To maximise their chances for survival, humans and animals need to base
their decisions not only
on the average consequences of chosen actions, but also on the variability
of the rewards resulting
from these actions. For example, when ones food reserves are depleted, one
should prefer to forage
in an area where food is guaranteed, rather than an area where the amount
of food is higher on
average but variable, thus avoiding the risk of starvation. To implement
such policies, the brain
needs to be able to learn about variability of rewards resulting from
taking different actions. This
talk will present a simple mathematical model describing how such learning
may be implemented in
the brain. The models suggests how the information about reward uncertainty
is used by the brain to
make decisions that not only maximize expected rewards but also take risks
into account. The
models is consistent with a wide range of experimental data from
neuroscience. The talk will also
include a discussion of the relationship between the model and the
probability weighting functions
from the prospect theory.
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Emanuela
Rosazza Gianin
Dipartimento di Statistica e Metodi Quantitativi
Università di Milano-Bicocca
Edificio U7 – 4° Piano
Via Bicocca degli
Arcimboldi, 8
20126 Milano
Tel. 02 64483208
Fax. 02
64483105
e-mail:
emanuela.rosazza1@unimib.it
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