Emotion as a Significant Change in Neural Activity

Biologically Inspired Cognitive Architectures, 2013

We address the BICA Challenge with a multi-agent system implementing Stanovich's Tripartite Framework, which we have augmented with a diffuse control system modeled on biological neuromodulations. The Tripartite Framework shows how adaptive yet reflective behavior can emerge from the interaction of three sets of processes: processes responsible for fast context-sensitive behaviors (an autonomous mind), processes responsible for cognitive control (an algorithmic mind), and processes responsible for deliberative processing and rational behavior (a reflective mind). Working within this augmented Tripartite Framework, we were able to build a fully situated, goal-directed sensorimotor agent that can plan its behavior by reasoning on counterfactual situations. In this paper, we put neuromodulations to work towards giving the system well fleshed out emotions. Without them, the system's emotions are purely semantic and cognitive. They are semantic in that the system's conceptual map contains emotion words that are fully linked to other relevant words, and they are cognitive in that algorithmic-level control can focus goal-directed attention on the emotion words when the task demands it (as in an emotional Stroop task). With neuromodulations, we believe we can integrate the main physiological component of Lindquist's situated conceptualization of emotions, core affect, understood physiologically as dynamical patterns of neuromodulations. Emotions in the resulting system are patterns of message passing activity between agents in which neuromodulations can increase sensitivity on salient emotional aspects of environments and focus attention on those aspects. We study the resulting emotions with the help of an emotional Stroop task in which the semantic and cognitive aspects of emotion are observed.

Behavioral and Brain Sciences, 2005

Efforts to bridge emotion theory with neurobiology can be facilitated by dynamic systems (DS) modeling. DS principles stipulate higher-order wholes emerging from lower-order constituents through bidirectional causal processes – offering a common language for psychological and neurobiological models. After identifying some limitations of mainstream emotion theory, I apply DS principles to emotion–cognition relations. I then present a psychological model based on this reconceptualization, identifying trigger, self-amplification, and self-stabilization phases of emotion-appraisal states, leading to consolidating traits. The article goes on to describe neural structures and functions involved in appraisal and emotion, as well as DS mechanisms of integration by which they interact. These mechanisms include nested feedback interactions, global effects of neuromodulation, vertical integration, action-monitoring, and synaptic plasticity, and they are modeled in terms of both functional inte...

KI - Künstliche Intelligenz, 2011

While classical theories systematically opposed emotion and cognition, suggesting that emotions perturbed the normal functioning of the rational thought, recent progress in neuroscience highlights on the contrary that emotional processes are at the core of cognitive processes, directing attention to emotionally-relevant stimuli, favoring the memorization of external events, valuating the association between an action and its consequences, biasing decision making by allowing to compare the motivational value of different goals and, more generally, guiding behavior towards fulfilling the needs of the organism. This article first proposes an overview of the brain areas involved in the emotional modulation of behavior and suggests a functional architecture allowing to perform efficient decision making. It then reviews a series of biologically-inspired computational models of emotion dealing with behavioral tasks like classical conditioning and decision making, which highlight the computational mechanisms involved in emotional behavior. It underlines the importance of embodied cognition in artificial intelligence, as emotional processing is at the core of the cognitive computations deciding which behavior is more appropriate for the agent.

The primary tasks of a cognitive system are to survive and to maximize a lifelong utility function, like the number of offsprings. A direct computational maximization of lifelong utility is however not possible in complex environments, especially in the context, of real-world time constraints. The central role of emotions is to serve as an intermediate layer in the space of policies available to agents and animals, leading to a large dimensional reduction of complexity. We review our current understanding of the functional role of emotions, stressing the role of the neuromodulators mediating emotions for the diffusive homeostatic control system of the brain. We discuss a recent proposal, that emotional diffusive control is characterized, in contrast to neutral diffusive control, by interaction effects, viz by interferences between emotional arousal and reward signaling. Several proposals for the realization of synthetic emotions are discussed in this context, together with key open issues regarding the interplay between emotional motivational drives and diffusive control.

Biologically Inspired Cognitive Architectures, 2015

This article represents an extension of authors' previous papers , 2013) in modeling cognitive systems on the base of the Dynamical Theory of Information. The paper focuses on the problem of account for emotions in artificial system. The main hypothesis consists in the assumption that emotions inherent in a living system could be simulated by variation of amplitude of the occasional component (noise) inherently embedded into the architecture of artificial system. Within this concept, increasing noise amplitude should correspond to negative emotions (anxiety), while its decreasing provides positive emotions (relaxation, pleasure). A rapid up-and-down spike in the noise amplitude could imitate a laugh. This hypothesis is secured by incorporation of an additional dynamical variable that represents an analogy to the compound of neural mediators in human beings. The system of linked equations in terms of ''noise amplitude -neurotransmitter compound'' is proposed to describe mutual influence of the cognitive process and emotional component. The model permits to reproduce qualitatively certain prominent effects typical for human emotional reactions (like stress and shock).

2010

2020

Social interactions are a part of day-to-day life of most human beings. Affect, decision-making and behavior are central to it. With increase in adaptation of technology in our society, interaction between humans and artificially intelligent agents is also increasing. Large-scale brain-inspired neural models have been equipped with capabilities to fulfil a variety of tasks, but there has been relatively limited focus on making them capable of handling social interaction. In this paper, NeuroACT, a neural computational model and implementation of a sociopsychological theory called Affect Control Theory (ACT) is presented. This is towards building an emotionally intelligent AI agent, that can handle interactions. It takes as input a continuous affective interpretation of a perceived event, consisting of an actor, behavior and an object and generates post-event predictions of the next optimal behavior to minimize deflection. The aim is to model the role of affect guiding decisionmaking...

International Journal of e-Education, e-Business, e-Management and e-Learning, 2020

Cognitive psychology, represented by the expansion-construction theory, believes that positive emotions can broaden the scope of cognition, promote cognitive processes such as creative problem solving, executive control, cognitive flexibility, attention and decision-making. However, they cannot explain the phenomena in some experiments that are contrary to this conclusion. Thus, the motivational dimension model came into being. The theory believes that the effect of emotion on cognition is related to the motivation of emotion rather than its valance. After combing the literatures, it was found that dopamine mediates the positive emotions promoting cognitive activities. The activation of frontal lobe, anterior cingulate cortex, orbitofrontal cortex, hippocampus and amygdala are all the brain mechanisms for positive emotions. Looking ahead, further research on neurophysiological mechanisms will promote scholars’ more comprehensive and profound understanding of positive emotions. AI + ...

Biological Psychology, 2013

Recent findings suggest the processing of emotional stimuli is prioritized compared to neutral stimuli; however, it is not necessarily automatic and depends on several modulating factors. The current paper highlights three major factors that affect the reactions to emotional stimuli: (i) stimulus properties, (ii) task demands and attention, and (iii) individual characteristics. The evidence reviewed here suggests that individual characteristics shape the structure, function and connectivity within a neural network that is involved in the reactions to emotional stimuli. This neural network includes regions related to emotion and attention, in line with evidence for reciprocal connections between these two processes. Activation in this network further depends on the emotional value of a certain item, as well as physical features of the stimulus. This integrative view can lead to better understanding of the underlying mechanisms of emotional reactions, as well as better therapeutic approaches.