TY - JOUR
T1 - State representation in mental illness
AU - Radulescu, Angela
AU - Niv, Yael
N1 - Funding Information:
We are grateful to Daniel Bennett and Eugenia Radulescu for comments on earlier drafts of the manuscript, and to Eran Eldar and Sam Zorowitz for helpful discussions. This work was supported by grant W911NF-14-1-0101 from the Army Research Office .
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/4
Y1 - 2019/4
N2 - Reinforcement learning theory provides a powerful set of computational ideas for modeling human learning and decision making. Reinforcement learning algorithms rely on state representations that enable efficient behavior by focusing only on aspects relevant to the task at hand. Forming such representations often requires selective attention to the sensory environment, and recalling memories of relevant past experiences. A striking range of psychiatric disorders, including bipolar disorder and schizophrenia, involve changes in these cognitive processes. We review and discuss evidence that these changes can be cast as altered state representation, with the goal of providing a useful transdiagnostic dimension along which mental disorders can be understood and compared.
AB - Reinforcement learning theory provides a powerful set of computational ideas for modeling human learning and decision making. Reinforcement learning algorithms rely on state representations that enable efficient behavior by focusing only on aspects relevant to the task at hand. Forming such representations often requires selective attention to the sensory environment, and recalling memories of relevant past experiences. A striking range of psychiatric disorders, including bipolar disorder and schizophrenia, involve changes in these cognitive processes. We review and discuss evidence that these changes can be cast as altered state representation, with the goal of providing a useful transdiagnostic dimension along which mental disorders can be understood and compared.
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U2 - 10.1016/j.conb.2019.03.011
DO - 10.1016/j.conb.2019.03.011
M3 - Review article
C2 - 31051434
AN - SCOPUS:85064873820
VL - 55
SP - 160
EP - 166
JO - Current Opinion in Neurobiology
JF - Current Opinion in Neurobiology
SN - 0959-4388
ER -