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Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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Related Experiment Video

Updated: Feb 28, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

A Multimodal Adaptive Framework for Social Interaction with the MiRo-E Robot.

Yufeng Yang¹, Pei Shan Yap¹, Sobanawartiny Wijeakumar²

¹School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

This study introduces an adaptive framework for human-robot interaction (HRI), integrating real-time emotion expression with large language models. The system enhances engagement and naturalness in social robots through coordinated verbal and nonverbal communication.

Keywords:

LLM-based adaptive interaction emotion estimation emotion expression human–robot interaction multimodality user experience evaluation

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Last Updated: Feb 28, 2026

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Area of Science:

Robotics
Artificial Intelligence
Human-Computer Interaction

Background:

Current human-robot interaction (HRI) often lacks adaptivity, relying on repetitive verbal and isolated nonverbal cues.
This leads to unappealing user engagement and less natural interactions.

Purpose of the Study:

To propose an integrated framework combining real-time emotion expression for nonverbal communication with a fine-tuned large language model for verbal communication.
To enhance user engagement, task performance, and perceived naturalness in social HRI.

Main Methods:

Utilized the MiRo-E zoomorphic social interaction platform.
Developed a coordinated nonverbal interaction system based on real-time emotion expression.
Integrated a fine-tuned large language model for adaptive verbal communication.

Main Results:

User study demonstrated significant enhancements in user experience.
Task completion rates, user engagement, and perceived naturalness were improved.
The framework showed improved consistency across verbal and nonverbal modalities.

Conclusions:

The integrated framework significantly improves user engagement and naturalness in social HRI.
Adaptive and emotionally aligned responses are key to more human-like robot interactions.
This approach offers a promising direction for developing more sophisticated and engaging social robots.