Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

Learning how to allocate attention properly is essential for success at many categorization tasks. Advances in our understanding of learned attention are stymied by a chicken-and-egg problem: there are no theoretical accounts of learned attention that predict patterns of eye movements, making data collection difficult to justify, and there are not enough datasets to support the development of a rich theory of learned attention. The present work addresses this by reporting five measures relating to the overt allocation of attention across 10 category learning experiments: accuracy, probability of fixating irrelevant information, number of fixations to category features, the amount of change in the allocation of attention (using a new measure called Time Proportion Shift - TIPS), and a measure of the relationship between attention change and erroneous responses. Using these measures, the data suggest that eye-movements are not substantially connected to error in most cases and that aggregate trial-by-trial attention change is generally stable across a number of changing task variables. The data presented here provide a target for computational models that aim to account for changes in overt attentional behaviors across learning.

[1]  R. Shepard,et al.  Learning and memorization of classifications. , 1961 .

[2]  H. Collewijn,et al.  The function of visual search and memory in sequential looking tasks , 1995, Vision Research.

[3]  Scott E. Maxwell,et al.  Pairwise Multiple Comparisons in Repeated Measures Designs , 1980 .

[4]  W. T. Maddox,et al.  Dissociating explicit and procedural-learning based systems of perceptual category learning , 2004, Behavioural Processes.

[5]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[6]  Bob Rehder,et al.  How prior knowledge affects selective attention during category learning: An eyetracking study , 2011, Memory & cognition.

[7]  W Todd Maddox,et al.  Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[8]  B. Kopp,et al.  Brain mechanisms of selective learning: event-related potentials provide evidence for error-driven learning in humans , 2000, Biological Psychology.

[9]  Mark Blair,et al.  Integrating novel dimensions to eliminate category exceptions: when more is less. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[10]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[11]  Marcus R. Watson,et al.  Extremely selective attention: eye-tracking studies of the dynamic allocation of attention to stimulus features in categorization. , 2009, Journal of experimental psychology. Learning, memory, and cognition.

[12]  A. Wills,et al.  On the adequacy of current empirical evaluations of formal models of categorization. , 2012, Psychological bulletin.

[13]  Benjamin W. Tatler,et al.  Current understanding of eye guidance , 2009 .

[14]  A. L. I︠A︡rbus.  Eye Movements and Vision , 1967 .

[15]  Rebecca M Foerster,et al.  Saccadic eye movements in a high-speed bimanual stacking task: changes of attentional control during learning and automatization. , 2011, Journal of vision.

[16]  Michael F. Land,et al.  From eye movements to actions: how batsmen hit the ball , 2000, Nature Neuroscience.

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

[18]  Daniel Memmert,et al.  The effects of eye movements, age, and expertise on inattentional blindness , 2006, Consciousness and Cognition.

[19]  Mark R. Blair,et al.  Errors, efficiency, and the interplay between attention and category learning , 2009, Cognition.

[20]  Marcus R. Watson,et al.  The impact of category type and working memory span on attentional learning in categorization , 2009 .

[21]  J. Kruschke,et al.  A model of probabilistic category learning. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[22]  D. Medin,et al.  SUSTAIN: a network model of category learning. , 2004, Psychological review.

[23]  Gregory Ashby,et al.  Decision rules in the perception and categorization of multidimensional stimuli. , 1988, Journal of experimental psychology. Learning, memory, and cognition.

[24]  J. Boster,et al.  Form or Function: A Comparison of Expert and Novice Judgments of Similarity Among Fish , 1989 .

[25]  D. E. Irwin,et al.  Eye movements and scene perception: Memory for things observed , 2002, Perception & psychophysics.

[26]  M. Hayhoe,et al.  In what ways do eye movements contribute to everyday activities? , 2001, Vision Research.

[27]  Ronald A. Rensink,et al.  On the Failure to Detect Changes in Scenes Across Brief Interruptions , 2000 .

[28]  Jordan I. Barnes,et al.  Rethinking the role of error in attentional learning , 2011, CogSci.

[29]  I. Biederman,et al.  Sexing Day-Old Chicks : A Case Study and Expert Systems Analysis of a Difficult Perceptual-Learning Task , 1987 .

[30]  M. Blair,et al.  Waiting and weighting: Information sampling is a balance between efficiency and error-reduction , 2013, Cognition.

[31]  Aaron B. Hoffman,et al.  Eyetracking and selective attention in category learning , 2005, Cognitive Psychology.

[32]  K. Plaisted,et al.  Top-Down Attentional Modulation in Autistic Spectrum Disorders Is Stimulus-Specific , 2005, Psychological science.

[33]  Daniel R. Little,et al.  Error discounting in probabilistic category learning. , 2011, Journal of experimental psychology. Learning, memory, and cognition.

[34]  David L. Faigman,et al.  Human category learning. , 2005, Annual review of psychology.

[35]  H. Haider,et al.  Eye movement during skill acquisition: More evidence for the information-reduction hypothesis , 1999 .

[36]  Mark R. Blair,et al.  A Tale of Two Processes: Categorization Accuracy and Attentional Learning Dissociate with Imperfect Feedback , 2011, CogSci.

[37]  Keith Rayner.  Eye movements and landing positions in reading: a retrospective. , 2009, Perception.

[38]  D. Mirman,et al.  Temporal dynamics of activation of thematic and functional knowledge during conceptual processing of manipulable artifacts. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[39]  Joseph H. Goldberg,et al.  Identifying fixations and saccades in eye-tracking protocols , 2000, ETRA.

[40]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[41]  Lihan Chen,et al.  Temporal characteristics of overt attentional behavior during category learning , 2012, Attention, Perception, & Psychophysics.

[42]  M. Corbetta,et al.  A Common Network of Functional Areas for Attention and Eye Movements , 1998, Neuron.