Perceived surface slant is systematically biased in the actively-generated optic flow

Perceiving depth from texture and disparity cues: Evidence for a non-probabilistic account of cue integration

Bayesian inference theories have been extensively used to model how the brain derives three-dimensional (3D) information from ambiguous visual input. In particular, the maximum likelihood estimation (MLE) model combines estimates from multiple depth cues according to their relative reliability to produce the most probable 3D interpretation. Here, we tested an alternative theory of cue integration, termed the intrinsic constraint (IC) theory, which postulates that the visual system derives the most stable, not most probable, interpretation of the visual input amid variations in viewing conditions. The vector sum model provides a normative approach for achieving this goal where individual cue estimates are components of a multidimensional vector whose norm determines the combined estimate. Individual cue estimates are not accurate but related to distal 3D properties through a deterministic mapping. In three experiments, we show that the IC theory can more adeptly account for 3D cue integration than MLE models. In Experiment 1, we show systematic biases in the perception of depth from texture and depth from binocular disparity. Critically, we demonstrate that the vector sum model predicts an increase in perceived depth when these cues are combined. In Experiment 2, we illustrate the IC theory radical reinterpretation of the just noticeable difference (JND) and test the related vector sum model prediction of the classic finding of smaller JNDs for combined-cue versus single-cue stimuli. In Experiment 3, we confirm the vector sum prediction that biases found in cue integration experiments cannot be attributed to flatness cues, as the MLE model predicts.

Don't worry, be active: how to facilitate the detection of errors in immersive virtual environments

The current research aims to study the link between the type of vision experienced in a collaborative immersive virtual environment (active vs. multiple passive), the type of error one looks for during a cooperative multi-user exploration of a design project (affordance vs. perceptual violations), and the type of setting in which multi-user perform (field in Experiment 1 vs. laboratory in Experiment 2). The relevance of this link is backed by the lack of conclusive evidence on an active vs. passive vision advantage in cooperative search tasks within software based on immersive virtual reality (IVR). Using a yoking paradigm based on the mixed usage of simultaneous active and multiple passive viewings, we found that the likelihood of error detection in a complex 3D environment was characterized by an active vs. multi-passive viewing advantage depending on: (1) the degree of knowledge dependence of the type of error the passive/active observers were looking for (low for perceptual violations, vs. high for affordance violations), as the advantage tended to manifest itself irrespectively from the setting for affordance, but not for perceptual violations; and (2) the degree of social desirability possibly induced by the setting in which the task was performed, as the advantage occurred irrespectively from the type of error in the laboratory (Experiment 2) but not in the field (Experiment 1) setting. Results are relevant to future development of cooperative software based on IVR used for supporting the design review. A multi-user design review experience in which designers, engineers and end-users all cooperate actively within the IVR wearing their own head mounted display, seems more suitable for the detection of relevant errors than standard systems characterized by a mixed usage of active and passive viewing.