Modeling Cognitive Dissonance via Clamped Perceptual Plasticity in Active Inference Frameworks

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Abstract

Cognitive dissonance—the psychological distress arising from a persistent conflict between internal mental models and external reality—remains a foundational concept in social psychology and computational psychiatry. This paper presents a neurocomputational formulation of cognitive dissonance grounded in Active Inference and Variational Free Energy (VFE) minimization. 

We model a dual-channel agent navigating a dynamic spatial environment under two experimental paradigms: 

• (1) Flexible Adaptation, where sensory prediction errors drive both perceptual inference (synaptic weight updates) and active inference (motor action); and 

• (2) Clamped Plasticity / Cognitive Dissonance, where perceptual learning is paralyzed, forcing the agent to resolve sensory prediction errors solely through motor behavior. 

Our empirical logs reveal that clamping generative weight plasticity prevents structural alignment, causing steady-state VFE to remain elevated —more than 56 times higher than the baseline convergence level. This chronic prediction error triggers an allostatic cascade, transitioning the agent from a baseline affective state, into states of sustained distress. 

These findings establish a formal mathematical bridge between clamped synaptic adaptation, unresolved VFE, and psychological rigidity.

1. Introduction

Festinger’s classical theory of cognitive dissonance asserts that human agents experience affective discomfort when confronted with sensory evidence or environmental feedback that contradicts held beliefs or generative assumptions. To resolve this tension, an agent must either adapt its internal beliefs to match reality or act upon the world to align environment feedback with expectations.

In computational neuroscience, Active Inference—derived from the Free Energy Principle (FEP)—formalizes brain function as a continuous minimization of Variational Free Energy. Minimization occurs through two distinct, complementary pathways:

• Perceptual Inference (Mind Updating): Adjusting internal generative parameters/weights to better predict sensory observations.

• Active Inference (World Updating): Executing motor actions to alter sensory inputs such that they conform to internal predictions.

When an agent encounters a structural mismatch between its internal model and physical environment, both pathways typically operate concurrently to restore sensory predictions. However, when psychological dogma, ideological rigidity, or neurological trauma clamps synaptic plasticity, the perceptual adaptation route is blocked.

This paper investigates the computational consequences of such clamped plasticity. We demonstrate that blocking perceptual inference under structural mismatch directly generates the mathematical and affective hallmarks of cognitive dissonance: persistent prediction error spikes, erratic motor compensation, and a systematic cascade through negative allostatic affective states.

2. Mathematical Framework & Generative Architecture

2.1 Dual-Channel Generative Model

The agent operates in a two-dimensional continuous spatial domain, tracking a preferred target position 

 and avoiding an environmental threat.

The subjective internal belief state:

maps to predicted physical space via a generative synaptic weight vector:

by the following:

The total Variational Free Energy of the Neural Network is decomposed into target and threat sub-losses parameterized by target and threat precisions:

For a visible target within visibility range rvis, the sensory prediction error and corresponding sub-loss are given by:

2.2 Synaptic Plasticity & Perceptual Learning

Under standard operating conditions, generative weights adapt via gradient descent on the Variational Free Energy of the Neural Network:

2.3 Active Inference & Motor Action

Motor commands a modulate the physical agent state to directly reduce prediction error:

2.4 Allostatic Accumulation Engine

To capture physiological stress caused by unmitigated prediction error, we implement an Allostatic Accumulation Engine. The engine tracks the target and threat related VFEs, alongside their derivatives. 

3. Experimental Setup & Methodology

We evaluate two primary conditions across approximately 950 simulation frames:

1- Baseline Condition (Flexible Adaptation), shown in Figure 1:

• Synaptic learning rate larger than zero.

• Initial generative weights initialized with structural error.

• Perceptual weights update freely to minimize VFE.

    Figure 1

2- Cognitive Dissonance Condition (Clamped Plasticity), shown in Figure 2:

• Learning rate clamped to zero.

• Generative weights fixed at distorted baseline.

• Perceptual updating blocked entirely due to zero learning rate.

    Figure 2

4. Empirical Results

4.1 Synaptic Convergence & VFE Minimization

In the Flexible Adaptation baseline, sensory prediction errors drives synaptic weight convergence. Over 969 iterations, weights update from initial values toward optimal alignment. As a result of this dual perceptual-active minimization, steady-state Neural Network VFE drops to nearly zero.

In contrast, under Cognitive Dissonance, weights remain frozen at initial values. Because the generative model cannot correct its spatial projection bias, active motor commands continuously overshoot or undershoot real-world spatial coordinates. Consequently, steady-state Neural Network VFE remains chronically trapped at an average of 56.5 times higher than baseline:

4.2 Affective State Distribution & Allostatic Cascades

The chronic presence of unresolvable VFE under clamped plasticity alters the agent's emotional trajectory.

• Baseline Scenario: The agent maintains baseline affective equilibrium.

• Cognitive Dissonance Scenario: As chronic prediction errors persist without resolution, VFE metrics continually accumulate, producing a clear affective cascade from baseline equilibrium towards affective states denoting distress.

Cumulatively, the clamped agent spends 37.82% of its operational lifespan in negative allostatic affective states, whereas the baseline agent spends 0.00% in these states.

5. Discussion & Neurocomputational Implications

5.1 Psychological Mechanism of Cognitive Dissonance

Our simulation demonstrates that cognitive dissonance can be formalized as an intractable Active Inference trap. When an agent's internal model makes incorrect predictions about the world, it experiences Variational Free Energy. Under normal conditions, the agent reduces this energy by either updating its beliefs or acting upon the environment.

When learning rates are clamped, perceptual inference is disabled. The agent cannot update its beliefs. It is forced to rely entirely on active inference (motor behavior) to resolve the error. However, because its motor actions are driven by a initially flawed internal model, its movements consistently fail to eliminate the prediction error.

This creates a self-reinforcing loop:

5.2 Clinical & Psychiatric Extensions

This formulation provides a clear framework for modeling several psychological phenomena:

• Dogmatism & Ideological Rigidity: Individuals who refuse to adjust core beliefs when presented with contradicting facts experience elevated chronic distress and often respond with heightened behavioral aggression (represented by the transition into anger states).

• Trauma & Rigid Schema: In conditions like PTSD, cognitive schema remain locked, preventing sensory updates and leaving the individual in a state of hyper-arousal and chronic distress.

• Anhedonia & Learned Helplessness: Extended exposure to unresolvable VFE eventually depletes active motor responses, causing the agent to cease exploratory behavior altogether.

6. Conclusion

By integrating Active Inference with an Allostatic Accumulation Engine, we have demonstrated that clamping perceptual learning rates under structural prediction errors reproduces the core dynamics of cognitive dissonance. Without synaptic weight adaptation, agents are unable to resolve Variational Free Energy, driving steady-state error levels up by 56.5 times and triggering a systematic emotional cascade into stress, frustration, and anger. This computational architecture offers a quantitative framework for studying psychological rigidity, affective disorders, and decision-making under unresolvable conflict.

References

Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford University Press.

Friston, K. (2010). The free-energy principle: a unified brain theory?. Nature Reviews Neuroscience, 11(2), 127-138.

Parr, T., Pezzulo, G., & Friston, K. J. (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press.

Badcock, P. B., Friston, K. J., & Ramstead, M. J. (2019). The hierarchically mechanics of the brain: An active inference hypothesis for evolutionary systems biology. Physics of Life Reviews, 31, 219-258.