From Prompts to Constructs: A Dual-Validity Framework for LLM Research in Psychology

• URL: http://arxiv.org/abs/2506.16697v1
• Date: Fri, 20 Jun 2025 02:38:42 GMT
• Title: From Prompts to Constructs: A Dual-Validity Framework for LLM Research in Psychology
• Authors: Zhicheng Lin,
• Abstract summary: We argue that building a robust science of AI psychology requires integrating the principles of reliable measurement and the standards for sound causal inference.<n>We present a dual-validity framework to guide this integration, which clarifies how the evidence needed to support a claim scales with its scientific ambition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are rapidly being adopted across psychology, serving as research tools, experimental subjects, human simulators, and computational models of cognition. However, the application of human measurement tools to these systems can produce contradictory results, raising concerns that many findings are measurement phantoms--statistical artifacts rather than genuine psychological phenomena. In this Perspective, we argue that building a robust science of AI psychology requires integrating two of our field's foundational pillars: the principles of reliable measurement and the standards for sound causal inference. We present a dual-validity framework to guide this integration, which clarifies how the evidence needed to support a claim scales with its scientific ambition. Using an LLM to classify text may require only basic accuracy checks, whereas claiming it can simulate anxiety demands a far more rigorous validation process. Current practice systematically fails to meet these requirements, often treating statistical pattern matching as evidence of psychological phenomena. The same model output--endorsing "I am anxious"--requires different validation strategies depending on whether researchers claim to measure, characterize, simulate, or model psychological constructs. Moving forward requires developing computational analogues of psychological constructs and establishing clear, scalable standards of evidence rather than the uncritical application of human measurement tools.

Related papers

• HUMANLLM: Benchmarking and Reinforcing LLM Anthropomorphism via Human Cognitive Patterns [59.17423586203706]
  We present HUMANLLM, a framework treating psychological patterns as interacting causal forces.<n>We construct 244 patterns from 12,000 academic papers and synthesize 11,359 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.<n>Our dual-level checklists evaluate both individual pattern fidelity and emergent multi-pattern dynamics, achieving strong human alignment.
  arXiv  Detail & Related papers  (2026-01-15T08:56:53Z)
• Cognitive Foundations for Reasoning and Their Manifestation in LLMs [63.12951576410617]
  Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning.<n>We synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations.<n>We develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems.
  arXiv  Detail & Related papers  (2025-11-20T18:59:00Z)
• Large language models replicate and predict human cooperation across experiments in game theory [0.8166364251367626]
  How closely large language models mirror actual human decision-making remains poorly understood.<n>We develop a digital twin of game-theoretic experiments and introduce a systematic prompting and probing framework for machine-behavioral evaluation.<n>We find that Llama reproduces human cooperation patterns with high fidelity, capturing human deviations from rational choice theory.
  arXiv  Detail & Related papers  (2025-11-06T16:21:27Z)
• Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality [7.68863194266262]
  Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs)<n>In this study, we evaluate the reliability and validity of human psychometric tests for three constructs: sexism, racism, and morality.<n>We find that psychometric test scores do not align, and in some cases even negatively correlate with, model behavior in downstream tasks, indicating low ecological validity.
  arXiv  Detail & Related papers  (2025-10-13T10:43:49Z)
• A validity-guided workflow for robust large language model research in psychology [0.0]
  Large language models (LLMs) are rapidly being integrated into psychological research as research tools, evaluation targets, human simulators, and cognitive models.<n>These "measurement phantoms"--statistical artifacts masquerading as psychological phenomena--threaten the validity of a growing body of research.<n>Guided by the dual-validity framework that integrates psychometrics with causal inference, we present a six-stage workflow that scales validity requirements to research ambition.
  arXiv  Detail & Related papers  (2025-07-06T18:06:12Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Designing LLM-Agents with Personalities: A Psychometric Approach [0.47498241053872914]
  This research introduces a novel methodology for assigning quantifiable, controllable and psychometrically validated personalities to Agents. It seeks to overcome the constraints of human subject studies, proposing Agents as an accessible tool for social science inquiry.
  arXiv  Detail & Related papers  (2024-10-25T01:05:04Z)
• Assessment and manipulation of latent constructs in pre-trained language models using psychometric scales [4.805861461250903]
  We show how standard psychological questionnaires can be reformulated into natural language inference prompts.<n>We demonstrate, using a sample of 88 publicly available models, the existence of human-like mental health-related constructs.
  arXiv  Detail & Related papers  (2024-09-29T11:00:41Z)
• Evaluating Large Language Models with Psychometrics [59.821829073478376]
  This paper offers a comprehensive benchmark for quantifying psychological constructs of Large Language Models (LLMs)<n>Our work identifies five key psychological constructs -- personality, values, emotional intelligence, theory of mind, and self-efficacy -- assessed through a suite of 13 datasets.<n>We uncover significant discrepancies between LLMs' self-reported traits and their response patterns in real-world scenarios, revealing complexities in their behaviors.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale [2.50194939587674]
  dissertation: quantifying and mitigating sources of arbitiness in ML, randomness in uncertainty estimation and optimization algorithms, in order to achieve scalability without sacrificing reliability. dissertation serves as an empirical proof by example that research on reliable measurement for machine learning is intimately bound up with research in law and policy.
  arXiv  Detail & Related papers  (2024-06-13T19:29:37Z)
• CausalGym: Benchmarking causal interpretability methods on linguistic tasks [52.61917615039112]
  We use CausalGym to benchmark the ability of interpretability methods to causally affect model behaviour. We study the pythia models (14M--6.9B) and assess the causal efficacy of a wide range of interpretability methods. We find that DAS outperforms the other methods, and so we use it to study the learning trajectory of two difficult linguistic phenomena.
  arXiv  Detail & Related papers  (2024-02-19T21:35:56Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Position: AI Evaluation Should Learn from How We Test Humans [65.36614996495983]
  We argue that psychometrics, a theory originating in the 20th century for human assessment, could be a powerful solution to the challenges in today's AI evaluations.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.