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Title:

From Evidence to Action: Multistage Neural Mechanisms in Perceptual Decision-Making—An EEG-Informed Mechanistic Review

Subject:

Cognitive-Social Applications of Brain Mapping, such as talent identification, neuroleadership, and neurocognitive recruitment

Presenter Author:
Name:	Zahra Zare

Author:
Name 1:	Zahra Zare - Corresponding - Presenter
Affiliation 1:	Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

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Abstract

Original Research

introduction :

Perceptual decision-making requires uncertain sensory information to be transformed into an action. Sequential-sampling models have traditionally explained this process through evidence accumulation toward a decision boundary. Similar behavioral patterns can nevertheless arise from boundary adjustment, urgency, changes in sensory processing, or downstream motor dynamics. EEG findings showing distinguishable decision-related and sensorimotor processes have further motivated a multistage account of the decision–action pathway. This review traces the progression from accumulation-to-bound models toward multistage architectures in which the evolving decision signal is transformed through downstream neural and motor processes, emphasizing how EEG measurements constrain the temporal and functional organization of these processes and their adaptive regulation across successive decisions.

Methods:

A structured narrative review was conducted using Scopus. The search identified 74 records. Following duplicate removal and sequential screening of titles and keywords, abstracts, and full texts, 21 studies were retained for the final synthesis. Studies were organized according to their contribution to the mechanistic progression from evidence accumulation to action and adaptive control. The synthesis was also used to identify unresolved mechanistic links and research gaps across these stages.

Results:

The reviewed literature converges on a multistage decision–action architecture in which evidence accumulation constitutes a central computation rather than the complete decision process. Behavioral model comparisons show that similar choice and response-time patterns can arise from different latent mechanisms, increasing the value of physiological measurements for mechanistic localization. EEG studies distinguish relatively effector-independent accumulation-related activity from effector-selective sensorimotor preparation, while EMG provides complementary evidence that perceptual and decisional variables can continue to influence processing during motor execution. The Leaky Integrating Threshold (LIT) model formalizes a downstream secondary integration process that temporally transforms the evolving decision signal before commitment. Neural findings link behaviorally estimated secondary integration dynamics to the temporal separation between primary accumulation-related activity and the LIT-derived secondary neural representation. Adaptive extensions further indicate that secondary integration can vary with the evolving state of the decision process. Decision–action and cascade models extend this processing trajectory toward muscle activation and overt response, while outcome-related neural activity links the decision–action pathway across trials through adaptive control of subsequent behavior. The synthesis identifies unresolved mechanistic relationships between the LIT-derived secondary representation and established effector-selective Mu/Beta physiology, between cortical motor-related processing and muscular execution, and across behavioral, EEG, and EMG measurements, as well as uncertainty regarding the computational target of feedback-dependent control.

Conclusion:

The reviewed literature supports viewing perceptual decision-making as a multistage decision–action process in which evidence accumulation constitutes a central computation and the evolving decision signal is subsequently shaped by interacting neural and motor processes before overt action. The architecture extends beyond individual decisions through outcome-dependent adaptive control of subsequent behavior. Integrating computational models with behavioral and EEG measurements, while using EMG to characterize downstream motor execution, provides a principled framework for linking latent decision processes to their physiological implementation across successive stages of the decision–action pathway.

Keywords:

Perceptual Decision-Making; Evidence Accumulation; Electroencephalography (EEG); Drift Diffusion Model; Neurocomputational Modeling; Sensorimotor Integration

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