape , 2000, Nature Neuroscience.

[5]  William D. Marslen-Wilson,et al.  The time course of visual word recognition as revealed by linear regression analysis of ERP data , 2006, NeuroImage.

[6]  Marty G Woldorff,et al.  Timing and Sequence of Brain Activity in Top-Down Control of Visual-Spatial Attention , 2007, PLoS biology.

[7]  S. Hillyard,et al.  Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. , 1991, Journal of experimental psychology. Human perception and performance.

[8]  R. Barry,et al.  EEG alpha activity and the ERP to target stimuli in an auditory oddball paradigm. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[9]  W. Klimesch Alpha-band oscillations, attention, and controlled access to stored information , 2012, Trends in Cognitive Sciences.

[10]  J. Schoffelen,et al.  Prestimulus Oscillatory Activity in the Alpha Band Predicts Visual Discrimination Ability , 2008, The Journal of Neuroscience.

[11]  Marty G Woldorff,et al.  Differential functional roles of slow-wave and oscillatory-α activity in visual sensory cortex during anticipatory visual-spatial attention. , 2011, Cerebral cortex.

[12]  Jonathan Evans In two minds: dual-process accounts of reasoning , 2003, Trends in Cognitive Sciences.

[13]  John J. Foxe,et al.  Anticipatory Attentional Suppression of Visual Features Indexed by Oscillatory Alpha-Band Power Increases:A High-Density Electrical Mapping Study , 2010, The Journal of Neuroscience.

[14]  R. Bakeman Recommended effect size statistics for repeated measures designs , 2005, Behavior research methods.

[15]  Jacob Cohen The Cost of Dichotomization , 1983 .

[16]  Jason J. Corneveaux,et al.  Analysis of Copy Number Variation in Alzheimer’s Disease in a Cohort of Clinically Characterized and Neuropathologically Verified Individuals , 2012, PloS one.

[17]  J. Algina,et al.  Generalized eta and omega squared statistics: measures of effect size for some common research designs. , 2003, Psychological methods.

[18]  Jan Theeuwes,et al.  Faster, more intense! The relation between electrophysiological reflections of attentional orienting, sensory gain control, and speed of responding , 2007, Brain Research.

[19]  J. Schoffelen,et al.  Parieto‐occipital sources account for the increase in alpha activity with working memory load , 2007, Human brain mapping.

[20]  K. Grill-Spector,et al.  Repetition and the brain: neural models of stimulus-specific effects , 2006, Trends in Cognitive Sciences.

[21]  M. Woldorff,et al.  Utilization of reward-prospect enhances preparatory attention and reduces stimulus conflict , 2014, Cognitive, Affective, & Behavioral Neuroscience.

[22]  J. Lisman,et al.  Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task. , 2002, Cerebral cortex.

[23]  Simon Hanslmayr,et al.  Prestimulus oscillations predict visual perception performance between and within subjects , 2007, NeuroImage.

[24]  Natasha M. Maurits,et al.  Mental Fatigue Affects Visual Selective Attention , 2012, PloS one.

[25]  F. Pulvermüller,et al.  Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study , 2014, Cerebral cortex.

[26]  W. Klimesch,et al.  Pre-stimulus alpha phase-alignment predicts P1-amplitude , 2011, Brain Research Bulletin.

[27]  Kristina M. Visscher,et al.  The neural bases of momentary lapses in attention , 2006, Nature Neuroscience.

[28]  Edward K. Vogel,et al.  The capacity of visual working memory for features and conjunctions , 1997, Nature.

[29]  Carlo Umiltà,et al.  Attentional selection and identification of visual objects are reflected by distinct electrophysiological responses , 2007, Experimental Brain Research.

[30]  Kristopher J Preacher,et al.  On the practice of dichotomization of quantitative variables. , 2002, Psychological methods.

[31]  E. Vogel,et al.  The visual N1 component as an index of a discrimination process. , 2000, Psychophysiology.

[32]  Ruxandra Sireteanu,et al.  Perceptual learning in visual search: Fast, enduring, but non-specific , 1995, Vision Research.

[33]  Kait Clark,et al.  Improvement in Visual Search with Practice: Mapping Learning-Related Changes in Neurocognitive Stages of Processing , 2015, The Journal of Neuroscience.

[34]  Jeff Miller,et al.  Measurement of ERP latency differences: a comparison of single-participant and jackknife-based scoring methods. , 2008, Psychophysiology.

[35]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[36]  W. Ziegler The Oxford Handbook Of Event Related Potential Components , 2016 .

[37]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[38]  Benoit Brisson,et al.  Dissociation of the N2pc and sustained posterior contralateral negativity in a choice response task , 2008, Brain Research.

[39]  O. Jensen,et al.  Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition , 2010, Front. Hum. Neurosci..

[40]  S. Luck,et al.  The Oxford handbook of event-related potential components , 2011 .

[41]  G. V. Simpson,et al.  Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific α-Bank Electroencephalography Increases over Occipital Cortex , 2000, The Journal of Neuroscience.

[42]  Maarten A. S. Boksem,et al.  Effects of mental fatigue on attention: an ERP study. , 2005, Brain research. Cognitive brain research.