The effects of motivation on response rate: A hidden semi-Markov model analysis of behavioral dynamics

Eran Eldar, Genela Morris, Yael Niv*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageAmerican English
Pages (from-to)251-261
Number of pages11
JournalJournal of Neuroscience Methods
Issue number1
StatePublished - 30 Sep 2011
Externally publishedYes

Bibliographical note

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.


  • Hidden semi-Markov model
  • Motivation
  • Response rate
  • Sequential data analysis


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