Independent effects of statistical learning and top-down attention

It is well known that spatial attention can be directed in a top-down way to task-relevant locations in space. In addition, through visual statistical learning (VSL), attention can be biased towards relevant (target) locations and away from irrelevant (distractor) locations. The present study investigates the interaction between the explicit task-relevant, top-down attention and the lingering attentional biases due to VSL. We wanted to determine the contribution of each of these two processes to attentional selection. In the current study, participants performed a search task while keeping a location in spatial working memory. In Experiment 1, the target appeared more often in one location, and appeared less often in other location. In Experiment 2, a color singleton distractor was presented more often in location than in all other locations. The results show that when the search target matched the location that was kept in working memory, participants were much faster at responding to the search target than when it did not match, signifying top-down attentional selection. Independent of this top-down effect, we found a clear effect of VSL as responses were even faster when target (Experiment 1) or the distractor (Experiment 2) was presented at a more likely location in visual field. We conclude that attentional selection is driven by implicit biases due to statistical learning and by explicit top-down processing, each process individually and independently modulating the neural activity within the spatial priority map.

[1]  Jan Theeuwes,et al.  Learning to suppress a distractor is not affected by working memory load , 2019, Psychonomic Bulletin & Review.

[2]  L. Chelazzi,et al.  Rewards teach visual selective attention , 2013, Vision Research.

[3]  J. Deutsch Perception and Communication , 1958, Nature.

[4]  Jan Theeuwes,et al.  Spatial suppression due to statistical regularities is driven by distractor suppression not by target activation , 2018, Attention, Perception, & Psychophysics.

[5]  Jeff Miller Components of the location probability effect in visual search tasks. , 1988 .

[6]  Morten H. Christiansen,et al.  Domain generality versus modality specificity: the paradox of statistical learning , 2015, Trends in Cognitive Sciences.

[7]  J. Jonides,et al.  Overlapping mechanisms of attention and spatial working memory , 2001, Trends in Cognitive Sciences.

[8]  J. Geng,et al.  Reward associations and spatial probabilities produce additive effects on attentional selection , 2014, Attention, perception & psychophysics.

[9]  D. E. Irwin,et al.  Attention on our mind: the role of spatial attention in visual working memory. , 2011, Acta psychologica.

[10]  Iain D Gilchrist,et al.  Target location probability effects in visual search: an effect of sequential dependencies. , 2006, Journal of experimental psychology. Human perception and performance.

[11]  Jan Theeuwes,et al.  Anticipatory distractor suppression elicited by statistical regularities in visual search , 2019, bioRxiv.

[12]  Jan Theeuwes,et al.  OpenSesame: An open-source, graphical experiment builder for the social sciences , 2011, Behavior Research Methods.

[13]  B. Anderson The attention habit: how reward learning shapes attentional selection , 2016, Annals of the New York Academy of Sciences.

[14]  Yuhong V Jiang,et al.  Task specificity of attention training: the case of probability cuing , 2015, Attention, perception & psychophysics.

[15]  Marlene Behrmann,et al.  Probability Cuing of Target Location Facilitates Visual Search Implicitly in Normal Participants and Patients with Hemispatial Neglect , 2002, Psychological science.

[16]  Yuhong V Jiang,et al.  Visual search and location probability learning from variable perspectives. , 2013, Journal of vision.

[17]  Gail M. Rosenbaum,et al.  Guidance of spatial attention by incidental learning and endogenous cuing. , 2013, Journal of experimental psychology. Human perception and performance.

[18]  J Miller,et al.  Components of the location probability effect in visual search tasks. , 1988, Journal of experimental psychology. Human perception and performance.

[19]  R. Remington,et al.  The Risks of Downplaying Top-Down Control , 2018, Journal of cognition.

[20]  J. Jonides,et al.  Rehearsal in spatial working memory. , 1998, Journal of experimental psychology. Human perception and performance.

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

[22]  M. Posner,et al.  Attention and the detection of signals. , 1980, Journal of experimental psychology.

[23]  J. Theeuwes,et al.  Top-down versus bottom-up attentional control: a failed theoretical dichotomy , 2012, Trends in Cognitive Sciences.

[24]  Saul Sternberg,et al.  The discovery of processing stages: Extensions of Donders' method , 1969 .

[25]  J. Theeuwes,et al.  Interactions between working memory, attention and eye movements. , 2009, Acta psychologica.

[26]  James L. McClelland On the time relations of mental processes: An examination of systems of processes in cascade. , 1979 .

[27]  Jan Theeuwes,et al.  Visual Selection: Usually Fast and Automatic; Seldom Slow and Volitional , 2018, Journal of cognition.

[28]  Jan Theeuwes,et al.  Goal-driven, stimulus-driven, and history-driven selection. , 2019, Current opinion in psychology.

[29]  Andrew B. Leber,et al.  It’s under control: Top-down search strategies can override attentional capture , 2006, Psychonomic bulletin & review.

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

[31]  Rebecca M. Todd,et al.  Implicit guidance of attention: The priority state space framework , 2017, Cortex.

[32]  J. Theeuwes Top-down and bottom-up control of visual selection. , 2010, Acta psychologica.

[33]  Jan Theeuwes,et al.  Selection history: How reward modulates selectivity of visual attention , 2017, Psychonomic Bulletin & Review.

[34]  M. Shaw,et al.  Optimal allocation of cognitive resources to spatial locations. , 1977, Journal of experimental psychology. Human perception and performance.

[35]  M. Behrmann,et al.  Spatial probability as an attentional cue in visual search , 2005, Perception & psychophysics.

[36]  M. Chun,et al.  Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention , 1998, Cognitive Psychology.

[37]  Steven J. Luck,et al.  “Top-down” Does Not Mean “Voluntary” , 2018, Journal of cognition.

[38]  J. Wolfe,et al.  Changing your mind: on the contributions of top-down and bottom-up guidance in visual search for feature singletons. , 2003, Journal of experimental psychology. Human perception and performance.

[39]  F. Gregory Ashby,et al.  Deriving Exact Predictions From the Cascade Model , 1982 .

[40]  Yuhong V Jiang,et al.  Rapid acquisition but slow extinction of an attentional bias in space. , 2012, Journal of experimental psychology. Human perception and performance.

[41]  Yuhong V. Jiang,et al.  Habitual versus goal-driven attention , 2017, Cortex.

[42]  Jan Theeuwes,et al.  Spatial working memory effects in early visual cortex , 2010, Brain and Cognition.

[43]  H. Egeth Comment on Theeuwes’s Characterization of Visual Selection , 2018, Journal of cognition.

[44]  Jan Theeuwes,et al.  On the limits of top-down control of visual selection , 2011, Attention, perception & psychophysics.

[45]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[46]  Jan Theeuwes,et al.  How to inhibit a distractor location? Statistical learning versus active, top-down suppression , 2018, Attention, Perception, & Psychophysics.

[47]  James W Bisley,et al.  The what, where, and why of priority maps and their interactions with visual working memory , 2015, Annals of the New York Academy of Sciences.

[48]  Jan Theeuwes,et al.  Statistical Regularities Modulate Attentional Capture , 2018, Journal of experimental psychology. Human perception and performance.

[49]  H. Pashler,et al.  Evidence for split attentional foci. , 2000, Journal of experimental psychology. Human perception and performance.

[50]  Laurent Itti,et al.  An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[51]  Leonardo Chelazzi,et al.  Altering spatial priority maps via statistical learning of target selection and distractor filtering , 2017, Cortex.

[52]  Jan Theeuwes,et al.  Statistical regularities modulate attentional capture independent of search strategy , 2018, Attention, Perception, & Psychophysics.

[53]  J Theeuwes,et al.  Effects of location and form cuing on the allocation of attention in the visual field. , 1989, Acta psychologica.

[54]  Michael Zehetleitner,et al.  Probability cueing of distractor locations: both intertrial facilitation and statistical learning mediate interference reduction , 2014, Front. Psychol..