Abstract
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.
Highlights
Integrating sensory informationHumans have access to a rich array of sensory data from both within and between modalities upon which to base perceptual estimates and motor actions
We present a technique consisting of simulating end-to-end experiments, which can be used to determine the probability with which a population of observers behaving in accordance with one model of sensory cue integration can distinguished from the predictions of a Scarfe set of alternative models
We focus on the extent to which the predictions of MVUE can be distinguished from two popular alternative models, (a) choose the cue with the minimum sigma (MS), and (b) probabilistic cue switching (PCS)
Summary
Humans have access to a rich array of sensory data from both within and between modalities upon which to base perceptual estimates and motor actions. These data are treated as consisting of quasi-independent sensory “cues.” Given a set of cues, the question becomes how information is integrated to generate a robust percept of the world (Ernst & Bulthoff, 2004). Whereas there are clearly multiple benefits of combining and integrating sensory information (Ernst & Bulthoff, 2004), MVUE posits that the optimizing criteria of sensory integration is to maximize the precision of the integrated cues sensory estimate. As the reliability of the cues becomes unbalanced, the increase in precision rapidly diminishes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
