[ACT-R-users] Error-driven learning at the Groningen Spring School on Cognitive Modeling

[Spring School, FA]

*Update: New tutorial on Error-driven learning*

Fourth Groningen Spring School on Cognitive Modeling
*– ACT-R, Nengo, PRIMs, & Error-driven learning–*

Date: April 8-12, 2019
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>*

As briefly announced earlier, this year we will be offering a new tutorial
on error-driven learning during the spring school. Error-driven learning
(also called discrimination learning) allows to simulate the time course of
learning. It can be applied for all domains in cognitive science, but is
especially useful for modeling language processing and language learning.
More information about this simple and elegant approach can now be found on
our website
<http://www.cognitive-modeling.com/springschool/home/error-driven-learning/>
.

As in previous years, the Spring School will also cover the ACT-R, Nengo,
and PRIMs paradigms. A preliminary version of the program can now be found
on our website.

The *early registration deadline ends on February 15*, so make sure to sign
up before then.

Please let us know if you have any questions or check out our website for
more information.

Best regards,
the spring school team

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

______________

*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.

*ACT-R*
Teachers: Jelmer Borst & 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: http://www.ai.rug.nl/~niels/actransfer.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.
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