Counter-Inferential Behavior in Natural and Artificial Cognitive Systems
It addresses cognitive vulnerabilities that can cause maladaptive stability in systems like AI and human cognition, but is incremental as it builds on existing concepts of rigidity and adaptation.
This study investigates counter-inferential behavior, where agents misattribute success or suppress adaptation, leading to epistemic rigidity, and identifies it as a general cognitive vulnerability across natural and artificial systems, suggesting design principles to resist rigidity.
This study explores the emergence of counter-inferential behavior in natural and artificial cognitive systems, that is, patterns in which agents misattribute empirical success or suppress adaptation, leading to epistemic rigidity or maladaptive stability. We analyze archetypal scenarios in which such behavior arises: reinforcement of stability through reward imbalance, meta-cognitive attribution of success to internal superiority, and protective reframing under perceived model fragility. Rather than arising from noise or flawed design, these behaviors emerge through structured interactions between internal information models, empirical feedback, and higher-order evaluation mechanisms. Drawing on evidence from artificial systems, biological cognition, human psychology, and social dynamics, we identify counter-inferential behavior as a general cognitive vulnerability that can manifest even in otherwise well-adapted systems. The findings highlight the importance of preserving minimal adaptive activation under stable conditions and suggest design principles for cognitive architectures that can resist rigidity under informational stress.