David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Minds and Machines 18 (1):53-91 (2008)
If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler systems. Its capabilities are described based on simulations calling for increasing levels of functionality and are used to show how the model can progress from fundamental sequence learning and recall tasks to sophisticated behaviors such as an ability to solve simple mathematical expressions and a creative capacity for the formation and application of inductive rules.
|Keywords||Predictive learning Memory interleaving Creativity Inductive reasoning Autonomous neural networks|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
José E. Burgos (2001). A Neural-Network Interpretation of Selection in Learning and Behavior. Behavioral and Brain Sciences 24 (3):531-533.
Giovanni B. Moneta (1993). A Model of Scientists' Creative Potential: The Matching of Cognitive Structure and Domain Structure. Philosophical Psychology 6 (1):23 – 37.
Ron Sun, Andrew Coward & Michael J. Zenzen (2005). On Levels of Cognitive Modeling. Philosophical Psychology 18 (5):613-637.
J. Bickle, C. Worley & M. Bernstein (2000). Vector Subtraction Implemented Neurally: A Neurocomputational Model of Some Sequential Cognitive and Conscious Processes. Consciousness and Cognition 9 (1):117-144.
Gualtiero Piccinini (2010). The Resilience of Computationalism. Philosophy of Science 77 (5):852-861.
Jürgen Hollatz (1999). Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic. Artificial Intelligence and Law 7 (2-3):289-301.
Alex Vereschagin, Mike Collins & Pete Mandik (2007). Evolving Artificial Minds and Brains. In Drew Khlentzos & Andrea Schalley (eds.), Mental States Volume 1: Evolution, function, nature. John Benjamins.
Peter R. Krebs, Models of Cognition: Neurological Possibility Does Not Indicate Neurological Plausibility.
Paul Thagard & Terrence C. Stewart (2011). The AHA! Experience: Creativity Through Emergent Binding in Neural Networks. Cognitive Science 35 (1):1-33.
Added to index2009-01-28
Total downloads5 ( #224,470 of 1,098,996 )
Recent downloads (6 months)0
How can I increase my downloads?