本研究旨在探討視覺知覺與動作兩個領域的學習機制，究竟是由領域共通（domain-general）還是領域特定（domain-specific）的機制所調控。儘管先前研究已發現這兩個系統在學習動態上具有相似性，知覺學習與動作學習之間直接的神經與行為關聯仍不明確。為釐清視覺知覺學習與基於錯誤的動作學習之間的關聯，我們以紋理辨識任務（TDT）評估視覺知覺學習，並以視覺動作適應任務（VAT）評估基於錯誤的動作學習。實驗一檢驗是否存在一個共通的學習機制，以支持跨領域的學習進步速度（學習速率）。透過非線性指數衰退模型捕捉動作適應的快速動態，我們發現在所有階段中，包括早期適應期（r = -.21)、 晚期適應期（r = -.15）與再適應期（r = .002）TDT與VAT 的學習表現之間皆無顯著相關。此一行為結果顯示，個體在視覺系統中快速可塑性的能力，未必對應其在基於錯誤之動作校正上的效率，這很可能反映兩者所涉及的神經機制有別，例如TDT 主要仰賴早期視覺皮層，而VAT則涉及小腦的處理。實驗二則是建立在行為層次彼此獨立的基礎上，進一步探討更複雜的指標，包括學習轉移與靜息態連結性。結合靜息態連結性、任務相關腦電圖（EEG）以及格蘭傑因果關係（GC）的分析，揭示了一種階層性的解離現象（hierarchical dissociation）。兩項學習任務皆誘發了共通的整體「神經效率」模式，其特徵為靜息態 alpha 頻段（8–12 Hz）與低頻 gamma 頻段（30–55 Hz）連結性的下降。然而，主動的處理路徑仍維持高度的領域特定性：視覺知覺學習呈現「整合」模式，表現為由後至前（posterior-to-anterior）傳向額葉—中央區域的資訊流增加；動作學習則遵循「自動化」模式。兩領域間更深層的連結，則反映於 TDT 轉移指數與動作再適應率之間的顯著相關（r = -.57），其中 theta 頻段（4–8 Hz）可能作為錯誤監控的領域共通控制中樞。綜上所述，本研究結果顯示，儘管以行為進步速率衡量的學習能力屬於領域特定的歷程，但其底層的神經可塑性與錯誤監控系統，則是透過跨越視覺與動作領域的領域共通架構而運作。;This study investigates whether learning across the visual perceptual and motor domains is governed by domain-general or domain-specific mechanisms. Although prior research has documented similarities in the learning dynamics of these two systems， the direct neural and behavioral relationship between perceptual and motor learning remains unclear. To clarify the link between visual perceptual learning and error-based motor learning， we employed the Texture Discrimination Task (TDT) to assess visual perceptual learning and the VAT to assess error-based motor learning. In Experiment 1 (N = 51)， we examined whether a shared mechanism supports the rate of improvement (learning rate) across the two domains. Using non-linear exponential decay modeling to capture the rapid dynamics of motor adaptation， we found no significant correlation between TDT and VAT performance in any phase， including the early adaptation (r = -.21)， late adaptation (r = -.15)， and de-adaptation (r = .002) phases. These behavioral findings indicate that an individual’s capacity for rapid plasticity in the visual system does not necessarily correspond to their efficiency in error-based motor correction. This likely reflects the distinct neural substrates involved， namely the early visual cortex for the TDT and cerebellar processing for the VAT. Experiment 2 (N = 26) built upon this baseline of behavioral independence by examining more complex measures， including learning transfer and resting-state connectivity. Analyses combining resting-state connectivity， task-based EEG， and Granger causality (GC) revealed a pattern consistent with a hierarchical dissociation. Both learning tasks induced a shared global pattern of neural efficiency， characterized by reduced resting-state alpha (8–12 Hz) and low-gamma (30–55 Hz) connectivity. In contrast， the active processing pathways remained highly domain-specific. Visual perceptual learning exhibited an integration pattern， reflecting increased posterior-to-anterior information flow toward the fronto-central region， whereas motor learning followed an automation pattern. A deeper cross-domain link was evidenced by a significant correlation between the TDT transfer index and the motor de-adaptation rate (r = -.57)， with the theta band (4–8 Hz) potentially serving as a shared， domain-general control hub for error monitoring. In sum， our findings suggest that although learning capacity， as measured by the behavioral rate of improvement， is a domain-specific process， the underlying neural flexibility and error-monitoring systems operate through a domain-general framework spanning the visual and motor domains.
