Cognitive architecture

Summary

A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. The formalized models can be used to further refine a comprehensive theory of cognition and as a useful artificial intelligence program. Successful cognitive architectures include ACT-R (Adaptive Control of Thought - Rational) and SOAR. The research on cognitive architectures as software instantiation of cognitive theories was initiated by Allen Newell in 1990. The Institute for Creative Technologies defines cognitive architecture as: "hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments." History Herbert A. Simon, one of the fo

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https://en.wikipedia.org/wiki/Cognitive_architecture

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Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity

Ahmad Abu-Akel, Amer Chamseddine, Christine Mohr, Niloufar Pouyan, Maya Roinishvili, Meng Zhang

Many of us "see red," "feel blue," or "turn green with envy." Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficientr= .88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

SAGE PUBLICATIONS INC2020

Cognitive Architecture for Mutual Modelling

Wafa Monia Benkaouar Johal, Pierre Dillenbourg, Alexis David Jacq, Ana Paiva

In social robotics, robots needs to be able to be understood by humans. Especially in collaborative tasks where they have to share mutual knowledge. For instance, in an educative scenario, learners share their knowledge and they must adapt their behaviour in order to make sure they are understood by others. Learners display behaviours in order to show their understanding and teachers adapt in order to make sure that the learners' knowledge is the required one. This ability requires a model of their own mental states perceived by others: ``has the human understood that I(robot) need this object for the task or should I explain it once again ?" In this paper, we discuss the importance of a cognitive architecture enabling second-order Mutual Modelling for Human-Robot Interaction in educative contexts

2016

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It is often necessary to understand each other’s motivations in order to cooperate. Reaching such a mutual understanding requires two abilities: to build models of other’s motivations in order to understand them, and to build a model of “my” motivations perceived by others in order to be understood. Having a self-image seen by others requires two recursive orders of modeling, known in psychology as the first and second orders of theory of mind. In this paper, we present a second-order theory of mind cognitive architecture that aims to facilitate mutual understanding in multi-agent scenarios. We study different conditions of empathy and gratitude leading to irrational cooperation in iterated prisoner’s dilemma.

Instituto Superiro Technico2017