Language — the most distinctive human trait — remains a ‘mystery’1 or even a ‘problem’2 for evolutionary theory. It is underpinned by cooperative turn-taking3, which has been implicated with highly sophisticated cognitive skills such as mindreading4. Some have claimed that this turn-taking system is uniquely human5,6, but others argue that it provides the evolutionary ‘missing link’ between animal and human communication7. This debate has been constrained by a lack of comparative data, methodological confounds that often prevent meaningful comparisons, and a lack of information on key components of social relationships8,9 that might strongly impact upon turn-taking propensities. Objectives. TURNTAKING will quantify turn-taking production and comprehension in human children, chimpanzees, and two distantly related species — geladas and common marmosets. It will apply a powerful combination of systematic behavioral observations, eye-tracking paradigms, and established measures from Conversational Analysis3,10 and Primatology9 that allow the same type of data to be collected and analyzed in directly comparable ways across species. This will provide the first rigorous test of whether cooperative turn-taking is uniquely human, ancestral in the primate lineage, or evolved independently in different species. TURNTAKING will identify which hallmarks of human turn-taking are shared across different primate species, and which key components of relationship quality8,9 act upon turn-taking skills. Outcomes. This project will found the field of comparative turn-taking, and provide pioneering insights into the behavioral flexibility underlying different turn-taking systems. It will go beyond the state of the art by exposing whether cooperative turn-taking is the evolutionary ‘missing link’ between our species and our inarticulate primate cousins, and whether pro-social behaviors drove its emergence.

The visual world around us is a source of rich semantic information that guides our higher-level cognitive processes and actions. To tap into this resource, the brain?s visual system engages in complex, intertwined computations to actively sample, extract, and integrate information across space and time. Surprisingly however, the integrative nature of vision hardly plays a role in the way we approach it in experimentation and computational modelling. Instead, higher-level vision is commonly treated as a largely bottom-up categorization process. TIME proposes a new approach. It will allow us to study vision in a more natural setting and as a process that is (a) geared towards semantic understanding instead of label-based categorisation, (b) naturally intertwined with active information sampling and (c) expanding across multiple timeframes, including network dynamics that unfold within and across eye fixations. This will be accomplished by an ambitious, three-step work program that combines cutting-edge non-invasive human brain imaging performed while participants visually explore tens of thousands of rich human-annotated natural scenes, the development of novel multivariate analysis techniques, and large-scale computational modelling using a new bio-inspired deep learning framework for active vision that closes the sensory-motor loop. Using this interdisciplinary approach, TIME will establish, for the first time, when, where, and how visual semantic understanding emerges in the brain as it actively samples and integrates information from the world in a continuously updating and dynamic decision process. These ground-breaking developments both in experimentation and deep neural network modelling build towards a fundamental paradigm shift in how we study, model, and understand vision, yielding new insights into its complex neural processes operating in more natural, ecologically valid conditions, as well as a closer alignment between biological and synthetic vision.