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Forensic feature-comparison expertise: Statistical learning facilitates visual comparison performance.
Journal of Experimental Psychology: Applied ( IF 2.813 ) Pub Date : 2020-03-09 , DOI: 10.1037/xap0000266
Bethany Growns 1 , Kristy A Martire 2
Affiliation  

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing samples of visual evidence. This article examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relationship between distributional learning and visual comparison performance and the impact of training on the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accurate training (informed novices; n = 32), or inaccurate training (misinformed novices; n = 32) in Experiment 1 and between forensic examiners (n = 26), informed novices (n = 29), and uninformed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and novices performed significantly above chance in a visual comparison task in which distributional learning was required for high performance. However, informed novices outperformed all participants, and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners' expertise is domain specific and doesn't generalize to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译:

法医特征比较专业知识:统计学习有助于视觉比较性能。

在比较视觉证据样本时,特定学科的法医特征比较检查员比新手更准确。本文研究了一种可能有助于这种卓越的视觉比较性能的关键认知机制:了解环境中刺激发生频率的能力(分布统计学习)。我们研究了分布式学习和视觉比较性能之间的关系以及训练对视觉比较任务中分布式信息诊断的影响。我们比较了实验 1 中未接受培训的新手(不知情的新手;n = 32)、准确的培训(知情的新手;n = 32)或不准确的培训(被误导的新手;n = 32)以及法医检查员(n = 26)之间的表现), 知情新手 (n = 29), 和不知情的新手 (n = 27) 在实验 2 中。在两个实验中,法医检查员和新手在视觉比较任务中的表现明显高于机会,在该任务中,分布式学习需要高性能。然而,知情新​​手的表现优于所有参与者,只有他们的视觉比较表现与他们的分布式学习显着相关。法医检查员的专业知识很可能是特定于领域的,不能推广到新的视觉比较任务。然而,诊断性训练对于分布式学习和视觉比较性能之间的关系可能至关重要。(PsycInfo 数据库记录 (c) 2020 APA,保留所有权利)。法医检查员和新手在视觉比较任务中的表现明显高于机会,在该任务中,高性能需要分布式学习。然而,知情新​​手的表现优于所有参与者,只有他们的视觉比较表现与他们的分布式学习显着相关。法医检查员的专业知识很可能是特定于领域的,不能推广到新的视觉比较任务。然而,诊断训练对于分布式学习和视觉比较性能之间的关系可能是至关重要的。(PsycInfo 数据库记录 (c) 2020 APA,保留所有权利)。法医检查员和新手在视觉比较任务中的表现明显高于机会,在该任务中,高性能需要分布式学习。然而,知情新​​手的表现优于所有参与者,只有他们的视觉比较表现与他们的分布式学习显着相关。法医检查员的专业知识很可能是特定于领域的,不能推广到新的视觉比较任务。然而,诊断性训练对于分布式学习和视觉比较性能之间的关系可能至关重要。(PsycInfo 数据库记录 (c) 2020 APA,保留所有权利)。只有他们的视觉比较表现与他们的分布式学习显着相关。法医检查员的专业知识很可能是特定于领域的,不能推广到新的视觉比较任务。然而,诊断训练对于分布式学习和视觉比较性能之间的关系可能是至关重要的。(PsycInfo 数据库记录 (c) 2020 APA,保留所有权利)。只有他们的视觉比较表现与他们的分布式学习显着相关。法医检查员的专业知识很可能是特定于领域的,不能推广到新的视觉比较任务。然而,诊断性训练对于分布式学习和视觉比较性能之间的关系可能至关重要。(PsycInfo 数据库记录 (c) 2020 APA,保留所有权利)。
更新日期:2020-03-09
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