An examination of the cognitive process of deception detection based on confidence

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K22268
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0110:Psychology and related fields
• Research Institution: Osaka University
• Principal Investigator: Daiku Yasuhiro 大阪大学, 大学院人間科学研究科, 招へい研究員 (30880322)
• Project Period (FY): 2020-09-11 – 2024-03-31
• Keywords: 嘘 / 欺瞞 / 虚偽検出 / 真偽判断 / 統計モデリング

Outline of Final Research Achievements

In this study, I revisited Smith and Leach's (2019) result that confidence in veracity judgments predicts accuracy using Bayesian statistical modeling with the following three models: 1. a model in which accuracy is constant regardless of confidence; 2. a logistic regression model in which higher confidence predicts higher accuracy; and 3. a segmented logistic regression model in which higher confidence predicts higher accuracy but the coefficient changes at a breakpoint. Model 3 was the best fit, but the very wide 95% HDI of the breakpoint and the coefficient did not indicate that confidence predicted accuracy. This suggested that it was unlikely that the judges were making evidence-based veracity judgments.

Free Research Field

社会心理学

Academic Significance and Societal Importance of the Research Achievements

嘘検知を扱った先行研究では、古典的な研究手法によって、要因操作と正答率の因果関係という入力と出力の関係のみに焦点が当てられてきており、真偽判断の認知プロセスそのものは研究対象になってこなかった。本研究は、近年指摘され始めた確信度と正答率の関係を、ベイズ統計モデリングという新しい手法で精緻に検討することによって、真偽判断の認知プロセスそのものに焦点を当てた。確信度と正答率の確固たる関係性は見られなかったものの、嘘検知の認知プロセスの検討にベイズ統計モデリングという新たな方法を持ち込み、嘘研究との親和性の高さを示した点で学術的意義がある。