Unpeeling the layers of language: Bonobos and chimpanzees engage in cooperative turn-taking sequences

Human language is a fundamentally cooperative enterprise, embodying fast-paced and extended social interactions. It has been suggested that it evolved as part of a larger adaptation of humans’ species-unique forms of cooperation. Although our closest living relatives, bonobos and chimpanzees, show general cooperative abilities, their communicative interactions seem to lack the cooperative nature of human conversation. Here, we revisited this claim by conducting the first systematic comparison of communicative interactions in mother-infant dyads living in two different communities of bonobos (LuiKotale, DRC; Wamba, DRC) and chimpanzees (Taï South, Côte d’Ivoire; Kanyawara, Uganda) in the wild. Focusing on the communicative function of joint-travel-initiation, we applied parameters of conversation analysis to gestural exchanges between mothers and infants. Results showed that communicative exchanges in both species resemble cooperative turn-taking sequences in human conversation. While bonobos consistently addressed the recipient via gaze before signal initiation and used so-called overlapping responses, chimpanzees engaged in more extended negotiations, involving frequent response waiting and gestural sequences. Our results thus strengthen the hypothesis that interactional intelligence paved the way to the cooperative endeavour of human language and suggest that social matrices highly impact upon communication styles

Evidence for Training-Induced Plasticity in Multisensory Brain Structures: An MEG Study

Multisensory learning and resulting neural brain plasticity have recently become a topic of renewed interest in human cognitive neuroscience. Music notation reading is an ideal stimulus to study multisensory learning, as it allows studying the integration of visual, auditory and sensorimotor information processing. The present study aimed at answering whether multisensory learning alters uni-sensory structures, interconnections of uni-sensory structures or specific multisensory areas. In a short-term piano training procedure musically naive subjects were trained to play tone sequences from visually presented patterns in a music notation-like system [Auditory-Visual-Somatosensory group (AVS)], while another group received audio-visual training only that involved viewing the patterns and attentively listening to the recordings of the AVS training sessions [Auditory-Visual group (AV)]. Training-related changes in cortical networks were assessed by pre- and post-training magnetoencephalographic (MEG) recordings of an auditory, a visual and an integrated audio-visual mismatch negativity (MMN). The two groups (AVS and AV) were differently affected by the training. The results suggest that multisensory training alters the function of multisensory structures, and not the uni-sensory ones along with their interconnections, and thus provide an answer to an important question presented by cognitive models of multisensory training

A new model for root growth in soil with macropores

Abstract: Background and aimsThe use of standard dynamic root architecture models to simulate root growth in soil containing macropores failed to reproduce experimentally observed root growth patterns. We thus developed a new, more mechanistic model approach for the simulation of root growth in structured soil. Methods: In our alternative modelling approach, we distinguish between, firstly, the driving force for root growth, which is determined by the orientation of the previous root segment and the influence of gravitropism and, secondly, soil mechanical resistance to root growth. The latter is expressed by its inverse, soil mechanical conductance, and treated similarly to hydraulic conductivity in Darcy’s law. At the presence of macropores, soil mechanical conductance is anisotropic, which leads to a difference between the direction of the driving force and the direction of the root tip movement. Results: The model was tested using data from the literature, at pot scale, at macropore scale, and in a series of simulations where sensitivity to gravity and macropore orientation was evaluated. Conclusions: Qualitative and quantitative comparisons between simulated and experimentally observed root systems showed good agreement, suggesting that the drawn analogy between soil water flow and root growth is a useful one

Electromagnetic Correlates of Musical Expertise in Processing of Tone Patterns

Using magnetoencephalography (MEG), we investigated the influence of long term musical training on the processing of partly imagined tone patterns (imagery condition) compared to the same perceived patterns (perceptual condition). The magnetic counterpart of the mismatch negativity (MMNm) was recorded and compared between musicians and non-musicians in order to assess the effect of musical training on the detection of deviants to tone patterns. The results indicated a clear MMNm in the perceptual condition as well as in a simple pitch oddball (control) condition in both groups. However, there was no significant mismatch response in either group in the imagery condition despite above chance behavioral performance in the task of detecting deviant tones. The latency and the laterality of the MMNm in the perceptual condition differed significantly between groups, with an earlier MMNm in musicians, especially in the left hemisphere. In contrast the MMNm amplitudes did not differ significantly between groups. The behavioral results revealed a clear effect of long-term musical training in both experimental conditions. The obtained results represent new evidence that the processing of tone patterns is faster and more strongly lateralized in musically trained subjects, which is consistent with other findings in different paradigms of enhanced auditory neural system functioning due to long-term musical training

Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models

Background and aimsThe main difficulty in the use of 3D root architecture models is correct parameterization. We evaluated distributions of the root traits inter-branch distance, branching angle and axial root trajectories from contrasting experimental systems to improve model parameterization.MethodsWe analyzed 2D root images of different wheat varieties (Triticum aestivum) from three different sources using automatic root tracking. Model input parameters and common parameter patterns were identified from extracted root system coordinates. Simulation studies were used to (1) link observed axial root trajectories with model input parameters (2) evaluate errors due to the 2D (versus 3D) nature of image sources and (3) investigate the effect of model parameter distributions on root foraging performance.ResultsDistributions of inter-branch distances were approximated with lognormal functions. Branching angles showed mean values <90°. Gravitropism and tortuosity parameters were quantified in relation to downwards reorientation and segment angles of root axes. Root system projection in 2D increased the variance of branching angles. Root foraging performance was very sensitive to parameter distribution and variance.Conclusions2D image analysis can systematically and efficiently analyze root system architectures and parameterize 3D root architecture models. Effects of root system projection (2D from 3D) and deflection (at rhizotron face) on size and distribution of particular parameters are potentially significant

Rhizosphere pH and phosphorus forms in an Oxisol cultivated with soybean, brachiaria grass, millet and sorghum

Plants have shown different responses to fertilization with rock phosphate, including responses through alteration of the attributes of rhizospheric soil. The objective of this study was to evaluate soil pH alterations and alterations in the contents of forms of phosphorus in the rhizosphere of soil fertilized with rock phosphate as a result of cultivation of species of plants. An experiment was developed under greenhouse conditions to evaluate alterations in the pH and in the forms of phosphorus in the rhizosphere of an Oxisol fertilized with rock phosphate and cultivated with four species. Treatments consisted of the cultivation of four species of soybean - Glycine max (L.) Merrill, brachiaria grass - Brachiaria brizantha Hochst Stapf, millet - Pennisetum glaucum (L.) R. Brown, and sorghum - Sorghum bicolor (L.) Moench grown in PVC columns filled with soil and divided with a nylon screen (25 µm mesh) to impede root growth in part of the column. After 45 days of cultivation, the soil was divided into the layers of 0-1, 1-2, 2-3, 3-4, 4-5, 5-7, 7-9, and 9-14 mm from the rhizoplane and air dried to determine pH and P contents through Hedley fractionation. In the 1-2 and 2-3 mm layers, soybean cultivation caused an increase in pH when compared to the control treatment (without plants). In the other layers, there were no alterations in pH due to cultivation of plants. The cultivation of millet, brachiaria grass, and sorghum reduced the inorganic P content in the most labile forms only in the 0-1 mm layer from the rhizoplane

Multi-feature classifiers for burst detection in single EEG channels from preterm infants

International audienceObjective: The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal as-phyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However , as the brain activity evolves rapidly during postna-tal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA ≥ 36 weeks) using multi-feature classification on a single EEG channel. Approach: Five EEG burst detectors relying on different machine learning approaches were compared: Logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36 – 41 weeks PMA. Main results: The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements , LR provided the highest scores (Cohen's kappa = 0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants ≥ 36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance: In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel