Experimental investigations of cue combination typically assume that individual cues provide noisy but unbiased sensory information about world properties. However, in numerous instances, including real-world settings, observers systematically misestimate properties of the world from sensory information. Two such instances are the estimation of shape from stereo and motion cues. Bias in single-cue estimates, therefore poses a problem for cue combination if the visual system is to maintain accuracy with respect to the world, particularly because knowledge about the magnitude of bias in individual cues is typically unknown. Here, we show that observers fail to take account of the magnitude of bias in each cue during combination and instead combine cues in proportion to their reliability so as to increase the precision of the combined-cue estimate. This suggests that observers were unaware of the bias in their sensory estimates. Our analysis of cue combination shows that there is a definable range of circumstances in which combining information from biased cues, rather than vetoing one or other cue, can still be beneficial, by reducing error in the final estimate.

Virtual reality (VR) is becoming an increasingly important way to investigate sensory processing. The converse is also true: in order to build good VR technologies, one needs an intimate understanding of how our brain processes sensory information. One of the key advantages of studying perception with VR is that it allows an experimenter to probe perceptual processing in a more naturalistic way than has been possible previously. In VR, one is able to actively explore and interact with the environment, just as one would do in real life. In this article, we review the history of VR displays, including the philosophical origins of VR, before discussing some key challenges involved in generating good VR and how a sense of presence in a virtual environment can be measured. We discuss the importance of multisensory VR and evaluate the experimental tension that exists between artifice and realism when investigating sensory processing.

Technological innovations have had a profound influence on how we study the sensory perception in humans and other animals. One example was the introduction of affordable computers, which radically changed the nature of visual experiments. It is clear that vision research is now at cusp of a similar shift, this time driven by the use of commercially available, low-cost, high-fidelity virtual reality (VR). In this review we will focus on: (a) the research questions VR allows experimenters to address and why these research questions are important, (b) the things that need to be considered when using VR to study human perception,

Observers generally fail to recover three-dimensional shape accurately from binocular disparity. Typically, depth is overestimated at near distances and underestimated at far distances [Johnston, E. B. (1991). Systematic distortions of shape from stereopsis. Vision Research, 31, 1351-1360]. A simple prediction from this is that disparity-defined objects should appear to expand in depth when moving towards the observer, and compress in depth when moving away. However, additional information is provided when an object moves from which 3D Euclidean shape can be recovered, be this through the addition of structure from motion information [Richards, W. (1985). Structure from stereo and motion. Journal of the Optical Society of America A, 2, 343-349], or the use of non-generic strategies [Todd, J. T., & Norman, J. F. (2003). The visual perception of 3-D shape from multiple cues: Are observers capable of perceiving metric structure? Perception and Psychophysics, 65, 31-47]. Here, we investigated shape constancy for objects moving in depth. We found that to be perceived as constant in shape, objects needed to contract in depth when moving toward the observer, and expand in depth when moving away, countering the effects of incorrect distance scaling (Johnston, 1991). This is a striking example of the failure of shape constancy, but one that is predicted if observers neither accurately estimate object distance in order to recover Euclidean shape, nor are able to base their responses on a simpler processing strategy.

Sensory cue integration is one of the primary areas in which a normative mathematical framework has been used to define the “optimal” way in which to make decisions based upon ambiguous sensory information and compare these predictions to behavior. The conclusion from such studies is that sensory cues are integrated in a statistically optimal fashion. However, numerous alternative computational frameworks exist by which sensory cues could be integrated, many of which could be described as “optimal” based on different criteria. Existing studies rarely assess the evidence relative to different candidate models, resulting in an inability to conclude that sensory cues are integrated according to the experimenter's preferred framework. The aims of the present paper are to summarize and highlight the implicit assumptions rarely acknowledged in testing models of sensory cue integration, as well as to introduce an unbiased and principled method by which to determine, for a given experimental design, the probability with which a population of observers behaving in accordance with one model of sensory integration can be distinguished from the predictions of a set of alternative models.