The origin of language is one of the most significant evolutionary milestones of life on Earth, but one of the most persevering scientific unknowns. Two decades ago, game theorists and mathematicians predicted that the first words and grammar emerged as a response to transmission errors and information loss in language's precursor system, however, empirical proof is lacking. Here, we assessed information loss in proto-consonants and proto-vowels in human pre-linguistic ancestors as proxied by orangutan consonant-like and vowel-like calls that compose syllable-like combinations. We played back and re-recorded calls at increasing distances across a structurally complex habitat (i.e. adverse to sound transmission). Consonant-like and vowel-like calls degraded acoustically over distance, but no information loss was detected regarding three distinct classes of information (viz. individual ID, context and population ID). Our results refute prevailing mathematical predictions and herald a turning point in language evolution theory and heuristics. Namely, explaining how the vocal–verbal continuum was crossed in the hominid family will benefit from future mathematical and computational models that, in order to enjoy empirical validity and superior explanatory power, will be informed by great ape behaviour and repertoire.
Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior. Flash bursts relay around vegetation across the swarm, illuminating the role of the environment in shaping self-organization. Flash bursts relay around vegetation across the swarm, illuminating the role of the environment in shaping self-organization.
Understanding how the brain computes choice from sensory information is a central question in perceptual decision-making research. From a behavioral perspective, paradigms suitable to study perceptual decision-making condition choice on invariant properties of the stimuli, thus decoupling stimulus-specific information from decision-related variables. From a neural perspective, powerful tools for the dissection of brain circuits are needed, which suggests the mouse as a suitable animal model. However, whether and how mice can perform an invariant visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task where the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a discrimination acuity of at least 6°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a novel probabilistic choice model that explained behavioral strategies in (n = 40) mice and identified unreported dimensions of variation associated with the circularity of the stimulus space. Furthermore, the model showed a dependence of history biases on task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice are capable of decoupling decision-relevant information from stimulus-specific information, thus demonstrating they are a useful animal model for studying neural representation of abstract learned categories in perceptual decision-making research.
Labyrinth fishes (Perciformes: Anabantoidei) are primary freshwater fishes with a disjunct African-Asian distribution that exhibit a wide variety of morphological and behavioral traits. These intrinsic features make them particularly well suited for studying patterns and processes of evolutionary diversification. We reconstructed the first molecular-based phylogenetic hypothesis of anabantoid intrarelationships using both mitochondrial and nuclear nucleotide sequence data to address anabantoid evolution. The mitochondrial data set included the complete cytochrome b, partial 12S rRNA, complete tRNA Val, and partial 16S rRNA genes (3332 bp) of 57 species representing all 19 anabantoid genera. The nuclear data set included the partial RAG1 gene (1494 bp) of 21 representative species. The phylogenetic analyses of a combined (mitochondrial + nuclear) data set recovered almost fully resolved trees at the intrafamily level with different methods of phylogenetic inference. Phylogenetic relationships at this taxonomic level were compared with previous morphology-based hypotheses. In particular, the enigmatic pike-head (Luciocephalus) was confidently placed within the “spiral egg” clade, thus resolving the long-standing controversy on its relative phylogenetic position. The molecular phylogeny was used to study the evolution of the different forms of parental care within the suborder. Our results suggest that the evolution of breeding behavior in anabantoids is highly correlated with phylogeny, and that brood care evolved three times independently from an ancestral free spawning condition without parental care. Ancestral character state reconstructions under maximum parsimony and maximum likelihood further indicated that both bubble nesting and mouthbrooding have evolved recurrently during anabantoid evolution. The new phylogenetic framework was also used to test alternative biogeographic hypotheses that account for the disjunct African-Asian distribution. Molecular divergence time estimates support either a drift vicariance linked to the breakup of Gondwana or Late Mesozoic Early Tertiary dispersal from Africa to Asia or vice versa.
Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.
Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.
Relative brain size has long been considered a reflection of cognitive capacities and has played a fundamental role in developing core theories in the life sciences. Yet, the notion that relative brain size validly represents selection on brain size relies on the untested assumptions that brain-body allometry is restrained to a stable scaling relationship across species and that any deviation from this slope is due to selection on brain size. Using the largest fossil and extant dataset yet assembled, we find that shifts in allometric slope underpin major transitions in mammalian evolution and are often primarily characterized by marked changes in body size. Our results reveal that the largest-brained mammals achieved large relative brain sizes by highly divergent paths. These findings prompt a reevaluation of the traditional paradigm of relative brain size and open new opportunities to improve our understanding of the genetic and developmental mechanisms that influence brain size. An in-depth look at mammalian brain size evolution prompts a reevaluation of a traditional paradigm. An in-depth look at mammalian brain size evolution prompts a reevaluation of a traditional paradigm.
A balanced intake of macronutrients—protein, carbohydrate and fat—is essential for the well-being of organisms. An adequate calorific intake but with insufficient protein consumption can lead to several ailments, including kwashiorkor1. Taste receptors (T1R1–T1R3)2 can detect amino acids in the environment, and cellular sensors (Gcn2 and Tor)3 monitor the levels of amino acids in the cell. When deprived of dietary protein, animals select a food source that contains a greater proportion of protein or essential amino acids (EAAs)4. This suggests that food selection is geared towards achieving the target amount of a particular macronutrient with assistance of the EAA-specific hunger-driven response, which is poorly understood. Here we show in Drosophila that a microbiome–gut–brain axis detects a deficit of EAAs and stimulates a compensatory appetite for EAAs. We found that the neuropeptide CNMamide (CNMa)5 was highly induced in enterocytes of the anterior midgut during protein deprivation. Silencing of the CNMa–CNMa receptor axis blocked the EAA-specific hunger-driven response in deprived flies. Furthermore, gnotobiotic flies bearing an EAA-producing symbiotic microbiome exhibited a reduced appetite for EAAs. By contrast, gnotobiotic flies with a mutant microbiome that did not produce leucine or other EAAs showed higher expression of CNMa and a greater compensatory appetite for EAAs. We propose that gut enterocytes sense the levels of diet- and microbiome-derived EAAs and communicate the EAA-deprived condition to the brain through CNMa.
Covariation among traits shapes both phenotypic evolution and ecological interactions across space and time. However, rampant geographical variation in the strength and direction of such correlations can be particularly difficult to explain through generalized mechanisms. By integrating population genomics, surveys of natural history collections and spatially explicit analyses, we tested multiple drivers of trait correlations in a coral snake mimic that exhibits remarkable polymorphism in mimetic and non-mimetic colour traits. We found that although such traits co-occur extensively across space, correlations were best explained by a mixture of genetic architecture and correlational selection, rather than by any single mechanism. Our findings suggest that spatially complex trait distributions may be driven more by the simple interaction between multiple processes than by complex variation in one mechanism alone. These interactions are particularly important in mimicry systems, which frequently generate striking geographical variation and genetic correlations among colour pattern traits.
The human brain has undergone rapid expansion since humans diverged from other great apes, but the mechanism of this human-specific enlargement is still unknown. Here, we use cerebral organoids derived from human, gorilla, and chimpanzee cells to study developmental mechanisms driving evolutionary brain expansion. We find that neuroepithelial differentiation is a protracted process in apes, involving a previously unrecognized transition state characterized by a change in cell shape. Furthermore, we show that human organoids are larger due to a delay in this transition, associated with differences in interkinetic nuclear migration and cell cycle length. Comparative RNA sequencing (RNA-seq) reveals differences in expression dynamics of cell morphogenesis factors, including ZEB2, a known epithelial-mesenchymal transition regulator. We show that ZEB2 promotes neuroepithelial transition, and its manipulation and downstream signaling leads to acquisition of nonhuman ape architecture in the human context and vice versa, establishing an important role for neuroepithelial cell shape in human brain expansion.