LaPsyDÉ

It takes two to tango: a synergistic approach to human-machine decision making

Artificial Intelligence (AI) holds enormous potential for enhancing human decisions, improving cognitive overload and lowering bias
in high-stakes scenarios. Adoption of AI-based support systems in such applications is however minimal, chiefly due to the difficulty
of assessing their assumptions, limitations and intentions. In order to realise the promise of AI for individuals, society and economy,
people should feel they can trust AIs in terms of reliability, capacity to understand the human’s needs, and guarantees that they are
genuinely aiming at helping them. TANGO will develop the theoretical basis and computational framework for hybrid decision
support systems (HDSS) in which humans and machines are aligned in terms of values and goals, know their respective strengths, and
work together to reach an optimal decision. To this end, TANGO will develop: 1) A cognitive theory of mutual understanding and
hybrid decision making, of intuitive vs deliberative approaches to decision making and of how they affect our trust in human and AI
teammates. 2) Cognition-aware explainable AIs implementing synergistic human-machine interaction, enabling machines to
determine what information a specific decision maker (e.g., layperson vs expert) needs, or does not need, to reach an informed
decision. 3) A “Human-in-the-loop” co-evolution of human decision making and machine learning models building on bi-directional,
explanation-augmented interlocution. The TANGO framework will be evaluated on four high impact use cases, namely supporting: i)
women during pregnancy and postpartum, ii) surgical teams in intraoperative decision making, iii) loan officers and applicants in
credit lending decision processes, and iv) public policy makers in designing incentives and allocating funds. Success in these case
studies will establish TANGO as the framework of reference for developing a new generation of synergistic AI systems, and will
strengthen the leadership of Europe in human-centric AI.

Myelin implications in executive control in adolescence and adulthood.

During brain development, the myelination process by oligodendrocytes is critical to shape action potential fidelity and neuronal network synchronization. Although myelin is critical for the proper function of neuronal circuits and behaviour, little is known on how myelination shapes complex cognitive processes during development. We hypothesize that myelination is a crucial developmental component for the construction of neuronal network activities leading to complex cognitive processes such as executive control. Specifically, we will perform a cross-species and multi-level analysis to assess the role of myelination on cognitive control in adolescence and adulthood. First, by combining behaviour and in vivo electrophysiology with behaviour in two mouse models displaying global or specific myelin defects, we will study the impact of myelin impairments on cortical oscillations and cognitive control using an intra-/extradimensional (ID/ED) task. Then, we will examine human cognitive control using a comparable ID/ED task analysed with behaviour, in vivo brain imaging and electrophysiology (anatomo-functional connectivity from EEG, diffusion and functional MRI) in adolescent and adult subjects. This will allow us to determine whether increasing myelination from adolescence to adulthood leads to an increased anatomical connectivity, and hence to an increased functional connectivity/integration that in turn enhances cognitive control at a behavioral level. Finally, we will integrate our data in a computational model of learning and cognitive control in both humans and rodents to assess specific and subtle common behaviors that may be influenced by myelination. In summary, we will use a multi-level approach to assess the impact of myelination from the cellular to the behavioural level on executive control.