Publications by Year: 2011

2011
Takahashi, Y. K., Roesch, M. R., Wilson, R. C., Toreson, K., O'Donnell, P., Niv, Y., & Schoenbaum, G. (2011). Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex. Nature Neuroscience , 14 (12), 1590–1597. Publisher's VersionAbstract
Nature Neuroscience 14, 1590 (2011). doi:10.1038/nn.2957
Eldar, E., Morris, G., & Niv, Y. (2011). The effects of motivation on response rate: A hidden semi-Markov model analysis of behavioral dynamics. Journal of Neuroscience Methods , 201 (1), 251–261. Publisher's VersionAbstract
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. ©2011 Elsevier B.V.
Ribas-Fernandes, J. J. F., Solway, A., Diuk, C., McGuire, J. T., Barto, A. G., Niv, Y., & Botvinick, M. M. (2011). A neural signature of hierarchical reinforcement learning. Neuron , 71 (2), 370–379. Publisher's VersionAbstract
Human behavior displays hierarchical structure: simple actions cohere into subtask sequences, which work together to accomplish overall task goals. Although the neural substrates of such hierarchy have been the target of increasing research, they remain poorly understood. We propose that the computations supporting hierarchical behavior may relate to those in hierarchical reinforcement learning (HRL), a machine-learning framework that extends reinforcement-learning mechanisms into hierarchical domains. To test this, we leveraged a distinctive prediction arising from HRL. In ordinary reinforcement learning, reward prediction errors are computed when there is an unanticipated change in the prospects for accomplishing overall task goals. HRL entails that prediction errors should also occur in relation to task subgoals. In three neuroimaging studies we observed neural responses consistent with such subgoal-related reward prediction errors, within structures previously implicated in reinforcement learning. The results reported support the relevance of HRL to the neural processes underlying hierarchical behavior.
McDannald, M. A., Lucantonio, F., Burke, K. A., Niv, Y., & Schoenbaum, G. (2011). Ventral Striatum and Orbitofrontal Cortex Are Both Required for Model-Based, But Not Model-Free, Reinforcement Learning. Journal of Neuroscience , 31 (7), 2700–2705. Publisher's VersionAbstract
In many cases, learning is thought to be driven by differences between the value of rewards we expect and rewards we actually receive. Yet learning can also occur when the identity of the reward we receive is not as expected, even if its value remains unchanged. Learning from changes in reward identity implies access to an internal model of the environment, from which information about the identity of the expected reward can be derived. As a result, such learning is not easily accounted for by model-free reinforcement learning theories such as temporal difference reinforcement learning (TDRL), which predicate learning on changes in reward value, but not identity. Here, we used unblocking procedures to assess learning driven by value- versus identity-based prediction errors. Rats were trained to associate distinct visual cues with different food quantities and identities. These cues were subsequently presented in compound with novel auditory cues and the reward quantity or identity was selectively changed. Unblocking was assessed by presenting the auditory cues alone in a probe test. Consistent with neural implementations of TDRL models, we found that the ventral striatum was necessary for learning in response to changes in reward value. However, this area, along with orbitofrontal cortex, was also required for learning driven by changes in reward identity. This observation requires that existing models of TDRL in the ventral striatum be modified to include information about the specific features of expected outcomes derived from model-based representations, and that the role of orbitofrontal cortex in these models be clearly delineated.