Tell Me What I Missed: Interacting with GPT during Recalling of One-Time Witnessed Events

TL;DR

This study explores how people interact with GPT during memory recall of a one-time witnessed event, revealing how trust, strategies, and perceptions are influenced by GPT prompts and user beliefs.

Contribution

It introduces a novel examination of GPT-assisted memory recall, comparing default and guided interactions, and uncovers how these influence user trust and recall strategies.

Findings

Participants' trust in GPT correlates with perceived understanding.

Guided GPT prompts improve alignment between perceived and actual recall.

Users develop diverse strategies, including building on recollections and requesting missing details.

Abstract

LLM-assisted technologies are increasingly used to support cognitive processing and information interpretation, yet their role in aiding memory recall, and how people choose to engage with them, remains underexplored. We studied participants who watched a short robbery video (approximating a one-time eyewitness scenario) and composed recall statements using either a default GPT or a guided GPT prompted with a standardized eyewitness protocol. Results show that, in the default condition, participants who believed they had a clearer understanding of the event were more likely to trust GPT's output, whereas in the guided condition, participants showed stronger alignment between subjective clarity and actual recall. Additionally, participants evaluated the legitimacy of the individuals in the incident differently across conditions. Interaction analysis further revealed that default-GPT users spontaneously developed diverse strategies, including building on existing recollections, requesting potentially missing details, and treating GPT as a recall coach. This work shows how GPT-user interplay can subconsciously shape beliefs and perceptions of remembered events.