Neural representations and adaptive states in human action control

Abstract

Adaptive behaviour requires goals to remain effective while actions are revised when circumstances change. This chapter integrates a collaborative research programme with wider theories and findings on cognitive control, episodic memory, neural dynamics, and clinical variation. It develops a selective narrative synthesis centred on the content, stability, retrieval, and reconfiguration of perception–action representations. Established accounts of goal maintenance, conflict monitoring, proactive and reactive control, and the expected value of control provide complementary explanations of how control is recruited. Event coding and binding–retrieval frameworks specify the representations on which these adjustments operate. EEG decomposition and multivariate analyses help distinguish representational contents and their temporal organisation, while oscillatory and connectivity findings identify candidate processes for coordinating their use. Aperiodic activity offers a further perspective on task-dependent processing biases, but pharmacological and biophysical evidence limits its interpretation as a unique measure of excitation–inhibition balance or metacontrol. Intervention studies connect neural dynamics with neurochemical modulation, while demonstrating dependence on baseline state, experience, and the operation targeted. Developmental and clinical evidence reveals both shared constraints and dissociable routes to similar behavioural outcomes. The proposed integration links representational operations, neural coordination, and control policy through testable predictions. It retains distinctions between descriptive markers, computational explanations, causal influences, and clinical benefit. Progress depends on separating competing accounts experimentally, establishing measurement reliability, and testing generalisation across tasks, samples, and settings.

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BibTeX RIS

BibTeX

@misc{beste2026neural,
  title = {{Neural representations and adaptive states in human action control}},
  author = {Christian Beste},
  howpublished = {PsyArXiv},
  year = {2026},
  month = sep,
  doi = {10.31234/osf.io/a3kd5_v1},
  url = {https://osf.io/preprints/psyarxiv/a3kd5_v1/},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Neural representations and adaptive states in human action control
AU  - Christian Beste
PY  - 2026
DA  - 2026/09/27
PB  - PsyArXiv
DO  - 10.31234/osf.io/a3kd5_v1
UR  - https://osf.io/preprints/psyarxiv/a3kd5_v1/
AB  - Adaptive behaviour requires goals to remain effective while actions are revised when circumstances change. This chapter integrates a collaborative research programme with wider theories and findings on cognitive control, episodic memory, neural dynamics, and clinical variation. It develops a selective narrative synthesis centred on the content, stability, retrieval, and reconfiguration of perception–action representations. Established accounts of goal maintenance, conflict monitoring, proactive and reactive control, and the expected value of control provide complementary explanations of how control is recruited. Event coding and binding–retrieval frameworks specify the representations on which these adjustments operate. EEG decomposition and multivariate analyses help distinguish representational contents and their temporal organisation, while oscillatory and connectivity findings identify candidate processes for coordinating their use. Aperiodic activity offers a further perspective on task-dependent processing biases, but pharmacological and biophysical evidence limits its interpretation as a unique measure of excitation–inhibition balance or metacontrol. Intervention studies connect neural dynamics with neurochemical modulation, while demonstrating dependence on baseline state, experience, and the operation targeted. Developmental and clinical evidence reveals both shared constraints and dissociable routes to similar behavioural outcomes. The proposed integration links representational operations, neural coordination, and control policy through testable predictions. It retains distinctions between descriptive markers, computational explanations, causal influences, and clinical benefit. Progress depends on separating competing accounts experimentally, establishing measurement reliability, and testing generalisation across tasks, samples, and settings.
ER  -

Abstract

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