September 2011
Volume 11, Issue 11
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2011
Feature-based attention enhances performance by increasing response gain
Author Affiliations
  • Katrin Herrmann
    Department of Psychology, New York University, USA
  • David J. Heeger
    Department of Psychology, New York University, USA
    Center for Neural Science, New York University, USA
  • Marisa Carrasco
    Department of Psychology, New York University, USA
    Center for Neural Science, New York University, USA
Journal of Vision September 2011, Vol.11, 131. doi:10.1167/11.11.131
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      Katrin Herrmann, David J. Heeger, Marisa Carrasco; Feature-based attention enhances performance by increasing response gain. Journal of Vision 2011;11(11):131. doi: 10.1167/11.11.131.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Objective: Characterize the contrast dependence of feature-based attention. Methods: Observers performed a 2IFC orientation-discrimination task. The first interval contained 32 randomly positioned Gabors (16 had identical orientation θR near 45° and 16 had identical orientation θL near −45°). In the second interval, 32 Gabors all shared identical orientation θtest chosen randomly from θR ± 1 JND or θL ± 1 JND. Observers indicated whether θtest was slightly clockwise or counter-clockwise of the corresponding orientation (θR or θL) in the first interval. Feature-based attention was manipulated with a 75%-valid pre-cue (a ±45° line at fixation) resulting in valid (pre-cue and θtest roughly matched) and invalid (mismatched) cue conditions. The precise values of θR and θL were randomly and independently varied over trials so that the first-interval display was uninformative as to which orientation would be queried. Performance (d′) was measured for each cue condition and several contrasts, randomly interleaved. Results: We fit psychometric functions with five free parameters (full model) to the data: asymptotic performance at high contrast (dmax) for each cue condition, contrast yielding half-maximum performance (c50) for each cue condition, and an exponent shared by both cue conditions. Feature-based attention significantly changed dmax, but not c50. Nested F-tests showed a similarly good fit of a restricted model using one c50 for both cue conditions (compared to the full model). A restricted model with one dmax for both cue conditions fit significantly worse than to the full model. Conclusion: We manipulated feature-based attention while minimizing effects of spatial attention. Our results are consistent with an increase in response gain and support a key prediction of the normalization model of attention (Figure 4c of Reynolds & Heeger, Neuron, 2009).

NIH R01-EY019693 to DJH and MC. 
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