Periodic Reporting for period 3 - ContentMAP (Contentotopic mapping: the topographical organization of object knowledge in the brain)
Berichtszeitraum: 2022-02-01 bis 2023-07-31
Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. In ContentMAP I will put forth a novel understanding of how object knowledge is organized in the brain, by proposing that this knowledge is topographically laid out in the cortical surface according to object-related dimensions that code for different types of representational content – I will call this contentotopic mapping. To study this fine-grain topography, I will use a combination of fMRI, behavioral, and neuromodulation approaches. I will first obtain patterns of neural and cognitive similarity between objects, and from these extract object-related dimensions using a dimensionality reduction technique. I will then parametrically manipulate these dimensions with an innovative use of a visual field mapping technique, and test how functional selectivity changes across the cortical surface according to an object’s score on a target dimension. Moreover, I will test the tuning function of these contentotopic maps. Finally, to mirror the complexity of implementing a highdimensional manifold onto a 2D cortical sheet, I will aggregate the topographies for the different dimensions into a composite map, and develop an encoding model to predict neural signatures for each object. To sum up, ContentMAP will have a dramatic impact in the cognitive sciences by describing how the stuff of concepts is represented in the brain, and providing a complete description of how fine-grain representations and functional selectivity within highlevel complex processes are topographically implemented.
ContentMAP tries to address how information about (manipulable) objects is organized in the brain. Specifically, ContentMAP proposes that, in part, the organization of object knowledge followed the typical organizational principles that the brain applies elsewhere – i.e. that object information is topographically organized by (object-related) dimensions. During this first part of ContentMAP, we have used the object-related dimensions we had obtained (and described in the Action), and tested whether these dimensions: a) are organizing principles for neural data (as they are for behavioral judgements); and b) whether that organization is topographic – i.e. are objects represented in different areas in neural proximity of each other based on whether they are similar in these object-related dimensions?
Our preliminary data suggests that 1) our object-related dimensions are used as organizing principles for neural data, in that decoding of object-specific neural patterns is influenced by object-specific score in these dimensions; and that 2) these dimensions drive a topography organization of information - what we call contentotopy, in that when we use visual mapping techniques such as population receptive field we obtain continuous maps in different areas. We have also developed a parallel (and not originally proposed in the Action) line of research focusing also on the organization of object knowledge in the brain and particularly on the role of connectivity in how conceptual information is processed and organized. Here, we have shown that object-related local computations are shaped by long-range connectivity with regions that share high-level object preferences in order to fulfill particular cognitive demands.
Our preliminary data suggests that 1) our object-related dimensions are used as organizing principles for neural data, in that decoding of object-specific neural patterns is influenced by object-specific score in these dimensions; and that 2) these dimensions drive a topography organization of information - what we call contentotopy, in that when we use visual mapping techniques such as population receptive field we obtain continuous maps in different areas. We have also developed a parallel (and not originally proposed in the Action) line of research focusing also on the organization of object knowledge in the brain and particularly on the role of connectivity in how conceptual information is processed and organized. Here, we have shown that object-related local computations are shaped by long-range connectivity with regions that share high-level object preferences in order to fulfill particular cognitive demands.
The data obtained thus far is still preliminary, but if these preliminary results are confirmed with full data analysis, we have potentially groundbreaking results that go beyond the state of the art, as they show that object-related dimensions are used as an organizing principle of neural object-related information. Moreover, and importantly, at least for some of these object-related dimensions, this organization follows topographically the score of each object in the target dimension. This is what was proposed as the main breakthourgh of ContentMAP. Moreover, the parallel line of research that we developed has led to a couple of papers (e.g. Almeida et al., 2021, Walbrin & Almeida, 2021) that are breakthroughs that were not expected. In Almeida et al., 2021, we showed for the first time that face processing requires representing faces in a 3D fashion. In Walbrin & Almeida, 2021, we showed that representations in a local area are dependent on the processing in a distally, yet related area.