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Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

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Conversational technology and reactions to withheld information.

Nikolos Gurney¹, George Loewenstein², Nick Chater³

¹Institute for Creative Technologies, University of Southern California, Los Angeles, CA, United States America.

|April 11, 2024

Summary

Conversational AI, like voice assistants, makes people more critical of withheld information. Users are less likely to evaluate products and more likely to remember missing details when information is provided conversationally.

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Area of Science:

Human-Computer Interaction
Cognitive Psychology
Artificial Intelligence

Background:

Decision-making often involves inferring missing information.
Conversational technologies (e.g., Google Assistant, Alexa) are increasingly used for information retrieval.
Traditional digital media presents information differently than conversational agents.

Purpose of the Study:

To investigate how conversational information provision affects user responses to withheld information.
To compare the impact of conversational versus traditional digital media on inference and recall.
To identify the underlying psychological mechanisms driving these effects.

Main Methods:

Experiments were conducted using various conversational modes: recorded phone calls, chat conversations, and scripts.
Participants interacted with conversational agents and traditional digital media.
Evaluations of products/services and recall of withheld information were measured.

Main Results:

Conversational information provision led to reduced evaluations of products/services with withheld information.
Users were more likely to recall that information was withheld when provided conversationally.
These effects were consistent across different conversational formats and with the Google Assistant.

Conclusions:

Conversational AI prompts more critical engagement with incomplete information.
User intuitions about the reasons for withheld information drive these critical responses.
The findings have implications for designing more transparent and user-centric AI interactions.