ID:
511454
Durata (ore):
36
CFU:
6
SSD:
PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA
Stato approvazione:
Bozza
Anno:
2024
Dati Generali
Periodo di attività
Primo Semestre (02/10/2024 - 20/12/2024)
Syllabus
Obiettivi Formativi
This course aims at developing knowledge and understanding in the way the brain and the cognitive system generate memories and process language. Generally speaking, the course sits in the area of Cognitive and Experimental Psychology and Neuroscience.
I’ll aim at:
- advancing the students’ comprehension of theoretical models, and specifically how they can be formalized to generate precise predictions, so that they can be assessed properly;
- deepening the students’ understanding of the methods that underlie the construction of these formal models.
Furthermore, the course aims at cultivating the ability to apply this knowledge and understanding effectively by:
- enhancing proficiency in executing and assessing applications within experimental contexts in the area of Cognitive Psychology and Experimental Psychology;
- advancing the students’ competence in applying these methods to the clinical setting;
- promoting critical thinking, analytical prowess, and the synthesis of ideas, again in the area of Cognitive Psychology and Experimental Psychology;
- using ethical principles in practical applications and research endeavours in the same areas.
I’ll aim at:
- advancing the students’ comprehension of theoretical models, and specifically how they can be formalized to generate precise predictions, so that they can be assessed properly;
- deepening the students’ understanding of the methods that underlie the construction of these formal models.
Furthermore, the course aims at cultivating the ability to apply this knowledge and understanding effectively by:
- enhancing proficiency in executing and assessing applications within experimental contexts in the area of Cognitive Psychology and Experimental Psychology;
- advancing the students’ competence in applying these methods to the clinical setting;
- promoting critical thinking, analytical prowess, and the synthesis of ideas, again in the area of Cognitive Psychology and Experimental Psychology;
- using ethical principles in practical applications and research endeavours in the same areas.
Prerequisiti
The course is entirely self-contained – there’s no strict entrance requirement, any motivated student will be able to take the course profitably.
There’s a set of skills and knowledge, however, that will certainly help students get the most of this course:
- Some notion of neural processing (e.g., the way individual neurons and neuronal assemblies process information) and some familiarity with fundamental concepts in cognitive psychology (e.g., information, cognitive module) will help seeing connections between the models we’ll cover in the course and the basic behaviour of the brain and intelligent systems.
- It would also help if students are familiar with some basic mathematical tools (e.g., linear equations, matrix algebra, Bayes theorem). People can get through this course very happily without any math at all, but a deep understanding of the models we’ll study together does require a bit of mathematical intuition.
- Having taken some introductory courses in memory and language will also help. If one already knows the general features of a theoretical model, they’ll be able to focus on its computational implementation. Again, this is not strictly necessary though; we will cover the general features of the models as well as their computational version.
There’s a set of skills and knowledge, however, that will certainly help students get the most of this course:
- Some notion of neural processing (e.g., the way individual neurons and neuronal assemblies process information) and some familiarity with fundamental concepts in cognitive psychology (e.g., information, cognitive module) will help seeing connections between the models we’ll cover in the course and the basic behaviour of the brain and intelligent systems.
- It would also help if students are familiar with some basic mathematical tools (e.g., linear equations, matrix algebra, Bayes theorem). People can get through this course very happily without any math at all, but a deep understanding of the models we’ll study together does require a bit of mathematical intuition.
- Having taken some introductory courses in memory and language will also help. If one already knows the general features of a theoretical model, they’ll be able to focus on its computational implementation. Again, this is not strictly necessary though; we will cover the general features of the models as well as their computational version.
Metodi didattici
The course will be made up of frontal lectures, integrated with seminars, hands-on laboratories with computational tools, case-based analyses, practical exercises, and group discussions on scientific journal papers – the precise mixture of which will be adapted to the number of students attending.
Verifica Apprendimento
The final grade will be based on an oral assessment. The interview will cover the topics discussed during the course, as well as those proposed in the reading material indicated in the syllabus (note that the latter will probably be a superset of the former). The evaluation will be obviously based on the knowledge acquired during the course. But more than that, I’m interested in the students’ ability to argue and critically apply the knowledge acquired during the course to real/realistic cases. The student's capacity to present their point of view clearly and coherently will also be taken into consideration. The aim of the evaluation is to assess the depth of the students’ understanding and their ability to effectively apply the new knowledge acquired. Students will be encouraged to demonstrate their critical analysis and problem-solving skills, as well as their ability to connect the various topics covered during the course.
For the students who will be able to attend classes (which is very strongly encouraged!), the final grade will also be based on the quality of their in-class participation – the quality of their questions, their ability to help their peers during the discussions, and their group-oriented learning attitude.
Depending on the number of students attending, some informal assignments might be given during the course, which might contribute to the final grade.
The grade scale goes from 0 to 30 cum laude (30L). 18 is the minimum passing grade.
For the students who will be able to attend classes (which is very strongly encouraged!), the final grade will also be based on the quality of their in-class participation – the quality of their questions, their ability to help their peers during the discussions, and their group-oriented learning attitude.
Depending on the number of students attending, some informal assignments might be given during the course, which might contribute to the final grade.
The grade scale goes from 0 to 30 cum laude (30L). 18 is the minimum passing grade.
Testi
The core reading materials are the following papers:
- Coltheart et al., (2001). DRC: A Dual Route Cascaded Model of Visual Word Recognition and Reading Aloud. Psychological Review, 108, 204-256.
- Seidenberg, M.S and McClelland, J. (1989). A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review, 96, 523-568.
- Davis, C.J. (2010). The Spatial Coding Model of Visual Word Identification. Psychological Review, 117, 713-758.
- Collins, A.M. and Loftus, E.F. (1975). A Spreading-Activation Theory of Semantic Processing, Psychological Review, 82, 407-428.
- McRea, K., De Sa, V.R. and Seidenberg, M. (1997). On the Nature and Scope of Featural Representations of Word Meaning. Journal of Experimental Psychology: General, 126, 99-130.
- Lenci, A. (2018). Distributional Models of Word Meaning. Annual Review of Linguistics, 4, 151-171.
- Endress, A.D. and Johnson, S.P. (2021). When Forgetting Fosters Learning: A Neural Network Model for Statistical Learning. Cognition, 213, 104621.
As more general reference textbooks, I’d suggest you consider:
- Computational Models of Reading: A Handbook, by Erik Reichle (Macquarie University, Sydney), published in 2020 by Oxford University Press. This book covers some of the models that we’ll discuss in class – with considerably lesser details in some case though. But here you’ll also find many other models that we will not cover during the course, thus providing very useful context for our target models. This book also has a nice introduction to computational models – what they are, why are they useful, what type of models are out there, and so on. This part will be directly useful for the course (and the exam).
- How to Build a Brain: A Neural Architecture for Biological Cognition, by Chris Eliasmith (University of Waterloo), published in 2013, again by OUP. This is the next step, for those who become really really interested in computational models of neural processing and cognition. It is a considerably more difficult book, which goes way deeper into things like representation, processing, neural dynamics, and the mathematical basis of state-of-the-art computational models of cognition and the brain. We will not cover this book during the course (nor is it strictly necessary for the exam), but we will touch onto most of its content – at a much more superficial level than the book goes.
- Coltheart et al., (2001). DRC: A Dual Route Cascaded Model of Visual Word Recognition and Reading Aloud. Psychological Review, 108, 204-256.
- Seidenberg, M.S and McClelland, J. (1989). A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review, 96, 523-568.
- Davis, C.J. (2010). The Spatial Coding Model of Visual Word Identification. Psychological Review, 117, 713-758.
- Collins, A.M. and Loftus, E.F. (1975). A Spreading-Activation Theory of Semantic Processing, Psychological Review, 82, 407-428.
- McRea, K., De Sa, V.R. and Seidenberg, M. (1997). On the Nature and Scope of Featural Representations of Word Meaning. Journal of Experimental Psychology: General, 126, 99-130.
- Lenci, A. (2018). Distributional Models of Word Meaning. Annual Review of Linguistics, 4, 151-171.
- Endress, A.D. and Johnson, S.P. (2021). When Forgetting Fosters Learning: A Neural Network Model for Statistical Learning. Cognition, 213, 104621.
As more general reference textbooks, I’d suggest you consider:
- Computational Models of Reading: A Handbook, by Erik Reichle (Macquarie University, Sydney), published in 2020 by Oxford University Press. This book covers some of the models that we’ll discuss in class – with considerably lesser details in some case though. But here you’ll also find many other models that we will not cover during the course, thus providing very useful context for our target models. This book also has a nice introduction to computational models – what they are, why are they useful, what type of models are out there, and so on. This part will be directly useful for the course (and the exam).
- How to Build a Brain: A Neural Architecture for Biological Cognition, by Chris Eliasmith (University of Waterloo), published in 2013, again by OUP. This is the next step, for those who become really really interested in computational models of neural processing and cognition. It is a considerably more difficult book, which goes way deeper into things like representation, processing, neural dynamics, and the mathematical basis of state-of-the-art computational models of cognition and the brain. We will not cover this book during the course (nor is it strictly necessary for the exam), but we will touch onto most of its content – at a much more superficial level than the book goes.
Contenuti
During the course, we will cover these areas/models:
- semantic memory (e.g., early semantic networks, prototypes, modern large language models);
- statistical learning (e.g., associative nets, chunking)
- visual word identification and reading aloud (e.g., SOLAR, the Overlap model, the DRC);
- eye movement during reading (e.g., SWIFT, EZ-READER, OBI-1).
More fundamentally, we will see why it is useful to go through the hassle of building computational models, and explore the different types of models we can build (e.g., neural networks, self-learning vs. hardwired, local vs. distributed representations).
- semantic memory (e.g., early semantic networks, prototypes, modern large language models);
- statistical learning (e.g., associative nets, chunking)
- visual word identification and reading aloud (e.g., SOLAR, the Overlap model, the DRC);
- eye movement during reading (e.g., SWIFT, EZ-READER, OBI-1).
More fundamentally, we will see why it is useful to go through the hassle of building computational models, and explore the different types of models we can build (e.g., neural networks, self-learning vs. hardwired, local vs. distributed representations).
Lingua Insegnamento
INGLESE
Corsi
Corsi
PSYCHOLOGY, NEUROSCIENCE AND HUMAN SCIENCES
Laurea Magistrale
2 anni
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