MAC-Brain: Developing a Multi-scale account of Attentional Control as the constraining interface between vision and action: A cross-species investigation of relevant neural circuits in the human and macaque Brain
The MAC-Brain project proposes an integrated approach for the study of visuo-spatial attention in the primate brain. Attention acts as an overarching mechanism that integrates signals from multiple sources in order to assign processing priorities to specific sensory inputs, gating selection at the service of goal-oriented behavior. Relevant signals include the low-level sensory characteristics of stimuli in the external world, as well as internally-stored information about current goals, expectations and value-related signals. However, these factors have been traditionally studied in isolation and using disparate stimuli and tasks.
Here we develop a unitary framework to investigate the role different attention control signals, and their interactions, and we aim to identify how these are represented in the human and non-human primate brain. By using standardized paradigms in both species we seek to bridge the gap between cellular-level monkey data and system-level human data.
This collaborative project is funded under the European FLAG-ERA JTC 2017 program, associated to the Human Brain Project, and comprises four partners:
Leonardo Chelazzi (project co-ordinator)
Nexus – Emergent Attention Lab
Department of Neuroscience, Biomedicine and Movement Sciences (DNBM), University of Verona, Italy
Lyon Neuroscience Research Center (CRNL), Lyon, France
Suliann Ben Hamed
Institut des Sciences Cognitives Marc-Jeannerod (ISC), Lyon, France
Department of Experimental Psychology, Ghent University, Belgium
The research activities at the ImpAct Team (CRNL) are carried out in collaboration with Dr. Fadila Hadj-Bouziane and focus primarily on functional imaging studies in healthy participants. However, wide-ranging exchanges with the project’s partners support the progress of innovative perspectives on data analysis and the modeling of attention control functions.