Human Confabulation vs. AI Hallucination
In the human brain, memory and perception do not function as exact recordings of reality. They result from reconstructive processes shaped by neural networks, context, and a natural inclination toward coherence. These processes can become distorted, leading to false memories or confabulations in both clinical settings and everyday life. A confabulation includes fabricated details or entire events that an individual recalls with confidence, even though these details are inaccurate. In the field of artificial intelligence, large language models sometimes produce similarly confident but factually incorrect statements, often called AI hallucinations. Despite clear differences between human biology and AI architectures, both humans and these models rely on pattern-based systems that try to complete missing or ambiguous information.
Confabulation in Neuroscience
In clinical neuroscience, confabulation is frequently observed in conditions associated with memory impairment such as Korsakoff syndrome, which often involves thiamine deficiency and alcohol misuse, or in certain types of amnesia and dementia. Individuals with these disorders struggle to recall events accurately. Rather than stating that they do not remember, they spontaneously generate stories that appear plausible to them.
"Confabulation is a lie told honestly. To confabulate is to replace missing information with something false that we believe to be true." - Brené Brown, American academic and podcaster
Many neuroscientists emphasize that memory retrieval differs from replaying a precise recording. Retrieving a memory involves reactivating and reconstructing neural circuits that encode fragments of past experiences. Contextual details are sometimes inserted to fill gaps, which can produce confabulated recollections when the original information is only partially available. In one influential theoretical model known as predictive coding, the brain constantly generates predictions about incoming sensory and contextual input. When this input is weak or unclear, top-down predictions may take over and produce experiences that feel real. In individuals who have reduced inhibitory control or damaged retrieval mechanisms, these predictions can form the basis of confabulation.
The Brain’s Latent Knowledge
Memory traces sometimes remain inaccessible in healthy individuals until triggered by a specific cue. An old photograph, a familiar melody, or a particular scent can suddenly revive a long forgotten memory, revealing that the memory was present but could not be recalled without an appropriate prompt. This arises from the associative networks of the brain, which link experiences and concepts in ways that facilitate rapid retrieval. This associative capacity is typically useful, but it can also contribute to memory distortions if the recalled content is partial and the brain fills in the remainder with inferences based on assumptions or schemas.
AI Hallucinations as a Parallel Phenomenon
Large language models are computational systems with extensive networks of weighted parameters. These parameters capture linguistic patterns gleaned from enormous text corpora, allowing the models to generate coherent text when prompted. When faced with contradictory data or strong prompts for detailed responses, the models may produce plausible-sounding but incorrect statements. This parallels human confabulation in the sense that both humans and AI rely on pattern completion to fill in missing or ambiguous content.
Dormant Pathways and Fine Tuning
Prompting or fine-tuning can activate previously underused or dormant pathways in human memory and AI. In humans, a cue can reawaken a memory that has not been accessed for a long time. In AI systems, a prompt can shift the model’s parameter configurations, sometimes for the better, yielding accurate results, or for the worse, combining unrelated information and producing incorrect or confabulated statements. Both humans and AI employ forms of pattern completion that sometimes result in the invention of details to maintain coherence.
The Core Issue
Human confabulation arises from the constructive nature of memory, which depends on neural circuits that rebuild rather than simply replay past events. When neurological damage or cognitive biases disrupt the balance, confabulation can occur. Reduced reality checking or inhibitory control, often involving frontal brain regions, makes it possible for false memories to replace or merge with real ones. In AI, hallucinations occur because linguistic models lack an inherent truth-checking mechanism. Training or fine-tuning incomplete data can lead the model to fill gaps with information drawn from correlated text patterns, resulting in incorrect yet self-consistent outputs. Although biological and computational mechanisms differ fundamentally, both humans and AI experience similar failures when relying on pattern-based recall or generation to provide missing content.
Concluding Thoughts
Scientists have long studied human confabulation to understand how the brain compensates for incomplete or corrupted memories. Now, large language models display a form of confabulation that produces coherent but incorrect responses for related reasons: both systems rely on underlying networks that try to complete patterns when data is insufficient. This parallel raises questions about whether AI systems function in ways that resemble human cognition more than previously assumed. The similarities, however, do not imply identical processes. Instead, they show that relying on pattern completion can lead any complex system, biological or computational, to produce accurate or convincing yet flawed outputs.
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