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Timing: representation-based
versus action-based accounts
Armando Machado
Indiana University, USA
In this talk I will survey a
set of studies designed to contrast two theories of how animals
time events, the Scalar Expectancy Theory (SET) and the Learning
to Time model (LeT).
My focus will be on a key assumption made by each theory concerning
the learning process: In SET temporal learning consists of storing
counts in distinct memory stores, whereas in LeT learning consists
of strengthening and weakening connections between distinct behavioral
states and the operant responses. I derive the predictions of
each theory for two timing tasks, and then compare these predictions
with the corresponding experimental findings. Finally, I will
draw some implications of the successes and failures of each
theory for our understanding of how animals time events. I will
argue in particular that by separating representation from action,
cognitive models continue to leave the animal "buried in
thought".
Keywords: models of timing, temporal
learning, representation, action. |