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Little is known about the interplay between affective and cognitive processes of decision making within the bounded rationality perspective, in particular for the debate on adaptive decision making and strategy selection. This gap in the knowledge is particularly important as affect and deliberation may direct preferences in opposite directions. How do decision makers solve such dissonance? In this paper, we address this question by exploring the use of integral affect as a choice heuristic in comparison with and in conjunction to “take the best,” and weighted addition of attributes (WADD). We operationalize theories of reliance on affect in choice through a "Take the emotionally best" algorithm. Its predictive power is experimentally tested against other models, including mixed-sequential cognitive/affective procedures. We find that individual decisions are better predicted by a sequential combination of "Take the emotionally best" and "Take the best" with a slight dominance of the former. Conditions of cognitive/affective ambivalence, low discrimination ability and high complexity provide the cognitive architecture where such blended choice strategies predict decisions more precisely. This implies that reliance on integral affect may precede the use of cognitive cues following an ecological rationality perspective rather than supporting a kind of competition between affect and cognition as implied in current literature. [ABSTRACT FROM AUTHOR]

Background and objective: The presence of executive deficits in patients with Amyotrophic Lateral Sclerosis is well established, even if standardized measures are difficult to obtain due to progressive physical disability of the patients. We present clinical data concerning a newly developed measure of cognitive flexibility, administered by means of Eye-Tracking (ET) technology in order to bypass verbal-motor limitations. Methods: 21 ALS patients and 21 age-and education-matched healthy subjects participated in an ET-based cognitive assessment, including a newly developed test of cognitive flexibility (Arrows and Colors Cognitive Test–ACCT) and other oculomotor-driven measures of cognitive functions. A standard screening of frontal and working memory abilities and global cognitive efficiency was administered to all subjects, in addition to a psychological self-rated assessment. For ALS patients, a clinical examination was also performed. Results: ACCT successfully discriminated between patients and healthy controls, mainly concerning execution times obtained at different subtests. A qualitative analysis performed on error distributions in patients highlighted a lower prevalence of perseverative errors, with respect to other type of errors. Correlations between ACCT and other ET-based frontal-executive measures were significant and involved different frontal sub-domains. Limited correlations were observed between ACCT and standard ‘paper and pencil’ cognitive tests. Conclusions: The newly developed ET-based measure of cognitive flexibility could be a useful tool to detect slight frontal impairments in non-demented ALS patients by bypassing verbal-motor limitations through the oculomotor-driven administration. The findings reported in the present study represent the first contribution towards the development of a full verbal-motor free executive test for ALS patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Cognitive systems research has predominantly been guided by the historical distinction between emotion and cognition, and has focused its efforts on modelling the “cognitive” aspects of behaviour. While this initially meant modelling only the control system of cognitive creatures, with the advent of “embodied” cognitive science this expanded to also modelling the interactions between the control system and the external environment. What did not seem to change with this embodiment revolution, however, was the attitude towards affect and emotion in cognitive science. This paper argues that cognitive systems research is now beginning to integrate these aspects of natural cognitive systems into cognitive science proper, not in virtue of traditional “embodied cognitive science”, which focuses predominantly on the body’s gross morphology, but rather in virtue of research into the interoceptive, organismic basis of natural cognitive systems. [Copyright &y& Elsevier]

Abstract: In our continuous attempts to model natural intelligence and emotions in machine learning, many research works emerge with different methods that are often driven by engineering concerns and have the common goal of modeling human perception in machines. This paper aims to go further in that direction by investigating the integration of emotion at the structural level of cognitive systems using the novel emotional DuoNeural Network (DuoNN). This network has hidden layer DuoNeurons, where each has two embedded neurons: a dorsal neuron and a ventral neuron for cognitive and emotional data processing, respectively. When input visual stimuli are presented to the DuoNN, the dorsal cognitive neurons process local features while the ventral emotional neurons process the entire pattern. We present the computational model and the learning algorithm of the DuoNN, the input information–cognitive and emotional–parallel streaming method, and a comparison between the DuoNN and a recently developed emotional neural network. Experimental results show that the DuoNN architecture, configuration, and the additional emotional information processing, yield higher recognition rates and faster learning and decision making. [ABSTRACT FROM AUTHOR]