Purchase this article with an account.
Steven S. Shimozaki, Kelly Y. Chen, Craig K. Abbey, Miguel P. Eckstein; The temporal dynamics of selective attention of the visual periphery as measured by classification images. Journal of Vision 2007;7(12):10. doi: https://doi.org/10.1167/7.12.10.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
This study estimates the temporal dynamics of selective attention with classification images, a technique assessing observer information use by tracking how responses are correlated with external noise added to the stimulus. Three observers performed a yes/no discrimination of a Gaussian signal that could appear at one of eight locations (eccentricity—4.6°). During the stimulus duration (300 ms), a peripheral cue indicated the potential signal location with 100% validity, and stimuli were presented in frames (37.5 ms/frame) of independently sampled Gaussian luminance image noise. Stimuli were presented either with or without a succeeding masking display (100 ms) of high-contrast image noise, with mask presence having little effect. The results from the classification images suggest that observers were able to use information at the cued location selectively (relative to the uncued locations), starting within the first (0–37.5 ms) or second (37.5–75 ms) frame. This suggests a selective attention effect earlier than those found in previous behavioral and event-related potential (ERP) studies, which generally have estimated the latency for selective attention effects to be 75–100 ms. We present a deconvolution method using the known temporal impulse response of early vision that indicates how the classification image results might relate to previous behavioral and ERP results. Applying the model to the classification images suggests that accounting for the known temporal dynamics could explain at least part of the difference in results between classification images and the previous studies.
This PDF is available to Subscribers Only