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Kristjansson, A.

Publications and source records attributed to Kristjansson, A..

2 recordsLinked to original sources
bioRxiv2021

Effortless integration of probabilistic visual input

Current theories of perception suggest that the brain represents features of the world as probability distributions, but can such uncertain foundations provide the basis for everyday vision? Perceiving objects and scenes requires knowing not just how features (e.g., colors) are distributed but also where they are and which other features they are combined with. Using a Bayesian computational model, we recover probabilistic representations used by human observers to search for odd stimuli among distractors. Importantly, we found that the brain integrates information between feature dimensions and spatial locations, leading to more precise representations compared to when information integration is not possible. We also uncover representational asymmetries and biases, showing their spatial organization and arguing against simplified "summary statistics" accounts. Our results confirm that probabilistically encoded visual features are bound with other features and to particular locations, proving how probabilistic representations can be a foundation for higher-level vision.

neuroscience↗
bioRxiv2021

Feature distribution learning by passive exposure

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects of feature distributions. In FDL, observers learn the full shape of a distribution of features in a set of distractor stimuli and use this information to improve visual search: response times (RT) are slowed if the target feature lies inside the previous distractor distribution, and the RT patterns closely reflect the distribution shape. FDL requires only a few trials and is markedly sensitive to different distribution types. It is unknown, however, whether our perceptual system encodes feature distributions automatically and by passive exposure, or whether this learning requires active engagement with the stimuli. In two experiments, we sought to answer this question. During an initial exposure stage, participants passively viewed a display of 36 lines that included one orientation singleton or no singletons. In the following search display, they had to find an oddly oriented target. The orientations of the lines were determined either by a Gaussian or a uniform distribution. We found evidence for FDL only when the passive trials contained an orientation singleton. Under these conditions, RTs decreased as a function of the orientation distance between the target and the exposed distractor distribution. These results suggest that FDL can occur by passive exposure, but only if an orientation singleton appears during exposure to the distribution.

neuroscience↗