Human sensory systems cannot simultaneously parse and reconstruct all available inputs into meaningful perceptual representations. Classic accounts address this processing limit by invoking a selection mechanism that preferentially encodes only the most salient and behaviorally relevant stimuli in the environment. This mechanism is typically referred to as selective attention, and empirical work has traditionally focused on understanding why and how relevant stimuli dominate perceptual awareness. However, other extra-retinal factors can also impact the efficiency of information processing, such as estimates of the prior probability of a particular stimulus (or configuration of stimuli) based on past experience in a particular context (i.e. expectation). Despite many demonstrations that expectation can profoundly influence a variety of perceptual phenomena ranging from low-level grouping to high-level object recognition, empirical and theoretical studies almost always conflate expectation and selective attention. This confusion persists even though these factors are logically dissociable: the probability that a stimulus will appear in a given context may have little or nothing to do with behavioral relevance. The conflation of these extra-retinal factors may seem inconsequential, as both might naively be expected to influence neural activity and behavior in a similar way. However, recent theories of cortical information processing - such as predictive coding - hold that stable perceptual representations emerge from the dynamic interplay between internal probability estimates about the state of the world (i.e. expectations) and the content and quality of incoming sensory information (which is shaped by task-relevance, or attention). Here, we adopt a Bayesian framework that casts perceptual inference as the product of prior beliefs and likelihoods (i.e. sensory evidence). We will use this framework to formulate and test the hypothesis that expectation operates on priors to modulate pre-stimulus responses in visual cortex and to bias the `read-out' of neural codes during decision-making, whereas attention directly impacts likelihood functions by shaping stimulus-evoked neural responses on the basis of task relevance. Our approach will combine psychophysics, quantitative models of perceptual and cognitive processes, and novel EEG and fMRI analysis methods that can determine how priors and likelihoods combine to shape the quality of feature-selective perceptual representations. Collectively, this work will provide key insights into how different extra-retinal biasing factors interact to shape perception, and will more broadly test generative models of cortical information processing that characterize perception as a problem of optimal statistical inference. In turn, this knowledge should improve our ability to isolate specific aspects of selective information processing that can sometimes go awry, thereby enabling more targeted diagnoses and interventions in clinical settings.
Whether listening to a teacher in a classroom or driving a car down the road, the ability to pay attention to important sensory stimuli in the environment is critical to our everyday ability to survive and to thrive. In the present research proposal, we wil use the visual system as a model to better understand how different factors such as expectation (i.e. knowledge about statistical regularities in the environment) and selective attention (knowledge about the relevance of stimuli in the environment) independently modulate changes in the activity of sensory neurons to promote more efficient perception, memory, and decision making. Without a comprehensive understanding of how expectation and attention operates to modulate populations of sensory neurons, we are not properly equipped to understand or recognize how deviations might give rise to various perceptual and cognitive disorders. The knowledge gained through this research will thus aid in the development of more objective diagnostic makers for common disorders of selective information processing - such as attention deficit disorder, schizophrenia, and depression - so that interventions can be initiated earlier and in a more targeted manner.
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