Cognition and Computation

Alberto Testolin
Dipartimento di Psicologia Generale, Università degli Studi di Padova,

The aim of the course is to introduce the student to the leading frameworks to understand human cognition from a computational perspective. The discussed approaches are relevant both for understanding how the mind works and for the design of modern artificial intelligence systems. In particular, besides the most popular neural network models (e.g., deep learning), we will introduce the framework of structured probabilistic models, which are based on Bayesian rationality principles. Theoretical discussion will be complemented by case studies from the cognitive modeling literature.

- Introduction to the computational approach to understand human cognition.
- Symbolic vs. emergentist models.
- Bayesian rationality and structured probabilistic models.
- Case studies from the cognitive modeling literature.

Introductory readings
McClelland, J., et al. (2010). Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in cognitive sciences
Tenenbaum et al. (2011). “How to grow a mind: Statistics, structure and abstraction.” Science
Hassabis et al. (2017). “Neuroscience-inspired Artificial Intelligence.” Neuron

Course requirements
The student is expected to have basic knowledge of probability theory and machine learning.

Examination modality

Course material, enrollment and last minute notifications
Made available by the teacher at this Moodle address

08 Feb 2021, 14:00-17:00
18 Feb 2021, 14:00-17:00

Zoom; the link is in the Moodle page of the course.

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