Reuse of learned subtask representations during human planning
- 1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, UK
Abstract
The notion of a compositional reuse of previously learned problem solutions predicts that the human brain should reinstate such learned subtask representations during planning. Here, we test this in a value-ranking task implemented in an online and a magnetoencephalography (MEG) experiment, respectively. Participants first learned pairwise rankings and subsequently solved problems where these learned rankings could either be reused or required reordering. Behaviourally, in the online experiment, we show the availability of a reusable subtask structure improved accuracy, with computational modelling indicating a greater weighting of subtask-level value when participants could avail of this learnt structure. In MEG, during planning, we show enhanced reactivation of task-relevant subtask representations in the reusable condition, and particularly so early in the experiment. In a subsequent probe task, we show subtask-identity representations generalised to a novel context and that stimuli belonging to the same learned subtask manifest an increase in their neural similarity. Temporally delayed linear modelling (TDLM) revealed forward and backward sequential reactivation at a subtask level, alongside backward stimulus-level replay, with subtask sequenceness varying according to task context. The findings provide convergent behavioural, computational and neural evidence for reactivation and reuse of learnt relational structures for novel problem solving.
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BibTeX
@misc{seak2026reuse,
title = {{Reuse of learned subtask representations during human planning}},
author = {L. C. U. Seak and L. Luettgau and M. K. Eckstein and R. Moran and R. J. Dolan},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758531},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758531v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Reuse of learned subtask representations during human planning
AU - L. C. U. Seak
AU - L. Luettgau
AU - M. K. Eckstein
AU - R. Moran
AU - R. J. Dolan
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758531
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758531v1
AB - The notion of a compositional reuse of previously learned problem solutions predicts that the human brain should reinstate such learned subtask representations during planning. Here, we test this in a value-ranking task implemented in an online and a magnetoencephalography (MEG) experiment, respectively. Participants first learned pairwise rankings and subsequently solved problems where these learned rankings could either be reused or required reordering. Behaviourally, in the online experiment, we show the availability of a reusable subtask structure improved accuracy, with computational modelling indicating a greater weighting of subtask-level value when participants could avail of this learnt structure. In MEG, during planning, we show enhanced reactivation of task-relevant subtask representations in the reusable condition, and particularly so early in the experiment. In a subsequent probe task, we show subtask-identity representations generalised to a novel context and that stimuli belonging to the same learned subtask manifest an increase in their neural similarity. Temporally delayed linear modelling (TDLM) revealed forward and backward sequential reactivation at a subtask level, alongside backward stimulus-level replay, with subtask sequenceness varying according to task context. The findings provide convergent behavioural, computational and neural evidence for reactivation and reuse of learnt relational structures for novel problem solving.
ER -