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Related Experiment Video

Updated: May 10, 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

Forward modelling requires intention recognition and non-impoverished predictions.

Jan P de Ruiter¹, Chris Cummins

¹Department of Psycholinguistics, Bielefeld University, 33501 Bielefeld, Germany. jan.deruiter@uni-bielefeld.de

The Behavioral and Brain Sciences

|June 25, 2013

Summary

Pickering & Garrod's theory needs computational modeling for early intention recognition. Fully specified predictions, not impoverished ones, are required to explain key linguistic phenomena in comprehension and production.

Related Experiment Videos

Last Updated: May 10, 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

Area of Science:

Cognitive Science
Computational Linguistics
Psycholinguistics

Background:

The Pickering & Garrod (P&G) theory offers a novel framework for understanding language processing.
A key aspect of the P&G theory involves the mechanism of early intention recognition.

Purpose of the Study:

To advocate for the implementation of the P&G theory within a computational model.
To highlight the necessity of efficient and reliable early intention recognition for the theory's success.
To address the limitations of impoverished predictions in explaining observed linguistic phenomena.

Main Methods:

Conceptual analysis of the P&G theory.
Theoretical critique of prediction generation mechanisms.

Main Results:

The P&G theory's reliance on early intention recognition is identified as a critical component.
Impoverished prediction generation is shown to be insufficient for explaining core linguistic phenomena.
The necessity of fully specified perceptual predictions in both language comprehension and production is established.

Conclusions:

Computational modeling is essential for validating and refining the P&G theory.
Future theoretical development must incorporate robust mechanisms for intention recognition.
Fully specified perceptual predictions are crucial for a comprehensive account of language processing phenomena.