A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

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Enhancing Customer Satisfaction Analysis with a Machine Learning Approach: From a Perspective of Matching Customer Comment and Agent Note

With the booming of UGCs, customer comments are widely utilized in analyzing customer satisfaction. However, due to the characteristics of emotional expression, ambiguous semantics and short text, sentiment analysis with customer comments is easily biased and risky. This paper introduces another important UGC, i.e., agent notes, which not only effectively complements customer comment, but delivers professional details, which may enhance customer satisfaction analysis. Moreover, detecting the mismatch on aspects between these two UGCs may further help gain in-depth customer insights. This paper proposes a machine learning based matching analysis approach, namely CAMP, by which not only the semantics and sentiment in customer comments and agent notes can be sufficiently and comprehensively investigated, but the granular and fine-grained aspects could be detected. The CAMP approach can provide practical guidance for following-up service, and the automation can help speed-up service response, which essentially improves customer satisfaction and retains customer loyalty

Virtual humans and Photorealism: The effect of photorealism of interactive virtual humans in clinical virtual environment on affective responses

The ability of realistic vs stylized representations of virtual characters to elicit emotions in users has been an open question for researchers and artists alike. We designed and performed a between subjects experiment using a medical virtual reality simulation to study the differences in the emotions aroused in participants while interacting with realistic and stylized virtual characters. The experiment included three conditions each of which presented a different representation of the virtual character namely; photo-realistic, non-photorealistic cartoon-shaded and non-photorealistic charcoal-sketch. The simulation used for the experiment, called the Rapid Response Training System was developed to train nurses to identify symptoms of rapid deterioration in patients. The emotional impact of interacting with the simulation on the participants was measured via both subjective and objective metrics. Quantitative objective measures were gathered using skin Electrodermal Activity (EDA) sensors, and quantitative subjective measures included Differential Emotion Survey (DES IV), Positive and Negative Affect Schedule (PANAS), and the co-presence or social presence questionnaire. The emotional state of the participants was analyzed across four distinct time steps during which the medical condition of the virtual patient deteriorated, and was contrasted to a baseline affective state. The data from the EDA sensors indicated that the mean level of arousal was highest in the charcoal-sketch condition, lowest in the realistic condition, with responses in the cartoon-shaded condition was in the middle. Mean arousal responses also seemed to be consistent in both the cartoon-shaded and charcoal-sketch conditions across all time steps, while the mean arousal response of participants in the realistic condition showed a significant drop from time step 1 through time step 2, corresponding to the deterioration of the virtual patient. Mean scores of participants in the DES survey seems to suggest that participants in the realistic condition elicited a higher emotional response than participants in both non-realistic conditions. Within the non-realistic conditions, participants in the cartoon-shaded condition seemed to elicit a higher emotional response than those in the charcoal-sketch condition

Estimation of Hierarchical Emotion in Mental State Transition Learning Network

In general, emotions are often appeared in the facial expressions, voice pitch, exaggerated gesticulation, and so on. They are outward signals of emotions, internal world in order to serve for human communications. Perlovsky described on aesthetic emotions and analyzed their role within joint functioning of cognition and language. This paper proposes the different method from his idea. The method uses Mental State Transition Network proposed by Ren and Emotion Generation Calculations. Moreover, the transition costs in the network are modified according to the stimulus from external world. The simulation results also are reported