Literature DB >> 23298793

Rethinking human visual attention: spatial cueing effects and optimality of decisions by honeybees, monkeys and humans.

Miguel P Eckstein1, Stephen C Mack, Dorion B Liston, Lisa Bogush, Randolf Menzel, Richard J Krauzlis.   

Abstract

Visual attention is commonly studied by using visuo-spatial cues indicating probable locations of a target and assessing the effect of the validity of the cue on perceptual performance and its neural correlates. Here, we adapt a cueing task to measure spatial cueing effects on the decisions of honeybees and compare their behavior to that of humans and monkeys in a similarly structured two-alternative forced-choice perceptual task. Unlike the typical cueing paradigm in which the stimulus strength remains unchanged within a block of trials, for the monkey and human studies we randomized the contrast of the signal to simulate more real world conditions in which the organism is uncertain about the strength of the signal. A Bayesian ideal observer that weights sensory evidence from cued and uncued locations based on the cue validity to maximize overall performance is used as a benchmark of comparison against the three animals and other suboptimal models: probability matching, ignore the cue, always follow the cue, and an additive bias/single decision threshold model. We find that the cueing effect is pervasive across all three species but is smaller in size than that shown by the Bayesian ideal observer. Humans show a larger cueing effect than monkeys and bees show the smallest effect. The cueing effect and overall performance of the honeybees allows rejection of the models in which the bees are ignoring the cue, following the cue and disregarding stimuli to be discriminated, or adopting a probability matching strategy. Stimulus strength uncertainty also reduces the theoretically predicted variation in cueing effect with stimulus strength of an optimal Bayesian observer and diminishes the size of the cueing effect when stimulus strength is low. A more biologically plausible model that includes an additive bias to the sensory response from the cued location, although not mathematically equivalent to the optimal observer for the case stimulus strength uncertainty, can approximate the benefits of the more computationally complex optimal Bayesian model. We discuss the implications of our findings on the field's common conceptualization of covert visual attention in the cueing task and what aspects, if any, might be unique to humans.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 23298793      PMCID: PMC3968950          DOI: 10.1016/j.visres.2012.12.011

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  65 in total

1.  Mechanisms of perceptual attention in precuing of location.

Authors:  B A Dosher; Z L Lu
Journal:  Vision Res       Date:  2000       Impact factor: 1.886

2.  The psychophysics of visual search.

Authors:  J Palmer; P Verghese; M Pavel
Journal:  Vision Res       Date:  2000       Impact factor: 1.886

3.  Optimal observer model of single-fixation oddity search predicts a shallow set-size function.

Authors:  Wade Schoonveld; Steve S Shimozaki; Miguel P Eckstein
Journal:  J Vis       Date:  2007-07-06       Impact factor: 2.240

Review 4.  Statistical decision theory to relate neurons to behavior in the study of covert visual attention.

Authors:  Miguel P Eckstein; Matthew F Peterson; Binh T Pham; Jason A Droll
Journal:  Vision Res       Date:  2009-01-10       Impact factor: 1.886

5.  Optimal feature integration in visual search.

Authors:  Benjamin T Vincent; Roland J Baddeley; Tom Troscianko; Iain D Gilchrist
Journal:  J Vis       Date:  2009-05-15       Impact factor: 2.240

6.  Attention and the detection of signals.

Authors:  M I Posner; C R Snyder; B J Davidson
Journal:  J Exp Psychol       Date:  1980-06

7.  Behavioral assessments of learning and attention in rats exposed perinatally to 3,3',4,4',5-pentachlorobiphenyl (PCB 126)

Authors:  P J Bushnell; D C Rice
Journal:  Neurotoxicol Teratol       Date:  1999 Jul-Aug       Impact factor: 3.763

8.  Covert visual search: prior beliefs are optimally combined with sensory evidence.

Authors:  Benjamin Vincent
Journal:  J Vis       Date:  2011-11-30       Impact factor: 2.240

9.  Reflexive social attention in monkeys and humans.

Authors:  Robert O Deaner; Michael L Platt
Journal:  Curr Biol       Date:  2003-09-16       Impact factor: 10.834

10.  Sensory coding of nest-site value in honeybee swarms.

Authors:  Thomas D Seeley; P Kirk Visscher
Journal:  J Exp Biol       Date:  2008-12       Impact factor: 3.312

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  23 in total

1.  EEG signatures of contextual influences on visual search with real scenes.

Authors:  Amir H Meghdadi; Barry Giesbrecht; Miguel P Eckstein
Journal:  Exp Brain Res       Date:  2021-01-04       Impact factor: 1.972

2.  Does the Superior Colliculus Control Perceptual Sensitivity or Choice Bias during Attention? Evidence from a Multialternative Decision Framework.

Authors:  Devarajan Sridharan; Nicholas A Steinmetz; Tirin Moore; Eric I Knudsen
Journal:  J Neurosci       Date:  2017-01-18       Impact factor: 6.167

3.  Changes in perceptual sensitivity related to spatial cues depends on subcortical activity.

Authors:  Lee P Lovejoy; Richard J Krauzlis
Journal:  Proc Natl Acad Sci U S A       Date:  2017-05-22       Impact factor: 11.205

Review 4.  Attention as an effect not a cause.

Authors:  Richard J Krauzlis; Anil Bollimunta; Fabrice Arcizet; Lupeng Wang
Journal:  Trends Cogn Sci       Date:  2014-06-19       Impact factor: 20.229

Review 5.  Dissociating the impact of attention and expectation on early sensory processing.

Authors:  Nuttida Rungratsameetaweemana; John T Serences
Journal:  Curr Opin Psychol       Date:  2019-03-23

6.  Attentional modulation: target selection, active search and cognitive processing.

Authors:  Marisa Carrasco; Miguel Eckstein; Rich Krauzlis; Preeti Verghese
Journal:  Vision Res       Date:  2013-06-07       Impact factor: 1.886

7.  Awareness-Dependent Normalization Framework of Visual Bottom-up Attention.

Authors:  Shiyu Wang; Ling Huang; Qinglin Chen; Jingyi Wang; Siting Xu; Xilin Zhang
Journal:  J Neurosci       Date:  2021-10-05       Impact factor: 6.167

8.  Expectations Do Not Alter Early Sensory Processing during Perceptual Decision-Making.

Authors:  Nuttida Rungratsameetaweemana; Sirawaj Itthipuripat; Annalisa Salazar; John T Serences
Journal:  J Neurosci       Date:  2018-05-17       Impact factor: 6.167

9.  Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.

Authors:  Vasilis M Karlaftis; Joseph Giorgio; Petra E Vértes; Rui Wang; Yuan Shen; Peter Tino; Andrew E Welchman; Zoe Kourtzi
Journal:  Nat Hum Behav       Date:  2019-03-01

10.  Learning predictive statistics from temporal sequences: Dynamics and strategies.

Authors:  Rui Wang; Yuan Shen; Peter Tino; Andrew E Welchman; Zoe Kourtzi
Journal:  J Vis       Date:  2017-10-01       Impact factor: 2.240

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