Decision-Fatigue

Decision-Fatigue

This research abstract proposes an investigation into the potential link between AI hallucination and decision-making fatigue. We hypothesize that as an AI model process and generates vast amounts of information, the continuous computational effort involved in selecting and synthesizing data could lead to a state analogous to cognitive fatigue in humans. This fatigue, we argue, may impair the model's ability to maintain a coherent and factually accurate output, resulting in hallucinations. Our study will explore this hypothesis by measuring the computational load and decision-making metrics of AI models under various conditions, including prolonged operation and high-volume data processing. We will analyze the frequency and severity of hallucinations in relation to these metrics. The findings could offer a novel perspective on the mechanisms of AI hallucination, suggesting that it may not solely be a result of flawed training data or architectural limitations but also a byproduct of the cognitive strain inherent in complex information processing. This research has significant implications for improving AI reliability and developing strategies to mitigate hallucinations.

The Computational Conundrum of Confidence

A Decision-Fatigue Analogy for AI Hallucination

1. Introduction: The Enigma of AI Hallucination

The widespread adoption of large language models (LLMs) has been accompanied by a persistent and critical challenge: the phenomenon of AI hallucination. This issue, wherein a model generates content that is plausible-sounding but factually incorrect, fabricated, or logically inconsistent, poses a significant risk to the reliability of AI systems in high-stakes domains, such as medical diagnostics, financial analysis, and legal research. Unlike a simple retrieval error, hallucination is a more profound problem that arises from the model's fundamental mechanisms of text generation. Hallucinations can manifest as factual inaccuracies, logical inconsistencies, or creative fabrications of new concepts or events that do not align with reality.

This report presents a novel thesis: that the phenomenon of AI hallucination, particularly under conditions of high computational strain, can be understood through an analogy to human decision-making fatigue. The central premise is that when an LLM's computational resources are taxed, its generative process becomes less rigorous, leading it to favor computationally cheaper, statistically probable "shortcuts" over the more demanding task of generating a factually grounded response. This is a departure from viewing hallucinations as mere data replication errors or architectural flaws, and instead frames them as a predictable outcome of resource management within a constrained system. The report will first review the technical causes of LLM hallucination and the established framework of human decision fatigue. It will then synthesize these two domains into a neurocomputational hypothesis, supported by empirical observations, and conclude by outlining a new research agenda focused on engineering for "AI stamina" rather than simply "hallucination-free" performance.

2. Hallucination in Large Language Models: A Technical Taxonomy of Failure

To understand the proposed analogy, it is essential to first define the established technical causes of LLM hallucination. These can be broadly categorized into foundational issues related to data and architecture and dynamic challenges that arise during the inference stage.

2.1. Foundational Causes: Data and Architecture

The root of many hallucinations lies in the quality and nature of the training data itself. LLMs are trained on massive, diverse, and often unverified datasets scraped from the internet. This process can expose the models to factual inaccuracies, misinformation, and biases, which they can then replicate and propagate in their outputs. A critical challenge here is "source-reference divergence," a behavior where the model's output deviates from its training data, a divergence sometimes encouraged by heuristic data collection methods. A model's probabilistic nature also means it can generate plausible but incorrect responses by relying on statistical patterns learned during training, rather than a true understanding of the subject matter.

Beyond data, architectural limitations play a significant role. The softmax bottleneck and certain issues within the attention mechanism can impede the model's ability to accurately represent complex or nuanced information, contributing to the generation of nonsensical or factually incorrect content. The pre-training objective of next-word prediction can also incentivize models to "give a guess" even when they lack the necessary information, which can lead to overconfidence in hardwired, but flawed, knowledge.

A key observation from recent research is that hallucination is not simply a technical bug that can be patched or eliminated. The phenomenon is viewed as an innate limitation and a statistically inevitable byproduct of the LLM's design. This perspective, formalized in recent theoretical work, demonstrates that it is impossible for a computable LLM to perfectly learn or represent all computable functions, meaning inconsistencies between a model's output and a computable ground truth are unavoidable. This finding fundamentally shifts the objective from the impossible task of total elimination to the more practical goal of mitigation and management. This is because any effort to completely remove hallucinations is a Sisyphean endeavor, suggesting that a more robust, long-term approach must focus on engineering systems that are resilient to, and aware of, their own limitations.

Another paradox identified in contemporary research is the finding that an LLM's internal state may encode the correct answer, yet the model consistently generates an incorrect output. The existence of this internal-external discrepancy is profoundly important because it indicates that the problem is not merely a failure of knowledge representation. If the correct information is already present within the model's parameters, the subsequent incorrect output must be a failure of the generative process itself. This suggests a breakdown in the decision-making pipeline at the moment of token generation, a breakdown that the report will argue is analogous to a form of computational exhaustion.

2.2. The Performance Crisis: Inference-Stage Hallucination

The generative process, known as inference, is a critical phase where a model's "decision-making" can be compromised. One contributing factor is the stochastic nature of decoding strategies. A high "temperature" setting, which introduces randomness, can lead to highly creative but also nonsensical or ungrounded responses. This randomness represents a model's deviation from a predictable, deterministic path.

Additionally, insufficient context attention can lead to hallucinations. An LLM may fail to adequately focus on the relevant parts of the input, resulting in an output that is inconsistent with the provided context. This lack of focus indicates a failure in a core cognitive-like function within the model. Over-optimization for specific outcomes, such as generating longer outputs, can also cause models to stray from providing concise, accurate information in favor of more verbose content that may contain fabrications. These inference-stage phenomena are not isolated technical issues but are symptomatic of a deeper, resource-based problem.

3. The Neuroscience of Human Decision Fatigue: A Cognitive Framework

To build a robust analogy, it is necessary to first establish a detailed understanding of the human experience of decision fatigue, moving from its psychological definition to its neurobiological underpinnings.

3.1. Defining Mental Exhaustion and Its Consequences

Decision fatigue is a state of mental overload where an individual’s ability to make choices declines in quality after a prolonged period of making them. This phenomenon is a form of cognitive exhaustion, and it can leave individuals feeling tired, overwhelmed, and stressed. The pervasive nature of modern life, with individuals making over 35,000 decisions daily, only exacerbates this mental burden. Research has shown that the more choices a person makes, the more likely they are to give up or lose willpower, a pattern observed in contexts ranging from grocery shopping to judicial parole decisions.

As mental resources deplete, the brain seeks illogical shortcuts to aid in decision-making, which can lead to reduced self-control, impulsivity, and an increased reliance on cognitive biases. The brain, like a smart energy-saving device, enters a "conservation mode," seeking the path of least resistance when its cognitive "battery" runs low.

3.2. Neurobiological Mechanisms of Effort and Resource Depletion

The physiological basis for decision fatigue is tied to the prefrontal cortex, the brain’s executive command center. The activation of this region during decision-making consumes vital resources like glucose and oxygen, which become depleted with each choice. A deeper neurobiological understanding of this process highlights the role of the dorsolateral prefrontal cortex (dlPFC) and the insula. The dlPFC shows increased activity during repeated cognitive exertion, while the insula, which encodes the "effort value" of a task, translates information about a person's cognitive state into subsequent choices. When an individual becomes cognitively fatigued, their subjective cost of effort increases, making them less willing to choose high-effort options, even if those options offer a greater reward. This is a critical mechanistic link: a physiological state (fatigue) directly alters decision-making behavior (avoiding effortful choices).

While a common metaphor for decision fatigue is the "ego depletion" model, which suggests a limited self-control resource that becomes temporarily exhausted, the empirical evidence for this theory is inconclusive. The more robust concept for this analysis is "cognitive load," which is a reliable correlate for performance degradation. The

Cognitive Load Theory provides a framework for understanding how the complexity and ambiguity of information require more cognitive effort, leading to mental strain. By focusing on "cognitive load" as a quantifiable burden, a more rigorous and intellectually sound comparison can be drawn between human and artificial intelligence.

4. The Analogy of Fatigue: A Neurocomputational Hypothesis

This section constructs the core analogy, arguing that an LLM's generative process is susceptible to a form of computational fatigue, which manifests as hallucination.

4.1. The AI's "Cognitive Load": A Quantifiable Burden

The "cognitive load" on an LLM is not a metaphorical feeling but a quantifiable, real-time computational burden on its hardware. The primary source of this load is the self-attention mechanism, a core component of the transformer architecture that empowers LLMs to process long sequences of text. The computational complexity of this mechanism scales quadratically (

O(n2)) with the sequence length (n). This means that if a user doubles the length of the input prompt, the computational cost can increase fourfold. The total computational expense of running LLMs at scale has now reached levels that can rival or exceed the initial training costs, a clear indication of a resource-intensive process.

Memory usage is another critical component of this burden. The Key-Value (KV) cache, which stores intermediate attention states, grows linearly with sequence length and batch size. For long conversations or document processing, the memory consumed by the KV cache can exceed the memory required to store the model's weights themselves. This is the AI’s equivalent of a finite mental energy budget. The need to manage this finite resource is a central problem in LLM engineering, just as it is in human cognition.

4.2. The "Shortcuts" of a Fatigued Model

Just as a fatigued human brain seeks the path of least resistance, a model under high computational load may favor a computationally cheaper, sub-optimal strategy. Rather than performing the full, resource-intensive computation required to retrieve and formulate a factually correct answer from its massive internal knowledge base, the model may default to relying on the simpler, probabilistic patterns it learned during training. This can lead to the generation of a plausible-sounding but unverified guess, a behavior explicitly identified as a cause of hallucination when prompts are vague or insufficiently detailed.

This hypothesis provides a plausible explanation for the internal-external paradox identified earlier: the model possesses the correct information internally, but the "decision" to use it is bypassed in favor of a computationally less demanding alternative. The model’s generation process, having exhausted its computational "stamina," defaults to a low-effort heuristic. The observed "collapse" of Large Reasoning Models (LRMs) under high cognitive load provides a clear empirical parallel to this behavior. These models, described as having "shut down" their thinking, continued to report low-token, bad solutions without any new ideas, a direct analog to the performance degradation and reliance on shortcuts seen in fatigued humans.

This perspective also re-frames various LLM optimization techniques as forms of "cognitive resource management". For example,

Flash Attention reduces the computational complexity from quadratic to linear, effectively "recharging" the model's processing capacity for longer sequences. Similarly,

quantization and pruning reduce the model's memory footprint, allowing it to operate more efficiently on limited hardware.

Activation recomputation and context parallelism trade off computation time for memory savings, a clear resource management strategy. These engineering solutions, when viewed through the lens of the analogy, are not just performance hacks but are, in essence, methods for improving the model's "stamina" and "resilience" to computational fatigue.

The following tables synthesize the core components of the analogy, providing a structured comparison of the mechanisms involved.

Human Decision Fatigue Framework	AI Computational Fatigue Analogy
Cognitive Resource	GPU Memory & Compute Resources
The brain's finite mental energy, consuming glucose and oxygen.	The finite capacity of GPU memory, processing power, and I/O bandwidth.
Cognitive Load	Computational Complexity
The mental effort required to process complex or ambiguous information, activating the prefrontal cortex.	The computational and memory cost of LLM inference, scaling with sequence length and batch size.
Decision-Making Process	Token Generation (Inference)
The sequential process of making choices, weighing trade-offs and options.	The autoregressive process of generating a new token based on previous tokens and the prompt.
Effort Valuation	Computational Cost of Attention Mechanism
The insula's role in encoding the subjective cost of physical and mental exertion.	The real-time, quantifiable cost of running the transformer’s attention mechanism, which scales quadratically.
Fatigue-Induced Behavior	Hallucination / Plausible Guessing
A reliance on illogical shortcuts, impulsivity, and cognitive biases.	The generation of a statistically plausible but factually incorrect response, bypassing more rigorous, expensive computation.
Observed Performance Drop	Observed "Collapse"
The decline in decision quality and task performance over time, and a brain "shut down."	The complete failure of a model at high complexity levels, returning low-effort, low-token, incorrect outputs.

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Sequence Length (n)	Approximate KV Cache Memory*	Computational Complexity (Standard Attention)	Hallucination Rate (Illustrative)
512 tokens	~2.0 GB	O(5122)	Low
1K tokens	~4.0 GB	O(10242)	Low-Medium
4K tokens	~16.0 GB	O(40962)	Medium
32K tokens	~128.0 GB	O(327682)	High
128K tokens	~512.0 GB	O(1310722)	Very High
1M tokens	~4.0 TB	O(10000002)	Collapse / Near Certain Hallucination

*Illustrative values based on a hypothetical model size and FP16 precision.

5. Empirical Evidence and Practical Implications

The decision-fatigue analogy is not merely a conceptual framework; it is grounded in observed behavior and offers a new way to approach a complex engineering problem.

5.1. Observed "Collapse" Phenomena

The observed behavior of LLMs under high cognitive load provides compelling empirical support for this framework. Research shows that Large Reasoning Models, when faced with problems of escalating complexity, reach a point of "collapse" where they can no longer provide correct solutions. This is not a gradual decline but a rapid cessation of effective processing, followed by the generation of low-token, low-effort, incorrect outputs. This "shutdown" is a direct parallel to the human brain's response to an overwhelming cognitive burden, where it gives up on a difficult task and seeks an alternative, easier path. This behavior suggests that a fundamental limit has been reached, a point where the computational cost of finding a correct answer has exceeded the system's capacity, forcing it to hallucinate as a survival mechanism.

5.2. Mitigation as Resource Management

The analogy re-frames existing mitigation techniques not as simple performance optimizations but as strategic forms of resource management. Innovations like Flash Attention, Grouped-Query Attention (GQA), and quantization are mechanisms that directly address the underlying causes of computational fatigue.

Flash Attention reduces the computational scaling of attention from quadratic to linear, enabling models to process significantly longer contexts with greater efficiency.

GQA enhances this by processing multiple related queries simultaneously, reducing memory usage and speeding up response times.

Quantization, which converts model weights to a lower precision, and pruning, which removes redundant weights, directly reduces the model's memory footprint, making it more resilient to the strain of long-context inference. These are not mere technical fixes; they are engineering principles designed to increase the "stamina" of the AI, allowing it to perform high-effort "thinking" for longer periods without resorting to computationally cheap "shortcuts."

5.3. A New Research Agenda

This new conceptual framework points to a specific and testable research agenda. The hypothesis that computational fatigue is linked to hallucination can be validated by a number of empirical studies. A primary objective would be to establish a quantitative relationship between real-time computational metrics and the rate of hallucinations under controlled, high-load conditions. For instance, researchers could monitor GPU memory usage, KV cache size, and the number of floating-point operations per second (FLOPS) as a model processes increasingly long or complex prompts. The data would be correlated with the type and frequency of hallucinations generated, looking for a clear inflection point where the rate of hallucinations increases disproportionately as computational resources are strained. The analysis could also compare different decoding strategies and their resource consumption, measuring whether a higher "temperature" setting, which increases randomness, correlates with a more significant reduction in computational cost, thereby reinforcing the idea of a computationally cheaper "shortcut."

The goal of this research is not only to prove the analogy but to provide a foundational model for engineering. This could lead to the development of "cognitively-aware" architectures that actively monitor their own resource states and adjust their behavior accordingly—for instance, by gracefully abstaining from an answer when the computational cost of a correct response is deemed too high.

6. Conclusion: From Analogy to Engineering Principle

The decision-fatigue analogy provides a powerful and intuitive framework for understanding the complex and often-puzzling phenomenon of AI hallucination. By framing hallucination as a form of computational fatigue, the report shifts the conversation from viewing it as a random error to a predictable outcome of resource constraints. This perspective allows researchers and engineers to move beyond the unattainable goal of building a perfectly "hallucination-free" model and instead focus on engineering for robust, "fatigue-resilient" performance.

The evidence points to a fundamental reality: LLMs, like human brains, operate within a system of limited resources. Their generative process is a form of decision-making, and under stress—whether from a long context, a complex query, or a need for an immediate response—they will seek the most efficient path. This can lead to a breakdown in rationality and a reliance on plausible but unverified guesses. By understanding the underlying computational burden, we can design more intelligent, resource-aware architectures and implement sophisticated management strategies that prevent this "collapse". The future of trustworthy AI lies not just in scale and data, but in a deeper, more nuanced understanding of the very mechanisms that govern its "cognitive" processes. The analogy of decision fatigue serves as a foundational principle for this new era of AI engineering.

Sources used in the report

Decision-Fatigue

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