Cognitive Control of Choices and Actions
DOI: 10.1007/978-3-031-45271-0_14
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Summary
This chapter reviews the application of model-based cognitive neuroscience to understand the cognitive control of choices and actions. The authors address the need for cognitive models that encompass the full range of psychological processes, from signal detection to action execution. They focus on evidence-accumulation models (EAMs), which posit that decisions are made by gradually accruing information until a threshold is reached. The review examines how these models link latent cognitive parameters to neural data, covering three primary domains: controlling when to act (speed-accuracy trade-offs), controlling which actions to take (conflict resolution and delay discounting), and controlling which actions to withhold (inhibitory control). The authors analyze experimental designs and modeling strategies across these domains. For timing control, they discuss the use of functional magnetic resonance imaging (fMRI) combined with the Linear Ballistic Accumulator (LBA) model to study speed-accuracy trade-offs. They contrast traditional "two-stage" modeling procedures with "joint-modelling" frameworks, such as the Neural Drift Diffusion Model (NDDM), which simultaneously estimate behavioral and neural parameters to account for trial-to-trial variability and uncertainty. In the domain of action selection, the review covers delay discounting tasks using LBA and Leaky Competing Accumulator (LCA) models, as well as conflict tasks like the Flanker task, where models incorporate attentional mechanisms like shrinking spotlights or reactive control signals linked to EEG data. For action withholding, the text focuses on the stop-signal paradigm, utilizing horse-race models and the BEESTS model to estimate stop-signal reaction times (SSRT) and account for violations of standard assumptions. Key findings indicate that specific brain regions correlate with cognitive control parameters. In speed-accuracy tasks, fMRI data revealed that the right anterior striatum and pre-supplementary motor area are modulated by threshold changes, supporting a cortico-striatal loop theory of action propensity. Joint modeling demonstrated that neural data improve the precision of behavioral parameter estimates and predictive validity. In delay discounting, studies identified the dorsal medial frontal cortex, posterior parietal cortex, and dorsolateral prefrontal cortex as loci of value accumulation, receiving inputs from ventromedial prefrontal cortex. Regarding conflict tasks, model selection favored reactive control mechanisms over time-based ones, with EEG correlations supporting activation-based control models. The review also highlights that while collapsing bounds and urgency models explain certain timing behaviors, they are computationally expensive, prompting the proposal of the more tractable Timed Racing Diffusion Model (TRDM). The significance of this work lies in demonstrating the synergy between cognitive modeling and neuroscience. By linking latent model parameters to physiological measures, researchers can resolve ambiguities in neural data and infer cognitive processes that are not directly observable. The authors emphasize that generative Bayesian estimation and joint modeling offer superior statistical rigor compared to two-stage approaches. They conclude by identifying opportunities for future research, particularly in applying joint modeling to complex tasks like prospective memory and utilizing tractable models like the TRDM to explore the overlap between decision-making and timing circuits.
Key finding
Joint modeling frameworks that integrate evidence-accumulation behavioral models with neural data provide more precise parameter estimates and clearer interpretations of cognitive control mechanisms than traditional two-stage approaches.
Methodology
review
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | unpaywall | — | — | 2 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | failed | — | — | — | 4 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model