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.

The role of perspective in event segmentation.

People divide their ongoing experience into meaningful events. This process, event segmentation, is strongly associated with visual input: when visual features change, people are more likely to segment. However, the nature of this relationship is unclear. Segmentation could be bound to specific visual features, such as actor posture. Or, it could be based on changes in the activity that are correlated with visual features. This study distinguished between these two possibilities by examining whether segmentation varies across first- and third-person perspectives. In two experiments, observers identified meaningful events in videos of actors performing everyday activities, such as eating breakfast or doing laundry. Each activity was simultaneously recorded from a first-person perspective and a third-person perspective. These videos presented identical activities but differed in their visual features. If segmentation is tightly bound to visual features then observers should identify different events in first- and third-person videos. In addition, the relationship between segmentation and visual features should remain unchanged. Neither prediction was supported. Though participants sometimes identified more events in first-person videos, the events they identified were mostly indistinguishable from those identified for third-person videos. In addition, the relationship between the video's visual features and segmentation changed across perspectives, further demonstrating a partial dissociation between segmentation and visual input. Event segmentation appears to be robust to large variations in sensory information as long as the content remains the same. Segmentation mechanisms appear to flexibly use sensory information to identify the structure of the underlying activity.