This paper examined the claims postulated by the somatic marker hypothesis and compares those claims to the current evidence surrounding the neural basis of alexithymia. The results were then compared to see if they contradict or have a distinct localisation in the brain separate from those behavioural brain regions hypothesized by the somatic marker hypothesis. It was concluded that the somatic marker hypothesis and the neural basis for alexithymia share certain regions of interest, primarily the amygdala and insula, but also potentially the anterior cingulate cortex.

Theoretical procedures are developed to account for the effect of emotion and perception in strategic conflict. The <em>possibility principle</em> facilitates modeling the effects of emotions on future scenarios contemplated by decision makers; <em>perceptual graph models</em> and a <em>graph model system</em> permit the decision makers (DMs) to experience and view the conflict independently; and <em>perceptual stability analysis</em>, which is based on individual- and meta-stability analysis techniques, is employed in analyzing graph model systems when the DMs have inconsistent perceptions. These developments improve the methodology of the Graph Model for Conflict Resolution by reconciling emotion, perception, and strategy to make predictions consistent with the actual unfolding of events. <br /><br /> Current research in neuroscience suggests that emotions are a necessary component of cognitive processes such as memory, attention, and reasoning. The somatic marker hypothesis, for example, holds that feelings are necessary to reasoning, especially during social interactions (Damasio, 1994, 2003). Somatic markers are memories of past emotions: we use them to predict future outcomes. To incorporate the effect of emotion in conflict, the underlying principle of Damasio?s hypothesis is used in developing the possibility principle, which significantly expands the paradigm of the Graph Model for Conflict Resolution of Fang, Hipel, and Kilgour (1993). <br /><br /> State identification is a crucial step in determining future scenarios for DMs. The possibility principle is integrated into the modeling stage of the Graph Model by refining the method of determining feasible states. The possibility principle enables analysts and DMs to include emotion in a conflict model, without sacrificing the parsimonious design of the Graph Model methodology, by focusing attention on two subsets of the set of feasible states: <em>hidden</em> and <em>potential</em> states. Hidden states are logically valid, feasible states that are invisible because of the presence of negative emotions such as anger and fear; potential states are logically valid, feasible states that are invisible because of missing positive emotions. Dissipating negative emotions will make the hidden states visible, while expressing the appropriate positive emotions will make the potential states visible. The possibility principle has been applied to a number of real world conflicts. In all cases, eliminating logically valid states not envisioned by any DM simplifies a conflict model substantially, expedites the analysis, and makes it an intuitive and a realistic description of the DMs' conceptualizations of the conflict. <br /><br /> A fundamental principle of the Graph Model methodology is that all DMs' directed graphs must have the same set of feasible states, which are integrated into a <em>standard</em> graph model. The possibility principle may modify the set of feasible states perceived by each DM according to his or her emotion, making it impossible to construct a single standard graph model. When logically valid states are no longer achievable for one or more DMs due to emotions, the apprehension of conflict becomes inconsistent, and resolution may become difficult to predict. Therefore, reconciling emotion and strategy requires that different apprehensions of the underlying decision problem be permitted, which can be accomplished using a perceptual graph model for each DM. A perceptual graph model inherits its primitive ingredients from a standard graph model, but reflects a DM's emotion and perception with no assumption of complete knowledge of other DMs' perceptions. <br /><br /> Each DM's perceptual graph model constitutes a complete standard graph model. Hence, conclusions drawn from a perceptual graph model provide a limited view of equilibria and predicted resolutions. A graph model system, which consists of a list of DMs' perceptual graph models, is defined to reconcile perceptions while facilitating conclusions that reflect each DM's viewpoint. However, since a DM may or may not be aware that other graph models differ from his or her own, different variants of graph model systems are required to describe conflicts. Each variant of graph model system corresponds to a configuration of awareness, which is a set of ordered combinations of DMs' viewpoints. <br /><br /> Perceptual stability analysis is a new procedure that applies to graph model systems. Its objective is to help an <em>outside</em> analyst predict possible resolutions, and gauge the robustness and sustainability of these predictions. Perceptual stability analysis takes a two-phase approach. In Phase 1, the stability of each state in each perceptual graph model is assessed from the point of view of the owner of the model, for each DM in the model, using standard or perceptual solution concepts, depending on the owner's awareness of others' perceptions. (In this research, only perceptual solution concepts for the 2-decision maker case are developed. ) In Phase 2, meta-stability analysis is employed to consolidate the stability assessments of a state in all perceptual graph models and across all variants of awareness. Distinctive modes of equilibria are defined, which reflect incompatibilities in DMs' perceptions and viewpoints but nonetheless provide important insights into possible resolutions of conflict. <br /><br /> The possibility principle and perceptual stability analysis are integrative techniques that can be used as a basis for empathetically studying the interaction of emotion and reasoning in the context of strategic conflict. In general, these new techniques expand current modeling and analysis capabilities, thereby facilitating realistic, descriptive models without exacting too great a cost in modeling complexity. In particular, these two theoretical advances enhance the applicability of the Graph Model for Conflict Resolution to real-world disputes by integrating emotion and perception, common ingredients in almost all conflicts. <br /><br /> To demonstrate that the new developments are practical, two illustrative applications to real-world conflicts are presented: the US-North Korea conflict and the confrontation between Russia and Chechen Rebels. In both cases, the analysis yields new strategic insights and improved advice.

When trying to make sense of uncertain situations, we might rely on summary information from a description, or information gathered from our personal experience. There are two approaches that both attempt to explain how we make risky decisions using descriptive or experiential information—the cognitive-based explanation from the description-experience gap, and the emotion-based explanation from the somatic marker hypothesis (SMH). This dissertation brings together these two approaches to better understand how we make risky decisions. Four options were presented, with options differing in terms of advantageousness and riskiness. How easy or difficult it was to consciously comprehend the reward structure, or cognitive penetrability, was manipulated by displaying single outcomes or multiple, diverse outcomes per trial. Within the description or experience task, participants were randomly assigned to the more or less penetrable version of an all gain or all loss set of options. How often the riskier or advantageous options were chosen served as a measure of risky or advantageous decision making. Regardless of penetrability, risk preferences were generally but not completely as predicted by the SMH. Instead, the primary effect of cognitive penetrability was on advantageous decision making. Furthermore, description was found to be more cognitively penetrable than experience. Overall, the results suggest that clarification is needed regarding how somatic markers are formed in the loss versus gain domain, and future research should consider the difference in penetrability between description and experience when trying to explain preferences between the two decisions.

Drug addiction is a complex behavioral disorder that has been extensively studied in an attempt to uncover its underlying biological mechanisms. This paper contributes to the literature on addiction by demonstrating that addiction is a result of an improperly functioning decision making process. The areas of the brain that are most implicated in decision making demonstrate significant overlap with those areas most affected by addiction. Specifically, the limbic structures of the brain (amygdala, basal ganglia, and mesolimbic reward pathway) and the prefrontal cortices (orbitofrontal cortex, dorsolateral prefrontal cortex, and ventromedial prefrontal cortex) are discussed in relation to their involvement in prominent theories of decision making such as Prospect Theory and the Somatic Marker Hypothesis. This paper will then use the above knowledge regarding the specific brain mechanisms that control decision making and apply it to neurobiological theories of addiction. The view that addiction is a behavioral disorder that results primarily from a degradation of the brain mechanisms involved in decision making processes is important to consider because it can help provide a concrete approach to developing more individualized and effective treatment programs in the future.

Theoretical procedures are developed to account for the effect of emotion and perception in strategic conflict. The <em>possibility principle</em> facilitates modeling the effects of emotions on future scenarios contemplated by decision makers; <em>perceptual graph models</em> and a <em>graph model system</em> permit the decision makers (DMs) to experience and view the conflict independently; and <em>perceptual stability analysis</em>, which is based on individual- and meta-stability analysis techniques, is employed in analyzing graph model systems when the DMs have inconsistent perceptions. These developments improve the methodology of the Graph Model for Conflict Resolution by reconciling emotion, perception, and strategy to make predictions consistent with the actual unfolding of events. <br /><br /> Current research in neuroscience suggests that emotions are a necessary component of cognitive processes such as memory, attention, and reasoning. The somatic marker hypothesis, for example, holds that feelings are necessary to reasoning, especially during social interactions (Damasio, 1994, 2003). Somatic markers are memories of past emotions: we use them to predict future outcomes. To incorporate the effect of emotion in conflict, the underlying principle of Damasio?s hypothesis is used in developing the possibility principle, which significantly expands the paradigm of the Graph Model for Conflict Resolution of Fang, Hipel, and Kilgour (1993). <br /><br /> State identification is a crucial step in determining future scenarios for DMs. The possibility principle is integrated into the modeling stage of the Graph Model by refining the method of determining feasible states. The possibility principle enables analysts and DMs to include emotion in a conflict model, without sacrificing the parsimonious design of the Graph Model methodology, by focusing attention on two subsets of the set of feasible states: <em>hidden</em> and <em>potential</em> states. Hidden states are logically valid, feasible states that are invisible because of the presence of negative emotions such as anger and fear; potential states are logically valid, feasible states that are invisible because of missing positive emotions. Dissipating negative emotions will make the hidden states visible, while expressing the appropriate positive emotions will make the potential states visible. The possibility principle has been applied to a number of real world conflicts. In all cases, eliminating logically valid states not envisioned by any DM simplifies a conflict model substantially, expedites the analysis, and makes it an intuitive and a realistic description of the DMs' conceptualizations of the conflict. <br /><br /> A fundamental principle of the Graph Model methodology is that all DMs' directed graphs must have the same set of feasible states, which are integrated into a <em>standard</em> graph model. The possibility principle may modify the set of feasible states perceived by each DM according to his or her emotion, making it impossible to construct a single standard graph model. When logically valid states are no longer achievable for one or more DMs due to emotions, the apprehension of conflict becomes inconsistent, and resolution may become difficult to predict. Therefore, reconciling emotion and strategy requires that different apprehensions of the underlying decision problem be permitted, which can be accomplished using a perceptual graph model for each DM. A perceptual graph model inherits its primitive ingredients from a standard graph model, but reflects a DM's emotion and perception with no assumption of complete knowledge of other DMs' perceptions. <br /><br /> Each DM's perceptual graph model constitutes a complete standard graph model. Hence, conclusions drawn from a perceptual graph model provide a limited view of equilibria and predicted resolutions. A graph model system, which consists of a list of DMs' perceptual graph models, is defined to reconcile perceptions while facilitating conclusions that reflect each DM's viewpoint. However, since a DM may or may not be aware that other graph models differ from his or her own, different variants of graph model systems are required to describe conflicts. Each variant of graph model system corresponds to a configuration of awareness, which is a set of ordered combinations of DMs' viewpoints. <br /><br /> Perceptual stability analysis is a new procedure that applies to graph model systems. Its objective is to help an <em>outside</em> analyst predict possible resolutions, and gauge the robustness and sustainability of these predictions. Perceptual stability analysis takes a two-phase approach. In Phase 1, the stability of each state in each perceptual graph model is assessed from the point of view of the owner of the model, for each DM in the model, using standard or perceptual solution concepts, depending on the owner's awareness of others' perceptions. (In this research, only perceptual solution concepts for the 2-decision maker case are developed. ) In Phase 2, meta-stability analysis is employed to consolidate the stability assessments of a state in all perceptual graph models and across all variants of awareness. Distinctive modes of equilibria are defined, which reflect incompatibilities in DMs' perceptions and viewpoints but nonetheless provide important insights into possible resolutions of conflict. <br /><br /> The possibility principle and perceptual stability analysis are integrative techniques that can be used as a basis for empathetically studying the interaction of emotion and reasoning in the context of strategic conflict. In general, these new techniques expand current modeling and analysis capabilities, thereby facilitating realistic, descriptive models without exacting too great a cost in modeling complexity. In particular, these two theoretical advances enhance the applicability of the Graph Model for Conflict Resolution to real-world disputes by integrating emotion and perception, common ingredients in almost all conflicts. <br /><br /> To demonstrate that the new developments are practical, two illustrative applications to real-world conflicts are presented: the US-North Korea conflict and the confrontation between Russia and Chechen Rebels. In both cases, the analysis yields new strategic insights and improved advice.

The present study examines the relationship of emotional awareness to anticipatory psychophysiological markers and performance on the Iowa Gambling Task (IGT). The IGT is a computerized card game that simulates real-life decisions through uncertainty of reward or punishment. The participant’s goal is to make advantageous card choices. Anticipatory somatic markers of physiological arousal, like electrodermal activity and heart rate, have been proposed to bias decisions in the IGT. The central hypothesis is that a participant’s emotional awareness is related to their ability to make advantageous decisions through biasing psychophysiological responses. The Toronto Alexithymia Scale was used to assess each participant’s emotional awareness. Less emotional awareness was associated with enhanced performance on the IGT. However, anticipatory physiological arousal (electrodermal activity and heart rate) and emotional awareness yielded no significant relationships. Findings suggest a need for further research on cognitive models, such as the expectancy valence model, in relation to decision-making.

In this doctoral thesis, I present four studies aim at investigating the role of affective and emotional reactions in shaping people’s risk-taking behaviors in conditions of uncertainty. In the first two studies, I use the Iowa Gambling Task (IGT) in the attempt to mimic real-life conditions of uncertainty in a laboratory setting. In Study 1 (Chapter 3), I manipulate IGT decks by associating the hearing of a highly unpleasant sound to the moment of selection from Bad (Congruent condition) or Good decks (Incongruent condition) to make participants experience a negative affective reaction towards them. Drawing on the affect heuristic and the SMH, I expect that this unpleasant reaction will lead participants to avoid the manipulated decks, thus having a detrimental effect when it is associated with Good decks (i.e., lower selections from the long-term advantageous Good decks) and a beneficial effect when it is associated with the Bad ones instead (i.e., lower selections from the long-term disadvantageous Bad decks). In Study 2 (Chapter 4), I replicate Study 1’s design to detect a similar effect while using an emotional reaction of disgust, induced by a disgust-eliciting image, in line with assumptions on the role of discrete, incidental emotions in decision-making. In both Study 1 and Study 2, I also investigate the generation of somatic markers in terms of enhanced autonomic activation measuring anticipatory SCR as in classical IGT studies. Moreover, in the attempt to reach a deeper understanding of this mechanism, I also include measures of heart rate (Study 1 and Study 2) and pupil dilation (Study 2) as suggested in previous studies presented in the first chapter. In the last two studies instead, I investigate the effect of emotional reactions on health-related risk-taking behaviors during the COVID-19 pandemic. In Study 3 (Chapter 5), I explore how the frame used in the media (positive frame: number of recovered vs negative frame: number of dead) to communicate information about the pandemic and the comparison between the COVID-19 and the seasonal flu can influence citizens compliance with self-protective behaviors through the mediation of emotional reaction (i.e., worry) and risk perception as suggested by the affect heuristic and the risk as feelings framework. Moreover, I also compare results from three different European samples (Italian vs Austrian vs English). Last, in Study 4 (Chapter 6), I investigate the effect of six information formats (five numerical and one verbal) commonly used in the media to report COVID-19 mortality rates on citizens' emotional reactions, risk perception and intention to comply with recommended self-protective behaviors against the virus.

The present study examines the relationship of emotional awareness to anticipatory psychophysiological markers and performance on the Iowa Gambling Task (IGT). The IGT is a computerized card game that simulates real-life decisions through uncertainty of reward or punishment. The participant’s goal is to make advantageous card choices. Anticipatory somatic markers of physiological arousal, like electrodermal activity and heart rate, have been proposed to bias decisions in the IGT. The central hypothesis is that a participant’s emotional awareness is related to their ability to make advantageous decisions through biasing psychophysiological responses. The Toronto Alexithymia Scale was used to assess each participant’s emotional awareness. Less emotional awareness was associated with enhanced performance on the IGT. However, anticipatory physiological arousal (electrodermal activity and heart rate) and emotional awareness yielded no significant relationships. Findings suggest a need for further research on cognitive models, such as the expectancy valence model, in relation to decision-making.

Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that the robots may directly deal with human lives in life-or-death situations, and they are therefore required to make highly intelligent decisions as rapidly as possible. The intelligence we are looking for in this type of situations is proactiveness: the ability to anticipate as well as improvise. Anticipation here means that the robot can assess the current situation, predict the future consequence of the situation, and execute an action to have desired outcome based on the determined assessment and prediction. On the other hand, improvisation is performed when the consequence of the situation is not fully known. In other words, it is the ability to deal with a novel situation based on knowledge or skill being acquired before. In this presentation, we introduce a biologically inspired computational model of proactive intelligent behavior for robots. Integrating multiple levels of machine learning techniques such as temporal difference learning, instance-based learning, and partially observable Markov decision process, aggregated episodic memories are processed in order to accomplish anticipation as well as improvisation. How this model can be implemented within a software architectural framework and integrated into a physically realized robotic system is also explained. The experimental results using a real robot and high fidelity 3D simulators are then presented in order to help us understand how extended experience of a robot influences its ability to behave proactively.

The neuroscience of decision making is laying the puzzle of how the brain computes decisions. It tries to sort out which factors are responsible for causing us to choose one way or the other. This thesis reviews to what extent emotional brain processes and their neural circuits impact decision making. The somatic marker hypothesis (SMH) provides a solid dual-system framework for decision making. Dissociating an impulsive system, in which the amygdala is central, and a reflective system mediated by the ventromedial prefrontal cortex(VMPFC). The SMH emphasizes the function of the VMPFC as necessary and crucial formaking favorable long-term decisions. Research on moral decision making also shows that similar systems as used by the SMH has a key role in how we think about moral dilemmas as well. Damage or maldevelopment of these neural circuits can cause myopia for the future and deeply immoral behavior. Abnormalities in emotional neuronal circuits can also be linked to addictive behavior and psychopathy. The findings on decision making and its neuralsubstrates dismantle the common sense notion of free will and moral responsibility. An explanation of how the feeling of free will arises is given using the Interpreter system theoryof consciousness. Moral responsibility without the need for a free will is defended by analternative approach with a framework of a brain in-control versus out-of-control.