Soru

Read the following passage carefully.

Historically, cognitive science modeled perception as a fundamentally "bottom-up" process, wherein raw sensory data is sequentially assembled and interpreted by increasingly sophisticated cortical hierarchies until a coherent representation of the external world emerges. However, the paradigm of "predictive processing" (PP) fundamentally inverts this architecture. According to PP, the brain does not passively receive data; rather, it functions as an inference engine that continuously generates "top-down" probabilistic predictions about the causes of sensory inputs. In this model, the only information that actually propagates upward through the cortical hierarchy is the "prediction error"—the discrepancy between what the brain anticipated and what the sensory organs actually encountered. The brain's overarching objective is to minimize this prediction error, either by updating its internal models to better match the sensory input (perceptual inference) or by physically acting upon the world to change the sensory input to match the prediction (active inference).

Crucially, however, not all prediction errors are treated equally. The PP framework posits that the brain must estimate the reliability, or "precision," of the sensory information it receives. Precision weighting acts as a gain control mechanism on prediction error signals, determining the extent to which a given error will update higher-level models. If the brain expects sensory input to be noisy or unreliable—such as when viewing an object in dense fog—it assigns low precision to the resulting prediction errors, causing the perceptual system to rely more heavily on its prior expectations. Conversely, in highly reliable sensory environments, prediction errors are assigned high precision and forcefully drive the updating of internal models.

Neurobiologically, theorists argue that this precision weighting is heavily mediated by specific neuromodulatory systems. While early PP models broadly implicated dopamine in the encoding of precision, contemporary neurocomputational accounts draw a sharper functional distinction. These accounts suggest that while dopamine is primarily involved in encoding the precision of prediction errors concerning action and motor control (facilitating active inference), it is the cholinergic system—specifically the neurotransmitter acetylcholine—that predominantly regulates the precision of prediction errors regarding sensory attention (facilitating perceptual inference).

This distinction carries profound clinical implications, particularly for neurodevelopmental conditions such as autism spectrum disorder (ASD). Some researchers hypothesize that the sensory overwhelm frequently observed in ASD may stem from an inflexibly high precision weighting assigned to sensory prediction errors, driven by cholinergic dysregulation. Under this view, if the brain chronically treats all sensory fluctuations as highly reliable signals, it cannot easily abstract away minor environmental noise, necessitating rigid environmental control to minimize the resulting cognitive burden.

Based on the passage, which of the following best describes the specific function that contemporary neurocomputational accounts attribute to acetylcholine within the predictive processing framework?