Air quality, cognitive reserve and decision-making in the Portuguese aging population
Description and Objectives:
The ability of the central neural system to withstand adverse changes related to aging is referred to as Cognitive Reserve (CR). The CR is known to be affected – positively and negatively – by a variety of factors, including environmental factors like ait pollution. CR in aging population modulates metacognition (i.e., the ability to evaluate one’s own decisions and cognitive performance) and decision-making strategies (i.e., the balance between exploration and exploitation, the strength of the uncertainty avoidance, the balance between risk-seeking and risk-aversion, use of prototypes in reasoning, etc.). In this project, we will analyze how air quality correlates with person’s cognitive functions, metacognition and decision-making strategies in Portuguese aging populations in rural and urban areas.
You will learn: 1) how to estimate the level of air pollution using instrumental measurements; 2) how to access satellite data and analyze it relative to particular geographical segment; 3) how to design and run cognitive tests, and adapt them for specific (hereby, aging) population(s); figure out causal relationships between different factors affecting public health and build models using structural equation modelling) to test competing theories of such causal relationships.
First, make a systematic review of literature on how air quality affect cognitive reserve. Optionally, run meta-analysis, based on the systematic review (if the student has sufficient skills, time reserves and interest for that). Second, learn experimental paradigms used in cognitive psychology to measure cognitive performance (related to attention, memory, executive functions). Third, adapt these paradigms to specificity of the aging population and design the experiments. Fourth, administer experiments to people and measure air quality at the time of the experiment, and pool satellite data on long-term conditions of air quality in these regions. Then data analysis, building and testing causal models using SEM (most probably, path analysis), and finally, writing a thesis.
Good communicative skills are required. Python skills will be beneficial. MatLab skills and expertise in (or desire to learn) the signal detection theory is highly beneficial.
The project might require multiple short travels around the country (if travels are necessary, funds will be provided for train and, if necessary, for basic accommodation).
The project potentially has practical implications for policy making and healthcare.
If reliable results are obtained, the student will receive support to present the results at scientific meetings, and publish them open access (probably this perspective goes beyond the master year programme, and will be of interest only to those who will benefit from co-authoring scientific publications for their future career).
EEG markers of decision-making in statistical learning
Description and Objectives:
Decision-making is affected by metacognitive skills. Metacognition is the ability to track one’s own cognitive performance with confidence ratings. Good metacognition is reflected in high confidence in one’s decisions that are correct, and low confidence in one’s decisions that are wrong.
This ability relies on two mechanisms: (1) error detection, or detection of those cases when the decision is wrong, but behavioural response is already initiated and cannot be inhibited, hence the person known he has committed an error, and assigns low confidence in such decisions; (2) estimation of probability of an error, or detecting those cases when committing an error is more likely, hence assigning lower confidence to decisions made in such cases. Both mechanisms are at place, and the aim of the project is to separate electrophysiological correlates of these two mechanisms.
During the first year, the student will use an existing dataset: EEG signal recorded during learning stage and EEG signal recorded during recognition test in a classical artificial grammar learning experiment. The experiment was run in two modalities (audio and visual), and we expect that they student will work with the data obtained in the visual modality (although we will also make the data for the audio modality available for comparison). The analysis will include: (1) time-frequency analysis of the data; (2) extracting ERPs from the data recorded during the recognition stage. Analysis of this dataset is a compulsory part of the project. The student will (1) review the methods of time-frequency EEG signal analysis; (2) review the literature on electrophysiological correlates of confidence and accuracy; (3) formulate the hypothesis and design an analysis pipeline to test the hypothesis; (4) present the results for (a) general public, and for (b) scientific output; (5) write the thesis outline.
During the subsequent years, the student will design and run his/her own decision-making experiment in an EEG laboratory, which will allow learning EEG data acquisition and experimental design principles, according to the thesis outline prepared by the end of the first year.
This is a good opportunity for people thinking about academic and rehabilitational use of the EEG technique.
This project will be linked to the European project and presupposes engagement with the main project and the team, including co-authoring publications for international journals. Also, the student will receive support in writing up his/her own first-author publications.
The student will benefit from experience with MatLab and deeper expertise in signal detection theory.