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系統之影響，結果發現兩者皆有影響。且根據 FN 的振幅顯示兩者影響的程度相 近。推估當非預期結果或高錯誤可能性畫面呈現時皆會傳送錯誤偵測訊號至前扣 帶皮質並活化之，進而觀察到較大的 FN。對照至本實驗結果，顯示當一個畫面 同時包含了非預期結果及高錯誤可能性訊息時，會有最大的 FN 產生；而非預期 結果及高錯誤可能性各別出現時會引發振幅相近的 FN；當畫面並未包含非預期 結果及高錯誤可能性訊息時則不會觀察到 FN。顯示無論單看增強學習理論或是 錯誤可能性理論皆無法良好解釋此研究結果，唯有綜合兩者。故建議在錯誤偵測 系統中納入錯誤可能性之評估因素，並待後續研究確認其可行性。

第四節、總結

綜合本研究的兩個實驗，主要發現錯誤可能性對於個體在評估事件的影響。

由於過去主要用以解釋錯誤偵測系統的理論為增強學習理論，而增強學習理論之 主要核心為比較實際結果與預期之間的差異。本研究的實驗一點出有些無法單純 由增強學習理論所解釋的現象（風險因素），並在實驗二中更直接指出錯誤可能 行對於事件評估的影響。如此的現象更值得讓研究者重新思考不論增強學習理論 或是錯誤可能性理論皆無法全面解釋現實生活中的事件評估依據，唯有將各種評 估之因素納入並統整。

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附表說明

表一：ERN 與 FN 異同整理比較表。

表二：增強學習理論及錯誤可能性理論異同整理比較表。

表三：實驗一牌局設計。

表四：實驗二腦波數值。

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附表

表一

ERN FN

全名 錯誤相關負波

（Error-Related Negativity）

回饋相關負波

（Feedback-Related Negativity）

別名 response ERN, rERN, N^E feedback ERN, fern, FN

作業型態 反映作業 賭博作業、估計作業

錯誤來源 內在 外在

來源 ACC ACC

發生時間 事件後 50～150ms 回饋後 250~350ms

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表二

增強學習理論 錯誤可能性理論 錯誤來源 回饋結果 錯誤可能性畫面 時間點 回饋結果呈現 錯誤可能性呈現時

來源 ACC ACC

注意力系統 不需介入 介入

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表三

CV Condition % Condition 高風險牌 低風險牌 高風險牌 低風險牌 + 50% 50% 50% 75%

- 50% 50% 50% 25%

CV 10.72 4.12 10.72 10.72 EV 100 100 100 100

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表四

預期結果 非預期結果

高錯誤可能性

-3.753 -4.072

(2.307) (2.740)

低錯誤可能性

-3.103 -3,750

(2.069) (1.858)

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圖一：典型 FN 波形（摘自 Gehring &Willoughby, 2002）。

圖二：增強學習理論機制示意圖 (摘自 Holroyd & Cole, 2002)。

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附圖

圖一

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圖二

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圖三

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圖四

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圖五

‐5

‐4.5

‐4

‐3.5

‐3

‐2.5

‐2

‐1.5

‐1

‐0.5

0

變異程度 輸贏比率

μV

高風險 低風險

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圖六

‐5

‐4

‐3

‐2

‐1

0

1

‐100 0 100 200 300 400 500

μV

Risk Level @ CV Condition

Low Risk@CV Condition High Risk@CV Condition Difference

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圖七

‐7

‐6

‐5

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‐3

‐2

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0

Negative Positive

μV

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圖八

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‐7

‐6

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‐4

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‐2

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0

變異程度 輸贏比率

μV

負向回饋 正向回饋

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圖九

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圖十

‐8

‐7

‐6

‐5

‐4

‐3

‐2

‐1

0

高風險 低風險

μV

負向回饋 正向回饋

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圖十一

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圖十二

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圖十四

‐7

‐6

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0

低錯誤可能性 中錯誤可能性 高錯誤可能性

μV

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圖十五

‐6

‐5

‐4

‐3

‐2

‐1

0

預期結果 非預期結果

μV

高錯誤可能性 低錯誤可能性

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圖十六

‐5

‐4.5

‐4

‐3.5

‐3

‐2.5

‐2

‐1.5

‐1

‐0.5

0

拒絕 接受

μV

獲利 損失

在文檔中 錯誤可能性與預期衝突對於錯誤偵測系統之影響-以回饋負波為例 - 政大學術集成 (頁 42-72)