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Attention and Emotions, Computational Perspectives

There is now a wealth of evidence that the deployment of attention is strongly modulated by emotional salience. This evidence has been provided by empirical studies using behavioral, imaging, and electroencephalographic approaches. However, only a handful of computational models of the interaction between attention and emotion exist. These models attempt to identify the mechanisms that underlie key empirical phenomena, by answering questions such as how the processing of a stimulus might be modulated by its emotionality and, more specifically, how task-irrelevant emotional stimuli compete for attentional resources. Such models are the subject matter of this entry. The relevance of theories and models of attention and emotion in computer applications (in particular, in human-computer interaction) will also be considered.

Because of their link to neurophysiology, connectionist approaches will be highlighted. In addition, because there has been very little modeling work on other emotions, the entry will exclusively consider the effects of threatening stimuli. Models are classified according to the form of attention-emotion interaction that they posit: direct, indirect, or strategic.

Direct Interference

Andrew Mathews, Bundy Mackintosh, and Eamon Fulcher laid out the first variety of attention-emotion models. They proposed that all stimuli are rapidly evaluated to determine their level of threat, with this evaluation probably occurring in the amygdala. Then, due to the salience of threatening stimuli, the activation of such stimuli is directly enhanced. Consequently, relative to emotionally neutral stimuli, threatening stimuli are given a competitive advantage. Behaviorally, this manifests in two ways. First, when presented as distracting stimuli, threatening items will (surreptitiously) attract attention more than will neutral items and will thus impair performance more on an attended task. Second, when attended, threat stimuli will be responded to faster than will neutral stimuli. Initial findings seemed to confirm this pattern of behavior. For example, in the emotional Stroop task, participants were slower to name the ink color in which a threatening word was written than they were the ink color of a neutral word, although strong effects were most evident in clinical populations, such as the clinically anxious.

Probably the most notable computational model of this kind was Gerald Matthews and Trevor Harley's connectionist model of the emotional Stroop. Although the model was not directly wired up to exhibit this pattern, competition between stimuli emerged during learning, and a threat-monitoring unit gave threatening stimuli a positive bias in this competition.

Indirect Interference

John Taylor and Nickolaos Fragopanagos proposed a more complex realization of attention-emotion interactions in their neurophysiologically constrained neural network model. Although their model contains a pathway by which representations of threatening stimuli can be directly enhanced (via the amygdala), their model also proposes indirect effects of threatening stimuli. Specifically, a ventral emotion circuit, including the amygdala and orbital frontal cortex, is connected to more dorsal attention networks, including areas such as the dorsolateral prefrontal cortex and posterior parietal cortex. The reciprocal connections between these emotion and attention networks are inhibitory; thus, top-down attentional control can be disrupted by the detection of negative stimuli. As a result, performance can be worse on an attended negative stimulus than on an attended neutral stimulus, because activation of the ventral emotion network disrupts attentional focus on the negative stimulus itself. This aspect of the Taylor and Fragopanagos model is supported by recent behavioral data and contrasts it with the earlier direct models.

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