Mélusine Boon-Falleur, Nicolas Baumard, Jean-Baptiste André (2021). Risk-seeking or impatient? Disentangling variance and time in hazardous behaviors. Evolution and Human Behavior. DOI: https://doi.org/10.1016/j.evolhumbehav.2021.04.001

Résumé :
Individual observations of risky behaviors present a paradox: individuals who take the most risks in terms of hazards (smoking, speeding, risky sexual behaviors) are also less likely to take risks when it comes to innovation, financial risks or entrepreneurship. Existing theories of risk-preferences do not explain these patterns. From a simple model, we argue that many decisions involving risk have a temporal dimension, and that this dimension is often the main determinant of individual choices. In many real life instances, risk taking amounts to damaging the individual's capital (whether embodied capital, financial capital, social reputation, etc.), which would affect her over a long period of time after the risky decision. In evolutionary terms, the marginal cost of this type of risky behavior depends on the relative importance of the future in the individual's fitness (e.g. her time horizon). Individuals with short time horizons will give less importance to a degradation of their capital because this degradation will be paid effectively for a shorter period of time. This approach explains patterns of behaviors observed across socio-economic groups and puts forward new approaches to prevent hazardous behaviors such as smoking.

Brian Buccola, Jeremy Kuhn & David Nicolas (2021). Groups versus covers revisited: Structured pluralities and symmetric readings. Nat Lang Semantics . DOI: https://doi.org/10.1007/s11050-021-09179-x

Résumé :
A number of natural language constructions seem to provide access to structured pluralities — that is, pluralities of pluralities. A body of semantic work has debated how to model this additional structure and the extent to which it depends on pragmatics. In this article, after controlling for the distinction between ambiguity and underspecification, we present new data showing that structured pluralities are sometimes but not always available, depending on the form of the plural noun phrase used. We show that these results challenge two longstanding theories of plurality. We sketch two different ways to account for these data and describe some of the diverging predictions they make.

Nikos Gekas, Pascal Mamassian (2021). Adaptation to one perceived motion direction can generate multiple velocity aftereffects. Journal of Vision, Vol.21, 17. doi:https://doi.org/10.1167/jov.21.5.17

Résumé :
Sensory adaptation is a useful tool to identify the links between perceptual effects and neural mechanisms. Even though motion adaptation is one of the earliest and most documented aftereffects, few studies have investigated the perception of direction and speed of the aftereffect at the same time, that is the perceived velocity. Using a novel experimental paradigm, we simultaneously recorded the perceived direction and speed of leftward or rightward moving random dots before and after adaptation. For the adapting stimulus, we chose a horizontally-oriented broadband grating moving upward behind a circular aperture. Because of the aperture problem, the interpretation of this stimulus is ambiguous, being consistent with multiple velocities, and yet it is systematically perceived as moving at a single direction and speed. Here we ask whether the visual system adapts to the multiple velocities of the adaptor or to just the single perceived velocity. Our results show a strong repulsion aftereffect, away from the adapting velocity (downward and slower), that increases gradually for faster test stimuli as long as these stimuli include some velocities that match some of the ambiguous ones of the adaptor. In summary, the visual system seems to adapt to the multiple velocities of an ambiguous stimulus even though a single velocity is perceived. Our findings can be well described by a computational model that assumes a joint encoding of direction and speed and that includes an extended adaptation component that can represent all the possible velocities of the ambiguous stimulus.

Jean-Rémi King, 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.

Jeremy Kuhn, Carlo Geracia, Philippe Schlenker, Brent Strickland (2021). Boundaries in space and time: Iconic biases across modalities. Cognition, Volume 210, May 2021, 104596

Résumé :
The idea that the form of a word reflects information about its meaning has its roots in Platonic philosophy, and has been experimentally investigated for concrete, sensory-based properties since the early 20th century. Here, we provide evidence for an abstract property of ‘boundedness’ that introduces a systematic, iconic bias on the phonological expectations of a novel lexicon. We show that this abstract property is general across events and objects. In Experiment 1, we show that subjects are systematically more likely to associate sign language signs that end with a gestural boundary with telic verbs (denoting events with temporal boundaries, e.g., die, arrive) and with count nouns (denoting objects with spatial boundaries, e.g., ball, coin). In Experiments 2–3, we show that this iconic mapping acts on conceptual representations, not on grammatical features. Specifically, the mapping does not carry over to psychological nouns (e.g. people are not more likely to associate a gestural boundary with idea than with knowledge). Although these psychological nouns are still syntactically encoded as either count or mass, they do not denote objects that are conceived of as having spatial boundaries. The mapping bias thus breaks down. Experiments 4–5 replicate these findings with a new set of stimuli. Finally, in Experiments 6–11, we explore possible extensions to a similar bias for spoken language stimuli, with mixed results. Generally, the results here suggest that ‘boundedness’ of words' referents (in space or time) has a powerful effect on intuitions regarding the form that the words should take.

Pierre Lelievre, Peter Neri (2021). A deep-learning framework for human perception of abstract art composition. Journal of Vision, Vol.21, 9. doi:https://doi.org/10.1167/jov.21.5.9

Résumé :
Artistic composition (the structural organization of pictorial elements) is often characterized by some basic rules and heuristics, but art history does not offer quantitative tools for segmenting individual elements, measuring their interactions and related operations. To discover whether a metric description of this kind is even possible, we exploit a deep-learning algorithm that attempts to capture the perceptual mechanism underlying composition in humans. We rely on a robust behavioral marker with known relevance to higher-level vision: orientation judgements, that is, telling whether a painting is hung “right-side up.” Humans can perform this task, even for abstract paintings. To account for this finding, existing models rely on “meaningful” content or specific image statistics, often in accordance with explicit rules from art theory. Our approach does not commit to any such assumptions/schemes, yet it outperforms previous models and for a larger database, encompassing a wide range of painting styles. Moreover, our model correctly reproduces human performance across several measurements from a new web-based experiment designed to test whole paintings, as well as painting fragments matched to the receptive-field size of different depths in the model. By exploiting this approach, we show that our deep learning model captures relevant characteristics of human orientation perception across styles and granularities. Interestingly, the more abstract the painting, the more our model relies on extended spatial integration of cues, a property supported by deeper layers.

Shannon Locke (2021). Affective Bias Through the Lens of Signal Detection Theory. Computational Psychiatry, 5(1), 4–20. DOI: http://doi.org/10.5334/cpsy.58

Résumé :
Affective bias – a propensity to focus on negative information at the expense of positive information – is a core feature of many mental health problems. However, it can be caused by wide range of possible underlying cognitive mechanisms. Here we illustrate this by focusing on one particular behavioural signature of affective bias – increased tendency of anxious/depressed individuals to predict lower rewards – in the context of the Signal Detection Theory (SDT) modelling framework. Specifically, we show how to apply this framework to measure affective bias and compare it to the behaviour of an optimal observer. We also show how to extend the framework to make predictions about bias when the individual holds incorrect assumptions about the decision context. Building on this theoretical foundation, we propose five experiments to test five hypothetical sources of this affective bias: beliefs about prior probabilities, beliefs about performance, subjective value of reward, learning differences, and need for accuracy differences. We argue that greater precision about the mechanisms driving affective bias may eventually enable us to better understand the mechanisms underlying mood and anxiety disorders.

Helena Miton, Olivier Morin (2021). Graphic complexity in writing systems. Cognition, volume 214, 104771, ISSN 0010-0277, DOI : https://doi.org/10.1016/j.cognition.2021.104771.

Résumé :
A writing system is a graphic code, i.e., a system of standardized pairings between symbols and meanings in which symbols take the form of images that can endure. The visual character of writing implies that written characters have to fit constraints of the human visual system. One aspect of this optimization lays in the graphic complexity of the characters used by scripts. Scripts are sets of graphic characters used for the written form of one language or more. Using computational methods over a large and diverse dataset (over 47,000 characters, from over 133 scripts), we answer three central questions about the visual complexity of written characters and the evolution of writing: (1) What determines character complexity? (2) Can we find traces of evolutionary change in character complexity? (3) Is complexity distributed in a way that makes character recognition easier? Our study suggests that (1) character complexity depends primarily on which linguistic unit the characters encode, and that (2) there is little evidence of evolutionary change in character complexity. Additionally (3) for an individual character, the half which is encountered first while reading tends to be more complex than that which is encountered last.

Olivier Morin, Pierre Jacquet, Krist Vaesen nd Alberto Acerbi (2021). Social information use and social information waste. Philosophical transactions of the Royal Society B, volume 376, issue 1828. DOI: https://doi.org/10.1098/rstb.2020.0052

Résumé :
Social information is immensely valuable. Yet we waste it. The information we get from observing other humans and from communicating with them is a cheap and reliable informational resource. It is considered the backbone of human cultural evolution. Theories and models focused on the evolution of social learning show the great adaptive benefits of evolving cognitive tools to process it. In spite of this, human adults in the experimental literature use social information quite inefficiently: they do not take it sufficiently into account. A comprehensive review of the literature on five experimental tasks documented 45 studies showing social information waste, and four studies showing social information being over-used. These studies cover ‘egocentric discounting’ phenomena as studied by social psychology, but also include experimental social learning studies. Social information waste means that human adults fail to give social information its optimal weight. Both proximal explanations and accounts derived from evolutionary theory leave crucial aspects of the phenomenon unaccounted for: egocentric discounting is a pervasive effect that no single unifying explanation fully captures. Cultural evolutionary theory's insistence on the power and benefits of social influence is to be balanced against this phenomenon.

Lou Safra, Adil Siljimassi, Coralie Chevallier (2021). Disease, perceived infectability and threat reactivity: A COVID-19 study. Personality and Individual Differences, Volume 180, 110945, DOI: https://doi.org/10.1016/j.paid.2021.110945

Résumé :
Using a two-wave online experiment, we investigate whether COVID-19 exposure changes participants' threat-detection threshold. Threat reactivity was measured in a signal detection task among 277 British adults who also reported how vulnerable they felt to infectious diseases. Participants' data were then matched to the local number of confirmed COVID-19 cases announced by the NHS every day. We found that participants who perceive themselves as more likely to catch infectious diseases displayed higher threat reactivity in response to increased COVID-19 cases.

Fleur Zeldenrust, Boris Gutkin, Sophie Denéve (2021). Efficient and robust coding in heterogeneous recurrent networks. PLoS Comput Biol, 17(4): e1008673. DOI: https://doi.org/10.1371/journal.pcbi.1008673

Résumé:
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, ‘type 1’ and ‘type 2’ neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that ‘type 2’ neurons are more coherent with the overall network activity than ‘type 1’ neurons.

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