Looking for a Synergy between Human and Artificial Cognition

Parker, Jeanne E.; Hollister, Debra; González, Avelino J.; Brézillon, Patrick; Parker, Shane T.

doi:10.1007/978-3-642-40972-1_4

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…As discussed in a previous paper [56], context familiarity has an important role in how artificial intelligence handles natural language processing (NLP) and generation (NLG) [53] [5]. One of the most widely accepted models of linguistic processing and representation activation is the spreading activation model, proposed by Collins and Loftus in 1975 [18].…”

Section: Linguistics Context and Aimentioning

confidence: 99%

Contextual Reasoning in Human Cognition and its Implications for Artificial Intelligence Systems

Hollister¹,

González²,

Hollister³

2019

MUC

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Les recherches de ces 50 dernières années ont montré que le raisonnement avec et à propos du contexte est un aspect essentiel de la cognition humaine, imprégnant le langage, la mémoire et les capacités de raisonnement. L'ensemble de ce processus est développé au cours du temps par un apprentissage basé sur l'expérience. En supposant que le but de l'intelligence artificielle est de doté une machine d'une intelligence proche de celle des humains, il est essentiel de considérer et incorporer le concept de contexte dans la conception et l'implémentation des systèmes intelligents. Ce papier discute du raisonnement contextuel chez l'humain et comment celui-ci aide les humains à faire face avec succès à des tâches cognitives complexes. Nous relions de plus cette recherche sur le contexte en psychologie cognitive aux recherches actuelles sur le raisonnement contextuel ans les systèmes d'intelligence artificielle. ABSTRACT. Research over the last 50 years has shown that reasoning with and about context is an essential aspect of human cognition, permeating language, memory, and reasoning capabilities. This integral process is developed over a lifetime through experiential learning. Given the goal of artificial intelligence to create human-like intelligence in a machine, it is essential to consider and incorporate the concept of context in intelligent system design and implementation. This paper discusses contextual reasoning in humans and how it helps humans successfully deal with complex cognitive tasks. We further link this research on context in cognitive psychology with the current research in contextual reasoning in Artificial Intelligence systems. MOTS-CLÉS. Contexte dans la cognition humaine, raisonnement contextuel, Contexte dans la cognition humaine, raisonnement contextuel. KEYWORDS. Context in human cognition, contextual reasoning, role of context in learning.

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Section: Linguistics Context and Aimentioning

confidence: 99%

Contextual Reasoning in Human Cognition and its Implications for Artificial Intelligence Systems

Hollister¹,

González²,

Hollister³

2019

MUC

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“…In (7) , to be able to completely mimic human intelligence, Artificial Intelligence needs to learn to identify contexuality. In the present study, the author used WWW as an experimental tool to study whether a conceptual combination obeys compositionality.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Non-compositionality of Conceptual Combinations

Singh

2024

IJST

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Objectives: This research is first of its kind to investigate the application of quantum mechanics and related mathematical models to cognitive science, specifically focusing on the challenge of modeling how the human mind assigns meaning to combinations of words. The primary objective is to classify whether given word combinations are compositional or non-compositional. Methods: The author introduces the "WWW method," which leverages the World Wide Web (WWW) as an experimental tool in place of human subjects, which makes this method cheaper and handy in comparison to the earlier methods which used human subjects for spoken and sign language studies and robots as non-human subjects. The "WWW method" involves checking the adherence to Bell inequalities and marginal selectivity. The term "meaning bound" is introduced to quantify the extent to which two words influence each other's meaning within a combination. This concept proves instrumental in devising a method for assessing compositionality using the WWW method. Findings: When the WWW serves as the information space, the verification of whether a word combination adheres to Bell inequalities entails determining if the meaning bound for the word combination (AB & C) exceeds that of its constituent parts (A & C, B & C) within the conceptual combination AB. Marginal selectivity is evaluated by establishing a vector model for grammatically framed word combinations, utilizing the WWW to collect data. If the word combination 'A then B' yields an equivalent number of hits as 'B then A,' it signifies that the word combination AB satisfies marginal selectivity. Novelty: Consequently, this research presents an accessible approach to identifying entangled pairs of word combinations using the WWW method, eliminating the need for human subject experiments. The earlier studies which include human subjects are costly and cannot be replicated anytime-anywhere, for obvious reasons. Keywords: Quantum cognition, Marginal selectivity, Compositionality, WWW method, CHSH inequality

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Tactical Reasoning Through Context-Based Reasoning

González

2014

Context in Computing

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Looking for a Synergy between Human and Artificial Cognition

Cited by 6 publications

References 31 publications

Contextual Reasoning in Human Cognition and its Implications for Artificial Intelligence Systems

Contextual Reasoning in Human Cognition and its Implications for Artificial Intelligence Systems

Analyzing the Non-compositionality of Conceptual Combinations

Tactical Reasoning Through Context-Based Reasoning

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