A Markov Model of Conditional Associative Learning in a Cognitive Behavioural Scenario - Foundations on Natural and Artificial Computation - page 10

A Markov Model of Conditional Associative
Learning in a Cognitive Behavioural Scenario

Stefan Gluge 1 , Oussama H. Hamid 2 , Jochen Braun 2 , and Andreas Wendemuth 1

1 Faculty of Electrical Engineering and Information Technology,
Otto von Guericke University Magdeburg,
Universitatsplatz 2, 39106 Magdeburg, Germany
[e-mail]
2 Cognitive Biology, Institute of Biology,
Otto von Guericke University Magdeburg, Leipziger Strasse 44,
39120 Magdeburg, Germany
[e-mail]

Abstract. In conditional learning, one investigates the computational
principles by which the human brain solves challenging recognition prob-
lems. The role of temporal context in the learning of arbitrary visuo-
motor associations has so far been studied mostly in primates. We model
the explicit learning task where a sequence of visual objects is presented
to human subjects. The computational modelling of the algorithms that
appear to underlie human performance shall capture the effects of confu-
sion in ordered and random presentation of objects. We present a Markov
model where the learning history of a subject on a certain object is rep-
resented by the states of the model. The analysis of the resulting Markov
chain makes it possible to judge the influence of two model parameters
without the simulation of a specific learning scenario. As the model is
able to reproduce the learning behaviour of human subjects it might be
useful in the development of future experiments.

1 Introduction

In biology, conditional associative learning is often studied in the context of
arbitrary sensorimotor mappings [9,2]. Typically, the experimental design takes
a set of visual stimuli from the same category and maps them randomly onto a set
of motor responses. Subjects learn by trial and error which response produces the
reward in the case of each stimulus. As all stimuli are potentially associated with
reward, the subject cannot simply learn stimulus-reward associations. Instead,
subjects must link each stimulus to the specific response that ensures the reward
in each case.
The complexity of this task implies that multiple brain areas may be involved.
In fact, studies with behaving non-human primates reveal an extensive network
of brain regions underlying conditional associative learning [10,1,8].
To study the effect of temporal order on conditional associative learning, we
modified Miyashita's classical experiments with non-human primates [6,7]. Our