1. Crowding by a single bar
ECVP 2013, August, Bremen, Germany
Crowding by a single bar
Endel Põder
University of Tartu, Estonia
Background
Visual crowding does not affect much the detection of the presence of simple visual features but perturbs heavily combining them into recognizable objects. Still, the crowding effects have been rarely related to general pattern recognition mechanisms.
Examples of stimuli
Example of display
Methods
In many aspects similar to Dakin et al (2010).
Observers had to identify the orientation (4AFC) of a rotated T presented briefly (60 ms) at a peripheral location (eccentricity 6 deg). Adjacent to the target, a single bar was presented. The bar was either horizontal or vertical, and located in a random direction (0-360 deg) from the target.
Orientation and position of the crowding bar and observers’ responses are expressed relative to the upright orientation of the target. The data are pooled across absolute orientations of the target.
Response panel
Results of the experiment
Very strong and regular effects of the crowding bar on the identification of target orientation. Wrong answers are evoked by a global stimulus configuration that resembles a correspondingly oriented target. Only rough relative positions of features matter, exact metrical relations are not important.
Modeling
Follows the ideas of the “standard model” (e.g. Riesenhuber & Poggio, 1999): local feature detectors, spatial pooling of their outputs, combining the results into second-order features. Assumes three kinds of simple features: horizontal and vertical bars plus a low-pass blob at the target position. Second-order features are combinations of the oriented and un-oriented features (e.g. presence of a horizontal bar above the object center). Particular combinations of second-order features provide evidence for each target orientation. The hypothesis with maximum support was selected as the response on a given trial.
Results of a simulation
(points – experiment, lines – model)
Supposed spatial arrangement of receptive fields
References
Dakin, S. C., Cass, J., Greenwood, J. A., & Bex, P. J. (2010). Probabilistic, positional averaging predicts object-level crowding effects with letter-like stimuli. Journal of Vision, 10(10):14, 1–16.
Pasupathy, A., & Connor, C. E. (2001). Shape representation in area V4: position-specific tuning for boundary conformation. Journal of Neurophysiology, 86, 2505–2519.
Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019–1025.
Conclusions
The results are broadly consistent with the “standard model”.
Crowding in visual periphery is caused by large pooling regions that include information from surrounding objects.
This study supports the idea that visual system uses second-order features that encode the presence of simple visual features in some approximate position relative to candidate object center (Pasupathy & Connor, 2001).