Core B: Bayesian and decision theoretic tools The production of movement sequences is inherently affected by uncertainty: to move rapidly the animal needs to estimate what to do next given past knowledge. Such estimates can never be certain. A colorful example from a recently popular book shows that we can never be certain about a sequence of events. The turkey that has been fed every day for close to a year gets slaughtered for Thanksgiving. Many communities such as robotics, economics, data mining and models of human behavior are converging on a common approach towards formalizing uncertainty: Bayesian decision theory. We will first use these methods to predict behaviors from each of the three experimental labs. We will continue to extract the relevant variables (timescales, probabilities) that need to be represented by the nervous system to efficiently produce sequences. These variables will then be correlated with measured neural signals to ask how these variables are represented. Moreover, uncertainty is central when analyzing data from neurons. When we are asking how neurons store and recall motor sequences we never directly measure the relevant variables, such as memory, we rather measure spikes or imaging signals that are affected by noise. A central topic for neural data analysis, therefore, is to combine many measurements (say 1000 spikes) into an estimate (of say tuning properties) that has small uncertainty (or narrow error-bars). We will use state of the art Bayesian data analysis techniques to analyze the data resulting from the proposed experiments in the other projects. Specifically we are interested in asking how neurons interact with one another using these Bayesian methods. Lastly, we will use state of the art decoding methods to ask how well various types of information are encoded by the measured signals. This is useful for the experimental projects as it allows asking how much information about a, variable of interest is encoded by neural signals.
The proposed work is central to the problem of understanding the mechanisms where practice leads to reorganization of the human motor system in the face of aging, neurodeneration, stroke or brain injury. Understanding these mechanisms has an impact on the design of therapies directed at preserving function, developing compensator movements and ultimately, developing novel motor capacity.
|Soto, Fabian A; Vucovich, Lauren; Musgrave, Robert et al. (2015) General recognition theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception. Psychon Bull Rev 22:88-111|
|Lawlor, Patrick Nathan; Kalisky, Tomer; Rosner, Robert et al. (2014) Conceptualizing cancer drugs as classifiers. PLoS One 9:e106444|
|Ashby, F Gregory (2014) Is state-trace analysis an appropriate tool for assessing the number of cognitive systems? Psychon Bull Rev 21:935-46|
|Devarajan, Karthik; Cheung, Vincent C K (2014) On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data. Neural Comput 26:1128-68|
|Smith, J David; Johnston, Jennifer J R; Musgrave, Robert D et al. (2014) Cross-modal information integration in category learning. Atten Percept Psychophys 76:1473-84|
|Acuna, Daniel E; Wymbs, Nicholas F; Reynolds, Chelsea A et al. (2014) Multifaceted aspects of chunking enable robust algorithms. J Neurophysiol 112:1849-56|
|Fernandes, Hugo L; Stevenson, Ian H; Vilares, Iris et al. (2014) The generalization of prior uncertainty during reaching. J Neurosci 34:11470-84|
|Klimm, Florian; Bassett, Danielle S; Carlson, Jean M et al. (2014) Resolving structural variability in network models and the brain. PLoS Comput Biol 10:e1003491|
|Smith, J David; Boomer, Joseph; Zakrzewski, Alexandria C et al. (2014) Deferred feedback sharply dissociates implicit and explicit category learning. Psychol Sci 25:447-57|
|Barany, Deborah A; Della-Maggiore, Valeria; Viswanathan, Shivakumar et al. (2014) Feature interactions enable decoding of sensorimotor transformations for goal-directed movement. J Neurosci 34:6860-73|
Showing the most recent 10 out of 72 publications