Integrating Statistical Learning into Cognitive Science
• 大类 : 医学 - 2区
• 小类 : 心理学 - 2区
Aims and scope of the special issue:
Statistical learning (SL)—learning from the distributional properties of sensory input across time and space—has become a major theoretical construct in cognitive science. Hypothesized as the primary means by which organisms learn about the regularities in the environment, SL is proposed to be involved in a wide range of basic and higher-order cognitive functions such as vision, audition, motor planning, event processing, reading, speech perception, language acquisition, semantic memory, and social cognition, to name a few. The important role of SL in cognitive science stems, therefore, from the wide range of processes it may subserve. However, each domain of learning is characterized by different types of regularities, and different types of computations. For SL theory to achieve its promise and establish itself as a key building block of such a diverse set of cognitive functions, evidence from a wide range of research areas should be integrated with SL research and vice versa. SL research should also converge with what we know about the general faculties that subserve cognition such as perception, memory, attention deployment, executive functions, etc. The aim of this special issue is to take a step in this direction. The volume takes as its starting point that a range of mechanisms, computations, and representations are involved in the detection and learning of regularities in different domains, and that these vary with different temporal windows of learning. It aims to incorporate empirical evidence from a wide range of domains, experimental paradigms, and timescales, and to offer theoretical discussions of how SL research is best integrated into cognitive science.