TY - JOUR
T1 - The effects of motivation on response rate
T2 - A hidden semi-Markov model analysis of behavioral dynamics
AU - Eldar, Eran
AU - Morris, Genela
AU - Niv, Yael
N1 - Funding Information: This work was funded by a start-up grant from the United States-Israel Binational Science Foundation . We thank Peter Dayan for helpful discussions of this work and Daphna Joel for assistance with execution of the rat experiment.
PY - 2011/9/30
Y1 - 2011/9/30
N2 - A central goal of neuroscience is to understand how neural dynamics bring about the dynamics of behavior. However, neural and behavioral measures are noisy, requiring averaging over trials and subjects. Unfortunately, averaging can obscure the very dynamics that we are interested in, masking abrupt changes and artificially creating gradual processes. We develop a hidden semi-Markov model for precisely characterizing dynamic processes and their alteration due to experimental manipulations. This method takes advantage of multiple trials and subjects without compromising the information available in individual events within a trial. We apply our model to studying the effects of motivation on response rates, analyzing data from hungry and sated rats trained to press a lever to obtain food rewards on a free-operant schedule. Our method can accurately account for punctate changes in the rate of responding and for sequential dependencies between responses. It is ideal for inferring the statistics of underlying response rates and the probability of switching from one response rate to another. Using the model, we show that hungry rats have more distinct behavioral states that are characterized by high rates of responding and they spend more time in these high-press-rate states. Moreover, hungry rats spend less time in, and have fewer distinct states that are characterized by a lack of responding (Waiting/Eating states). These results demonstrate the utility of our analysis method, and provide a precise quantification of the effects of motivation on response rates.
AB - A central goal of neuroscience is to understand how neural dynamics bring about the dynamics of behavior. However, neural and behavioral measures are noisy, requiring averaging over trials and subjects. Unfortunately, averaging can obscure the very dynamics that we are interested in, masking abrupt changes and artificially creating gradual processes. We develop a hidden semi-Markov model for precisely characterizing dynamic processes and their alteration due to experimental manipulations. This method takes advantage of multiple trials and subjects without compromising the information available in individual events within a trial. We apply our model to studying the effects of motivation on response rates, analyzing data from hungry and sated rats trained to press a lever to obtain food rewards on a free-operant schedule. Our method can accurately account for punctate changes in the rate of responding and for sequential dependencies between responses. It is ideal for inferring the statistics of underlying response rates and the probability of switching from one response rate to another. Using the model, we show that hungry rats have more distinct behavioral states that are characterized by high rates of responding and they spend more time in these high-press-rate states. Moreover, hungry rats spend less time in, and have fewer distinct states that are characterized by a lack of responding (Waiting/Eating states). These results demonstrate the utility of our analysis method, and provide a precise quantification of the effects of motivation on response rates.
KW - Hidden semi-Markov model
KW - Motivation
KW - Response rate
KW - Sequential data analysis
UR - http://www.scopus.com/inward/record.url?scp=80052309250&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.jneumeth.2011.06.028
DO - https://doi.org/10.1016/j.jneumeth.2011.06.028
M3 - مقالة
C2 - 21782849
SN - 0165-0270
VL - 201
SP - 251
EP - 261
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -