Social and Behavioral Sciences Commons^™

Modeling Perceptual Grouping Strategies In Visual Search Tasks, Maria Kon, Gregory Francis

MODVIS Workshop

No abstract provided.

Modeling Distribution Learning In Visual Search, Andrey Chetverikov

MODVIS Workshop

Chetverikov, Campana, and Kristjansson (2017) used visual search to demonstrate that human observers are able to extract statistical distributions of visual features. Observers searched for an odd-one-out target with distractors randomly drawn from the same distribution over the course of several “prime” trials. Then, on test trials parameters of the target and distractors changed and response times (RT) were analyzed as a function of the distance between the target position in feature space and the mean of distractor features during prime trials. The resulting RT curves followed the probability density of prime distractor distributions. This approach provides a detailed estimation …

Learning Object Representations For Modeling Attention In Real World Scenes, Alex Schwarz, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

Models of visual attention have been rarely used in real world tasks as they have been typically developed for psychophysical setups using simple stimuli. Thus, the question remains how objects must be represented to allow such models an operation in real world scenarios. We have previously presented an attention model capable of operating on real-world scenes (Beuth, F., and Hamker, F. H. 2015, NCNC, which is a successor of Hamker, F. H., 2005, Cerebral Cortex), and show here how its object representations have been learned. We have used a learning rule based on temporal continuity (Földiák, P., 1991, Neural Computation) …

Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.

To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by …