Theory of Mind and Empathy. Part I - Model of Social Emotional Thinking

• Autorzy: Przybyszewski A. W. , Polkowski L. T.

Identyfikatory

DOI

10.3233/FI-2017-1466

• Języki publikacji: EN

Abstrakty

EN

There are two very different approaches to understand functioning of the brain. First, there is a huge progress in the research of the neurological and neurophysiological properties of different brain substructures, circuits, networks, single cells, synapses and their molecular properties. It contributes to the progress of research in the fields of basic medical sciences and the dramatic increase in average life expectancy. On the another side that does not directly follows neurological developments, it is our introspection related to individual ways of thinking in order to solve different problems that also involve human creativity (cognitive theory of mind). We use many diverse ways of thinking, and they depend on different circumstances. Especially interesting are influences of intuition, feelings and emotions on our creativity, which is in a large part are also related to the social interactions (affective empathy). In this work, we formalise emotional scales and transfer of emotions between individuals (social emotional thinking). We also demonstrate a continuity of the emotion transfer mappings, and an importance of the interactions between emotional faces. It is not only human specific to show and to react to face emotions, but strong and wide human social interactions are based on the precise emotional social thinking. By measuring critical values of face deformations that may influence mutual emotions, we can test precision and tolerance of human visual and emotional systems. By introduction indiscernibility relations between individual reading of face parts deformation, we have used rough set theory to probe social emotional thinking. As one of us have demonstrated that the visual system has properties that follows rough set theory (cognitive theory of mind), this work extends this concept to the social emotional interactions (cognitive and affective theory of mind). As in modern world IT - information technology - has became driving factor in the process of globalisation by creating effective channels of information exchange; hence it becomes extremely important to analyse emotional meaning (cognitive empathy) for this vast information flow. By using as described here, rough set theory to determine, which parts of information have significant emotional influence, our model may give grounds to increase the collective well-being.

Słowa kluczowe

EN

amygdala; insula; similarities; driver logical rules; modulatory logical rules; cognitive empathy; affective empathy

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2017
• Tom: Vol. 150, nr 2
• Strony: 221--230
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Przybyszewski A. W.

• Polish–Japanese Institute of Information Technology, Koszykowa 86, 00-097 Warsaw, Poland

autor

Polkowski L. T.

• Polish–Japanese Institute of Information Technology, Koszykowa 86, 00-097 Warsaw, Poland

Bibliografia

• [1] Przybyszewski AW. The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory. Transactions on Rough Sets IX (J. F. Peters et al. Eds.), LNCS 5390, 2008, p. 287-317. doi: 10.1007/978-3-540-89876-4_16.
• [2] Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht, 1991. doi: 10.1007/978-94-011-3534-4.
• [3] Minsky M. The Emotion Machine. Simon and Schuster, New York, 2006. ISBN: 0743276639.
• [4] Sugase Y, Yamane S, Ueno SK, Kawano K. Global and fine information coded by single neurons in the temporal visual cortex. Nature, 1999; 400: 869-873. doi: 10.1038/23703.
• [5] Damasio AR. Descartes’ Error. New York: Avon Books, 1995. ISBN: 9780143036227.
• [6] Bourbaki N. Topologie Generale. Herrmann, Paris, 1958.
• [7] Birkhoff G. Lattice Theory. New York, 1948.
• [8] Rizzolatti G, Craighero L. The mirror-neuron system. Annu Rev Neurosci., 2004; 27: 169-92. doi: 10.1146/annurev.neuro.27.070203.144230.
• [9] Rizzolatti G, Fabbri-Destro M. The mirror system and its role in social cognition. Curr Opin Neurobiol.. 2009; 5 (l): 24-34.
• [10] Di Cesare G, Di Dio C, Marchi M, Rizzolatti G. Expressing our internal states and understanding those of others. Proc. Natl. Acad. Sci„ USA, 2015; 112 (33): 10331-10345. doi: 10.1073/pnas.1512133112.
• [11] Gallese V, Keysers C, Rizzolatti G. A unifying view of the basis of social cognition. Trends Cogn Sci. 2004; 8 (9): 396-403. doi: 10.1016/j.tics.2004.07.002.
• [12] Singer T, et al. Empathy for pain involves the affective but not sensory components of pain. Science. 2004; 303 (5661): 1157-1162. doi: 10.1126/science.1093535.
• [13] Przybyszewski AW. Applying Data Mining and Machine Learning Algorithms to predict symptoms development in PD Annales Academiae Medicae Silesiensis, 2014; 68 (5): 332-349.
• [14] Przybyszewski AW, Kon M, Szlufik S, Szymański A, Habela P, Koziorowski M. Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients. Sensors. 2016; 16 (9): 1498. doi: 10.3390/s16091498.
• [15] Eckman P. Emotion in the Human Face, Malor Books, Los Angeles, 2015. ISBN-10: 1933779829, 13: 978-1933779829.
• [16] Vandal T, McDuff D, El Kaliouby R. Event detection: Ultra large-scale Clustering of Facial Expressions In: Automatic Face and Gesture Recognition (FG), 11-th IEEE International Conference and Workshops on, vol. 1, 2015, p. 1-8.
• [17] Plutchik R. Emotions: A Psychoevolutionary Synthesis. New York. Harper and Row, 1980. ISBN-10: 0060452358, 13: 978-0060452353.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).