2011
DOI: 10.1007/978-3-642-24955-6_2
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A Computational Agent Model for Hebbian Learning of Social Interaction

Abstract: Abstract. In social interaction between two persons usually a person displays understanding of the other person. This may involve both nonverbal and verbal elements, such as bodily expressing a similar emotion and verbally expressing beliefs about the other person. Such social interaction relates to an underlying neural mechanism based on a mirror neuron system, as known within Social Neuroscience. This mechanism may show different variations over time. This paper addresses this adaptation over time. It presen… Show more

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Cited by 7 publications
(1 citation statement)
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“…[2,[4][5][6]8,15,16]) has an equilibrium and when it increases or decreases. More specifically, assume the following dynamic model (also see [5]) for Hebbian learning for the strength ω of a connection from a state X 1 to a state X 2 with maximal connection strength 1, learning rate η > 0, and extinction rate ζ ≥ 0 (here X 1 (t) and X 2 (t) denote the activation levels of the states X 1 and X 2 at time t; sometimes the t is left out of X i (t) and simply X i is written)…”
Section: Mathematical Analysis For Equilibrium States: Hebbian Learningmentioning
confidence: 99%
“…[2,[4][5][6]8,15,16]) has an equilibrium and when it increases or decreases. More specifically, assume the following dynamic model (also see [5]) for Hebbian learning for the strength ω of a connection from a state X 1 to a state X 2 with maximal connection strength 1, learning rate η > 0, and extinction rate ζ ≥ 0 (here X 1 (t) and X 2 (t) denote the activation levels of the states X 1 and X 2 at time t; sometimes the t is left out of X i (t) and simply X i is written)…”
Section: Mathematical Analysis For Equilibrium States: Hebbian Learningmentioning
confidence: 99%