Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness (2025)

Abstract: Cognitive decline often surfaces in language years before diagnosis. It is frequently non-experts, such as those closest to the patient, who first sense a change and raise concern. As LLMs become integrated into daily communication and used over prolonged periods, it may even be an LLM that notices something is off. But what exactly do they notice–and should be noticing–when making that judgment? This paper investigates how dementia is perceived through language by non-experts. We presented transcribed picture descriptions to non-expert humans and LLMs, asking them to intuitively judge whether each text was produced by someone healthy or with dementia. We introduce an explainable method that uses LLMs to extract high-level, expert-guided features representing these picture descriptions, and use logistic regression to model human and LLM perceptions and compare with clinical diagnoses. Our analysis reveals that human perception of dementia is inconsistent and relies on a narrow, and sometimes misleading, set of cues. LLMs, by contrast, draw on a richer, more nuanced feature set that aligns more closely with clinical patterns. Still, both groups show a tendency toward false negatives, frequently overlooking dementia cases. Through our interpretable framework and the insights it provides, we hope to help non-experts better recognize the linguistic signs that matter.

## introduction dementia is a progressive neurodegenerative condition caused by various underlying pathologies, most commonly alzheimer's disease (ad). tens of millions are currently living with the disease worldwide, with this figure expected to double with each passing generation @xcite . early diagnosis is critical for maximizing the effectiveness of both symptomatic and disease-modifying interventions, especially during mild cognitive impairment (mci), a transitional stage between normal aging and dementia, where cognitive deficits are present but do not yet impair daily functioning @xcite @xcite . subtle language dysfunctions have long been recognized as early signs of cognitive decline, making linguistic signals valuable for early detection @xcite @xcite @xcite . this has spurred hundreds of studies applying natural language processing (nlp) methods to transcribed cognitive assessments @xcite , primarily to extract linguistic markers (disfluencies, rephrasing, part-of-speech ratios, etc; @xcite @xcite and to detect cognitive decline. in reality, however, the initial "detection" of dementia symptoms rarely begins with a clinician or structured cognitive assessments. instead, it is often the individuals themselves or their close environment, who first notice signs of cognitive decline and initiates a medical evaluation @xcite @xcite . this highlights the importance of understanding how dementia is perceived by non-experts, i.e., those who are not yet patients, caregivers, or clinicians. identifying which linguistic behaviors are perceived as related to dementia can reveal where public intuition aligns with clinical insight, and where it falls short and can benefit from further education. additionally, nowadays, it is not just humans who can track linguistic changes and raise concerns. adults over 55, an age group at increased risk for cognitive decline @xcite , now regularly use large language models (llms) @xcite . given these models' extensive world knowledge and continuous linguistic signal from the user, one can imagine llms flagging subtle shifts in language to indicate early signs of cognitive im-pairment. it is therefore important to understand which cues drive llms' perceptions. our paper explores the concept of dementia perception by non-experts: which cues lead humans and llms to perceive someone as cognitively impaired based on their language. to study this, we collected perceptions from 27 humans and 3 llms-llama 3.1 @xcite , @xcite , and gemini-1.5-pro @xcite -who were presented with 514 transcriptions of a spontaneous speech task (cookie theft picture descriptions from the pitt corpus; @xcite ; further detailed in section 3.1). humans and llms were asked to give their best intuitive judgment as to whether each text was produced by someone healthy or cognitively impaired. throughout this work, we use "humans" to refer to non-expert humans, and "clinicians" or "clinical diagnosis" to refer to expert medical judgment. to analyze perceptions, we propose a 4-step explainable method, inspired by studies such as @xcite , @xcite , and @xcite : (1) design intuitive, human-centered features in consultation with domain experts; (2) extract these features using an llm as an annotator, with quality control; (3) train inherently simple and interpretable logistic regression models to predict perceptions and clinical diagnosis; and (4) analyze coefficients to identify the linguistic cues influencing how dementia is perceived. sections 4 to 7 outline the full method and results. our pipeline is rooted in high-level features that capture nuanced aspects of picture descriptions. these 38 binary features ( traditionally, extracting such high-level features would require extensive feature engineering and a dedicated algorithmic logic for each feature, posing a significant scalability challenge. manual annotation is also impractical, as our dataset includes 38 features for hundreds of transcriptions, amounting to over 19,000 annotations. to address this, we use llms as annotators and validate their output with statistical tests to ensure quality comparable to human annotations @xcite . our analysis reveals that human judgments are highly inconsistent and show a tendency toward false negatives, i.e., labeling clinically diagnoses cases as healthy. humans appear to rely on a narrow set of simple, objective features, and sometimes interpret cues in ways that contradict clinical patterns. notably, when asked to describe which linguistic cues shaped their perception, they often reported features that align with our predefined set, reinforcing its validity. however, these self-reports do not match the actual decision patterns, suggesting that people do not rely on what they think they do. llms, while also prone to false negatives, appear to rely on a much richer feature set, including sentiment-related cues. this highlights their surprisingly nuanced use of language in this task. to summarize, our contributions are as follows: (1) we propose an interpretable four-step method building on llms-as-annotators with statistical significance quality test for analyzing the linguistic features driving dementia perception; (2) we analyze the perceptions of non-experts (27 human annotators and three llms) and identify the linguistic behaviors they associate with dementia; (3) we examine the extent to which these behaviors overlap with features associated with clinical diagnosis and analyse human and llm misperceptions. we hope this study lays a foundation for future research on dementia perception from the perspective of all stakeholders. by shedding light on the linguistic cues that non-experts and llms rely on when assessing cognitive decline, we believe our findings can contribute to broader public awareness and support earlier detection. finally, we aspire to encourage the interpretable and statistically grounded use of llms in sensitive domains, fostering interdisciplinary trust and real-world impact. these datasets have been used to extract linguistic markers such as syntactic complexity @xcite , idea density @xcite , topic structure @xcite , and meta-semantic terms, i.e., words expressing emotion or opinion @xcite . disfluencies, pauses, and context shifts are other markers known to significantly influence model predictions @xcite @xcite . markers are then used to train dementia detection algorithms, using traditional classifiers such as svms and random forests @xcite @xcite or transformers @xcite @xcite . recently, llms have also been leveraged for feature extraction or embedding-based representations supporting dementia detection @xcite @xcite @xcite @xcite . despite these advances, automated detection has yet to be adopted in clinical settings, and early diagnosis still relies on selfreports from patients or their environment. ## conclusions this study explores how non-expert humans and llms perceive dementia in transcribed picture descriptions, and how their perceptions align with clinical diagnoses. we present an inherently interpretable method using high-level, expert-guided features annotated by gpt-4o, followed by logistic regression and coefficient analysis. ultimately, our work highlights: (1) the inherent difficulty of this task, both in perceiving dementia through text alone and in modeling such a complex phenomenon; (2) the importance of educating both humans and llms to recognize a broader range of linguistic signals to improve early detection; and