The extent to which animals other than humans can reason about physical problems is contentious. The benchmark test for this ability has been the trap-tube task. We presented New Caledonian crows with a series of two-trap versions of this problem. Three out of six crows solved the initial trap-tube. These crows continued to avoid the trap when the arbitrary features that had previously been associated with successful performances were removed. However, they did not avoid the trap when a hole and a functional trap were in the tube. In contrast to a recent primate study, the three crows then solved a causally equivalent but visually distinct problem-the trap-table task. The performance of the three crows across the four transfers made explanations based on chance, associative learning, visual and tactile generalization, and previous dispositions unlikely. Our findings suggest that New Caledonian crows can solve complex physical problems by reasoning both causally and analogically about causal relations. Causal and analogical reasoning may form the basis of the New Caledonian crow's exceptional tool skills.

The ability of some bird species to pull up meat hung on a string is a famous example of spontaneous animal problem solving. The “insight” hypothesis claims that this complex behaviour is based on cognitive abilities such as mental scenario building and imagination. An operant conditioning account, in contrast, would claim that this spontaneity is due to each action in string pulling being reinforced by the meat moving closer and remaining closer to the bird on the perch. We presented experienced and naïve New Caledonian crows with a novel, visually restricted string-pulling problem that reduced the quality of visual feedback during string pulling. Experienced crows solved this problem with reduced efficiency and increased errors compared to their performance in standard string pulling. Naïve crows either failed or solved the problem by trial and error learning. However, when visual feedback was available via a mirror mounted next to the apparatus, two naïve crows were able to perform at the same level as the experienced group. Our results raise the possibility that spontaneous string pulling in New Caledonian crows may not be based on insight but on operant conditioning mediated by a perceptual-motor feedback cycle.

Binocular vision is a visual property that allows fine discrimination of in-depth distance (stereopsis), as well as enhanced light and contrast sensitivity. In mammals enhanced binocular vision is structurally associated with a large degree of frontal binocular overlap, the presence of a corresponding retinal specialization containing a fovea or an area centralis, and well-developed ipsilateral retinal projections to the lateral thalamus (GLd). We compared these visual traits in two visually active species of the genus Octodon that exhibit contrasting visual habits: the diurnal Octodon degus, and the nocturnal Octodon lunatus. The O. lunatus visual field has a prominent 100° frontal binocular overlap, much larger than the 50° of overlap found in O. degus. Cells in the retinal ganglion cell layer were 40% fewer in O. lunatus (180,000) than in O. degus (300,000). O. lunatus has a poorly developed visual streak, but a well developed area centralis, located centrally near the optic disk (peak density of 4,352 cells/mm2). O. degus has a highly developed visual streak, and an area centralis located more temporally (peak density of 6,384 cells/mm2). The volumes of the contralateral GLd and superior colliculus (SC) are 15% larger in O. degus compared to O. lunatus. However, the ipsilateral projections to GLd and SC are 500% larger in O. lunatus than in O. degus. Other retinorecipient structures related to ocular movements and circadian activity showed no statistical differences between species. Our findings strongly suggest that nocturnal visual behavior leads to an enhancement of the structures associated with binocular vision, at least in the case of these rodents. Expansion of the binocular visual field in nocturnal species may have a beneficial effect in light and contrast sensitivity, but not necessarily in stereopsis. We discuss whether these conclusions can be extended to other mammalian and non-mammalian amniotes.

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BackgroundGlioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re‐incidence and therapy resistance. Thus, it is urgent to characterize the mechanisms of GB pathogenesis to help researchers identify novel therapeutic targets to cure this devastating disease. Recently, a promising approach to identifying novel therapeutic targets is the integration of tumor omics data with clinical information using machine learning (ML) techniques.Recent findingsML has become a valuable addition to the researcher's toolbox, thanks to its flexibility, multidimensional approach, and a growing community of users. The goal of this review is to introduce basic concepts and applications of ML for studying GB to clinicians and practitioners who are new to data science. ML applications include exploring large data sets, finding new relevant patterns, predicting outcomes, or merely understanding associations of the complex molecular networks presented within the tumor. Here, we review ML applications published between 2008 and 2018 and discuss ML strategies intending to identify new potential therapeutic targets to improve the management and treatment of GB.ConclusionsML applications to study GB vary in purpose and complexity, with positive results. In GB studies, ML is often used to analyze high‐dimensional datasets with prediction or classification as a primary goal. Despite the strengths of ML techniques, they are not fail‐safe and methodological issues can occur in GB studies that use them. This is why researchers need to be aware of these issues when planning and appraising studies that apply ML to the study of GB.