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Daniel Kersten

Publications and source records attributed to Daniel Kersten.

6 recordsLinked to original sources

Bayesian models of object perception.

The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. Recent advances in Bayesian models of computer vision and in the measurement and modeling of natural image statistics are providing the tools to test and constrain theories of human object perception. In turn, these theories are having an impact on the interpretation of cortical function.

Animals↗

Is color an intrinsic property of object representation?

The role of color in object representation was examined by using a variation of the Stroop paradigm in which observers named the displayed colors of objects or words. In experiment 1, colors of color-diagnostic objects were manipulated to be either typical or atypical of the object (eg a yellow banana versus a purple banana). A Stroop-like effect was obtained, with faster color-naming times for the typical as compared to the atypical condition. In experiment 2, naming colors on words specifying these same color-diagnostic objects reversed this pattern, with the typical condition producing longer response times than the atypical condition. In experiment 3, a blocked condition design that used the same words and colors as experiment 2 produced the standard Stroop-like facilitation for the typical condition. These results indicate that color is an intrinsic property of an object's representation at multiple levels. In experiment 4, we examined the specific level(s) at which color-shape associations arise by following the tasks used in experiments 1 and 2 with a lexical-decision task in which some items were conceptually related to items shown during color naming (eg banana/monkey). Priming for these associates was observed following color naming of words, but not pictures, providing further evidence that the color-shape associations responsible for the differing effects obtained in experiments 1 and 2 are due to the automatic activation of color-shape associations at different levels of representation.

Adult↗

Bootstrapped learning of novel objects.

Recognition of familiar objects in cluttered backgrounds is a challenging computational problem. Camouflage provides a particularly striking case, where an object is difficult to detect, recognize, and segment even when in "plain view." Current computational approaches combine low-level features with high-level models to recognize objects. But what if the object is unfamiliar? A novel camouflaged object poses a paradox: A visual system would seem to require a model of an object's shape in order to detect, recognize, and segment it when camouflaged. But, how is the visual system to build such a model of the object without easily segmentable samples? One possibility is that learning to identify and segment is opportunistic in the sense that learning of novel objects takes place only when distinctive clues permit object segmentation from background, such as when target color or motion enables segmentation on single presentations. We tested this idea and discovered that, on the contrary, human observers can learn to identify and segment a novel target shape, even when for any given training image the target object is camouflaged. Further, perfect recognition can be achieved without accurate segmentation. We call the ability to build a shape model from high-ambiguity presentations bootstrapped learning.

Animals↗

Three-dimensional symmetric shapes are discriminated more efficiently than asymmetric ones.

Objects with bilateral symmetry, such as faces, animal shapes, and many man-made objects, play an important role in everyday vision. Because they occur frequently, it is reasonable to conjecture that the brain may be specialized for symmetric objects. We investigated whether the human visual system processes three-dimensional (3D) symmetric objects more efficiently than asymmetric ones. Human subjects, having learned a symmetric wire object, discriminated which of two distorted copies of the learned object was more similar to the learned one. The distortion was achieved by adding 3D Gaussian positional perturbations at the vertices of the wire object. In the asymmetric condition, the perturbation was independent from one vertex to the next. In the symmetric condition, independent perturbations were added to only half of the object; perturbations on the other half retained the symmetry of the object. We found that subjects' thresholds were higher in the symmetric condition. However, since the perturbation in the symmetric condition was correlated, a stimulus image provided less information in the symmetric condition. Taking this in to consideration, an ideal-observer analysis revealed that subjects were actually more efficient at discriminating symmetric objects. This reversal in interpretation underscores the importance of ideal-observer analysis. A completely opposite, and wrong, conclusion would have been drawn from analyzing only human discrimination thresholds. Given the same amount of information, the visual system is actually better able to discriminate symmetric objects than asymmetric ones.

Differential Threshold↗

Shape perception reduces activity in human primary visual cortex.

Visual perception involves the grouping of individual elements into coherent patterns that reduce the descriptive complexity of a visual scene. The physiological basis of this perceptual simplification remains poorly understood. We used functional MRI to measure activity in a higher object processing area, the lateral occipital complex, and in primary visual cortex in response to visual elements that were either grouped into objects or randomly arranged. We observed significant activity increases in the lateral occipital complex and concurrent reductions of activity in primary visual cortex when elements formed coherent shapes, suggesting that activity in early visual areas is reduced as a result of grouping processes performed in higher areas. These findings are consistent with predictive coding models of vision that postulate that inferences of high-level areas are subtracted from incoming sensory information in lower areas through cortical feedback.

Brain Mapping↗