機率式傾向遮蓋者與傾向條件句分析

Probabilistic Dispositional Masks and Conditional Analysis of Dispositions

傾向的展現可以被遮蓋。例如，一個致命毒藥在被人吃下之後並沒有致人於死，因為她在吃下毒藥之後立刻吃下解毒劑。在此案例中，解毒劑扮演的角色一般稱為遮蓋者（masks / maskers）：在刺激條件滿足後，遮蓋了相應的傾向展現。文獻上，對遮蓋者的研究來自於「傾向條件句分析」（conditional analysis of dispositions, CAD）的相關討論，其中「是否有CAD理論可免於遮蓋者反例的挑戰？」更是近30年來的核心議題。圖林（Turyn, 2021）對遮蓋者提出一種全新分析，稱為「機率式傾向遮蓋者」（probabilistic dispositional masks, PDM）。他宣稱，PDM相容於所有傾向觀點，甚至能幫助CAD徹底解決遮蓋者反例，所以是最有用的遮蓋者分析。本文論證，PDM與CAD之間具有內在衝突，因此PDM並不像圖林所宣稱的相容於所有傾向觀點，當然也無法幫助CAD擺脫遮蓋者反例的威脅。

The manifestation of dispositions can be masked. For example, a lethal poison does not cause death after being ingested because the person immediately takes an antidote. In this case, the antidote serves as a masker: something that, even after the stimulus condition is met, prevents the corresponding disposition from manifesting. In the literature, research on maskers has emerged from discussions surrounding the Conditional Analysis of Dispositions (CAD). A central issue over the past thirty years has been whether any version of CAD can withstand the challenge posed by masker counterexamples. Turyn (2021) proposed a novel analysis of maskers, termed Probabilistic Dispositional Masks (PDM). He claimed that PDM is compatible with any view of dispositions and could enable CAD to resolve masker counterexamples once and for all, making it the most useful analysis of maskers. This article argues that there is a fundamental tension between PDM and CAD. Consequently, PDM is not as universally compatible as Turyn suggests and cannot rescue CAD from the threat posed by masker counterexamples.