The human visual system obtains information about object depth from a large number of distinct cues. A full understanding of visual depth perception requires an understanding of how information provided by these cues is combined by our visual systems. Although nearly all theories of visual depth perception use the concept of cue reliability, we lack a good understanding of what this concept means, of how observers can measure cue reliability, and of what observers can do once they have measured it. The proposed research program places much importance on the need to understand observers' estimates of cue reliabilities, and on the need to understand how observers use these reliabilities during visual reliability judgments, and the roles that these factors play in experience-dependent adaptation of visual depth perception. Two types of experience-dependent adaptation are considered. Cue combination learning refers to the adaptation of the integration process that combines depth estimates based on individual cues into a single, composite depth estimate. Cue recalibration refers to the adaptation of depth interpretations of individual visual cues, such as adaptation of depth-from-motion estimates or adaptation of depth-from-texture estimates. The research program hypothesizes that observers regard a depth cue as reliable when: (i) depth estimates based on that cue are less variable than estimates based on other cues; or when (ii) depth estimates based on that cue are positively correlated with estimates based on other cues. It also hypothesizes that observers adapt their depth perception strategies so as to: (i) rely more heavily on reliable cues during cue integration; and to (ii) recalibrate depth judgments based on unreliable cues so that they more closely match those based on reliable cues.
| Michel, Melchi M; Jacobs, Robert A (2008) Learning optimal integration of arbitrary features in a perceptual discrimination task. J Vis 8:3.1-16 |
| Michel, Melchi M; Jacobs, Robert A (2007) Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning. J Vis 7:4 |
| Ivanchenko, Volodymyr; Jacobs, Robert A (2007) Visual learning by cue-dependent and cue-invariant mechanisms. Vision Res 47:145-56 |
| Chhabra, Manu; Jacobs, Robert A (2006) Properties of synergies arising from a theory of optimal motor behavior. Neural Comput 18:2320-42 |
| Michel, Melchi M; Jacobs, Robert A (2006) The costs of ignoring high-order correlations in populations of model neurons. Neural Comput 18:660-82 |
| Aslin, Richard N; Battaglia, Peter W; Jacobs, Robert A (2004) Depth-dependent contrast gain-control. Vision Res 44:685-93 |
| Jacobs, Robert A; Dominguez, Melissa (2003) Visual development and the acquisition of motion velocity sensitivities. Neural Comput 15:761-81 |
| Fine, Ione; MacLeod, Donald I A; Boynton, Geoffrey M (2003) Surface segmentation based on the luminance and color statistics of natural scenes. J Opt Soc Am A Opt Image Sci Vis 20:1283-91 |
| Ivanchenko, Volodymyr; Jacobs, Robert A (2003) A developmental approach AIDS motor learning. Neural Comput 15:2051-65 |
| Battaglia, Peter W; Jacobs, Robert A; Aslin, Richard N (2003) Bayesian integration of visual and auditory signals for spatial localization. J Opt Soc Am A Opt Image Sci Vis 20:1391-7 |
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