{'fraction_non_alphanumeric': 0.03125, 'fraction_numerical': 0.002533783783783784, 'mean_word_length': 5.771428571428571, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A neural dynamic model of associative two-process theory: The differential outcomes effect and infant development

In animal and human learning, outcome expectancy is understood to control action under a number of learning paradigms. One such paradigm, the differential outcomes effect (DOE), entails faster learning when responses have differential, rather than non-differential, outcomes. The associative two-process theory has provided an increasingly accepted explanation as to how outcome expectancies influence action selection, though it is computationally not well understood. In this paper, we describe a neural-dynamic model of this theory implemented as an Actor-Critic like architecture. The model utilizes expectation-based, or prospective, action control that following differential outcomes training suppresses stimulus-based, or retrospective, action control (known as overshadowing in the learning literature). It thereby facilitates learning. The neural-dynamics of the model are evaluated in a simulation of experiments with young children (aged 4-8.6 years) that uses a differential outcomes procedure. We assess development parametrically in neural-dynamic terms.