About this Research Topic

Background

Introduction
The human mind is driven toward understanding. Beyond perceiving correlations in the environment, we want to understand the spatial, temporal, and psychological mechanisms from which events are created. The capacity to infer the causal structure of experience and to generate explanations is central to our sense of understanding, making possible the formation of conceptual representations that constrain inference, guide generalization, and provide the basis for goal-directed, intelligent behavior. The purpose of this Research Topic in Frontiers in Human Neuroscience is to further our understanding of the psychological, computational, and neural foundations of causal inference and explanation.

Psychology
Emerging research in psychology investigates how children and adults construct causal interpretations of the world around them. How are people able to infer the causal structure of experience and to generate explanations? When do we seek explanations and what makes some explanations better than others? When do these capacities emerge in development and how are they shaped by intellectual ability, society and culture, and the structure of experience? How do these processes interact with cognitive systems for learning and memory? What role do they play in the formation of cognitive, social and affective processes?

Computation
In computational cognitive science, theorists have applied formal models (drawing on Bayesian statistics and symbolic neural networks) to characterize causal inference and explanation in computational terms. Such models have sharpened our understanding of the nature and origins of causal inference and offer new insight into cognitive algorithms for human explanation. Key questions for further exploration include: How such structured symbolic representations are implemented within cognitive and neural systems. To what extent does the information processing architecture of the human mind/brain operate on the basis of flexibly structured causal models, symbolic neural networks or other forms of representation?

Neuroscience
Accumulating neuroscience evidence indicates that the human prefrontal cortex (PFC) extracts statistical regularities across experience in an effort to infer general patterns and causal relationships that establish the proper mappings between situations, actions and consequences necessary for goal-directed behavior. Future research should develop increasingly precise theories of how causal representations are encoded and processed by the PFC, and examine how these mechanisms interact with cognitive, affective, and social knowledge networks to guide specific forms of thought and behavior. It is equally important to identify genes and regulatory sequences that contribute to the organization and functioning of the observed neural circuits and molecular pathways. Finally, future research should investigate how these mechanisms are altered in psychiatric illness and neurological disease and develop cognitive neuroscience directed interventions.

Solicitation
We solicit original empirical work, review and opinion papers, and methodological papers that can promote our understanding of explanatory inference. This Research Topic is designed to promote an interdisciplinary approach to understanding the nature and origins of causal inference, and to elucidate the remarkable cognitive, neural, and genetic architecture from which explanatory inference and our sense of understanding emerge.

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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