Tag Archives: Emotions

A model of others’ emotions that predicts very well experimental results

Rebecca Saxe, Seeing Other Minds in 3D, Trends in Cognitive Sciences, Volume 22, Issue 3, 2018, Pages 193-195, DOI: 10.1016/j.tics.2018.01.003.

Tamir and Thornton [1] have identified three key dimensions that organize our understanding of other minds. These dimensions (glossed as valence, social impact, and rationality) can capture the similarities and differences between concepts of internal experiences (anger, loneliness, gratitude), and also between concepts of personalities (aggressive, introverted, agreeable). Most impressively, the three dimensions explain the patterns of hemodynamic activity in our brains as we consider these experiences [2] (Box 1). States such as anger and gratitude are invisible, but the patterns evoked in our brain as we think about them are as predictable by the model of Tamir and Thornton as the patterns evoked in our visual cortex when we look at chairs, bicycles, or pineapples are predictable by models of high-level vision [3]. Human social prediction follows the same dimensions: observers predict that transitions are more likely between states that are ‘nearby’ in this abstract 3D space [4]. Thus, we expect that a friend now feeling ‘anxious’ will be more likely to feel ‘sluggish’ than ‘energetic’ later.

Evidence of the dicotomy reactive/predictive control in the brain

Mattie Tops, Markus Quirin, Maarten A.S. Boksem, Sander L. Koole, Large-scale neural networks and the lateralization of motivation and emotion, International Journal of Psychophysiology, Volume 119, 2017, Pages 41-49, DOI: 10.1016/j.ijpsycho.2017.02.004.

Several lines of research in animals and humans converge on the distinction between two basic large-scale brain networks of self-regulation, giving rise to predictive and reactive control systems (PARCS). Predictive (internally-driven) and reactive (externally-guided) control are supported by dorsal versus ventral corticolimbic systems, respectively. Based on extant empirical evidence, we demonstrate how the PARCS produce frontal laterality effects in emotion and motivation. In addition, we explain how this framework gives rise to individual differences in appraising and coping with challenges. PARCS theory integrates separate fields of research, such as research on the motivational correlates of affect, EEG frontal alpha power asymmetry and implicit affective priming effects on cardiovascular indicators of effort during cognitive task performance. Across these different paradigms, converging evidence points to a qualitative motivational division between, on the one hand, angry and happy emotions, and, on the other hand, sad and fearful emotions. PARCS suggests that those two pairs of emotions are associated with predictive and reactive control, respectively. PARCS theory may thus generate important new insights on the motivational and emotional dynamics that drive autonomic and homeostatic control processes.

A computational cognitive architecture that models emotion

Ron Sun, Nick Wilson, Michael Lynch, Emotion: A Unified Mechanistic Interpretation from a Cognitive Architecture, Cognitive Computation, February 2016, Volume 8, Issue 1, pp 1–14, DOI: 10.1007/s12559-015-9374-4.

This paper reviews a project that attempts to interpret emotion, a complex and multifaceted phenomenon, from a mechanistic point of view, facilitated by an existing comprehensive computational cognitive architecture—CLARION. This cognitive architecture consists of a number of subsystems: the action-centered, non-action-centered, motivational, and metacognitive subsystems. From this perspective, emotion is, first and foremost, motivationally based. It is also action-oriented. It involves many other identifiable cognitive functionalities within these subsystems. Based on these functionalities, we fit the pieces together mechanistically (computationally) within the CLARION framework and capture a variety of important aspects of emotion as documented in the literature.

Reinforcement learning to explain emotions

Joost Broekensa, Elmer Jacobsa & Catholijn M. Jonker, A reinforcement learning model of joy, distress, hope and fear, Connection Science, DOI: 10.1080/09540091.2015.1031081.

In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, V(s), models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework – coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human–robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.