Sophie Bavard, Aldo Rustichini, Stefano Palminteri (2021). Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning.Sciences Advances , 7, 14, eabe0340, 10.1126/sciadv.abe0340.

Résumé :
Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: Decreasing task difficulty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of participants performing variants of an RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing task difficulty. Last, we propose a range-adapting model and show that it is able to parsimoniously capture all the behavioral results.

Diego Candia-Rivera, Jitka Annen, Olivia Gosseries, Charlotte Martial, Aurore Thibaut, Steven Laureys, Catherine Tallon-Baudry (2021). Neural responses to heartbeats detect residual signs of consciousness during resting state in post-comatose patients. Journal of Neuroscience. DOI:10.1523/JNEUROSCI.1740-20.2021

Résumé :
The neural monitoring of visceral inputs might play a role in first-person perspective, i.e. the unified viewpoint of subjective experience. In healthy participants, how the brain responds to heartbeats, measured as the heartbeat-evoked response (HER), correlates with perceptual, bodily, and self-consciousness. Here we show that HERs in resting-state EEG data distinguishes between post-comatose male and female human patients (n=68, split into training and validation samples) suffering from the unresponsive wakefulness syndrome and patients in minimally conscious state with high accuracy (random forest classifier, 87% accuracy, 96% sensitivity and 50% specificity in the validation sample). Random EEG segments not locked to heartbeats were useful to predict (un)consciousness, but HERs were more accurate, indicating that HERs provide specific information on consciousness. HERs also led to more accurate classification than heart rate variability. HER-based consciousness scores correlate with glucose metabolism in the default mode network node located in the right superior temporal sulcus, as well as with the right ventral occipito-temporal cortex. These results were obtained when consciousness was inferred from brain glucose metabolism measured with Positron Emission Topography. HERs reflected the consciousness diagnosis based on brain metabolism better than the consciousness diagnosis based on behavior (Coma Recovery Scale-Revised, 77% validation accuracy). HERs thus seem to capture a capacity for consciousness that does not necessarily translate into intentional overt behavior. These results confirm the role of HERs in consciousness, offer new leads for future bedside testing, and highlight the importance of defining consciousness and its neural mechanisms independently from behavior.

Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni (2021). Communicating artificial neural networks develop efficient color-naming systems. Proceedings of the National Academy of Sciences Mar 2021, 118 (12) e2016569118; 10.1073/pnas.2016569118

Résumé :
Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. Focusing on the well-studied color domain, we show that artificial neural networks trained with deep-learning techniques to play a discrimination game develop communication systems whose distribution on the accuracy/complexity plane closely matches that of human languages. The observed variation among emergent color-naming systems is explained by different degrees of discriminative need, of the sort that might also characterize different human communities. Like human languages, emergent systems show a preference for relatively low-complexity solutions, even at the cost of imperfect communication. We demonstrate next that the nature of the emergent systems crucially depends on communication being discrete (as is human word usage). When continuous message passing is allowed, emergent systems become more complex and eventually less efficient. Our study suggests that efficient semantic categorization is a general property of discrete communication systems, not limited to human language. It suggests moreover that it is exactly the discrete nature of such systems that, acting as a bottleneck, pushes them toward low complexity and optimal efficiency.

Diego Candia-Rivera, Jitka Annen, Olivia Gosseries, Charlotte Martial, Aurore Thibaut, Steven Laureys, Catherine Tallon-Baudry (2021). Neural responses to heartbeats detect residual signs of consciousness during resting state in post-comatose patients. Journal of Neuroscience. DOI:10.1523/JNEUROSCI.1740-20.2021

Charles Findling, Valentin Wyart (2021). Computation noise in human learning and decision-making: origin, impact, function. Current Opinion in Behavioral Sciences, 38, 124-132, ISSN 2352-1546, 10.1016/j.cobeha.2021.02.018.

Résumé
Making sense of uncertain and volatile environments, a cognitive process modeled across domains as statistical inference, constitutes a difficult yet ubiquitous challenge for human intelligence. Beside sensory errors and exploratory choices, recent research has identified the limited computational precision of cognitive inference as a surprisingly large contributor to the variability and suboptimality of perceptual and reward-guided decisions made under uncertainty. This focused review discusses the theoretical and experimental evidence scattered across psychology and neuroscience which, taken together, provides key insights into the origin, impact and function of this ‘computation noise’ for learning and decision-making. Moving beyond the classical description of internal noise as performance-limiting constraint on neural function and cognition, we outline the possible emergent benefits of computation noise for adaptive behavior in adverse conditions and highlight open questions for future research.

Jean-Remi King and Valentin Wyart (2021). The Human Brain Encodes a Chronicle of Visual Events at each Instant of Time thanks to the Multiplexing of Traveling Waves. Journal of Neuroscience, JN-RM-2098-20; DOI: https://doi.org/10.1523/JNEUROSCI.2098-20.2021

Résumé :
The human brain continuously processes streams of visual input. Yet, a single image typically triggers neural responses that extend beyond one second. To understand how the brain encodes and maintains successive images, we analyzed with electro-encephalography the brain activity of human subjects of either sex, while they watched ∼5,000 visual stimuli presented within fast sequences. First, we confirm that each stimulus can be decoded from brain activity for ∼1 sec, and demonstrate that the brain simultaneously represents multiple images at each time instant. Second, we source-localize the corresponding brain responses in the expected visual hierarchy, and show that distinct brain regions represent different snapshots of past stimulations. Third, we propose a simple framework to further characterize the dynamical system of these traveling waves. Our results show that a chain of neural circuits, which consist of (i) a hidden maintenance mechanism, and (ii) an observable update mechanism, accounts for the dynamics of macroscopic brain representations elicited by successive visual stimuli. Together, these results detail a simple architecture explaining how successive visual events and their respective timings can be simultaneously represented in brain activity.

Alan L. F. Lee, Vincent de Gardelle & Pascal Mamassian (2021). Global visual confidence. Psychon Bull Rev. 10.3758/s13423-020-01869-7

Résumé :
Visual confidence is the observers’ estimate of their precision in one single perceptual decision. Ultimately, however, observers often need to judge their confidence over a task in general rather than merely on one single decision. Here, we measured the global confidence acquired across multiple perceptual decisions. Participants performed a dual task on two series of oriented stimuli. The perceptual task was an orientation-discrimination judgment. The metacognitive task was a global confidence judgment: observers chose the series for which they felt they had performed better in the perceptual task. We found that choice accuracy in global confidence judgments improved as the number of items in the series increased, regardless of whether the global confidence judgment was made before (prospective) or after (retrospective) the perceptual decisions. This result is evidence that global confidence judgment was based on an integration of confidence information across multiple perceptual decisions rather than on a single one. Furthermore, we found a tendency for global confidence choices to be influenced by response times, and more so for recent perceptual decisions than earlier ones in the series of stimuli. Using model comparison, we found that global confidence is well described as a combination of noisy estimates of sensory evidence and position-weighted response-time evidence. In summary, humans can integrate information across multiple decisions to estimate global confidence, but this integration is not optimal, in particular because of biases in the use of response-time information.

Niels Lettinga, Hugo Mell, Yann Algan, Pierre Jacquet, & Coralie Chevallier (2021). Childhood environmental adversity is not linked to lower levels of cooperative behaviour in economic games. Evolutionary Human Sciences, 3, e29, 1-22, 10.1017/ehs.2021.21

Résumé :
Cooperation is a universal phenomenon, it is present in all human cultures from hunter–gatherers to industrialised societies, and it constitutes a fundamental aspect of social relationships. There is, however, variability in the amount of resources people invest in cooperative activities. Recent findings indicate that this variability may be partly explained as a contextually appropriate response to environmental conditions. Specifically, adverse environments seem to be associated with less cooperation and recent findings suggest that this effect is partly mediated by differences in individuals’ life-history strategy. In this paper, we set out to replicate and extend these findings by measuring actual cooperative behaviour in three economic games – a Dictator game, a Trust game and a Public Goods game – on a nationally representative sample of 612 people. Although we found that the cooperation and life-history strategy latent variables were adequately captured by the models, the hypothesised relationship between childhood environmental adversity and adult cooperation and the mediation effect by life-history strategy were not found.

Barbara Pavlek, James Winters, Olivier Morin (2021). Standards and quantification of coin iconography: possibilities and challenges Digital Scholarship in the Humanities, fqab030, 10.1093/llc/fqab030

Résumé :
The use of digital technologies and big data in the humanities and social sciences provided many opportunities for cultural heritage management and research, enabling data sharing and interdisciplinary collaborations. These developments increased the need for standardized data formats. General and domain-specific standards for describing and classifying cultural data, based on linked data principles, are developed to support increasingly numerous digital collections. However, the existing standards do not fully address the particular challenges concerning the standardized descriptions of images. Here we focus on ancient coins, an official image-bearing medium. We present current approaches to coin iconography, including the application of statistical measures to infer patterns in the use of images for communication. We discuss the importance of consistent, standardized data for quantitative research, and propose a generalized approach, focused on basic concepts and limiting the level of detail for the sake of simplicity, interoperability, and compatibility with statistical methods, as a necessary first step towards creating reliable iconographic standards.

Thomas Schatz, Naomi H. Feldman, Sharon Goldwater, Xuan-Nga Cao, Emmanuel Dupoux (2021). Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input. Proceedings of the National Academy of Sciences. 118 (7) e2001844118; DOI: 10.1073/pnas.2001844118

Résumé :
Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get better at distinguishing English and [l], as in “rock” vs. “lock,” relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories—like and [l] in English—through a statistical clustering mechanism dubbed “distributional learning.” The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here, we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning, as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that, contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants’ attunement.

Aurélien Weiss, Valérian Chambon, Junsoek K. Lee, Jan Drugowitsch, Valentin Wyart (2021). Interacting with volatile environments stabilizes hidden-state inference and its brain signatures. Nat Commun. 12, 2228. 10.1038/s41467-021-22396-6

Résumé :
Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

Nicolai Wolpert, Catherine Tallon-Baudry (2021). Coupling between the phase of a neural oscillation or bodily rhythm with behavior: evaluation of different statistical procedures. NeuroImage. 118050, ISSN 1053-8119, 10.1016/j.neuroimage.2021.118050.

Résumé :
Growing experimental evidence points at relationships between the phase of a cortical or bodily oscillation and behavior, using various circular statistical tests. Here, we systematically compare the performance (sensitivity, False Positive rate) of four circular statistical tests (some commonly used, i.e. Phase Opposition Sum, Circular Logistic Regression, others less common, i.e., Watson test, Modulation Index). We created semi-artificial datasets mimicking real two-alternative forced choice experiments with 30 participants, where we imposed a link between a simulated binary behavioral outcome with the phase of a physiological oscillation. We systematically varied the strength of phase-outcome coupling, the coupling mode (1:1 to 4:1), the overall number of trials and the relative number of trials in the two outcome conditions. We evaluated different strategies to estimate phase-outcome coupling chance level, as well as significance at the individual or group level. The results show that the Watson test, although seldom used in the experimental literature, is an excellent first intention test, with a good sensitivity and low False Positive rate, some sensitivity to 2:1 coupling mode and low computational load. Modulation Index, initially designed for continuous variables but that we find useful to estimate coupling between phase and a binary outcome, should be preferred if coupling mode is higher than 2:1. Phase Opposition Sum, coupled with a resampling procedure, is the only test retaining a good sensitivity in the case of a large unbalance in the number of occurrences of the two behavioral outcomes.

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