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Affective-Associative Two-Process theory: A neural network investigation of adaptive behaviour in differential outcomes training

Artikel i vetenskaplig tidskrift
Författare Robert Lowe
E. Billing
Publicerad i Adaptive Behavior
Volym 25
Nummer/häfte 1
Sidor 5-23
ISSN 1059-7123
Publiceringsår 2017
Publicerad vid Institutionen för tillämpad informationsteknologi (GU)
Sidor 5-23
Språk en
Länkar doi.org/10.1177/1059712316682999
Ämnesord Actor-Critic, reward learning, animal models, Associative-Two Process theory, neo-behaviourism, conditional discrimination performance, computational model, recognition, memory, decision-making, reward, expectancies, children, dopamine, amygdala, cortex, Computer Science, Psychology, Social Sciences - Other Topics
Ämneskategorier Tvärvetenskapliga studier, Psykologi, Datavetenskap (datalogi)

Sammanfattning

In this article we present a novel neural network implementation of Associative Two-Process (ATP) theory based on an Actor-Critic-like architecture. Our implementation emphasizes the affective components of differential reward magnitude and reward omission expectation and thus we model Affective-Associative Two-Process theory (Aff-ATP). ATP has been used to explain the findings of differential outcomes training (DOT) procedures, which emphasize learning differentially valuated outcomes for cueing actions previously associated with those outcomes. ATP hypothesizes the existence of a prospective' memory route through which outcome expectations can bring to bear on decision making and can even substitute for decision making based on the retrospective' inputs of standard working memory. While DOT procedures are well recognized in the animal learning literature they have not previously been computationally modelled. The model presented in this article helps clarify the role of ATP computationally through the capturing of empirical data based on DOT. Our Aff-ATP model illuminates the different roles that prospective and retrospective memory can have in decision making (combining inputs to action selection functions). In specific cases, the model's prospective route allows for adaptive switching (correct action selection prior to learning) following changes in the stimulus-response-outcome contingencies.

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