Date: Friday, November 5
Time: 10.00am to 11.30am
Venue: Tóth István György Room, CEU, 1051 Nádor u. 11. Courtyard (NOTE the different
venue!)
Speaker:
Máté Lengyel, Computational and Biological Learning Lab, Cambridge University
Title:
Learning and memory: the powers and perils of Bayesian inference
Abstract:
The theory of Bayesian inference presents a normative approach to understanding how
animals and humans learn about their environment. To demonstrate this, I will start by
introducing the theory and show as an example how it explains aspects of human chunk
learning in a visual learning paradigm that cannot be captured by traditional associative
learning accounts. I will then turn to a complementary view of Bayesian inference: how it
can be used as a data analysis tool to estimate mental representations of object classes
from simple binary response data collected in psychophysical experiments. Such methods can
be used to track as humans develop complex internal representations, with minimal changes
to already existing experimental paradigms. Finally, I will take a step back, and place
learning and memory within the wider context of behavioural economics. I will argue that
even though Bayesian inference offers a statistically optimal way for learning, the
representations it learns — internal models — can be highly inefficient for decision
making. This leaves room for qualitatively different ways of learning to be advantageous
under some ecologically relevant conditions. I will show how one such alternative,
episodic memory, can be understood as a better way to support optimal decision making
under risk and uncertainty in complex environments, and how this normative view of
episodic memory accounts for many of its behavioural and neural correlates. These studies
together provide a principled framework to explore complex learning and developmental
phenomena reported in humans and animals.
Everyone is welcome to attend.
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