Friday, December 1, 2023
12/1/2023
Project Summary One of the most influential theories of biological vision considers visual perception as a process of Bayesian inference. In order to make inferences about the external world a successful visual system must take into account the uncertainty of neural computations. In the particular case of depth perception, the focus of this project, Bayesian models postulate that uncertainty is explicitly represented as probability distributions defined over possible 3D interpretations of a scene. Only this knowledge allows the integration of multiple sources of 3D information to achieve Bayesian optimality. A large body of data compatible with this theory comes from studies involving depth discrimination, where it is indeed found that variability in perceptual responses becomes smaller as more depth cues are added to a stimulus. Here it is questioned whether this data is evidence that behavioral variability stems from neural noise representing uncertainty of 3D estimates. Instead, an alternate theory is proposed, which does not require this representation. Beyond being more parsimonious, this theory can also predict the same findings that seem to confirm the Bayesian predictions. This exploratory research proposal lays out two testable predictions of this new theory, termed Intrinsic Constraint (IC), for which (1) the brain does not represent probability distributions over 3D properties and (2) perceptual variability in depth discrimination tasks does not reflect uncertainty encoded in these probability distributions. In contrast to the Bayesian account, the IC theory postulates that responses to different 3D stimuli vary in magnitude instead of perceptual noise. In particular, stimuli that according to Bayesian models allegedly have different reliabilities for the IC model elicit different perceptual gains. Combining cues increases the perceptual gain and this factor, not higher precision, enhances performance in depth discriminations tasks. This prediction gives the IC model the explanatory power necessary to support its viability as a theory of 3D perception. Testing the validity of either theoretical account will be achieved through the synergetic collection of behavioral and fMRI data. First, it will be determined whether the Just Noticeable Difference (JND) of a two- interval depth discrimination task measures stimulus reliability or noise associated with memory retention. According to this second interpretation, it is the perceptual gain that determines the changes in physical depth required to overcome this task related noise, in agreement with the IC account. Second, an fMRI technique that can estimate both the magnitude and noise of probability distributions encoded in neural population activity will provide critical converging evidence of the existence (or absence) of neural encoding of 3D uncertainty. In summary, this research project will bring together the two separate fields of research of visual perception and visual short-term memory, with investigations leveraging behavioral and fMRI methods for addressing a fundamental problem in vision science.
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