一个自然语言模型,用于自传记忆的自动评分
Meladel Mistica1, Patrick Haylock2, Aleksandra Michalewicz3
1Melbourne Data Analytics Platform (MDAP), University of Melbourne, Melbourne Connect, Carlton, 3053, Victoria, Australia. misticam@unimelb.edu.au.
Behavior research methods
|April 25, 2024
概括
一个新的自然语言模型准确地分类了自传记忆类型,有助于心理健康评估. 该工具有助于识别与不良临床结果相关的记忆偏差.
科学领域:
- 认知心理学 认知心理学
- 计算语言学 计算语言学
- 临床心理学 临床心理学
背景情况:
- 记忆偏差是精神疾病的关键指标,影响预后.
- 目前的记忆偏差评估受到手动评分或单一类型识别的限制.
研究的目的:
- 开发一种自然语言模型来分类五种类型的自传记忆.
- 为了能够更广泛地评估记忆偏差,包括特异性和灵活性.
主要方法:
- 一个基于BERT的自然语言模型被微调和训练了17632个人类得分的记忆.
- 该模型在5,880个记忆中使用20倍交叉验证进行了验证.
- 性能与基准和支持矢量模型进行了比较.
主要成果:
- 该模型在分类内存类型方面实现了95.7%的准确性和91.0%的精度.
- 它成功地发现了比现有方法更广泛的记忆偏差.
- 为临床应用开发了一个开源的,用户友好的版本.
结论:
- 开发的自然语言模型为评估自传记忆偏差提供了一个精确而有效的方法.
- 它的可访问性促进了临床使用和基于记忆的干预措施的实施.
- 这种工具可以改善与记忆偏差相关的心理健康障碍的理解和治疗.
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