Modelling of Human Cognition Data

Instructors: 

Davide Crepaldi

Amount of frontal teaching: 

Around 12 hours of classes and around 25 hours of individual work

ECTS: 

7

In this course we go together over a set of human cognition data, trying to extract reliable inferences from it. If it sounds very general, that's exactly it—you can think of this course as a series of hands-on meetings on what is our everyday task as scientists, that is, transforming experimental data into solid science (which is somewhat correlated to have a publishable paper, or a solid chance to get your PhD ;-)).

In doing this, we'll go over a number of basic and not-so-basic statistical issues, which includes graphical data exploration, data manipulation (e.g., standardisation, centring), regression, mixed-effect modelling, predictor collinearity, non linearity, and bootstrap. Much attention will be given to monitoring our own work on issues such as data overstretch, p hacking, and other bad research practices (no external system will ever vicariate rigorous self inquiry in this respect).  

We won't cover these subjects systematically, from a theoretical standpoint. Rather, we'll go through them as they arise from our practical analysis work on the course dataset. This will allow a better grasping of the statistical concepts in context, and give the students knowledge that is more immediately transferrable to their everyday routine, without compromising on depth.    

The software I'll use for the course is R. There's no commitment in this statement, of course, and students with advanced background are most welcome to explore other options (Python is a very good one). I'll also make use of a visual interface to R called RStudio—same as above, seeing the same thing on your computer and mine will probably help out, particularly students with a rather basic background on stats/R, but there are surely several valid alternatives that students may like better.

Please bring your laptop with you at classes, with a working installation of whatever software you intend to use. This will be the best possible learning environment for you, independently of whether classes will be held in rooms with other computers available (which may or may not happen).         

This is as advanced course, and is on stats, not on software—I won't cover things such as software installation and basic software functionalities. These are considered to be pre–requisites for the course. You should spend a substantial amount of time before the course begins to get yourself familiar with things like R objects (numbers, vectors, matrices, lists, data frame); modes and attributes (character, factors, logical, numeric); basic functions (concatenation, tapply, indexing, binding, merge, table); import/export of data (read.table, scan, sink, write.table); probability distributions; grouping, loops, and conditional statements; writing your own function; and graphs (or their counterparts in the software you will be using at the course). This knowledge/familiarity can be obtained in several ways, and primarily by attending the Coding course offered by our PhD. If you decide to focus on R, a good entry point is studying and practicing chapters 1 to 10, and 12 of "An Introduction to R" (available here). Depending on a student's skills/attitude/commitment, this can require anything from a few hours to several weeks of work (=start well in advance).

The course will be rather intensive: in addition to the meetings, please consider that an effective learning will require at least 3-4 hours of individual work between meetings.

Finally, the course is NOT mandatory for students in the SISSA Cognitive Neuroscience PhD; therefore, there will be no final exam/grades by default. However: (i) SISSA students who would like to obtain a grade for the course; and (ii) Trento Master students who include this course in their "piano di studi"; may ask for/need an exam, which I will be happy to organize and will consist in a short essay (a commented R code) to be produced on a data frame provided by the teacher. The essay will be done remotely. People interested in the final exam should raise the issue with me as soon as possible after the course will begin.

The course will be held, most liklely, towards the end of February; a calendar for the classes will be announced soon, and will appear in the SISSA Cognitive Neuroscience website, precisely in the Calendar page.