How we see the world can be influenced strongly by our experiences. We can learn to associate certain visual images with impending rewards or punishments (e.g., seeing the front of a candy store). We can also learn to better differentiate weak, noisy, or obscure images with practice (e.g., an experienced bird- watcher identifying a bird in flight). The goal of our proposed research is to understand neural mechanisms common to these two seemingly different forms of visual learning, one that forms associations between visual input and reward-seeking behavior, the other that improves perceptual sensitivity. The key idea is that visual information is represented across heterogeneous populations of neurons in the brain, and both forms of learning involve a process of selecting neurons from these populations that have the appropriate characteristics for a given task. Associative learning requires selecting neurons that represent the appropriate visual features that predict reward. Perceptual learning requires selecting neurons that represent a particular visual feature with the highest sensitivity. Our three specific Aims will use computational modeling, human psychophysics, and combined psychophysics and physiology in monkeys to identify neural mechanisms that govern this selection process.
Aim 1 will introduce a computational model that can account for both associative and perceptual learning by selecting outputs from a sensory representation based on their ability to guide behavior that maximizes reward. We will test the model in several ways, including a comparison to behavioral and neural data from monkeys learning a demanding visual discrimination task and behavioral data from human subjects learning a similar task. The model is based on two computational principles that will guide the experiments in the other two Aims. The first principle is that learning results from changes in functional connectivity between sensory and decision neurons.
Aim 2 will test whether changes in interactions between two cortical areas reflect these predicted changes in functional connectivity. The second principle is that learning is driven by a process that identifies discrepancies between predicted and actual reward.
Aim 3 will test whether neurons in a subcortical structure known as the caudate encode this kind of reward prediction error during learning. These studies will help to unify previously disparate fields of associative and perceptual learning and provide a far-reaching perspective on mechanisms that allow experiences to shape the functions of a healthy visual system.
The proposed work is basic research, designed to provide new insights into how a healthy nervous system learns from experience to more effectively process visual information. Thus, direct benefits to public health are intended to come in the longer term, as these new insights can be used to design new ways to diagnose and treat disorders of visual perception (i.e., visual agnosias) and learning.
|Kim, Timothy Doyeon; Kabir, Mohammad; Gold, Joshua I (2017) Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment. J Neurosci 37:3632-3645|
|Krishnamurthy, Kamesh; Nassar, Matthew R; Sarode, Shilpa et al. (2017) Arousal-related adjustments of perceptual biases optimize perception in dynamic environments. Nat Hum Behav 1:|
|Barack, David L; Gold, Joshua I (2016) Temporal trade-offs in psychophysics. Curr Opin Neurobiol 37:121-125|
|Tsunada, Joji; Liu, Andrew S K; Gold, Joshua I et al. (2016) Causal contribution of primate auditory cortex to auditory perceptual decision-making. Nat Neurosci 19:135-42|
|Kalwani, Rishi M; Joshi, Siddhartha; Gold, Joshua I (2014) Phasic activation of individual neurons in the locus ceruleus/subceruleus complex of monkeys reflects rewarded decisions to go but not stop. J Neurosci 34:13656-69|
|Ding, Long; Gold, Joshua I (2013) The basal ganglia's contributions to perceptual decision making. Neuron 79:640-9|
|Gold, Joshua I; Ding, Long (2013) How mechanisms of perceptual decision-making affect the psychometric function. Prog Neurobiol 103:98-114|
|Wilson, Robert C; Nassar, Matthew R; Gold, Joshua I (2013) A mixture of delta-rules approximation to bayesian inference in change-point problems. PLoS Comput Biol 9:e1003150|
|Nassar, Matthew R; Gold, Joshua I (2013) A healthy fear of the unknown: perspectives on the interpretation of parameter fits from computational models in neuroscience. PLoS Comput Biol 9:e1003015|
|Nassar, Matthew R; Rumsey, Katherine M; Wilson, Robert C et al. (2012) Rational regulation of learning dynamics by pupil-linked arousal systems. Nat Neurosci 15:1040-6|
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