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Abstract Background Dementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia. Methods Multiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50 years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4–7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or “Likely Dementia” prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the “Likely Dementia” cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1–9, between 2002 and 2019, 7840 participants at baseline). Results Our algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722–0.787] to 0.830 [0.800–0.861]). “Likely Dementia” status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy. Conclusions Machine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking. International audience

The success of human cooperation crucially depends on mechanisms enabling individuals to detect unreliability in their conspecifics. Yet, how such epistemic vigilance is achieved from naturalistic sensory inputs remains unclear. Here we show that listeners’ perceptions of the certainty and honesty of other speakers from their speech are based on a common prosodic signature. Using a data-driven method, we separately decode the prosodic features driving listeners’ perceptions of a speaker’s certainty and honesty across pitch, duration and loudness. We find that these two kinds of judgments rely on a common prosodic signature that is perceived independently from individuals’ conceptual knowledge and native language. Finally, we show that listeners extract this prosodic signature automatically, and that this impacts the way they memorize spoken words. These findings shed light on a unique auditory adaptation that enables human listeners to quickly detect and react to unreliability during linguistic interactions. It remains unclear whether and how listeners can infer speakers’ reliability from the sound of their voice. Here, the authors show that listeners from several languages rely on a common prosodic signature to infer whether speakers are lying or doubting.

AbstractYoung children can exploit the syntactic context of a novel word to narrow down its probable meaning. But how do they learn which contexts are linked to which semantic features in the first place? We investigate if 3‐ to 4‐year‐old children (n = 60) can learn about a syntactic context from tracking its use with only a few familiar words. After watching a 5‐min training video in which a novel function word (i.e., ‘ko’) replaced either personal pronouns or articles, children were able to infer semantic properties for novel words co‐occurring with the newly learned function word (i.e., objects vs. actions). These findings implicate a mechanism by which a distributional analysis, associated with a small vocabulary of known words, could be sufficient to identify some properties associated with specific syntactic contexts.

International audience; Sprouse and Almeida (2017) provide a first systematic investigation of the sensitivity of four acceptability judgment tasks. In this project, we build on these results by decomposing those tasks into three constituent task features (single versus joint presentation, number of response options, and use of response labels), and explore the consequences of those task features on the sensitivity of acceptability judgment experiments. We present 6 additional experiments (for a total of 10) designed to probe the effect of those task features on sensitivity, both independently and in combination. Our results suggest three notable conclusions: (i) there is a clear advantage to joint presentation of theoretically-related sentence types, regardless of the type of response scale used in the experiment; (ii) tasks involving a continuous slider (which have an infinite number of response options, and few labels) offer good sensitivity, while relying solely on spatial reasoning rather than numeric reasoning; and (iii) there are a number of subtle interactions among the three task features that may warrant further investigation. We discuss the potential benefits and concerns of each of these features in detail, along with the relevance of these findings for deciding how to investigate both simple and higher-order acceptability contrasts.

International audience; Some sentences are globally informative but still deviant because they contain an expression that is redundant relative in its local environment, as in: #Ann is staying in Paris and she in France. In this case, the second conjunct seems to be evaluated after the first. In several frameworks, order of evaluation follows from linear order. Focusing on nominal modification in Japanese and Korean, Ingason (2016) argued that the correct notion of order is hierarchical, not linear, and he proposed that structurally higher elements are evaluated before lower elements, a conclusion that might dovetail with Romoli and Mandelkern's (2017) proposal for postposed if-clauses in conditionals. While agreeing with Ingason's conclusion that for nominal modification evaluation order is hierarchical, we amend his theory by considering sentences with several pre-or post-nominal modifiers (in English, Mandarin and French). We argue that a Noun Phrase is evaluated 'inside out', starting with the head noun and adding modifiers by order of structural proximity to the head-with the result that higher modifiers are evaluated later than lower modifiers (against the 'higher is earlier' view). We explore in an Appendix how this finding can be integrated with existing accounts of evaluation order for other constituent types, such as conjunction.

Vector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose and empirically analyze the classic arithmetic word analogy test, to motivate two new metrics that address the issues with the standard test, and which distinguish between class-wise offset concentration (similar directions between pairs of words drawn from different broad classes, such as France-London, China-Ottawa,. . .) and pairing consistency (the existence of a regular transformation between correctly-matched pairs such as France:Paris::China:Beijing). We show that, while the standard analogy test is flawed, several popular word embeddings do nevertheless encode linguistic regularities. International audience