Cognitive Science in Telemedicine: From Psychology to Artificial Intelligence

2015 ◽  
pp. 5-22 ◽  
Author(s):  
Gabriella Pravettoni ◽  
Raffaella Folgieri ◽  
Claudio Lucchiari
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science

scholarly journals Representations, Affordances, and Interactive Systems

2021 ◽  
Vol 5 (5) ◽  
pp. 23
Author(s):  
Robert Rowe
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Data Structures ◽  
Digital Computer ◽  
Mental Representations ◽  
Interactive Systems ◽  
Algorithmic Composition ◽  
The World ◽  
History Of

The history of algorithmic composition using a digital computer has undergone many representations—data structures that encode some aspects of the outside world, or processes and entities within the program itself. Parallel histories in cognitive science and artificial intelligence have (of necessity) confronted their own notions of representations, including the ecological perception view of J.J. Gibson, who claims that mental representations are redundant to the affordances apparent in the world, its objects, and their relations. This review tracks these parallel histories and how the orientations and designs of multimodal interactive systems give rise to their own affordances: the representations and models used expose parameters and controls to a creator that determine how a system can be used and, thus, what it can mean.

Modular Representations of Cognitive Phenomena in AI, Psychology and Neuroscience

2011 ◽  
pp. 66-89 ◽  
Author(s):  
Joanna J. Bryson
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Conscious Experience ◽  
Modular Representations ◽  
Domain Specific ◽  
Massive Modularity ◽  
Multi Agent ◽  
Individual Domain ◽  
Behavior Based

Many architectures of mind assume some form of modularity, but what is meant by the term ‘module’? This chapter creates a framework for understanding current modularity research in three subdisciplines of cognitive science: psychology, artificial intelligence (AI), and neuroscience. This framework starts from the distinction between horizontal modules that support all expressed behaviors vs. vertical modules that support individual domain-specific capacities. The framework is used to discuss innateness, automaticity, compositionality, representations, massive modularity, behavior-based and multi-agent AI systems, and correspondence to physiological neurosystems. There is also a brief discussion of the relevance of modularity to conscious experience.

Extensions and Limitations to Logic

2017 ◽  
pp. 141-195
Author(s):  
Thomas J. Marlowe
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Cognitive Science ◽  
Probabilistic Models ◽  
Mathematical Reasoning ◽  
The State ◽  
Modern World ◽  
Advanced Students ◽  
Solid Foundation

Classical (Aristotelean or Boolean) logics provide a solid foundation for mathematical reasoning, but are limited in expressivity and necessarily incomplete. Effective understanding of logic in the modern world entails for the instructor and advanced students an understanding of the wider context. This chapter surveys standard extensions used in mathematical reasoning, artificial intelligence and cognitive science, and natural language reasoning and understanding, as well as inherent limitations on reasoning and computing. Initial technical extensions include equality of terms, integer arithmetic and quantification over sets and relations. To deal with natural reasoning, the chapter explores temporal and modal logics, fuzzy logic and probabilistic models, and relevance logic. Finally, the chapter considers limitations to logic and knowledge, via an overview of the fundamental results of Turing, Gödel, and others, and their connection to the state of mathematics, computing and science in the modern world.

Return to Cognitive Science

2018 ◽  
Author(s):  
James A. Anderson
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Machine Intelligence ◽  
Active Memory ◽  
Practical Project ◽  
Digital Computers

Is ambiguity unavoidable? It is found in vision and everywhere in language. Semantic nets for disambiguation are realized in George Miller’s WordNet, a practical project helping disambiguate search strings using contextual disambiguation. Simple association using traditional passive memory is boring compared to complex association using active memory with multiple associative links active at the same time to perform a clearly defined task. A “mixer” is used to recognize items from a list, and generalization of the mixer is used for disambiguation. The chapter also discusses artificial intelligence, both its origins and currently ignored questions: Are biological intelligence and machine intelligence the same thing? Can digital computers really mimic in digital software a largely analog brain? The important question is not why machines are becoming so smart but why humans are still so good. Artificial intelligence is missing something important probably based on hardware differences.

Sign in / Sign up

Export Citation Format

Share Document