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Probabilistic reasoning by neurons Tianming Yang1

Probabilistic reasoning by neurons
Tianming Yang1
& Michael N. Shadlen1
Our brains allow us to reason about alternatives and to make choices that are likely to pay off. Often there is no one correct
answer, but instead one that is favoured simply because it is more likely to lead to reward. A variety of probabilistic
classification tasks probe the covert strategies that humans use to decide among alternatives based on evidence that bears
only probabilistically on outcome. Here we show that rhesus monkeys can also achieve such reasoning. We have trained two
monkeys to choose between a pair of coloured targets after viewing four shapes, shown sequentially, that governed the
probability that one of the targets would furnish reward. Monkeys learned to combine probabilistic information from the
shape combinations. Moreover, neurons in the parietal cortex reveal the addition and subtraction of probabilistic quantities
that underlie decision-making on this task.
Decision-making is a complicated process that is often based on
more than one source of evidence. The brain needs to combine
these sources to maximize the chance of achieving a correct decision
or to achieve another related goal. Recent advances in neuroscience
are beginning to expose the neurobiological mechanisms that
underlie simple decisions16
. It has been demonstrated that, when
the outcome of a decision is an eye movement, a neural correlate of


Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra


Collections: Computer Technologies and Information Sciences