The visual system is sensitive to statistical properties of complex scenes and can encode feature probability distributions in detail. This encoding could reflect a passive process due to the visual system’s sensitivity to temporal perturbations in the input or a more active process of building probabilistic representations. To investigate this, we examined how observers temporally integrate two different orientation distributions from sequentially presented visual search trials. If the encoded probabilistic information is used in a Bayesian optimal way, observers should weigh more reliable information more strongly, such as feature distributions with low variance. We therefore manipulated the variance of the two feature distributions. Participants performed sequential odd-one-out visual search for an oddly oriented line among distractors. During successive learning trials, the distractor orientations were sampled from two different Gaussian distributions on alternating trials. Then, observers performed a ‘test trial’ where the orientations of the target and distractors were switched, allowing to assess observer’s internal representation of distractor distributions based on changes in response times. In three experiments we observed that observer’s search times on test trials depended mainly on the very last learning trial, indicating little temporal integration. Since temporal integration has been previously observed with this method, we conclude that when the input is unreliable, the visual system relies on the most recent stimulus instead of integrating it with previous ones. This indicates that the visual system prefers to utilize sensory history when the statistical properties of the environment are relatively stable
Temporal integration of feature probability distributions
