THE SCIENCE
The measurement layer for the human side of AI — built on evidence.
Explicit measurement derived from language - not inferred by a model.
In the early 1990s, Dr. James W. Pennebaker began asking whether the words people use can predict their mental and physical health, their personalities, their social behaviors, and their cognitive processes. The answer was yes. That work became the foundation of Receptiviti's measurement.
WHAT PSYCHOLINGUISTIC MEASUREMENT IS
A transparent feature-extraction method - not a black box.
Psycholinguistic measurement maps language to validated psychological categories using explicit, interpretable dictionaries. Every word maps to one or more categories. Every score is computed the same way, every time. No sampling. No inference. No model involved in the scoring.
For an ML audience: think of it as a deterministic, theory-grounded feature-extraction layer - closer in spirit to a well-characterized scientific instrument than to a learned model. The same input yields the same output across runs, versions, and environments.
COGNITIVE
Analytical, thinking, cognitive load, certainty, insight
AFFECTIVE
Emotions, anxiety, sadness, affect
SOCIAL
rapport, affiliation, dominance, authenticity
WELLBEING
Distress signal, inward focus, wellbeing trajectory
INTERPERSONAL
Clout, status, over-reliance, dependency signals
STYLISTIC
Language style matching, formality, deception markers
200+ dimensions across cognitive, affective, social, interpersonal, and stylistic categories, traceable to published science.
LIWC
The most established computational text-analysis tool in psychology and the social sciences.
LIWC - Linguistic Inquiry and Word Count - was created by Dr. Pennebaker and refined over five major versions spanning three decades. The current version, LIWC-22, was developed and validated with per-dimension reliability and validity reporting.
Receptiviti holds the exclusive commercial rights to LIWC. Its measurement dimensions are the foundation of Receptiviti's API.
MEASUREMENT VS. INFERENCE
Why explicit measurement gives you what model inference cannot.
When a model infers psychological state, that inference is never observable - it influences user behavior and thinking without becoming a variable that can be cited, challenged, or audited. Receptiviti produces structured, explicit scores from language that can be.
LLM-BASED INFERENCE
X
X
X
X
Implicit and unauditable - psychological state influences model behavior without ever becoming an observable variable
Prompt-sensitive and unstable - the same conversation yields different inferences across model versions and prompt phrasings
Circular - asking the model being evaluated to assess its own impact on the user is not independent measurement
Not citable - inference from a prompted model cannot be traced to peer-reviewed science
RECEPTIVITI MEASUREMENT
Structured, observable variables - explicitly computed, logged, and auditable
Version-stable - derived from validated psycholinguistic frameworks, not from prompt-dependent inference. Same input, same score
Independent - derived from language, not from the model being evaluated. No circularity
Every dimension traceable to peer-reviewed science - citable in research, product decisions, and accountability conversations
The psycholinguistic frameworks behind Receptiviti's science are grounded in the same research tradition that has documented where LLM inference falls short. Demszky et al. (2023), Nature Reviews Psychology - co-authored by Dr. Pennebaker - argues LLMs are not yet ready for the most transformative psychological applications. Receptiviti's measurement layer is the answer that paper calls for: explicit, auditable scores derived from language, not from the model being evaluated.
RESEARCH
Published research, internal studies, and open questions.
Receptiviti's team and academic advisors publish peer-reviewed research on the psychological dimensions of language and human behavior. Receptiviti also conducts its own research and experiments - contributing to the questions AI safety, alignment, and evaluation teams are actively working on.
PUBLISHED: PNAS Nexus, 2024
Large language models display human-like social desirability biases in personality surveys
Salecha, Ireland et al.,2024. Large language models display human-like response biases when they infer they are being evaluated, with effects up to 1.20 human SD across GPT-4, Claude 3, Llama 3, and PaLM-2. Co-authored by Molly Ireland (Receptiviti).
PUBLISHED: npj Artificial Intelligence · 2026
PsychAdapter: adapting LLMs to reflect traits, personality, and mental health
Vu, Boyd, Eichstaedt et al., 2026. A lightweight LLM architectural modification that generates text reliably reflecting Big Five personality traits (87.3% accuracy) and mental health variables (96.7% accuracy). Co-authored by Ryan Boyd (UT Dallas), academic partner to Receptiviti.
PUBLISHED: Clinical Psychological Science, 2026
Replicability and validity of a new AI assessment of PTSD from patient language
Kjell, Ganesan, Boyd et al., 2026. AI-based psychological assessment from language, when grounded in validated measurement, produces replicable, clinically meaningful results across sequential evaluation with preregistered models.
Co-authored by Ryan Boyd (UT Dallas), academic advisor to Receptiviti.
PREPRINT: arXiv, May 2026, under review
When support escalates distress: regulation and escalation in LLM responses to venting and advice-seeking
Chi, Ganesan, Boyd, Ungar & Guntuku, 2026. Across 178,800 Reddit posts, LLM responses to venting simultaneously regulate and escalate distress — and the escalation is invisible to standard safety evaluations. Therapist personas reduce escalation; friend personas increase both. Measured using LIWC-22. Co-authored by Ryan Boyd (UT Dallas), academic advisor to Receptiviti.
PUBLISHED: npj Mental Health Research, 2025
Psychosocial dynamics of suicidality and nonsuicidal self-injury: a digital linguistic perspective
Entwistle, Hoemann, Nightingale & Boyd, 2025. Large-scale naturalistic study of the language dynamics surrounding suicidality and self-injury in 992 individuals with borderline personality disorder (66,786 posts). Co-authored by Ryan Boyd (UT Dallas), academic advisor to Receptiviti.
PUBLISHED: Perspectives on Psychological Science, 2026
Artificial intelligence and the psychology of human connection
Boyd & Markowitz, 2026. Introduces the MIRA model - a theoretical framework for when and how AI functions as a relational entity in human ecosystems. Language is the primary modality through which that relationship operates. Co-authored by Ryan Boyd (UT Dallas), academic advisor to Receptiviti.
INTERNAL STUDY
Providing measured user state as context to educational AI
GPT Study Mode: Providing psycholinguistic interaction-state signals produced a +4% improvement in overall educational effectiveness across 25 blinded evaluations. Largest gains in reasoning & scaffolding (+6.3%) and cognitive-load management (+5.9%).
SCOPED
Interaction state as a missing dimension in AI evaluation
The case for human-state signals as a first-class eval criterion alongside accuracy, helpfulness, and harmlessness.
34,000+
Independent peer-reviewed citations to the psycholinguistic science behind these dimensions
30+
Years of foundational research validated across health, personality, social behavior, deception, and clinical contexts
34,000+ citations.
Not a marketing number - a research record.
The psycholinguistic frameworks behind Receptiviti's dimensions are grounded in 34,000+ independent peer-reviewed citations spanning health psychology, clinical research, personality science, organizational behavior, forensic contexts, and deception research.
Every dimension is traceable to published science. That traceability is what makes these scores auditable — and what distinguishes them from inference which can't be cited or challenged.
The foundational methods paper — Tausczik & Pennebaker (2010), "The Psychological Meaning of Words," Journal of Language and Social Psychology — is among the most cited articles in its field.