В статье рассматриваются вопросы прогнозирования индивидуально-психологических характеристик человека (личностных черт, эмоциональных состояний, ценностей, мотивов и др.) на основании его цифровых следов. Как показали исследования, такие характеристики можно весьма точно выявлять на основании самых разных видов цифровых следов (текстов, изображений, «лайков» (мне нравится) и др.). Наибольшая точность прогноза достигается в случае личностных черт. Эмоциональные состояния, ценности, мотивы и удовлетворенность жизнью прогнозируются несколько хуже. Одновременное использование нескольких видов цифровых следов, а также более совершенных процедур сбора и анализа данных позволяет существенно повысить точность прогноза. Оцениваются ближайшие и более отдаленные перспективы исследований в данной области.
One of the important sources of failures in visual working memory (VWM) is that individual items can interfere with each other. Here, we tested how two causes of such interference—poor categorical distinctiveness and imperfect feature binding—interact. In three experiments, we showed low and high distinctive objects and tested VWM for objects alone, for locations alone and for object-location conjunctions. We found that low object distinctiveness impairs object recognition and increases the number of object-location binding errors. Also, we dissociated the probabilities that these binding errors are due to recognition impairment or a failure of correct binding. Results show that poor distinctiveness increases binding errors rate only due to lacking recognition but not to binding impairment. Together, our findings suggest that object distinction and object-location binding act upon different components of VWM and are separate sources of interference. This study was funded by RSCF #18-18-00334.
The question of whether visual working memory (VWM) stores individual features or bound objects as basic units is actively debated. Evidence exists for both feature-based and object-based storages, as well as hierarchically organized representations maintaining both types of information at different levels. One argument for feature-based storage is that features belonging to different dimensions (e.g., color and orientations) can be stored without interference suggesting independent capacities for every dimension. Here, we studied whether the lack of cross-dimensional interference reflects genuinely independent feature storages or mediated by common objects. In three experiments, participants remembered and recalled the colors and orientations of sets of objects. We independently manipulated set sizes within each feature dimension (making colors and orientations either identical or differing across objects). Critically, we assigned to-be-remembered colors and orientations either to same spatially integrated or to different spatially separated objects. We found that the precision and recall probability within each dimension was not affected by set size manipulations in a different dimension when the features belonged to integrated objects. However, manipulations with color set sizes did affect orientation memory when the features were separated. We conclude therefore that different feature dimensions can be encoded and stored independently but the advantage of the independent storages are mediated at the object-based level. This conclusion is consistent with the idea of hierarchically organized VWM.
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.
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.
Illusory conjunctions (IC) refer to errors in which an observer correctly reports features present in the display, but incorrectly pairs features or parts from multiple objects. There is a long-standing debate in the literature about the nature of ICs; for example, whether they arise from the lack of focused attention (Treisman & Schmidt, 1982) or from lossy peripheral representations (Rosenholtz et al., 2012). Here, we test the hypothesis that the occurrence of ICs relates to spatial uncertainty of features falling within the same noisy “window”. According to this idea, ICs occur when the spatial uncertainty is large compared to the distance between items, causing confusion over which features belong to which item. In Experiment 1, we directly measured the spatial noise at 3°, 6°, 9°, 12° from fixation. A compact “crowd” of four dots briefly appeared, followed by the presentation of a probe circle at various distances from the “crowd”. Observers had to respond whether any dot had fallen within the probed region. The probability of perceiving the dots as outside the probe as a function of distance provides a measure of spatial noise as a function of eccentricity. In Experiment 2, we presented four differently colored and oriented bars, located on an invisible circle with a diameter varying from 1° to 3.5° (the “separation”), and centered at one of three eccentricities (4°, 8°, 12°). Participants had to report the color, orientation, and location of any of the bars. The number of correct answers, guesses (reporting non-presented features), and ICs were estimated. The number of IC increased with eccentricity and decreased with separation. There is good resemblance between the spatial noise and the IC pattern. We conclude that there can be an overlap between the mechanisms of spatial localization and IC in peripheral vision.