Shengelia Tamara, Lagnado David
Department of Experimental Psychology, University College London, London, United Kingdom.
Front Psychol. 2021 Feb 5;11:519262. doi: 10.3389/fpsyg.2020.519262. eCollection 2020.
In criminal trials, evidence often involves a degree of uncertainty and decision-making includes moving from the initial presumption of innocence to inference about guilt based on that evidence. The jurors' ability to combine evidence and make accurate intuitive probabilistic judgments underpins this process. Previous research has shown that errors in probabilistic reasoning can be explained by a misalignment of the evidence presented with the intuitive causal models that people construct. This has been explored in abstract and context-free situations. However, less is known about how people interpret evidence in context-rich situations such as legal cases. The present study examined participants' intuitive probabilistic reasoning in legal contexts and assessed how people's causal models underlie the process of belief updating in the light of new evidence. The study assessed whether participants update beliefs in line with Bayesian norms and if errors in belief updating can be explained by the causal structures underpinning the evidence integration process. The study was based on a recent case in England where a couple was accused of intentionally harming their baby but was eventually exonerated because the child's symptoms were found to be caused by a rare blood disorder. Participants were presented with a range of evidence, one piece at a time, including physical evidence and reports from experts. Participants made probability judgments about the abuse and disorder as causes of the child's symptoms. Subjective probability judgments were compared against Bayesian norms. The causal models constructed by participants were also elicited. Results showed that overall participants revised their beliefs appropriately in the right direction based on evidence. However, this revision was done without exact Bayesian computation and errors were observed in estimating the weight of evidence. Errors in probabilistic judgments were partly accounted for, by differences in the causal models representing the evidence. Our findings suggest that understanding causal models that guide people's judgments may help shed light on errors made in evidence integration and potentially identify ways to address accuracy in judgment.
在刑事审判中，证据往往存在一定程度的不确定性，决策过程包括从最初的无罪推定转向基于该证据对有罪的推断。陪审员整合证据并做出准确直观概率判断的能力是这一过程的基础。先前的研究表明，概率推理中的错误可以通过所呈现的证据与人们构建的直观因果模型之间的不一致来解释。这一点已在抽象且无背景的情境中得到探讨。然而，对于人们在诸如法律案件这种背景丰富的情境中如何解释证据，我们了解得较少。本研究考察了参与者在法律情境中的直观概率推理，并评估了人们的因果模型如何在新证据的基础上支撑信念更新过程。该研究评估了参与者是否按照贝叶斯规范更新信念，以及信念更新中的错误是否可以通过支撑证据整合过程的因果结构来解释。这项研究基于英国最近的一个案例，一对夫妇被指控故意伤害他们的婴儿，但最终被宣告无罪，因为发现孩子的症状是由一种罕见的血液疾病引起的。研究人员一次向参与者展示一系列证据，包括实物证据和专家报告。参与者对虐待和疾病作为孩子症状的原因进行概率判断。将主观概率判断与贝叶斯规范进行比较。同时还引出了参与者构建的因果模型。结果表明，总体而言，参与者根据证据朝着正确的方向适当地修正了他们的信念。然而，这种修正并非通过精确的贝叶斯计算完成，并且在估计证据权重时出现了错误。概率判断中的错误部分可由表示证据的因果模型的差异来解释。我们的研究结果表明，理解指导人们判断的因果模型可能有助于揭示证据整合中出现的错误，并有可能找到提高判断准确性的方法。
