Attentional Facilitation of Constituent Features of an Object Does Not Spread Automatically along Object-defining Cortical Boundaries

The integrated object account predicts that attention is spread across all features that constitute one object, regardless of their task relevance. We challenge that prediction with a novel stimulation technique that allows for simultaneous electrophysiological measurements of the allocation of attention to two distinct features within one object. A rotating square that flickers in different colors evoked two distinct steady-state visual evoked potentials (SSVEPs) for rotation and color, respectively. If the integrated object account were true, we would expect identical SSVEP amplitudes regardless of what feature participants attended. We found greater SSVEP amplitudes for the to-be-attended feature compared with the to-be-ignored feature. SSVEP amplitudes averaged across both features were significantly reduced when participants attended to both features, which was mirrored in behavioral costs, implying competitive interactions or a division of attentional resources. Surprisingly, this reduction in amplitude was mainly driven by the SSVEP amplitude elicited by color changes. In conclusion, our results challenge the integrated object account and highlight the extent to which color is “special” within feature space.

[1]  Matthias M. Müller,et al.  Early visual and auditory processing rely on modality-specific attentional resources , 2013, NeuroImage.

[2]  Dietrich Lehmann,et al.  Spatial analysis of evoked potentials in man—a review , 1984, Progress in Neurobiology.

[3]  B. Rockstroh,et al.  Statistical control of artifacts in dense array EEG/MEG studies. , 2000, Psychophysiology.

[4]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[5]  S A Hillyard,et al.  Feature-selective attention enhances color signals in early visual areas of the human brain , 2006, Proceedings of the National Academy of Sciences.

[6]  Roelfsema Pieter Cortical algorithms for perceptual grouping , 2008 .

[7]  Denis Brunet,et al.  Topographic ERP Analyses: A Step-by-Step Tutorial Review , 2008, Brain Topography.

[8]  Matthias M. Müller,et al.  Attentional Selection of Feature Conjunctions Is Accomplished by Parallel and Independent Selection of Single Features , 2015, The Journal of Neuroscience.

[9]  H. Müller,et al.  Dimension-based visual attention modulates dual-judgment accuracy in Duncan's (1984) one- versus two-object report paradigm. , 2000, Journal of experimental psychology. Human perception and performance.

[10]  E. Macaluso,et al.  fMRI correlates of object-based attentional facilitation vs. suppression of irrelevant stimuli, dependent on global grouping and endogenous cueing , 2013, Front. Integr. Neurosci..

[11]  J. Duncan Cooperating brain systems in selective perception and action. , 1996 .

[12]  D. Heeger,et al.  The Normalization Model of Attention , 2009, Neuron.

[13]  Jeffrey N. Rouder,et al.  Bayesian t tests for accepting and rejecting the null hypothesis , 2009, Psychonomic bulletin & review.

[14]  Hans-Jochen Heinze,et al.  Object-based attention involves the sequential activation of feature-specific cortical modules , 2014, Nature Neuroscience.

[15]  M. Posner,et al.  Orienting of Attention* , 1980, The Quarterly journal of experimental psychology.

[16]  J. Duncan Selective attention and the organization of visual information. , 1984, Journal of experimental psychology. General.

[17]  H Stanislaw,et al.  Calculation of signal detection theory measures , 1999, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[18]  L. M. M.-T. Theory of Probability , 1929, Nature.

[19]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[20]  Michael S. Pratte,et al.  Using MCMC chain outputs to efficiently estimate Bayes factors , 2011 .

[21]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[22]  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.

[23]  L. Busse,et al.  Attention to the Color of a Moving Stimulus Modulates Motion-Signal Processing in Macaque Area MT: Evidence for a Unified Attentional System , 2009, Front. Syst. Neurosci..

[24]  E. Vogel,et al.  Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. , 1998, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[25]  Steven A. Hillyard,et al.  Attention Facilitates Multiple Stimulus Features in Parallel in Human Visual Cortex , 2008, Current Biology.

[26]  Jeffrey N. Rouder,et al.  Default Bayes factors for ANOVA designs , 2012 .

[27]  Yaoda Xu,et al.  The Neural Fate of Task-Irrelevant Features in Object-Based Processing , 2010, The Journal of Neuroscience.

[28]  Michael X. Cohen,et al.  Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation , 2016, NeuroImage.

[29]  R. M. Boynton,et al.  Comparison of four methods of heterochromatic photometry. , 1972, Journal of the Optical Society of America.

[30]  Leonardo Chelazzi,et al.  Selective Attention to Specific Features within Objects: Behavioral and Electrophysiological Evidence , 2006, Journal of Cognitive Neuroscience.

[31]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

[32]  A. Kreiter,et al.  Attentional spreading to task-irrelevant object features: experimental support and a 3-step model of attention for object-based selection and feature-based processing modulation , 2014, Front. Hum. Neurosci..

[33]  Jeffrey N. Rouder,et al.  Bayes factor approaches for testing interval null hypotheses. , 2011, Psychological methods.

[34]  John H Reynolds,et al.  Object-based attention to one of two superimposed surfaces alters responses in human early visual cortex. , 2011, Journal of neurophysiology.

[35]  Nancy Kanwisher,et al.  fMRI evidence for objects as the units of attentional selection , 1999, Nature.

[36]  John Duncan,et al.  Objects and attributes in divided attention: Surface and boundary systems , 1996, Perception & psychophysics.

[37]  Matthias M. Müller,et al.  Competitive Interactions of Attentional Resources in Early Visual Cortex during Sustained Visuospatial Attention within or between Visual Hemifields: Evidence for the Different-hemifield Advantage , 2014, Journal of Cognitive Neuroscience.