ERC FUNDED PROJECTS

"The question about the origin and dispersal of the Transeurasian languages (i.e. Japonic, Koreanic, Tungusic, Mongolic and Turkic) is one of the most disputed issues in linguistic history. Eurasia3angle will address this question from an interdisciplinary perspective. My key objective is to effectively synthesize linguistic, archaeological and genetic evidence in a single approach, for which I use the term ""triangulation"". To this end, my project will bring together a highly qualified interdisciplinary team of doctoral and postdoctoral researchers along with world-eminent experts, who will focus on testing the Farming/Language Dispersal Hypothesis for the Transeurasian languages. The FLDH attributes the dispersal of some of the world's major language families to the adoption of agriculture and subsequent population expansion, whereby the language of new farmers displaced that of preexisting hunter gatherers. In contrast to its application to the major language families in East Asia, the FLDH has not been tested yet for the Transeurasian languages. My research team will specifically investigate the hypothesis that the Transeurasian languages derive from a homeland in South Manchuria and that their early dispersal should be associated with the spread of cultivation of millet and beans. For this purpose, we will use advanced techniques recently introduced to the individual disciplines, such as the application of phylogenetic methods to linguistic classification, a focus on derivational morphology in the reconstruction of subsistence-related language, a matrix-based comparison of archaeological cultures and a model-based approach applied to genome-wide autosomal data. Converging these partial perspectives into a more holistic understanding of what really happened in the past is quite a challenge. However, if successful, this research will be a break-through in the investigation of human prehistory in general and in the long-standing Transeurasian debate in particular. "

2 000 000 €

Start date: 2015-09-01, End date: 2020-08-31

Visual awareness affords flexibility and experiential richness, and its loss following brain damage has devastating effects. However, patients with blindness following cortical damage may retain visual functions, despite visual awareness is lacking (blindsight). But, how can we translate non-conscious visual abilities into conscious ones after damage to the visual cortex? To place our understanding of visual awareness on firm neurobiological and mechanistic bases, I propose to integrate human and monkey neuroscience. Next, I will translate this wisdom into evidence-based clinical intervention. First, LIGHTUP will apply computational neuroimaging methods at the micro-scale level, estimating population receptive fields in humans and monkeys. This will enable analyzing fMRI signal similar to the way tuning properties are studied in neurophysiology, and to clarify how brain areas translate visual properties into responses associated with awareness. Second, LIGHTUP leverages a behavioural paradigm that can dissociate nonconscious visual abilities from awareness in monkeys, thus offering a refined animal model of visual awareness. Applying behavioural-Dynamic Causal Modelling to combine fMRI and behavioral data, LIGHTUP will build up a Bayesian framework that specifies the directionality of information flow in the interactions across distant brain areas, and their causal role in generating visual awareness. In the third part, I will devise a rehabilitation protocol that combines brain stimulation and visual training to promote the (re)emergence of lost visual awareness. LIGHTUP will exploit non-invasive transcranial magnetic stimulation (TMS) in a novel protocol that enables stimulation of complex cortical circuits and selection of the direction of connectivity that is enhanced. This associative stimulation has been proven to induce Hebbian plasticity, and we have piloted its effects in fostering visual awareness in association with visual restoration training.

1 994 212 €

Start date: 2018-08-01, End date: 2023-07-31

“What would I remove if I had a magic wand? Overconfidence” Daniel Kahneman’s famous fairy-tale wish (The Guardian 18 Jul 15) conveyed a deeply seated pessimism in cognitive scientists that overconfidence is hardwired into human cognition. This bias is pervasive, costly and the root cause of many human failures. Previous failed attempts at reducing overconfidence targeted individual decisions. I propose to reduce this bias at social level of decision making by determining the underlying mental, neural and social processes involved in overconfidence and testing these models by causal interventions. I focus on 3 common forms of social decisions. 1.In honest communication of uncertainty (e.g. 2 doctors disagreeing over a diagnosis), overconfidence impairs joint decisions. Combining computational analysis of behavior and brain response, I develop a real-time feedback loop that allows each disagreeing agent to weight her opinion by an individually-tailored signature of her own uncertainty, freeing joint decisions from overconfidence. 2.Focusing on advising and consulting, I use a novel laboratory model (i.e. Advising Game) to develop a theoretical and empirical understanding of overconfidence in the presence of conflict of interest to understand the mental and neural processes underlying strategic manipulation of others. Plus, by connecting the social use of overconfidence to self-esteem and self-worth, I translate this research to a Mental Health application looking at social dysfunctions in Depression. 3.Overconfidence impairs group processes (e.g. a panel selecting among grants) by promoting herding (blindly following others) and polarization to extreme viewpoints. Inspired by a recent discovery from my lab, I develop a novel causal intervention that puts together seeking consensus within each group and aggregating consensus opinions across groups to remove herding and polarization. Collective decisions can be made better and rid-O could help this wish come true.

1 928 912 €

Start date: 2019-09-01, End date: 2024-08-31

The neural bases of adaptive behavior in social environments are far from being understood. We propose to use both computational and neuroscientific methodologies to provide new and more accurate models of learning in interactive settings. The long-term objective is to develop a neural theory of learning: a mathematical framework that describes the computations mediating social learning in terms of neural signals, structures and plasticity. We plan to develop a model of adaptive learning based on three basic principles: (1) the observation of the outcome of un-chosen options improves the decisions taken in the learning process, (2) learning can be transferred from one domain to another, and (3) learning can be transferred from one agent to another (i.e. social learning). In all three cases, humans appear able to construct and transfer knowledge from sources other than their own direct experience, an underappreciated though we believe critical aspect of learning. Our approach will combine neural and behavioral data with computational models of learning. The hypotheses will be formalized into machine learning algorithms and neural networks of “regret” learning, to quantify the evolution of the learning computations on a trial-by-trial basis from the sequence of stimuli, choices and outcomes. The existence and accuracy of the predicted computations will be then tested on neural signals recorded with functional magnetic resonance imaging (fMRI). The potential findings of this project could lead us to suggest general principles of social learning, and we will be able to measure and model neural activation to show those general principles in action. In addition, our results could have important implications into policy-making - by revealing what type of information agents are naturally inclined to better learn from - and clinical practice - by outlining potential diagnostic procedures and behavioral therapies for disorders affecting social behavior.

1 999 998 €

Start date: 2014-08-01, End date: 2020-01-31