Hernan Anlló, Katsumi Watanabe, Jérôme Sackur, Vincent de Gardelle (2022). Effects of false statements on visual perception hinge on social suggestibility. J Exp Psychol Hum Percept Perform. doi: 10.1037/xhp0001024.

Résumé :
Verbal hints can bias perceptual decision-making, even when the information they provide is false. What makes individuals more or less susceptible to such influences, however, remains unclear. Here, we inquire whether suggestibility to social influence, a high-level trait measured by a standard suggestibility scale, could predict changes in perceptual judgments. We asked naive participants to indicate the dominant color in a series of stimuli after giving them a short, false verbal statement about which color would likely dominate. We found that this statement biased participants' perceptual judgments of the dominant color, as shown by a correlated shift of their discrimination performance, confidence judgments, and response times. Crucially, this effect was more pronounced in participants with higher levels of susceptibility to social influence. Together, these results indicate that social suggestibility can determine how much simple (albeit false) verbal hints influence perceptual judgments.

Alexis Dubreuil, Adrian Valente, Manuel Beiran, et al. (2022). The role of population structure in computations through neural dynamics. Nat Neurosci, 25, 783–794 (2022). doi:10.1038/s41593-022-01088-4

Résumé:
Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally complementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple subpopulations. Our analyses revealed that such a subpopulation structure enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, for inactivation experiments and for the implication of different neurons in multi-tasking.

Léo Fitouchi, Jean-Baptiste André & Nicolas Baumard (2022). From supernatural punishment to big gods to puritanical religions: clarifying explanatory targets in the rise of moralizing religions. Religion, Brain & Behavior, doi: 10.1080/2153599X.2022.2065352

Elie Grinfeder, Christian Lorenzi, Sylvain Haupert, Jérôme Sueur (2022). What Do We Mean by “Soundscape”? A Functional Description. Front. Ecol. Evol., Sec.Population, Community, and Ecosystem Dynamics. doi:10.3389/fevo.2022.894232

Résumé :
The concept of soundscape was originally coined to study the relationship between humans and their sonic environment. Since then, several definitions of soundscapes have been proposed based on musical, acoustical and ecological perspectives. However, the causal mechanisms that underlie soundscapes have often been overlooked. As a consequence, the term “soundscape” is frequently used in an ambiguous way, alternatively pointing to objective realities or subjective percepts. Through an interdisciplinary review, we identified the main biotic and abiotic factors that condition non-anthropogenic terrestrial soundscapes. A source-filter approach was used to describe sound sources, sound propagation phenomena and receiver’s characteristics. Interdisciplinary information was cross-referenced in order to define relationships between factors, sound sources and filters. Those relationships and the associated references were organized into a functional block diagram. This representation was used to question the different uses and meanings of the soundscape concept found in the literature. Three separate categories were then suggested: distal soundscape, proximal soundscape and perceptual soundscape. Finally, practical examples of these different categories were described, in relation to the diagram. This new systemic approach to soundscapes should help ecoacousticians, bioacousticians, psychoacousticians and environmental managers to better understand soundscapes and protect natural areas in a more significant way.

Enzo Laurenti, Nils Bourgon, Farah Benamara, Alda Mari, Véronique Moriceau, et al.. Give me your Intentions, I'll Predict Our Actions: A Two-level Classification of Speech Acts for Crisis Management in Social Media. 13th Conference on Language Resources and Evaluation (LREC 2022), European Language Resources Association (ELRA), Jun 2022, Marseille, France. pp.4333-4343.

Résumé:
Discovered by (Austin,1962) and extensively promoted by (Searle, 1975), speech acts (SA) have been the object of extensive discussion in the philosophical and the linguistic literature, as well as in computational linguistics where the detection of SA have shown to be an important step in many down stream NLP applications. In this paper, we attempt to measure for the first time the role of SA on urgency detection in tweets, focusing on natural disasters. Indeed, SA are particularly relevant to identify intentions, desires, plans and preferences towards action, providing therefore actionable information that will help to set priorities for the human teams and decide appropriate rescue actions. To this end, we come up here with four main contributions: (1) A two-layer annotation scheme of SA both at the tweet and subtweet levels, (2) A new French dataset of 6,669 tweets annotated for both urgency and SA, (3) An in-depth analysis of the annotation campaign, highlighting the correlation between SA and urgency categories, and (4) A set of deep learning experiments to detect SA in a crisis corpus. Our results show that SA are correlated with urgency which is a first important step towards SA-aware NLP-based crisis management on social media.

Maureen de Seyssel, Guillaume Wisniewski, Emmanuel Dupoux (2022). Is the Language Familiarity Effect gradual ? A computational modelling approach. Proceedings of the Annual Meeting of the Cognitive Science Society, 44.

Résumé :
According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited number of language pairs and their results only show the presence of the effect without yielding a gradual measure that may vary across language pairs. In this work, we show that the computational model of LFE introduced by Thorburn, Feldman, and Schatz (2019) can address these two limitations. In a first experiment, we attest to this model's capacity to obtain a gradual measure of the LFE by replicating behavioural findings on native and accented speech. In a second experiment, we evaluate LFE on a large number of language pairs, including many which have never been tested on humans. We show that the effect is replicated across a wide array of languages, providing further evidence of its universality. Building on the gradual measure of LFE, we also show that languages belonging to the same family yield smaller scores, supporting the idea of an effect of language distance on LFE.

Andrés Soria-Ruiz, Mora Maldonado, Isidora Stojanovic (2022). Good and Ought in Argumentation: COVID-19 as a Case Study. In: Steve Oswald, Marcin Lewiński, Sara Greco, Serena Villata, (eds).The Pandemic of Argumentation. Argumentation Library, vol 43. Springer, Cham. doi:10.1007/978-3-030-91017-4_3

Résumé :
The present chapter concerns arguments whose conclusions take the form of a prescription such as you ought to do such-and-such, which have driven much public discussion and policy since the beginning of the COVID-19 pandemic. We aim to tackle a hitherto under-explored characteristic of many such normative arguments, namely, the relationship between evaluative and deontic propositions, depending on whether they occur as premises or conclusions in such arguments. In order to investigate how willing people are to argue from what is good to what one ought to do, and the other way round, we conducted an Inferential Judgment Experiment. Participants were presented with arguments involving deontic and evaluative propositions, and had to judge whether they could infer conclusion from premise. The stimuli that we used are tightly related to the argumentation surrounding the pandemic, regarding the measures of preventing the spread of COVID-19. The results of the study show that there is a robust inferential connection between evaluatives and deontics, but at the same time, a significant asymmetry as well. We explore several theoretical approaches to the relationship between the deontic and the evaluative realm, and test their predictions against the results of our study.

Henri Vandendriessche, Ammel Demmou, Sophie Bavard, Julien Yadak, Cédric Lemogne, Thomas Mauras, Stefano Palminteri (2022). Contextual influence of reinforcement learning performance of depression: Evidence for a negativity bias? Psychological Medicine, 1-11. doi:10.1017/S0033291722001593

Résumé :
Backgrounds: Value-based decision-making impairment in depression is a complex phenomenon: while some studies did find evidence of blunted reward learning and reward-related signals in the brain, others indicate no effect. Here we test whether such reward sensitivity deficits are dependent on the overall value of the decision problem.

Methods: We used a two-armed bandit task with two different contexts: one 'rich', one 'poor' where both options were associated with an overall positive, negative expected value, respectively. We tested patients (N = 30) undergoing a major depressive episode and age, gender and socio-economically matched controls (N = 26). Learning performance followed by a transfer phase, without feedback, were analyzed to distangle between a decision or a value-update process mechanism. Finally, we used computational model simulation and fitting to link behavioral patterns to learning biases. Results: Control subjects showed similar learning performance in the 'rich' and the 'poor' contexts, while patients displayed reduced learning in the 'poor' context. Analysis of the transfer phase showed that the context-dependent impairment in patients generalized, suggesting that the effect of depression has to be traced to the outcome encoding. Computational model-based results showed that patients displayed a higher learning rate for negative compared to positive outcomes (the opposite was true in controls).

Conclusions: Our results illustrate that reinforcement learning performances in depression depend on the value of the context. We show that depressive patients have a specific trouble in contexts with an overall negative state value, which in our task is consistent with a negativity bias at the learning rates level.

Camille Michèle Williams, Hugo Peyre, Roberto Toro, Franck Ramus (2022). Comparing brain asymmetries independently of brain size. NeuroImage, 254, 119118, ISSN 1053-8119, doi:10.1016/j.neuroimage.2022.119118.

Résumé :
Studies examining cerebral asymmetries typically divide the l-R Measure (e.g., Left–Right Volume) by the L + R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L + R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries in the UK Biobank (N = 40, 028). We used 306 global and regional brain measures provided by the UK Biobank. Global gray and white matter volumes were taken from Freesurfer ASEG, subcortical gray matter volumes from Freesurfer ASEG and subsegmentation, cortical gray matter volumes, mean thicknesses, and surface areas from the Destrieux atlas applied on T1-and T2-weighted images, cerebellar gray matter volumes from FAST FSL, and regional white matter volumes from Freesurfer ASEG. We analyzed the extent to which the L + R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and l-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L + R Measure, the TCM, and the l-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13–42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.

AGENDA

Evénements grand public à venir à l'ENS-PSL

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