Visual Field Analysis: A reliable method to score left and right eye use using automated tracking.

Brain and behavioural asymmetries have been documented in various taxa. Many of these asymmetries involve preferential left and right eye use. However, measuring eye use through manual frame-by-frame analyses from video recordings is laborious and may lead to biases. Recent progress in technology has allowed the development of accurate tracking techniques for measuring animal behaviour. Amongst these techniques, DeepLabCut, a Python-based tracking toolbox using transfer learning with deep neural networks, offers the possibility to track different body parts with unprecedented accuracy. Exploiting the potentialities of DeepLabCut, we developed Visual Field Analysis, an additional open-source application for extracting eye use data. To our knowledge, this is the first application that can automatically quantify left-right preferences in eye use. Here we test the performance of our application in measuring preferential eye use in young domestic chicks. The comparison with manual scoring methods revealed a near perfect correlation in the measures of eye use obtained by Visual Field Analysis. With our application, eye use can be analysed reliably, objectively and at a fine scale in different experimental paradigms.

Debiasing thinking among non-WEIRD reasoners.

Human reasoning has been shown to be biased in a variety of situations. While most studies have focused on samples of WEIRD participants (from Western Educated Industrialized Rich and Democratic societies), the sparse non-WEIRD data on the topic suggest an even stronger propensity for biased reasoning. This could be explained by a competence issue (people lack the ability to integrate logical knowledge into their reasoning) or a performance issue (people possess the logical knowledge but do not know it is relevant). We addressed this question using a debiasing paradigm with the base-rate task on a sample of non-industrialized people, the Himba of Namibia. After a short training, most participants were debiased, lending credence to the performance account. Debiasing was however to some extent boosted by schooling and living environment suggesting that competence also plays a role (in that more acquired knowledge allows for a higher training benefit). Results imply that debias interventions can be successfully employed to boost sound reasoning around the world.

How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents.

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent-based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long-held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well-connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation.

Trend judgment as a perceptual building block of graphicacy and mathematics, across age, education, and culture.

Data plots are widely used in science, journalism and politics, since they efficiently allow to depict a large amount of information. Graphicacy, the ability to understand graphs, has thus become a fundamental cultural skill comparable to literacy or numeracy. Here, we introduce a measure of intuitive graphicacy that assesses the perceptual ability to detect a trend in noisy scatterplots ("does this graph go up or down?"). In 3943 educated participants, responses vary as a sigmoid function of the t-value that a statistician would compute to detect a significant trend. We find a minimum level of core intuitive graphicacy even in unschooled participants living in remote Namibian villages (N = 87) and 6-year-old 1st-graders who never read a graph (N = 27). The sigmoid slope that we propose as a proxy of intuitive graphicacy increases with education and tightly correlates with statistical and mathematical knowledge, showing that experience contributes to refining graphical intuitions. Our tool, publicly available online, allows to quickly evaluate and formally quantify a perceptual building block of graphicacy.

Environment and culture shape both the colour lexicon and the genetics of colour perception.

Many languages express 'blue' and 'green' under an umbrella term 'grue'. To explain this variation, it has been suggested that changes in eye physiology, due to UV-light incidence, can lead to abnormalities in blue-green color perception which causes the color lexicon to adapt. Here, we apply advanced statistics on a set of 142 populations to model how different factors shape the presence of a specific term for blue. In addition, we examined if the ontogenetic effect of UV-light on color perception generates a negative selection pressure against inherited abnormal red-green perception. We found the presence of a specific term for blue was influenced by UV incidence as well as several additional factors, including cultural complexity. Moreover, there was evidence that UV incidence was negatively related to abnormal red-green color perception. These results demonstrate that variation in languages can only be understood in the context of their cultural, biological, and physical environments.

Stability and individual variability of social attachment in imprinting.

Filial imprinting has become a model for understanding memory, learning and social behaviour in neonate animals. This mechanism allows the youngs of precocial bird species to learn the characteristics of conspicuous visual stimuli and display affiliative response to them. Although longer exposures to an object produce stronger preferences for it afterwards, this relation is not linear. Sometimes, chicks even prefer to approach novel rather than familiar objects. To date, little is known about how filial preferences develop across time. This study aimed to investigate filial preferences for familiar and novel imprinting objects over time. After hatching, chicks were individually placed in an arena where stimuli were displayed on two opposite screens. Using an automated setup, the duration of exposure and the type of stimuli were manipulated while the time spent at the imprinting stimulus was monitored across 6 days. We showed that prolonged exposure (3 days vs 1 day) to a stimulus produced robust filial imprinting preferences. Interestingly, with a shorter exposure (1 day), animals re-evaluated their filial preferences in functions of their spontaneous preferences and past experiences. Our study suggests that predispositions influence learning when the imprinting memories are not fully consolidated, driving animal preferences toward more predisposed stimuli.

Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks.

Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that "majority rules," and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as "idiosyncratic" or outright "pathological." In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution ["sampler"] or picking the value with the maximum probability ["MAP"]). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias.

Nitrate signaling promotes plant growth by upregulating gibberellin biosynthesis and destabilization of DELLA proteins.

Nitrate, one of the main nitrogen (N) sources for crops, acts as a nutrient and key signaling molecule coordinating gene expression, metabolism, and various growth processes throughout the plant life cycle. It is widely accepted that nitrate-triggered developmental programs cooperate with hormone synthesis and transport to finely adapt plant architecture to N availability. Here, we report that nitrate, acting through its signaling pathway, promotes growth in Arabidopsis and wheat, in part by modulating the accumulation of gibberellin (GA)-regulated DELLA growth repressors. We show that nitrate reduces the abundance of DELLAs by increasing GA contents through activation of GA metabolism gene expression. Consistently, the growth restraint conferred by nitrate deficiency is partially rescued in global-DELLA mutant that lacks all DELLAs. At the cellular level, we show that nitrate enhances both cell proliferation and elongation in a DELLA-dependent and -independent manner, respectively. Our findings establish a connection between nitrate and GA signaling pathways that allow plants to adapt their growth to nitrate availability.

Root-derived GA12 contributes to temperature-induced shoot growth in Arabidopsis.

Plants are able to sense a rise in temperature of several degrees, and appropriately adapt their metabolic and growth processes. To this end, plants produce various signalling molecules that act throughout the plant body. Here, we report that root-derived GA12, a precursor of the bioactive gibberellins, mediates thermo-responsive shoot growth in Arabidopsis. Our data suggest that root-to-shoot translocation of GA12 enables a flexible growth response to ambient temperature changes.