Trends in cognitive sciences
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Trends Cogn. Sci. (Regul. Ed.) · Apr 2014
ReviewSupra-personal cognitive control and metacognition.
The human mind is extraordinary in its ability not merely to respond to events as they unfold but also to adapt its own operation in pursuit of its agenda. This 'cognitive control' can be achieved through simple interactions among sensorimotor processes, and through interactions in which one sensorimotor process represents a property of another in an implicit, unconscious way. So why does the human mind also represent properties of cognitive processes in an explicit way, enabling us to think and say 'I'm sure' or 'I'm doubtful'? We suggest that 'system 2 metacognition' is for supra-personal cognitive control. It allows metacognitive information to be broadcast, and thereby to coordinate the sensorimotor systems of two or more agents involved in a shared task.
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Precise timing is crucial to decision-making and behavioral control, yet subjective time can be easily distorted by various temporal contexts. Application of a Bayesian framework to various forms of contextual calibration reveals that, contrary to popular belief, contextual biases in timing help to optimize overall performance under noisy conditions. Here, we review recent progress in understanding these forms of temporal calibration, and integrate a Bayesian framework with information-processing models of timing. We show that the essential components of a Bayesian framework are closely related to the clock, memory, and decision stages used by these models, and that such an integrated framework offers a new perspective on distortions in timing and time perception that are otherwise difficult to explain.
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Music is used to regulate mood and arousal in everyday life and to promote physical and psychological health and well-being in clinical settings. However, scientific inquiry into the neurochemical effects of music is still in its infancy. In this review, we evaluate the evidence that music improves health and well-being through the engagement of neurochemical systems for (i) reward, motivation, and pleasure; (ii) stress and arousal; (iii) immunity; and (iv) social affiliation. We discuss the limitations of these studies and outline novel approaches for integration of conceptual and technological advances from the fields of music cognition and social neuroscience into studies of the neurochemistry of music.
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Trends Cogn. Sci. (Regul. Ed.) · Sep 2012
ReviewEmotion and decision-making: affect-driven belief systems in anxiety and depression.
Emotion processing and decision-making are integral aspects of daily life. However, our understanding of the interaction between these constructs is limited. ⋯ We discuss how studies of individuals with emotional dysfunctions provide evidence that alterations of decision-making can be viewed in terms of altered probability and value computation. We argue that the probabilistic representation of belief states in the context of partially observable Markov decision processes provides a useful approach to examine alterations in probability and value representation in individuals with anxiety and depression, and outline the broader implications of this approach.
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Trends Cogn. Sci. (Regul. Ed.) · Sep 2012
ReviewMapping discrete and dimensional emotions onto the brain: controversies and consensus.
A longstanding controversy in the field of emotion research has concerned whether emotions are better conceptualized in terms of discrete categories, such as fear and anger, or underlying dimensions, such as arousal and valence. In the domain of neuroimaging studies of emotion, the debate has centered on whether neuroimaging findings support characteristic and discriminable neural signatures for basic emotions or whether they favor competing dimensional and psychological construction accounts. This review highlights recent neuroimaging findings in this controversy, assesses what they have contributed to this debate, and offers some preliminary conclusions. Namely, although neuroimaging studies have identified consistent neural correlates associated with basic emotions and other emotion models, they have ruled out simple one-to-one mappings between emotions and brain regions, pointing to the need for more complex, network-based representations of emotion.