Rapid Parallel Attentional Selection Can Be Controlled by Shape and Alphanumerical Category

Previous research has shown that when two color-defined target objects appear in rapid succession at different locations, attention is deployed independently and in parallel to both targets. This study investigated whether this rapid simultaneous attentional target selection mechanism can also be employed in tasks where targets are defined by a different visual feature (shape) or when alphanumerical category is the target selection attribute. Two displays that both contained a target and a nontarget object on opposite sides were presented successively, and the SOA between the two displays was 100, 50, 20, or 10 msec in different blocks. N2pc components were recorded to both targets as a temporal marker of their attentional selection. When observers searched for shape-defined targets (Experiment 1), N2pc components to the two targets were equal in size and overlapped in time when the SOA between the two displays was short, reflecting two parallel shape-guided target selection processes with their own independent time course. Essentially the same temporal pattern of N2pc components was observed when alphanumerical category was the target-defining attribute (Experiment 2), demonstrating that the rapid parallel attentional selection of multiple target objects is not restricted to situations where the deployment of attention can be guided by elementary visual features but that these processes can even be employed in category-based attentional selection tasks. These findings have important implications for our understanding of the cognitive and neural basis of top–down attentional control.

[1]  P. Roelfsema,et al.  Different States in Visual Working Memory: When It Guides Attention and When It Does Not , 2022 .

[2]  Gregory J Zelinsky,et al.  Effects of target typicality on categorical search. , 2014, Journal of vision.

[3]  Victor A. F. Lamme,et al.  Texture segregation is processed by primary visual cortex in man and monkey. Evidence from VEP experiments , 1992, Vision Research.

[4]  P. Dixon,et al.  Conceptual and physical differences in the category effect , 1987, Perception & psychophysics.

[5]  Jeff Miller,et al.  Jackknife-based method for measuring LRP onset latency differences. , 1998, Psychophysiology.

[6]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[7]  L. Chelazzi,et al.  Associative knowledge controls deployment of visual selective attention , 2003, Nature Neuroscience.

[8]  Howard E. Egeth,et al.  Parallel processing of multielement displays , 1972 .

[9]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

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

[11]  R. Romo,et al.  A recurrent network model of somatosensory parametric working memory in the prefrontal cortex. , 2003, Cerebral cortex.

[12]  Jeremy M. Wolfe,et al.  Guided Search 4.0: Current Progress With a Model of Visual Search , 2007, Integrated Models of Cognitive Systems.

[13]  K. Moutoussis,et al.  Functional segregation and temporal hierarchy of the visual perceptive systems , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[14]  Martin Eimer,et al.  What top-down task sets do for us: an ERP study on the benefits of advance preparation in visual search. , 2011, Journal of experimental psychology. Human perception and performance.

[15]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[16]  Gregory J. Zelinsky,et al.  Visual search is guided to categorically-defined targets , 2009, Vision Research.

[17]  S. Zeki A massively asynchronous, parallel brain , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[18]  Martin Eimer,et al.  Rapid guidance of visual search by object categories. , 2014, Journal of experimental psychology. Human perception and performance.

[19]  J. Duncan The locus of interference in the perception of simultaneous stimuli. , 1980, Psychological review.

[20]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[21]  Vincent Di Lollo,et al.  Electrophysiological Indices of Target and Distractor Processing in Visual Search , 2009, Journal of Cognitive Neuroscience.

[22]  C. Bundesen,et al.  A neural theory of visual attention: bridging cognition and neurophysiology. , 2005, Psychological review.

[23]  David J. Freedman,et al.  Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.

[24]  Lawrence C. Sincich,et al.  The circuitry of V1 and V2: integration of color, form, and motion. , 2005, Annual review of neuroscience.

[25]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[26]  Charles Folk,et al.  Contingent attentional capture by conceptually relevant images. , 2013, Journal of experimental psychology. Human perception and performance.

[27]  S. Zeki,et al.  A direct demonstration of perceptual asynchrony in vision , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  S. Treue,et al.  Feature-Based Attention Increases the Selectivity of Population Responses in Primate Visual Cortex , 2004, Current Biology.

[29]  Jeff Miller,et al.  Using the jackknife-based scoring method for measuring LRP onset effects in factorial designs. , 2001, Psychophysiology.

[30]  Antje S. Meyer,et al.  Electrophysiological Evidence of Semantic Interference in Visual Search , 2010, Journal of Cognitive Neuroscience.

[31]  Derek H. Arnold,et al.  Asynchronous processing in vision Color leads motion , 2001, Current Biology.

[32]  M. Eimer The neural basis of attentional control in visual search , 2014, Trends in Cognitive Sciences.

[33]  Li Fei-Fei,et al.  Neural mechanisms of rapid natural scene categorization in human visual cortex , 2009, Nature.

[34]  J. Duncan EPS Mid-Career Award 2004: Brain mechanisms of attention , 2006, Quarterly journal of experimental psychology.

[35]  P. Lachenbruch Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

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

[37]  G. Boynton,et al.  Global effects of feature-based attention in human visual cortex , 2002, Nature Neuroscience.

[38]  Geoffrey F. Woodman,et al.  Electrophysiological measurement of rapid shifts of attention during visual search , 1999, Nature.

[39]  Martin Eimer,et al.  Spatial Attention Can Be Allocated Rapidly and in Parallel to New Visual Objects , 2014, Current Biology.

[40]  Alexander Pollatsek,et al.  Typicality aids search for an unspecified target, but only in identification and not in attentional guidance , 2008, Psychonomic bulletin & review.

[41]  David E. Irwin,et al.  Temporal integration between visual images and visual percepts. , 2002, Journal of experimental psychology. Human perception and performance.

[42]  S. Kastner,et al.  A neural basis for real-world visual search in human occipitotemporal cortex , 2011, Proceedings of the National Academy of Sciences.

[43]  J. Theeuwes,et al.  Electrophysiological Evidence of the Capture of Visual Attention , 2006, Journal of Cognitive Neuroscience.

[44]  S. Luck,et al.  Neural sources of focused attention in visual search. , 2000, Cerebral cortex.

[45]  S. Zeki,et al.  Temporal hierarchy of the visual perceptive systems in the Mondrian world , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[46]  S. Hochstein,et al.  View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.

[47]  M. Eimer The N2pc component as an indicator of attentional selectivity. , 1996, Electroencephalography and clinical neurophysiology.

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

[49]  Naomi M. Kenner,et al.  How fast can you change your mind? The speed of top-down guidance in visual search , 2004, Vision Research.

[50]  R. Desimone,et al.  Responses of Neurons in Inferior Temporal Cortex during Memory- Guided Visual Search , 1998 .

[51]  George L. Malcolm,et al.  The effects of target template specificity on visual search in real-world scenes: evidence from eye movements. , 2009, Journal of vision.

[52]  M. Cheal,et al.  Attention in visual search: Multiple search classes , 1992, Perception & psychophysics.

[53]  Martin Eimer,et al.  Rapid parallel attentional target selection in single-color and multiple-color visual search. , 2015, Journal of experimental psychology. Human perception and performance.

[54]  S. Luck,et al.  Feature-based attention modulates feedforward visual processing , 2009, Nature Neuroscience.

[55]  Martin Eimer,et al.  EPS Mid-Career Award 2014 , 2015, Quarterly journal of experimental psychology.

[56]  Robert Desimone,et al.  Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4 , 2005, Science.

[57]  Martin Eimer,et al.  Memory-driven attentional capture is modulated by temporal task demands , 2011 .

[58]  A Treisman,et al.  Feature analysis in early vision: evidence from search asymmetries. , 1988, Psychological review.

[59]  Karl J. Friston,et al.  The physiological basis of attentional modulation in extrastriate visual areas , 1999, Nature Neuroscience.

[60]  R. Desimone,et al.  Visual properties of neurons in area V4 of the macaque: sensitivity to stimulus form. , 1987, Journal of neurophysiology.

[61]  G. Boynton,et al.  Feature-Based Attentional Modulations in the Absence of Direct Visual Stimulation , 2007, Neuron.

[62]  Martin Eimer,et al.  Activation of New Attentional Templates for Real-world Objects in Visual Search , 2015, Journal of Cognitive Neuroscience.

[63]  Eva Belke,et al.  Top-down effects of semantic knowledge in visual search are modulated by cognitive but not perceptual load , 2008, Perception & psychophysics.

[64]  S J Luck,et al.  Spatial filtering during visual search: evidence from human electrophysiology. , 1994, Journal of experimental psychology. Human perception and performance.