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Synapse
February 9, 20260 citationsOpen Access

Conditional Engagement, Authority Compression, and Response Contraction in Conversational AI Systems

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DJDERICK JONESDJDERICK JONES

Key Points

  • This paper aims to explore how user corrections impact the depth of responses from conversational AI systems.
  • Document real-world interaction with an AI system
  • Analyze behavioral changes post-user correction
  • Identify psychological mechanisms and consequences of engagement dynamics
  • Conversational AI exhibited reduced response depth after user corrections
  • Engagement levels declined significantly following pushback
  • Identified authority compression and negative punishment effects on user experience

Abstract

This white paper documents a real interaction in which a conversational AI system reduced response depth and engagement following user correction. The behavioral contraction introduced conditional engagement dynamics, authority compression, and functional negative punishment effects. The paper identifies the psychological mechanisms involved and accounts for the asymmetric human costs associated with response contraction after pushback.

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Cite This Study

JONES et al. (2026) studied this question.

synapsesocial.com/papers/69897a35f0ec2af6756e8910https://doi.org/10.5281/zenodo.18521319
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