MAC-Brain

The MAC-Brain project proposes a fresh new, integrated and systematic approach to the study of Attentional Control (AC) in the primate brain. AC acts as an overarching mechanism that integrates signals from multiple cognitive domains in order to assign processing priorities to specific sensory inputs and gate motor response selection at the service of goal-oriented behavior. While several sources of AC, and the corresponding neural substrate, have been identified in previous studies, the highly specialized nature of prior experiments prevented a systematic study of the interactions between these factors. Moreover, studies in humans and behaving macaques have been typically optimized according to different constraints, holding back the integration of system-level human data and cellular-level monkey data within a single framework. To fill these major gaps, the research endeavor of MAC-Brain will be designed to achieve two clear-cut strategic objectives: 1) Generating a set of highly standardized protocols for investigating the unique and combined influence of specific priority signals, including sensory, cognitive and value-related factors, within an identical experimental framework across species (humans/monkeys) and techniques. 2) Gathering a cross-species, multi-dimensional dataset associated with the neural implementation of these priority signals, as obtained by a collection of methodological approaches characterized by highly diverse spatial and temporal resolution (behavior, EEG, TMS, fMRI and electrophysiological recordings). The main scientific goal, and foreseen theoretical advancement, of the project will be that of uncovering the functional architecture of attention to unveil the degree of independence vs. synergy between different priority signals and, in ultimate terms, to understand the role of functional segregation vs. integration/convergence in the brain implementation of AC. Specifically, we will be able to directly address the issue of whether different priority signals act as fully independent sources of AC, each taking precedence at any given time, or whether they exert synergistic effects (as implemented by linear summation algorithms or more complex computations). New to the field, the neural implementation of AC will be systematically investigated not only in relation to mechanisms of target selection, but also to address the putative dedicated mechanisms for distractor filtering, in order to unveil whether independent functional architectures do implement the two attentional operations. While pioneering the development of an integrated theoretical and computational account of AC, the proposed research will provide new constraints for validating and extending existing neural simulations of visuo-motor integration, and insights for the development of theoretical models linking observations at the neuronal level with data on network dynamics, thus being of immediate value for computation-oriented units of the HBP consortium.