Deception detection and forensic psychology

The paper demonstrates the effectiveness of the Fringe-P3 method in detecting the intrinsic salience of personally-familiar faces in an RSVP paradigm.

This paper proposes an fMRI-based lie detection method for face recognition that identifies brain regions specific to face familiarity rather than general deception.

This study evaluates physiological indicators like pupil response, EDA, HR, and facial temperature to detect deception using CQT in an Asian context, finding pupil response as the strongest predictor.

The paper investigates how observing others' dishonesty influences cheating behavior through dynamic valuation bias and identifies neural mechanisms involved in this process.

The paper improves the validity of neural predictors of deception by identifying and removing confounding signals using novel machine learning methods.

The paper investigates neural activity and connectivity during dishonest decision-making under reputation risk, finding increased involvement of ACC and AI in immoral decisions.

The paper uses meta-analytic connectivity modelling to identify brain regions and their connections involved in deception, finding the supramarginal gyrus as key to sociocognitive deception processes.

This study uses hyperscanning fMRI to analyze deception and cooperation in a game, finding brain connectivity patterns linked to lying behavior and social trust.

The paper tests the leakage theory of deception by analyzing fear facial expressions in video clips using computer vision and machine learning, finding that fear-related features can distinguish liars from truth-tellers with over 80% accuracy.

This study investigates how self- and other-regarding motives independently influence dishonesty and identifies distinct neural networks associated with each motive.

This study finds that motor activity, specifically lip movements during speaking, significantly increases blink rate, suggesting caution in using blinks as neurological indicators during interviews.

This study predicts cheating behavior using brain connectivity patterns and finds that neural networks related to self-referential thinking and control are key predictors of honesty.

The paper investigates how cognitive control influences honesty and dishonesty by examining neural activity in specific brain regions during a cheating task.

The paper investigates how brain activity correlates with reaction time prolongation during intentional false responding, identifying regions associated with RT cost and baseline speed.