Other Research: CNAGI

Sally-Anne “false belief” test” - Simon Baron-Cohen - Borat’s brother

“Sally has a basket. Anne has a box. Sally has a marble. She puts the marble into her basket. Sally goes out for a walk. Anne takes the marble out of the basket and puts it into the box. Now Sally comes back. She wants to play with her marble. Where will Sally look for the marble?” - Human children cannot pass this test until the age of four. 

“Theory of mind refers to the capacity to understand other individuals by ascribing mental states to them. A theory of mind includes the understanding that others' beliefs, desires, intentions, emotions, and thoughts may be different from one's own.” Wikipedia

Mechanistic Empathy: Decoding by Contrasting Layers (DoLa) as a Functional Analogue to Inhibitory Control in Theory of Mind

Abstract

https://github.com/goldspruce/DoLa

Large Language Models (LLMs) frequently struggle with hallucination. This phenomenon occurs when a model prioritizes high-probability statistical associations over factual truth or specific context. Recent advancements in interpretability, specifically Decoding by Contrasting Layers (DoLa), have mitigated this issue by subtracting the logits of early transformer layers from those of later layers (Chuang et al., 2024). This paper proposes that DoLa is not merely a noise-reduction technique. Instead, it functions as an analogue to the cognitive mechanism of inhibitory control required for Theory of Mind (ToM) in human psychology.

In cognitive science, successful ToM performance requires the inhibition of the egocentric or reality-centric perspective to allow the representation of another's mental state to emerge. We argue that the early layers of an LLM function as the reflexive cognitive substrate. These layers encode strong statistical priors that mirror a reality bias, such as the most common association with an object. The later layers, conversely, encode context-dependent reasoning but remain polluted by these initial reflexes. By mathematically subtracting the early-layer logits, DoLa effectively decouples the raw statistical reflexes of the model from its higher-order reasoning.

This subtraction operation mirrors the decoupling mechanism in the human brain, where the inhibition of the default mode allows for the simulation of alternative perspectives. We demonstrate that applying contrastive decoding to ToM tasks in LLMs significantly improves performance on False Belief benchmarks. These findings suggest that hallucination in AI and egocentric bias in humans may share a common structural etiology, which is the failure to inhibit lower-order associations. These findings offer a novel framework for Mechanistic Theory of Mind and posit that empathy in artificial systems may be an emergent property of subtractive processing rather than additive complexity.

Keywords: Large Language Models, Theory of Mind, DoLa, Inhibitory Control, Mechanistic Interpretability, False Belief Task.

References

Chuang, Y.-S., Dang, Y., Wang, N., & Glass, J. (2024). DoLa: Decoding by contrasting layers improves factuality in large language models. arXiv. https://arxiv.org/abs/2309.03883

Li, X. L., & Liang, P. (2023). Contrastive decoding: Open-ended text generation as optimization. arXiv. https://arxiv.org/abs/2210.15097

Prelec, D., Seung, H. S., & McCoy, J. (2017). A solution to the single-question crowd wisdom problem. Nature, 541(7638), 532–535. https://doi.org/10.1038/nature21054

SAMPLE CODE

LLM Theory of Mind Subtraction Test 

See more here

Why Harrisburg Wins: Even though Philadelphia got more votes (65%), it performed worse than expected (85%). Harrisburg received fewer votes (35%), but it performed better than expected (10%). The "Surprise" signal reveals the hidden expert knowledge.

2. The Transformer Analogy (DoLa / Contrastive Decoding)

Recent research (such as DoLa and Contrastive Decoding) applies this exact logic to Large Language Models (LLMs) to detect hallucinations.

In this analogy, the Layers of the Transformer act as the "Population."

• The "Crowd" = Early Layers (e.g., Layer 2 of 32)
  The early layers function like the uninformed majority. They rely on "n-gram probability" and superficial associations. When they see "Capital of Pennsylvania," they reflexively activate "Philadelphia" because those words appear together frequently in the training data.

  • Analogy: This is the "Predicted Vote" (The Baseline/Prior).

• The "Expert" = Late Layers (e.g., Layer 32 of 32)
  The late layers function like the informed minority. They have processed the full context and logic of the sentence. They activate "Harrisburg" because they have done the reasoning. However, they are still "polluted" by the signals from the early layers.

  • Analogy: This is the "Actual Vote" (The Mixture).

The "SP" Calculation in AI

To find the truth, DoLa performs a mathematical operation equivalent to Prelec's algorithm: