[ACT-R-users] Groningen Spring School on Cognitive Modeling: March 30 to April 3, 2020

[Spring School, FA]

*Fifth Groningen Spring School on Cognitive Modeling*
*– ACT-R, Nengo, PRIMs, error-driven learning, and dynamical systems. –*

Date: *March 30 to April 3, 2020 *
Location: Groningen, the Netherlands
Fee: € 250 (late fee after February 15 will be € 300)
More information and registration:* www.cognitive-modeling.com/springschool
<http://www.cognitive-modeling.com/springschool>*

We are happy to announce the fifth Groningen Spring School on Cognitive
Modeling (March 30 to April 3, 2020). This year, the Spring School will
again cover four different modeling paradigms: ACT-R, Nengo, PRIMs, and
error-driven learning. It thereby offers a unique opportunity to learn the
relative strengths and weaknesses of these approaches.

Moreover, this year we are offering a lecture series on dynamical systems,
which should be interesting for anyone looking into modeling cognitive
dynamics at some level of abstraction. We recommend this lecture series as
an excellent combination with Nengo, for those interested in neuromorphic
computing.

The first day will provide an introduction to all five topics. From day
two, spring school students will be asked to commit to one topic, for which
they will attend lectures as well as tutorials. In addition, students can
sign up for a second topic, for which they will attend lectures only. All
students are invited to join a series of plenary research talks on the
different paradigms.

On the first day, spring school students are asked to introduce themselves
and their research interests in a poster session.

Registration is now open. For more information and registration, please see
the website: www.cognitive-modeling.com/springschool

Please feel free to forward the information to anyone who might be
interested in the Spring School.

We are looking forward to welcoming you in Groningen,

The Spring School team

______________

*ACT-R*
Teachers: Jelmer Borst, Maarten van der Velde, Stephen Jones, & Katja
Mehlhorn (University of Groningen)
Website: http://act-r.psy.cmu.edu.

ACT-R is a high-level cognitive theory and simulation system for developing
cognitive models for tasks that vary from simple reaction time experiments
to driving a car, learning algebra, and air traffic control. ACT-R can be
used to develop process models of a task at a symbolic level. Participants
will follow a compressed five-day version of the traditional summer school
curriculum. We will also cover the connection between ACT-R and fMRI.

*Nengo*
Teacher: Terry Stewart (University of Waterloo)
Website: http://www.nengo.ca

Nengo is a toolkit for converting high-level cognitive theories into
low-level spiking neuron implementations. In this way, aspects of model
performance such as response accuracy and reaction times emerge as a
consequence of neural parameters such as the neurotransmitter time
constants. It has been used to model adaptive motor control, visual
attention, serial list memory, reinforcement learning, Tower of Hanoi, and
fluid intelligence. Participants will learn to construct these kinds of
models, starting with generic tasks like representing values and positions,
and ending with full production-like systems. There will also be special
emphasis on extracting various forms of data out of a model, such that it
can be compared to experimental data.

*PRIMs*
Teacher: Niels Taatgen (University of Groningen)
Website: https://www.ai.rug.nl/~niels/prims/index.html

How do people handle and prioritize multiple tasks? How can we learn
something in the context of one task, and partially benefit from it in
another task? The goal of PRIMs is to cross the artificial boundary that
most cognitive architectures have imposed on themselves by studying single
tasks. It has mechanisms to model transfer of cognitive skills, and the
competition between multiple goals. In the tutorial we will look at how
PRIMs can model phenomena of cognitive transfer and cognitive training, and
how multiple goals compete for priority in models of distraction.

*Error-driven learning*
Teachers: Jacolien van Rij and Dorothée Hoppe (University of Groningen)

Error-driven learning (also called discrimination learning) allows to
simulate the time course of learning. It is based on the Rescorla-Wagner
model (Rescorla & Wagner, 1972) for animal cognition, which assumes that
learning is driven by expectation error, instead of behaviorist association
(Rescorla, 1988). The equations formulated by Rescorla and Wagner have been
used to investigate different aspects of cognition, including language
acquisition (e.g., Hsu, Chater, and Vitányi, 2011; St. Clair, Monaghan, and
Ramscar, 2009), second language learning (Ellis, 2006), and reading of
 complex words (Baayen et al, 2011). Although error-driven learning can be
applied for all domains in cognitive science, in this course we will focus
on how it could be used for modeling language processing and language
learning.

*Dynamical Systems: a Navigation Guide *
Teacher: Herbert Jaeger (University of Groningen)

This lecture-series gives a broad overview over the zillions of formal
models and methods invented by mathematicians and physicists for describing
“dynamical systems”. Here is a list of covered items: Finite-state automata
with and without input, deterministic and non-deterministic,
probabilistic), hidden Markov models and partially observable Markov
decision processes, cellular automata, dynamical Bayesian networks,
iterated function systems, ordinary differential equations, stochastic
differential equations, delay differential equations, partial differential
equations, (neural) field equations, Takens’ theorem, the engineering view
on “signals”, describing sequential data by grammars, Chomsky hierarchy,
exponential and power-law long-range interactions, attractors, structural
stability, bifurcations, phase transitions, topological dynamics,
nonautonomous attractor concepts. In the lectures, I try to work out the
underlying connecting lines between the “dots” listed above.
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