Machine psychology

Machine Psychology: The Foundational Paper

The 2023 paper (updated 2024) from DeepMind and collaborators that establishes machine psychology as a discipline. This is the theoretical foundation for our research approach.

Paper: arXiv:2303.13988 Authors: Hagendorff, Dasgupta, Binz, Chan, Lampinen, Wang, Akata, Schulz

Core Thesis

Study LLMs through behavioral experiments rather than (or alongside) mechanistic interpretability. Examine input-output relationships at the user-facing interface where outcomes matter, rather than inspecting weights and activations.

Why behavioral over mechanistic?

• Scales with model size (no architecture access needed)
• Works with closed-source models
• Captures emergent behaviors too complex to predict from weights alone
• Operates where real-world impact occurs

Four Primary Research Domains

1. Heuristics and Biases

Decision-making shortcuts and cognitive distortions. Studies found GPT-3 displayed human-like biases, but newer models largely eliminated them (possibly due to training improvements or data contamination).

Paradigms: Framing effects, ecological rationality, semantic content variations

2. Social Interactions

Theory of mind, cooperative/competitive behavior, communicative trade-offs.

Paradigms: False belief tasks (Wimmer & Perner), recursive mental state reasoning, negotiation dilemmas, network formation (preferential attachment, triadic closure)

3. Psychology of Language

Syntax, semantics, pragmatics processing.

Paradigms: Surprisal measures, priming techniques, garden path sentences, filler-gap dependencies, entailment assessment

4. Learning

In-context learning mechanisms, inductive biases, generalization.

Paradigms: Rule-based vs exemplar-based generalization, curriculum learning effects, spacing/repetition impacts, developmental psychology comparisons

Design Standards: Good Behavioral Experimentation

The paper’s methodological guidelines are essential reading for anyone running psych evals on LLMs.

Avoid Data Contamination

• Don’t copy psychology stimuli verbatim - models may have seen them
• Create “novel variants of classic” tasks with new wording, agents, scenarios
• Use procedurally generated experiments when possible

Use Representative Sampling

• “Batteries of varied prompts” not small convenience samples
• LLMs are highly sensitive to minor wording variations
• Test multiple versions of each task systematically

Control for Technical Biases

• Recency bias: LLMs overweight end-of-prompt information
• Common token bias: Models favor frequent training tokens
• Majority label bias: Few-shot examples skew toward frequent labels

Mitigations: Shuffle answer orders, use varied prompt formulations, document temperature settings

Performance-Competence Distinction

From Chomsky: performance in a situation may not capture underlying competence. Poor results don’t prove absence of capability - behavioral inconsistency isn’t evidence of lacking abstract proficiency.

Capability Elicitation Caveat

Chain-of-thought prompting improves performance, but:

• Enhanced results may be prompt artifacts, not fundamental capabilities
• Different augmentations help some tasks, hinder others
• Omitting them underestimates abilities; using them universally obscures true competencies

Self-Report Limitations

Critical warning: Properties like personality, morality, clinical disorders are “famously sensitive to prompting.” LLMs can simulate different personas when prompted differently.

Self-reports should be understood “as a property of a specific system prompt” not as fundamental model characteristics. This is directly relevant to our HEXACO work - see HEXACO Personality Profiles.

Behavioral vs. Mechanistic Interpretability

The paper argues these complement each other:

Behavioral	Mechanistic
Input-output relationships	Weights and activations
Scales with model size	Requires architecture access
Works on closed-source	Open-source only
User-facing interface	Internal mechanisms
What models do	How models work

Neither replaces the other. Behavioral methods become more valuable as models grow “more powerful, opaque, multi-modal, and integrated into complex real-world settings.”

Future Directions Proposed

1. Multimodal expansion: Apply paradigms to vision, sensory processing
2. Longitudinal studies: Track capability development over time
3. AI safety applications: Forecast alignment implications
4. Embodied interaction: LLMs with tool use
5. Domain expansion: Creativity, moral reasoning, clinical psychology

Relevance to Our Research

This paper provides the theoretical foundation for our approach:

1. Behavioral over mechanistic: We focus on behavioral measurement, aligning with their argument that this is where real-world impact occurs. This insight led us to skip psychometric abstraction entirely—if behavioral testing scales for LLMs (unlike humans), go straight to behavior.

2. Self-report caveat validated: Their warning about personality measures being “famously sensitive to prompting” was borne out by the literature. The Personality Illusion paper (2025) showed self-report fails to predict interactive behavior.

3. Methodology alignment: Our behavioral evaluation work follows their contamination avoidance and representative sampling guidelines.

4. Performance-competence: Variation in responses doesn’t mean incompetence—important for interpreting behavioral probe results.

5. Domain expansion: Their taxonomy (heuristics, social interactions, language, learning) maps well to multi-agent dynamics research.

Key Citations

• Binz & Schulz (2023) - GPT-3 displays human-like cognitive biases
• Dasgupta et al. (2022) - Semantic content effects on logical reasoning
• Chan et al. (2022) - Rule-based vs exemplar-based generalization
• Wilcox et al. (2023) - Surprisal measures for syntax
• Street et al. (2024) - Higher-order theory of mind

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See also: Research Notes, Behavioral Evaluation Tools, HEXACO Profiling, LLM Psychometrics, Research Log