Nicolas Baumard, Elise Huillery, Alexandre Hyafil, Lou Safra (2022). The cultural evolution of love in literary history. Nat Hum Behav, 6, 506–522 (2022). doi:10.1038/s41562-022-01292-z

Résumé :
Since the late nineteenth century, cultural historians have noted that the importance of love increased during the Medieval and Early Modern European period (a phenomenon that was once referred to as the emergence of ‘courtly love’). However, more recent works have shown a similar increase in Chinese, Arabic, Persian, Indian and Japanese cultures. Why such a convergent evolution in very different cultures? Using qualitative and quantitative approaches, we leverage literary history and build a database of ancient literary fiction for 19 geographical areas and 77 historical periods covering 3,800 years, from the Middle Bronze Age to the Early Modern period. We first confirm that romantic elements have increased in Eurasian literary fiction over the past millennium, and that similar increases also occurred earlier, in Ancient Greece, Rome and Classical India. We then explore the ecological determinants of this increase. Consistent with hypotheses from cultural history and behavioural ecology, we show that a higher level of economic development is strongly associated with a greater incidence of love in narrative fiction (our proxy for the importance of love in a culture). To further test the causal role of economic development, we used a difference-in-difference method that exploits exogenous regional variations in economic development resulting from the adoption of the heavy plough in medieval Europe. Finally, we used probabilistic generative models to reconstruct the latent evolution of love and to assess the respective role of cultural diffusion and economic development.

T.Y. Branch, Gloria Origgi & Tiffany Morisseau (2022). Why Trust Raoult? How Social Indicators Inform the Reputations of Experts. Social Epistemology.

Résumé :
The COVID-19 crisis has highlighted the considerable challenge of sourcing expertise and determining which experts to trust. Dissonant information fostered controversy in public discourse and encouraged an appeal to a wide range of social indicators of trustworthiness in order to decide whom to trust. We analyze public discourse on expertise by examining how social indicators inform the reputation of Dr. Didier Raoult, the French microbiologist who rose to international prominence as an early advocate for using hydroxychloroquine to treat COVID-19. To comprehend how these indicators came to inform his reputation, we outline Dr. Raoult’s rise to fame based on discourse about hydroxychloroquine. We then discuss why we trust in experts like scientist-practitioners. This is followed by an examination of how social indicators of trust like status, epistemic authority, influence and values have informed Dr. Raoult’s reputation. We conclude with recommendations for how to improve the selection and evaluation of social indicators of trust and reputations. Our aim here, instead of making a claim about the efficacy of hydroxychloroquine or Dr. Raoult’s reputation per se, is to outline through this case study how social indicators of trust inform reputation and the challenge they present to evaluating expertise.

Axel Cleeremans, Catherine Tallon-Baudry (2022). Consciousness matters: phenomenal experience has functional value. Neuroscience of Consciousness. Volume 2022, Issue 1, 2022, niac007, https://doi.org/10.1093/nc/niac007

Résumé :
‘Why would we do anything at all if the doing was not doing something to us?’ In other words: What is consciousness good for? Here, reversing classical views, according to many of which subjective experience is a mere epiphenomenon that affords no functional advantage, we propose that subject-level experience—‘What it feels like’—is endowed with intrinsic value, and it is precisely the value agents associate with their experiences that explains why they do certain things and avoid others. Because experiences have value and guide behaviour, consciousness has a function. Under this hypothesis of ‘phenomenal worthiness’, we argue that it is only in virtue of the fact that conscious agents ‘experience’ things and ‘care’ about those experiences that they are ‘motivated’ to act in certain ways and that they ‘prefer’ some states of affairs vs. others. Overviewing how the concept of value has been approached in decision-making, emotion research and consciousness research, we argue that phenomenal consciousness has intrinsic value and conclude that if this is indeed the case, then it must have a function. Phenomenal experience might act as a mental currency of sorts, which not only endows conscious mental states with intrinsic value but also makes it possible for conscious agents to compare vastly different experiences in a common subject-centred space—a feature that readily explains the fact that consciousness is ‘unified’. The phenomenal worthiness hypothesis, in turn, makes the ‘hard problem’ of consciousness more tractable, since it can then be reduced to a problem about function.

Alejandrina Cristia (2022). A systematic review suggests marked differences in the prevalence of infant-directed vocalization across groups of populations. Developmental Science, e13265.

Résumé :
Anthropological reports have long suggested that speaking to young children is very infrequent in certain populations (notably farming ones), which is in line with scattered quantitative studies. A systematic review was undertaken to use available literature in order to estimate the extent of population variation. Database searches, expert lists, and citation searches led to the discovery of 29 reports on the frequency of vocalizations directed to infants aged 24 months or younger, based on systematic observations of spontaneous activity in the infant's natural environment lasting at least 30 min in length. Together, these studies provide evidence on 1314 infants growing up in a range of communities (urban, foraging, farming). For populations located outside of North America, the frequency with which vocalization was directed to urban infants was much higher than that for rural infants (including both foraging and farming, medians = 12.6 vs. 3.6% of observations contained infant-directed vocalization behaviors). We benchmarked this effect against socio-economic status (SES) variation in the United States, which was much smaller. Infants in high SES American homes were spoken to only slightly more frequently than those in low SES homes (medians = 16.4 vs. 15.1% of observations contained infant-directed vocalization behaviors). Although published research represents a biased sample of the world's populations, these results invite further cross-population research to understand the causes and effects of such considerable population group differences.

Andrew Isaac Meso, Nikos Gekas, Pascal Mamassian and Guillaume S Masson (2022). Speed estimation for visual tracking emerges dynamically from nonlinear frequency interactions. eNeuro .013;9(3):ENEURO.0511-21.2022. doi: 10.1523/ENEURO.0511-21.2022.

Résumé :
Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probed human speed representation using ocular tracking of stimuli of different statistics. First, we compared ocular responses to single drifting gratings with a given set of spatiotemporal frequencies to broadband Motion Clouds of matched mean frequencies. Motion energy distributions of gratings and clouds are point-like, and ellipses oriented along the constant speed axis, respectively. Sampling frequency space, Motion Clouds elicited stronger, less variable and speed-tuned responses. Drifting gratings yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supra-linearly integrated along an iso-velocity line and sub-linearly integrated away from it. A dynamical probabilistic model characterises these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal “scale” axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organised along speed and scale axes.

Stefano Palminteri and Maël Lebreton (2022). The computational roots of positivity and confirmation biases in reinforcement-learning. Trends in Cognitive Science. doi:10.1016/j.tics.2022.04.005

Résumé :
Humans do not integrate new information objectively: outcomes carrying a positive affective value and evidence confirming one’s own prior belief are overweighed. Until recently, theoretical and empirical accounts of the positivity and confirmation biases assumed them to be specific to ‘high-level’ belief updates. We present evidence against this account. Learning rates in reinforcement learning (RL) tasks, estimated across different contexts and species, generally present the same characteristic asymmetry, suggesting that belief and value updating processes share key computational principles and distortions. This bias generates over-optimistic expectations about the probability of making the right choices and, consequently, generates over-optimistic reward expectations. We discuss the normative and neurobiological roots of these RL biases and their position within the greater picture of behavioral decision-making theories.

Rachid Riad, Marine Lunven, Hadrien Titeux et al. (2022). Predicting clinical scores in Huntington’s disease: a lightweight speech test. J Neurol . doi:10.1007/s00415-022-11148-1

Résumé :
Objectives
Using brief samples of speech recordings, we aimed at predicting, through machine learning, the clinical performance in Huntington’s Disease (HD), an inherited Neurodegenerative disease (NDD).

Methods
We collected and analyzed 126 samples of audio recordings of both forward and backward counting from 103 Huntington’s disease gene carriers [87 manifest and 16 premanifest; mean age 50.6 (SD 11.2), range (27–88) years] from three multicenter prospective studies in France and Belgium (MIG-HD (ClinicalTrials.gov NCT00190450); BIO-HD (ClinicalTrials.gov NCT00190450) and Repair-HD (ClinicalTrials.gov NCT00190450). We pre-registered all of our methods before running any analyses, in order to avoid inflated results. We automatically extracted 60 speech features from blindly annotated samples. We used machine learning models to combine multiple speech features in order to make predictions at individual levels of the clinical markers. We trained machine learning models on 86% of the samples, the remaining 14% constituted the independent test set. We combined speech features with demographics variables (age, sex, CAG repeats, and burden score) to predict cognitive, motor, and functional scores of the Unified Huntington’s disease rating scale. We provided correlation between speech variables and striatal volumes.

Results
Speech features combined with demographics allowed the prediction of the individual cognitive, motor, and functional scores with a relative error from 12.7 to 20.0% which is better than predictions using demographics and genetic information. Both mean and standard deviation of pause durations during backward recitation and clinical scores correlated with striatal atrophy (Spearman 0.6 and 0.5–0.6, respectively).
Rachid Riad, Hadrien Titeux, Laurie Lemoine, Justine Montillot, Agnes Sliwinski, Jennifer Bagnou, Xuan Cao, Anne-Catherine Bachoud-Levi, and Emmanuel Dupoux. 2022. A comparison study on patient-psychologist voice diarization. In Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022), pages 30–36, Dublin, Ireland. Association for Computational Linguistics. Conversations between a clinician and a patient, in natural conditions, are valuable sources of information for medical follow-up. The automatic analysis of these dialogues could help extract new language markers and speed up the clinicians’ reports. Yet, it is not clear which model is the most efficient to detect and identify the speaker turns, especially for individuals with speech disorders. Here, we proposed a split of the data that allows conducting a comparative evaluation of different diarization methods. We designed and trained end-to-end neural network architectures to directly tackle this task from the raw signal and evaluate each approach under the same metric. We also studied the effect of fine-tuning models to find the best performance. Experimental results are reported on naturalistic clinical conversations between Psychologists and Interviewees, at different stages of Huntington’s disease, displaying a large panel of speech disorders. We found out that our best end-to-end model achieved 19.5 % IER on the test set, compared to 23.6% achieved by the finetuning of the X-vector architecture. Finally, we observed that we could extract clinical markers directly from the automatic systems, highlighting the clinical relevance of our methods.

Mathieu Rita, Florian Strub, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux (2022). Population size and agent heterogeneity in simulated language emergence Proc. of ICLR

Résumé :
Populations have always been perceived as a structuring component for language to emerge and evolve: the larger the population, the more systematic the language. While this observation is widespread in the sociolinguistics literature, it has not been reproduced in computer simulations with deep reinforcement learning agents. In this paper, we thus aim to clarify this apparent contradiction. We thoughtfully explore emergent language properties by varying agent population size in the speaker-listener Lewis Game. After reproducing the experimental paradox, we challenge the simulation assumption that the agent community is homogeneous. To do so, we control the training speed or capacity of the agents by either asymetryzing the speaker-listeners properties or modifing each individuals within the population. We show that introducing such heterogeneities naturally sort out the initial paradox for larger communities start developing more systematic and structured languages.

Camila Scaff, Laila Fibla, & Alex Cristia (2022). Factors structuring lexical development in toddlers: The effects of parental education, language exposure, and age. Journal of Child Language, 1-21. doi:10.1017/S0305000922000186

Résumé :
A growing body of research suggests that individual variation in young children’s word comprehension (indexed by response times and accuracy) is structured and meaningful. In this paper, we assess how children’s word comprehension correlates with three factors: socio-economic status (indexed by maternal education), lingual status (based on language exposure), and age. We present results from 91 2- to 3-year-old children using a paired forced-choice task built on a child-friendly touch screen. Effects associated with maternal education and exposure to the tested language (French) were small, and they were greater for accuracy than response times. This pattern of results is compatible with an interpretation whereby the greatest effects of these two variables are on cumulative knowledge (vocabulary size) rather than on processing. Effects for age were larger and affected both accuracy and response times. Finally, response time variation did not mediate the effects of socio-economic status on accuracy or vice versa.

AGENDA

Agenda des événements du DEC

Retrouvez tous les événements organisés par le DEC sur l'agenda du département.

Un grand nombre de séminaires et de conférences données par les chercheur.ses du DEC ou par nos invité.e.s sont accessibles sur :

- notre chaîne youtube,

- le site des Savoirs de l'ENS,

- la chaîne youtube de l'école