研究目的: 錯誤放大(EA)是一種藉由放大執行錯誤的視覺回饋來訓練視覺動作技巧。然而，因為學習進步所導致錯誤回饋量減少，因此限制了受試者在學習後期所能獲得的學習效益。本篇研究要試驗漸進式錯誤放大相較於傳統恆定式錯誤放大是否可以獲得額外的學習效益。

研究方法: 32 位年輕健康成年人隨機分配至漸進式錯誤放大 (P-EA) 組 (共 16位; 男性 8 位, 女性 8 位; 年齡: 24.1 ± 2.8 歲) 和恆定放大 (C-EA) 組 (共 16位; 男性 8 位, 女性 8 位; 年齡: 23.3 ± 2.6 歲) 訓練視覺動作追蹤。訓練內容包含 15 次頻率為 0.2 赫茲的單一正弦波，以 20% 的最大自主收縮 (MVC) 訂為基值，振幅設為正負 10% 的最大自主收縮，訓練次數之間會有 2 分鐘的休息。P-EA組的錯誤放大倍率是在訓練中逐漸從 1 上升到 2 (2 倍實際錯誤)，而 C-EA 組的錯誤放大倍率則是一直維持在 1.5 (1.5 倍實際錯誤)。前、後測為兩種轉移測試，會在兩種視覺回饋 (實際錯誤回饋、1.75 倍實際錯誤) 情境下，執行複合式正弦波 (0.1 赫茲和 0.3 赫茲) 的視覺動作追蹤測試。錯誤量均方根 (Error_RMS)、最大線性相關(R_max)、力量表現和目標線均方根比值 (R_TF) 以及力量在 0.1 赫茲 (P01) 和 0.3 赫茲 (P03) 的頻譜值分析作為代表追蹤表現。追蹤表現的各項參數由二因子(時間與組別)變異數分析，組間人口資料的差異以卡方檢定或獨立樣本 t 檢定來檢定。

研究結果: P-EA 和 C-EA 組在真實視覺回饋 (G=1) 和錯誤放大回饋 (G=1.75)的轉移測試的表現，在訓練後錯誤量均方根 (Error_RMS) 都顯著減少 (G=1: F1,30=53.45, p < 0.001; G=1.75, F1,30=61.97, p < 0.001)。轉移測試的波形相似度部份，最大線性相關 (R_max) 在 G=1 和 G=1.75 的情境下，P-EA 和 C-EA 組都有顯著的時間效應，在訓練後相關性都呈現上升的趨勢 (G=1: F1,30=37.98, p < 0.001, G=1.75: F1,30=42.44; p < 0.001)。同樣地，統計結果顯示：轉移測試的力量表現和目標線均方根比值 (R_TF)出現時間效應，兩種情境下 P-EA 和 C-EA 組的力量表現和目標線均方根相似度在訓練後都有所提升 (G=1: F1,30=7.90, p=0.009, G=1.75: F1,30=10.04, p=0.004)。在轉移測試目標頻率的頻譜層面，兩組訓練後 0.1 赫茲 (P01) 的統計結果呈現顯著時間效應，並且在訓練後 P-EA 和 C-EA 組在兩種情境下的數值均下降(G=1: F1,30=5.75, p=0.023, G=1.75: F1,30=5.18, p=0.030)。然而相較於 P01，P-EA 和 CEA 組在 0.3 赫茲 (P03)也有時間效應， P03 的數值在後測明顯地上升(G=1: F1,30=48.96, p < 0.001, G=1.75: F1,30=60.08, p < 0.001)。值得注意的是：上述各項表現變數，都沒有出現顯著的組間差異(p > 0.05)。

結論: P-EA 和 C-EA 組均在視覺動作訓練後出現視覺追蹤作業的進步，在追蹤作業的高頻率成分更為明顯。然而，P-EA 組在時間和頻譜準確度上，並沒有如預期較 C-EA 組出現更多的學習增益。對於視覺動作技巧，不論是用漸進式錯誤放大或是恆定式錯誤放大訓練均可以達到相等的效果。

Objectives: Error amplification (EA) is a strategy to train visuomotor skills by virtual amplification of execution errors via visual feedback. However, task improvements result in less error feedback, which limits the learning benefits in the late stage of motor learning. This study aimed to examine whether progressive EA can produce additional learning benefits than traditional EA with constant EA.

Methods: Thirty-two healthy young adults were randomly assigned into progressive error amplification (P-EA) group (n=16; 8males, 8females; age: 24.1 ± 2.8 years old) and constant error amplification (C-EA) group (n=16; 8males, 8females; age: 23.3 ± 2.6 years old) to train visuomotor tracking. The training session consisted of fifteen visuomotor tracking trials of a simple sinusoidal target at 0.2 Hz, with target amplitude varying 10%maximal voluntary contraction (MVC) around a baseline force of 20% MVC. The error amplification of P-EA during the training session was progressively increased from 1 to 2 (2 times of real error) across trials, while error amplification of C-EA was consistently set at 1.5 for all trials (1.5 times of real error). Two transfer tasks were evaluated in the pre-test and post-test, including a compound sinusoidal target (0.1 Hz and 0.3 Hz) with two visual conditions (traditional visual feedback that displayed real error feedback (G=1)); error amplification (EA) feedback that displayed 1.75 times of real error (G=1.75)). Root mean square of the task errors (Error_RMS), maximal linear correlation between the target and force (R_max), ratio of the target RMS to force RMS (R_TF), spectral peak at the target frequencies of 0.1 Hz (P01) and at 0.3 Hz (P03) were used to characterize tracking performance of the transfer tasks. The main effects of time (pre-test vs. post-test) and groups (P-EA vs. C-EA) on tracking performance variables were examined by two-way ANOVA (time by group). Group differences in the demographic data were contrasted using Chi-square test or independent t statistics.

Results: For both transfer tasks, Error_RMS with real visual feedback (G=1) and EA feedback (G=1.75) were significantly decreased after training for the P-EA and C-EA groups (G=1: F1,30=53.45, p < 0.001; G=1.75, F1,30=61.97, p < 0.001). The waveform similarity indexed with R_max in the P-EA and C-EA groups was significantly subject to time effect, with higher correlations for both the G=1 and G=1.75 conditions (G=1: F1,30=37.98, p < 0.001, G=1.75: F1,30=42.44; p < 0.001). Likewise, R_TF of the two transfer tasks was subject to significant time effect in the real feedback and the EA conditions, with higher ratio of R_TF in the post-test (G=1: F1,30=7.91, p = 0.009, G=1.75: F1,30=10.04, p =0.004). For the spectral peak at the target frequency of the transfer tasks, both groups exhibited a significant lower P01 for both the visual feedback conditions after training (G=1: F1,30=5.75, p=0.023, G=1.75: F1,30=5.18, p=0.030). However, in contrast with P01, P03 of the two groups was subject to time effect, with obvious increase in P03 in the posttest (G=1: F1,30=48.961, p < 0.001, G=1.75: F1,30=60.08, p < 0.001). Of note, no significant group difference was noted for all aspects of performance variables (p > 0.05).

Conclusion: Both visuomotor training with P-EA and C-EA led to task improvement, especially for fast tracking maneuver. However, contrary to expectation, P-EA did not outweigh C-EA from the aspects of temporal accuracy and spectral accuracy. In conclusion, progressive error amplification and constant error amplification are equally effective to train a visuomotor skill.

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