Cited by 439Decision-Making and Behavioral EconomicsNeural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on Behavior

The DOI matches its Crossref record; 69 of the 70 references it cites were found in a registry or an index; none of the 29 sources its claims cite is checked for support yet.

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

Open access
Huys, Quentin J. M., Eshel, Neir, O'Nions, Elizabeth, Sheridan, Luke, Dayan, Peter, Roiser, Jonathan P.·2012·PLoS Computational Biology

Abstract

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.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Humans planning sequences prune decision trees by curtailing evaluation after large losses, a Pavlovian response that persists even when counterproductive. Computational modeling shows this specific pruning is distinct from general discounting and loss aversion, and correlates with subclinical depressive symptoms.

Key findings

  • Subjects adopted a simple pruning strategy: they stopped evaluating a sequence when they encountered a large loss, even when this was costly. In group −140 pruning was never disadvantageous, but in groups −70 and −100 it was increasingly counterproductive, yet subjects continued to prune.
  • The most parsimonious model was “Pruning & Learned,” which separated general pruning from specific pruning after large punishments. Specific pruning exceeded general pruning in 14/15, 12/16, and 14/15 subjects in the −70, −100, and −140 groups, respectively. This model correctly predicted 91% of choices; the full look-ahead model predicted 77% of choices and had a mean predictive probability of 0.71.
  • Pruning was evident above and beyond loss aversion. Subjects were approximately 1.4 times more sensitive to punishments than to equal-sized rewards, but adding loss aversion to the best model did not improve parsimony.
  • BDI score positively correlated with the specific pruning parameter, but not with general pruning. A negative correlation between BDI and reward sensitivity did not survive correction for multiple comparisons. NEO agreeableness positively correlated with the Learned Pavlovian influence and survived full correction for 60 comparisons; no significant correlation with NEO neuroticism was found.

Methods

  • Forty-six volunteers (23 female, mean age 23.84 years) were recruited and screened; groups received different large losses: −70 (n=15), −100 (n=16), and −140 (n=15). Subjects learned a deterministic six-state transition matrix, then completed two blocks of 24 choice episodes with sequence lengths of 2–8; the first block was discarded and the second analyzed. On half the trials, subjects planned their last 2–4 moves together without intermittent feedback.
  • Computational models included full look-ahead, discounting, pruning (general and specific), learned Pavlovian attraction/repulsion, loss aversion, and a habitual SARSA model. Models were fit with Bayesian model fitting and compared using integrated Bayesian Information Criterion at the group level.
  • Psychometric correlates were assessed with a weighted hierarchical multivariate regression, orthogonalizing age, sex, IQ, Digit Span, NEO measures, STAI, and BDI.

Limitations

  • The paper states that the sample was healthy volunteers with only very mild depressive symptoms, so the BDI correlation cannot be taken as direct evidence that mildly raised BDI scores measure vulnerability to depression; predictions about clinical depression and serotonin depletion remain to be tested. It also states that no significant correlation with NEO neuroticism was found.
  • The paper notes that in group −140 there was always an optimal sequence avoiding the large loss, making that group’s behavioral statistic difficult to interpret; at depths of 6 or more, the number of events was small, limiting power; and some missing age/STAI data excluded subjects from psychometric analyses.
  • Evident from the text: the task uses a fixed deterministic transition matrix and specific reinforcement structure, and the conclusions depend on the fitted model space; model comparison did not support separate group differences in pruning despite apparent behavioral differences.

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Integrity signals

Registry

Crossref · 10.1371/journal.pcbi.1002410

Cites

69 of 69 references verified0 citation errors · 0 not checkable

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Claims the paper makes

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Show all 41 claims

  • It may state two separate claims as one. 24
  • Probably not a substantial claim. 18
33 of 33
  • The paper relies on this without support.

    The large losses in the task are aversive/punishing events capable of evoking Pavlovian behavioural inhibition.

    “We term this heuristic pruning a “Pavlovian” response because it is invoked, as an immediate consequence of encountering the large loss, when searching the tree in one's mind.”
  • The paper relies on this without support.

    The BDI validly measures sub-clinical depressive symptoms/mood disturbance.

    “We found that BDI was positively correlated with the specific pruning parameter ().”
  • The authors state this as an assumption.

    Actions are independent given the stimuli, and thus factorize over trials.

    “We assume that actions are independent (given the stimuli, which we omit for notational clarity), and thus factorize over trials.”
  • The authors state this as an assumption.

    The value of each choice depends on how many choices are left after it, but not on the choices preceding it.

    “we emphasize that the value of each choice depends on how many choices are left after , but not on the choices preceding it.”
  • The paper relies on this without support.

    The one-touch Tower of London (OTT) task engages goal-directed processing.

    “Contrary to this, there is a recent report that mood manipulation, but not acute tryptophan depletion, impairs processing on the one-touch Tower of London (OTT) task [40], which should certainly engage goal-directed processing.”
  • The paper relies on this without support.

    The pruning parameter mediates the inhibition of iterative thought processes.

    “if serotonin indeed mediates behavioural inhibition in the face of punishments [10], [12]–[14] then it is a strong prediction that the pruning parameter , which mediates the inhibition of iterative thought processes, should be related to, and modulated by, serotonergic activity.”
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Metadata

Authors
Huys, Q. J. M., Eshel, N., O'Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • University College London (GB)
  • Gatsby Computational Neuroscience Unit (GB)
Corresponding author
Huys, Quentin J. M.
Venue
PLoS Computational Biology
Published
2012-03-08
Volume
8
Issue
3
Pages
e1002410
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
OpenAlex
W2059569317
Cited by
439
References
70
Citations by year
2026: 16 · 2025: 35 · 2024: 32 · 2023: 42 · 2022: 38 · 2021: 30 · 2020: 33 · 2019: 32 · 2018: 44 · 2017: 34 · 2016: 27 · 2015: 27 · 2014: 29 · 2013: 11 · 2012: 5
FWCI
17.75
Type
journal article
Language
en
Open access
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Staff Publications Online (The Tavistock and Portman) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Funders
Gatsby Charitable Foundation
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
Keywords
decision tree pruning, goal-directed behavior, sequential decision making, loss aversion, serotonin, mood disorders, reinforcement learning, human decision-making
MeSH terms
Algorithms, Computer Simulation, Decision Making, Goals, Humans, Models, Neurological, Reinforcement, Psychology, Task Performance and Analysis

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