Alexei V. Samsonovich

Toward a BICA-Model-Based Study of Cognition Using Brain Imaging Techniques☆

Abstract The aim of this study is to develop an approach to evaluation of a biologically inspired, causal model of cognition that exposes the mechanistic requirements for achieving fluid intelligence and makes testable predictions of neurophysiological measures. In order to build human-level-efficient tools for data analysis, it is necessary to have a theory of how concepts are represented in the human brain. This theory should specify (a) the structure and semantics of concept representations in the human brain, and (b) types, formats and specific patterns of neuronal activity instantiating these representations. The key to a biologically-informed human brain model begins with the mapping of (a) to (b), i.e., of the emotional Biologically Inspired Cognitive Architecture (eBICA) to informative features and characteristics of brain activity. The result is a detailed description of the information processing level of the dynamics of emotional evaluation of other agents and relationships with them in the process of joint activities, and the role of this evaluation in decision-making and generation of behavior based on the selected emotional cognitive architecture.

Functional Neural Networks in Behavioral Motivations

Functional magnetic resonance imaging (fMRI) is an effective non-invasive tool for exploration and analysis of brain functions. Here functional neural networks involved in behavioral motivations are studied using fMRI. It was found that behavioral conditions producing different motivations for action can be associated with different patterns of functional network activity. At the same time, connection can be made to dynamics of socio-emotional cognition, decision making and action control, described by the Virtual Actor model based on the eBICA cognitive architecture. These preliminary observations encourage further fMRI-based study of human social-emotional cognition. The impact is expected on the emergent technology of humanlike collaborative robots (cobots) and creative cognitive assistants.

Designing an Emotionally-Intelligent Assistant of a Virtual Dance Creator

Intelligent agents and co-robots, or cobots, become increasingly popular today as creators of digital art, including robotic or virtual dancing. Arguably, the creativity of such tools is linked to their social-emotional intelligence. In this work we question this hypothesis, extending the general paradigm of an emotionally-intelligent creative assistant (Samsonovich [1]) to virtual dance creation. For this purpose, a semantic map of dance patterns is constructed. Transitions between dance patterns are selected among local transitions on the map, following general rules. The outcome is judged by subjects as a more confident dance, compared to control conditions, when the semantic map was not used. In the proposed creative assistant of a choreographer, the state of emotional coherence of the cobot-assistant and the human user is maintained dynamically. Using the semantic map and M-schemas, the assistant will suggest variants of dance continuation, based on the current emotional state of the human choreographer and the appraisals of choices. It is expected that this approach, combining efforts of the human and the automaton working together in a state of emotional coherence, will be more user-favored and will yield higher productivity and creativity, compared to more traditional tools for virtual dance generation.

Empirical Measure of Learnability: A Tool for Semantic Map Validation☆

Abstract The many approaches to semantic mapping developed recently demand a precise measuring device that would, on the one hand, be sensitive to human subjective experiences (and therefore must involve a human in the loop), and on the other hand, allow comparative study and validation of consistency of individual semantic maps. The idea explored in this work is to measure the ability of a human subject to learn a given semantic map, and in this sense to be able to “make sense” of the map, as estimated based on a given set of test words. The paradigm includes allocating previously unseen test words in the map coordinates. The quantitative measure is the Pearsons correlation between actual map coordinates of test words and coordinates assigned by subjects. The preliminary study indicates that the proposed measure is sufficiently sensitive to discriminate individual semantic maps from each other and to rank them by their learnability, related to their internal consistency. Potential applications include evaluation of methods for automated semantic map construction, as well as diagnostics of semantic dementia, affective and personality disorders.

Cognitive Processes in Preparation for Problem Solving

Abstract The aim of this study was to examine the role of a software tool in diagnosing students thinking during problem solving in mathematics with 41 college students. Students were asked to select relevant steps, facts and strategies represented on the screen and connect them by arrows, indicating their plan of solution. Only after the diagram was completed, students were allowed to solve the problem. The findings are: (i) forward chaining is significantly more predominant, and backward chaining is significantly less frequent, compared to other possibilities or arrow entering. This result is unexpected, because classical planning methods produce backward chaining in this task. (ii) Students scoring in the middle are more likely to enter convergent pairs of arrows compared to students who scored low or high. This finding enables diagnosing student problem solving. Both findings imply constraints on selection of cognitive architectures used for modeling student problem solving.