Performance on visual tasks improves with training. Our long-term goal is to understand the neural changes that give rise to this form of perceptual learning. We use a task that requires a decision about the direction of weak motion signals in a random-dot stimulus. With training, monkeys, like humans, learn to interpret these motion signals more accurately and quickly. We will identify the neural substrate of these performance gains. In monkeys, several mechanisms contribute to decision formation. Neurons in the middle temporal area (MT) represent the motion information used to perform the task. When the decision is indicated with an eye movement, oculomotor circuits appear to accumulate this motion information over time to form the decision. Our three Specific Aims will identify changes in these mechanisms as performance improves with training.
Aim 1 will identify changes in how the brain represents the sensory stimulus. We will record from MT neurons to test whether their sensitivity to motion changes as performance improves.
Aim 2 will identify changes in how the brain interprets motion information to form the behavioral response. We will evoke saccadic eye movements with electrical microstimulation of the frontal eye field. These evoked saccades are sensitive to developing oculomotor commands and thus will be used to test how the accumulation process represented in these commands changes with training.
Aim 3 will identify changes in how the brain computes the decision. We will record from neurons in the lateral intraparietal area that represent formation of the decision and formation of the eye-movement response in a trained monkey. We will determine how these different neural computations become linked over the course of training. In the long term, these studies will help connect systems-level electrophysiology with the study of plasticity and learning. These connections will aid in the development of computational tools to treat learning disabilities and clinical disorders (e.g., psychosis, dementia and agnosia) that affect the brain's ability to process and interpret information. ? ?
|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:|
|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|
|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|
|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|
|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|
|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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