Neuroeconomics: How Neuroscience Can Inform Economics

Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the "black box" of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems ("executive function") interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, "dual-process" models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are "rationally" neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in "theory of mind", correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information

The influence of affect on attitude

Priests of the medieval Catholic Church understood something about the relationship between affect and attitude. To instill the proper attitude in parishioners, priests dramatized the power of liturgy to save them from Hell in a service in which the experience of darkness and fear gave way to light and familiar liturgy. These ceremonies “were written and performed so as to first arouse and then allay anxieties and fears ” (Scott, 2003, p. 227): The service usually began in the dark of night with the gothic cathedral’s nave filled with worship-pers cast into total darkness. Terrifying noises, wailing, shrieks, screams, and clanging of metal mimicked the chaos of hell, giving frightened witnesses a taste of what they could expect if they were tempted to stray. After a prolonged period of this imitation of hell, the cathedral’s interior gradually became filled with the blaze of a thousand lights. As the gloom diminished, cacophony was supplanted by the measured tones of Gregorian chants and polyphony. Light and divine order replaced darkness and chaos (R. Scott, personal correspondence, March 15, 2004). This ceremony was designed to buttress beliefs by experience and to transfigure abstractions into attitudes. In place of merely hearing about “the chaos and perdition of hell that regular performances of liturgy were designed to hold in check ” (Scott, 2003), parishioners shoul

Neurophysiological Assessment of Affective Experience

In the field of Affective Computing the affective experience (AX) of the user during the interaction with computers is of great interest. The automatic recognition of the affective state, or emotion, of the user is one of the big challenges. In this proposal I focus on the affect recognition via physiological and neurophysiological signals. Long‐standing evidence from psychophysiological research and more recently from research in affective neuroscience suggests that both, body and brain physiology, are able to indicate the current affective state of a subject. However, regarding the classification of AX several questions are still unanswered. The principal possibility of AX classification was repeatedly shown, but its generalisation over different task contexts, elicitating stimuli modalities, subjects or time is seldom addressed. In this proposal I will discuss a possible agenda for the further exploration of physiological and neurophysiological correlates of AX over different elicitation modalities and task contexts

The Limits of Emotion in Moral Judgment

I argue that our best science supports the rationalist idea that, independent of reasoning, emotions aren’t integral to moral judgment. There’s ample evidence that ordinary moral cognition often involves conscious and unconscious reasoning about an action’s outcomes and the agent’s role in bringing them about. Emotions can aid in moral reasoning by, for example, drawing one’s attention to such information. However, there is no compelling evidence for the decidedly sentimentalist claim that mere feelings are causally necessary or sufficient for making a moral judgment or for treating norms as distinctively moral. I conclude that, even if moral cognition is largely driven by automatic intuitions, these shouldn’t be mistaken for emotions or their non-cognitive components. Non-cognitive elements in our psychology may be required for normal moral development and motivation but not necessarily for mature moral judgment

Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception

Psychopathy, autism, and basic moral emotions: Evidence for sentimentalist constructivism

Philosophers and psychologists often claim that moral agency is connected with the ability to feel, understand, and deploy moral emotions. In this chapter, I investigate the nature of these emotions and their connection with moral agency. First, I examine the degree to which these emotional capacities are innate and/or ‘basic’ in a philosophically important sense. I examine three senses in which an emotion might be basic: developmental, compositional, and phylogenetic. After considering the evidence for basic emotion, I conclude that emotions are not basic in a philosophically important sense. Emotions, I argue, are best understood as socially constructed concepts. I then investigate whether these emotions are necessary for moral agency. In order to do this I examine the philosophical and psychological literature on psychopathy and autism (two conditions defined in terms of empathic and emotional deficits). Persons with psychopathy appear incapable of distinguishing moral from non-moral norms. Additionally, while persons with autism often struggle to develop their empathic capacities, they are capable of understanding and deploying moral emotions like guilt and shame. I conclude that, in line with the conceptual act theories of emotion, that only contagion-based empathy is necessary for the acquisition of moral concepts

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

The influence of angry customer outbursts on service providers’ facial displays and affective states

This article explores the existence and extent of emotional contagion, as measured by facial displays and reported affective states, in a service failure event. Using video vignettes of customers complaining about a service failure as stimulus material, the authors measured the facial displays and affective states of service providers as proxies for emotional contagion. Following a two-step approach, service providers’ facial expressions were first recorded and assessed, revealing that service providers’ facial displays matched those of the angry consumer. Second, a mixed ANOVA revealed service providers reported stronger negative affective states after exposure to an angry complaint than prior to exposure. The results demonstrated that during a complaint situation, angry outbursts by consumers can initiate the emotional contagion process, and service providers are susceptible to “catch” consumer anger through emotional contagion. Implications for complaint management and future research are discussed