Bonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees

By Quentin J. M. Huys, Neir Eshel, Elizabeth J. P. O'Nions, Luke Sheridan, Peter S. Dayan, Jonathan P. Roiser. PLoS Comput. Biol., 2012

452 citations recorded by the source corpus.

When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/09c66a4231400ba276c92700fe993d6c185bc4ac

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Citations in the corpus

Affective cognition: Exploring lay theories of emotion. (2015, background): “One noteworthy and emerging field is that of Computational Psychiatry (Huys et al., 2012; Montague, Dolan, Friston, & Dayan, 2012; Sharp, Monterosso, & Montague, 2012), which assumes that social and emotional functioning can be characterized by computational ‘‘phenotypes’’, which comprise genetic, n”
Computational psychiatry as a bridge from neuroscience to clinical applications (2016, background): “RL modeling has started to identified specific aspects of these processes, such as a role for aversive outcomes in guiding resource allocation process 132,133 ( Fig.”
Neuroscience-Inspired Artificial Intelligence. (2017, background): “…in humans are ‘‘jumpy,’’ bridging multiple temporal scales at a time; for example, humans seem to plan hierarchically, by considering in parallel terminal solutions, interim choice points, and piecemeal steps toward the goal (Balaguer et al., 2016; Solway et al., 2014; Huys et al., 2012).”
Reinforcement Learning and Causal Models (2017, background): “While there is evidence that humans carry out something resembling tree search (e.g., De Groot, 1978; Holding & Pfau, 1985; Huys et al., 2012, 2015), our current knowledge about model-based planning in the brain is very limited (see Daw & Dayan, 2014, for further discussion).”
Goals and Habits in the Brain (2013, unknown): “Other related search tasks have found behavioral evidence for these calculations Neuron 80, October 16, 2013 ª2013 The Authors 319 and have started to look at heuristics for pruning the tree, a necessity when it gets too wide or deep (Huys et al., 2012).”
Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task (2015, background): “A number of tasks have recently been developed to study planning in which subjects choose trajectories through multi-state environments to obtain reward, but are not returned to the same state at the start of each trial [9,13,47].”