OBJECTIVES To date, most research on plea bargaining has used some form of the shadow of the trial (SOT) model to frame defendant decisions. In this research, we proposed and tested a new conceptual model of plea decision-making, based on fuzzy-trace theory (FTT), for the context in which a nondetained, guilty defendant chooses between a guilty plea or trial, where both the plea and potential trial sentence entail incarceration. HYPOTHESES We predicted that plea decisions would be affected by (a) meaningful, categorical changes in conviction probability (e.g., low to moderate, moderate to high), as opposed to more granular changes within categories and (b) the presence and magnitude of categorical distinctions between plea offer and potential trial sentence rather than fine-grained differences between individual offers. METHOD We conducted three vignette-based experiments (Study 1: N = 1,701, Study 2: N = 1,098, Study 3: N = 1,232), using Mechanical Turk participants. In Studies 1 and 2, we manipulated potential trial sentence and conviction probability, asking participants to indicate either the maximum plea sentence they would accept (Study 1) or whether they would plead guilty to a specific offer (Study 2). In Study 3, we manipulated plea discount and potential trial sentence and measured plea acceptance. RESULTS Maximum acceptable plea sentences were similar within and different between "groupings" of meaningfully similar conviction probabilities (Study 1). Plea rates were similar within and different between groupings that comprised plea offers of similarly meaningful distance from the potential trial sentence (Study 3). The results also provide insight into the plea rates that might be expected under different combinations of the independent variables (Studies 2 and 3). CONCLUSIONS These results support a new conceptual model of plea decision-making that may be better suited to explaining case-level differences in plea outcomes than the SOT model and suggest that future research extending this model to a wider range of contexts would be fruitful. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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开发认罪决策模型：模糊痕迹理论、要点和分类边界。


目标 迄今为止，大多数关于辩诉交易的研究都使用某种形式的审判影子 (SOT) 模型来制定被告的决定。在这项研究中，我们提出并测试了一种基于模糊追踪理论（FTT）的认罪决策的新概念模型，适用于未拘留的有罪被告在认罪或审判之间做出选择的情况，其中认罪和审判均适用可能的审判判决包括监禁。假设 我们预测，认罪决定将受到以下因素的影响：(a) 定罪概率的有意义的、分类的变化（例如，低到中，中到高），而不是类别内更细粒度的变化；(b) 分类的存在和程度。认罪提议和潜在审判判决之间的区别，而不是各个提议之间的细粒度差异。方法 我们使用 Mechanical Turk 参与者进行了三项基于小插图的实验（研究 1：N = 1,701，研究 2：N = 1,098，研究 3：N = 1,232）。在研究 1 和 2 中，我们操纵了潜在的审判判决和定罪概率，要求参与者表明他们愿意接受的最高认罪判决（研究 1）或他们是否会针对特定提议认罪（研究 2）。在研究 3 中，我们操纵了认罪折扣和潜在的审判判决，并测量了认罪接受程度。结果 最大可接受的认罪判决在具有有意义的相似定罪概率的“分组”内相似，但不同（研究 1）。各组内的抗辩率相似，而各组之间的抗辩率则不同，这些组中的抗辩提议与潜在审判判决的距离具有相似的有意义距离（研究 3）。 结果还提供了对自变量不同组合下可能预期的抗辩率的深入了解（研究 2 和 3）。结论 这些结果支持了一种新的认罪决策概念模型，该模型可能比 SOT 模型更适合解释认罪结果中案件层面的差异，并表明未来的研究将该模型扩展到更广泛的背景将是富有成效的。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。