A decisional account of subjective inflation of visual perception at the periphery
Human peripheral vision appears vivid compared to foveal vision; the subjectively perceived level of detail does not seem to drop abruptly with eccentricity. This compelling impression contrasts with the fact that spatial resolution is substantially lower at the periphery. A similar phenomenon occurs in visual attention, in which subjects usually overestimate their perceptual capacity in the unattended periphery. We have previously shown that at identical eccentricity, low spatial attention is associated with liberal detection biases, which we argue may reflect inflated subjective perceptual qualities. Our computational model suggests that this subjective inflation occurs because under the lack of attention, the trial-by-trial variability of the internal neural response is increased, resulting in more frequent surpassing of a detection criterion. In the current work, we hypothesized that the same mechanism may be at work in peripheral vision. We investigated this possibility in psychophysical experiments in which participants performed a simultaneous detection task at the center and at the periphery. Confirming our hypothesis, we found that participants adopted a conservative criterion at the center and liberal criterion at the periphery. Furthermore, an extension of our model predicts that detection bias will be similar at the center and at the periphery if the periphery stimuli are magnified. A second experiment successfully confirmed this prediction. These results suggest that, although other factors contribute to subjective inflation of visual perception in the periphery, such as top-down filling-in of information, the decision mechanism may be relevant too.
KeywordsPeripheral vision Subjective perception Perceptual decision making Psychophysics Signal detection theory
This work is partially supported by a grant from the Templeton Foundation (6–40689) awarded to Hakwan Lau. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Megan Peters and Jorge Morales for valuable comments on the manuscript, Brian Maniscalco for assistance with model fitting, and Dobromir Rahnev for task design suggestions.
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