Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms

Morgado, Luís; Gaspar, Graça

doi:10.1007/978-3-540-77343-6_4

Cited by 3 publications

(2 citation statements)

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“…Morgado and Gaspar’s agent flow model aims at giving a unified view of emotional and cognitive process [ 40 , 41 ]. The authors argue that the emotional appraisal depends on two base factors: the agent potential to produce change in environment (achievement potential) and the environment conduciveness or resistance to that change (achievement conductance).…”

Section: Discussionmentioning

confidence: 99%

“…Yet, the achievement potential and conductance respectively correspond to the agent skills and the environmental challenges or task demands in this theory. This model is evaluated in a simple 2D environment where the cognitive attributes determining the agent emotional dispositions is easy to compute [ 40 ]. However, there is a possible gap in terms of scaling up to real world experiments.…”

Section: Discussionmentioning

confidence: 99%

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Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task

2017

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Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal. Second, cognitive processes, such as those involved in self-assessment, influence emotional states by eliciting affects like boredom and frustration. Third, emotional processes such as those triggered by self-assessment influence attentional processes. Last, close emotion-cognition interactions implement an efficient feedback loop for the purpose of top-down behavior regulation. The latter is what we call ‘Emotional Metacontrol’. We introduce a model based on artificial neural networks. This architecture is used to control a robotic system in a visual search task. The emotional metacontrol intervenes to bias the robot visual attention during active object recognition. Through a behavioral and statistical analysis, we show that this mechanism increases the robot performance and fosters the exploratory behavior to avoid deadlocks.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%