Abstract
Purpose: Human observers are often modelled as linear discriminators (e.g., as noisy cross-correlators) in shape discrimination tasks. One prediction of such models is that the influence of a stimulus pixel on an observer's response is proportional to the contrast at that pixel. We used a new variant of the reverse correlation technique to test this prediction. Methods: Observers performed several two-alternative identification tasks in external white noise: dot detection, orientation discrimination, and face discrimination, as well as shape discriminations involving illusory contours and occluded contours. We computed classification images to determine what regions of the stimuli observers used to perform the task, and within these regions we computed the correlation between the contrast level at each pixel and the observer's responses. Results: We confirmed the prediction of the linear discriminator model: the influence of each pixel on the observer's decision was linearly related to the contrast at that pixel. This was true even when observers used illusory and occluded contours to perform the task. Conclusions: These results have several implications. (1) Either there is no early transduction nonlinearity, or any such nonlinearity is compensated for and effectively undone during shape discrimination. This is consistent with Chubb and Nam's (2000) findings for judgements of texture luminance and texture variance. (2) The visual system is optimized for an approximately Gaussian noise distribution in the external world. (3) Illusory and occluded contours are used in the same way as luminance-defined contours in threshold shape discrimination tasks. (4) Observers are linear discriminators in threshold shape discrimination tasks.
Support: NSERC OGP0105494 and OGP0042133.