September 2011
Volume 11, Issue 11
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2011
fMRI evidence for the neural representation of target detection in natural scenes
Author Affiliations
  • Fei Guo
    Department of Psychology, Univ. of California, Santa Barbara, Santa Barbara, CA
    Inst. for Collaborative Biotechnologies, Univ. of California, Santa Barbara, Santa Barbara, CA
  • Tim Preston
    Department of Psychology, Univ. of California, Santa Barbara, Santa Barbara, CA
    Inst. for Collaborative Biotechnologies, Univ. of California, Santa Barbara, Santa Barbara, CA
  • Barry Giesbrecht
    Department of Psychology, Univ. of California, Santa Barbara, Santa Barbara, CA
    Inst. for Collaborative Biotechnologies, Univ. of California, Santa Barbara, Santa Barbara, CA
  • Miguel Eckstein
    Department of Psychology, Univ. of California, Santa Barbara, Santa Barbara, CA
    Inst. for Collaborative Biotechnologies, Univ. of California, Santa Barbara, Santa Barbara, CA
Journal of Vision September 2011, Vol.11, 1344. doi:10.1167/11.11.1344
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    • Get Citation

      Fei Guo, Tim Preston, Barry Giesbrecht, Miguel Eckstein; fMRI evidence for the neural representation of target detection in natural scenes. Journal of Vision 2011;11(11):1344. doi: 10.1167/11.11.1344.

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

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Abstract

Studies in humans and monkeys indicate that regions of parietal cortex are engaged in attention and target detection (Corbetta, 2000, Shulman et al., 2003), the accumulation of evidence (Bisley & Goldberg, 2003, Gold & Shadlen, 2007), and choice confidence (Kiani & Shadlen, 2009). However, little is known about the role of these parietal mechanisms in visual search of real scenes. Here we use fMRI and multivariate pattern classifiers to show that several high-order areas can reliably predict the presence/absence of arbitrary target objects in natural scenes. Eleven observers searched for a target object in a natural scene (250 ms) that was specified by a cue word (400 ms) presented prior to the search display. Observers rated target presence/absence using a 10-point confidence rating scale. A multivariate pattern classifier was used to predict the presence/absence of target objects within natural scenes from single-trial fMRI data acquired during the task in regions identified by standard localizers. Classifier performance indicated that while both anterior IPS and FEF predicted target object presence/absence, aIPS was the best predictor. In addition, the correlation between image-specific choices of human observers and those predicted by the classifier was highest in aIPS (r = 0.36, p < 0.001) and FEF (r = 0.30, p < 0.001). Classifier decision variables extracted using single-trial aIPS responses also resulted in the highest positive correlation between behavioral confidence ratings and the neural decision variables. We provide evidence that two areas within the frontoparietal attention network (aIPS and FEF) are involved in detecting the presence of arbitrary target objects within natural scenes. We show that the neural decision variables extracted from the classification analysis correlated with observers' decision confidence and with ensemble observer behavior elicited by an image, indicating a robust correlation between behavior and neural activity during search of natural scenes.

Army grant W911NF-09-D-0001. 
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