Attribution, memorability, and understanding in human-AI interaction

Contact person: Thomas Espeseth

Status: Project

Our research asks how information is prioritised in minds, brains and artificial networks, and what happens when information moves between such systems. Does priority in one system affect what is selected for transmission, and what becomes prioritised or preserved in the next?

We use memorability as a measurable expression of what persists in memory. Some images and texts are reliably more memorable than others, and these differences can be measured, predicted and experimentally varied.

In WP1, we study how AI systems select information. We use attribution and intervention methods to investigate what information models rely on, and compare this with what they ultimately retrieve, summarise or present. One possibility is that AI systems preferentially surface information that humans also find memorable, even though they were not designed to do so. We also test whether properties of the models' own representations better explain what gets selected. Where these measures diverge may be as informative as where they converge.

WP2 moves to the human receiver. We vary the memorability of AI-generated information and examine retention, understanding, inference, transfer, confidence and calibration. We also ask whether gist-level information can influence later cognition even when detail is lost.

WP3 asks whether these effects extend beyond a single encounter, influencing what people later seek, repeat or use, and what adaptive AI systems subsequently present.

We welcome collaboration involving AI selection, retrieval, explanation or summarisation, and human-AI, learning or decision settings where information exposure can be linked to later cognition or behaviour.