Trends in Cognitive Sciences
The cognitive control of emotion
Introduction
Conflicts, failures, and losses at times seem to conspire to ruin us. Yet, as Marcus Aurelius observed nearly two millennia ago, we humans have an extraordinary capacity to regulate the emotions occasioned by such travails. Importantly, these regulatory efforts largely determine the impact such difficulties will have on our mental and physical well-being 1, 2, 3. Given its importance to adaptive functioning, it is not surprising that research on emotion regulation has a long history (Box 1). Past work has investigated the cellular responses to stress, the behavioral consequences of adopting specific regulatory strategies, and the neural systems involved in simple forms of affective learning and social behavior in rodents and nonhuman primates 1, 4, 5, 6, 7. In recent years, research on emotion regulation has entered a new phase as functional imaging studies of regulatory phenomena in humans have developed rapidly. This growth has facilitated investigation of human analogs to affective behaviors studied in animals, but, perhaps more importantly, has allowed study of the emotion regulatory power of higher cognitive control processes that are difficult to study in animal models. In so doing, current work on the āhotā control of emotion draws on rapidly developing cognitive neuroscience models of the ācoldā control of attention and memory (e.g. 8, 9). The aim of this review is to evaluate recent imaging studies that, in the context of evidence from allied human and animal work, help to elucidate the functional architecture underlying the cognitive control of emotion.
Section snippets
Emotion and emotion regulation
An essential part of understanding emotion regulatory mechanisms is characterizing the processes that generate emotions. Current models posit that emotions are valenced responses to external stimuli and/or internal mental representations that (i) involve changes across multiple response systems (e.g. experiential, behavioral, peripheral physiological [10]), (ii) are distinct from moods, in that they often have identifiable objects or triggers, (iii) can be either unlearned responses to stimuli
Attentional control
Attention is often referred to as the selective aspect of information processing, enabling us to focus on goal-relevant (e.g. our writing) and ignore goal-irrelevant (e.g. loud music next door) information. In general, studies have indicated that behavioral and neural responses to attended as compared with unattended stimuli (or stimulus features) are either facilitated or inhibited, respectively (e.g. [19]). When responses to attended and unattended inputs do not differ, processing is
Cognitive change
The use of higher cognitive abilities such as working memory, long-term memory and mental imagery to support learning, judgment and reasoning has been a primary focus of research in cognitive neuroscience. In general, these abilities have been shown to depend upon interactions between prefrontal systems that support control processes and posterior cortical and subcortical systems that represent different types of modality specific (e.g. visual, spatial, auditory) information 8, 36. In the
Towards a functional architecture of cognitive control of emotion
The goal of this review was to evaluate recent imaging studies whose results can help to elucidate the functional architecture underlying the cognitive control of emotion.
Work using animal models of affective learning and imaging studies of either cognitive control or emotional responding in both healthy and psychiatric populations have implicated regions of PFC, OFC and ACC in specific types of control processes and subcortical regions, such as the amygdala, in different types of emotional
Future directions
Although current research provides converging evidence for a functional architecture for emotion control, it is important to note that for each type of control examined here, limited data and/or variability in activations across studies make it difficult to draw firm and highly specific inferences concerning which control computations are carried out by specific systems, and how they configure for different strategies in different contexts. To address these issues, future work will need to: (1)
Acknowledgements
The writing of this review was supported by National Science Foundation Grant BCS-93679 and National Institute of Health Grant MH58147.
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