Periodic Reporting for period 2 - COGNITIVE THREADS (Separating parallel threads of cognition to better explain behaviour)
Période du rapport: 2022-05-01 au 2023-10-31
The study of the human brain pursues the understanding of how neural structure and function translate into cognition and behaviour. This general goal has two complementary directions. The first is to uncover fundamental principles that generalise across an entire population. The second is to describe the neural underpinnings of specific individual differences; that is, instead of generalising, the aim is to investigate the particularities of specific individuals. Focusing on the study of stimulus processing, this project bridges between these two approaches by developing novel statistical techniques aimed at dissecting the different parallel aspects of this processing —which is based on general principles— and characterising how such aspects are unique to each individual. This endeavour is important because, bringing closer together goals of basic and applied science, it will contribute methods that can, for example, help to provide better and more individualised treatments and more accurate diagnoses/prognoses by being better at dissecting the heterogeneity of both the causes and the symptoms.
In this first period of the grant, the group has produced a number of scientific papers that build the foundations for our general objective. These cover questions about how to use effectively these models to predict subject traits (given the difficulties of using the raw data for prediction), about the reliability of the model estimations (critical for their use in practical scenarios) and about how to optimally perform model selection (i.e. how do we choose our models).
Furthermore, we are about the release a software package in Python to make these methods publicly available.
Furthermore, we are about the release a software package in Python to make these methods publicly available.
By the end of the project, we expect to move beyond theoretical work and apply these methods to real world applications, as was originally proposed in the project