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To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable\u2014called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants ([Formula: see text]) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.<\/jats:p>","DOI":"10.1162\/neco_a_01196","type":"journal-article","created":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T17:12:46Z","timestamp":1563901966000},"page":"1751-1788","source":"Crossref","is-referenced-by-count":32,"title":["Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach"],"prefix":"10.1162","volume":"31","author":[{"given":"Ali","family":"Yousefi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, U.S.A."}]},{"given":"Ishita","family":"Basu","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A."}]},{"given":"Angelique 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