Numerous studies report that observers are good at evaluating various ensemble statistics, such as mean or range. Recent studies have shown that, in the perception of mean size, the visual system relies on size information individually rescaled to distance for each item (Utochkin & Tiurina, 2018). Here, we directly tested this rescaling mechanism on the perception of variance. In our experiment, participants were stereoscopically shown a sample set of circles with different sizes and in different apparent depths. Then they had to adjust a test set so that the range of sizes to match the range of the sample. We manipulated the correlation between sizes and depth for both samples and tests. In positive size-depth correlation, bigger circles were presented farther and had to seem larger and small circles were presented closer and had to seem smaller; therefore, the apparent range had to increase. In negative size-depth correlation, the apparent range had to decrease, since bigger circles had to become smaller, and vice versa. We tested all possible couplings of correlation conditions between samples and tests. We found that in general, observers tended to overestimate the range of the sample (over-adjusted it on the test). Yet, the strongest underestimation was shown when the sample had a negative correlation and the test had a positive correlation. This pattern is consistent with the prediction following from the idea of rescaling. As the negative correlation reduced an apparent range, participants had to under-adjust the range of a positively correlated test to compensate for the difference in variance impressions. We conclude, therefore, that multiple sizes are automatically rescaled in accordance with their distances and this rescaling can be used to judge ensemble variance.
Classical change-point detection procedures assume a change-point model to be known and a change consisting in establishing a new observations regime, i.e. the change lasts infinitely long. These modeling assumptions contradicts applied problems statements. Therefore, even theoretically optimal statistics in practice very often fail when detecting transient changes online. In this work in order to overcome limitations of classical change-point detection procedures we consider approaches to constructing ensembles of change-point detectors, i.e. algorithms that use many detectors to reliably identify a change-point. We propose a learning paradigm and specific implementations of ensembles for change detection of short-term (transient) changes in observed time series. We demonstrate by means of numerical experiments that the performance of an ensemble is superior to that of the conventional change-point detection procedures.
The adaptation aftereffect (AAE) of mean size suggests that mean size is coded as a basic visual property. Also, size-distance rescaling of individual objects occurs prior to averaging. Because it is unclear whether the AAE is based on rescaled mean size, we tested the degree of AAE as a function the apparent mean size of stimuli presented at different depths. Observers were stereoscopically shown an adapting patch of dots with either a large or small mean size, followed by a brief test circle. Adaptors and tests were presented at a near and a far plane, both in the same or in different planes. Observers then adjusted the size of a probe in the middle plane to match the test size. We found evidence of the AAE and for test size rescaling, but no effect of whether the adaptor and test were presented in the same or in different planes. Our results suggest that the AAE of mean size take places at a lower level of visual processing than size-distance rescaling. This study was funded by RFBR #18-313-00253.