Plants, Cognition, Time (& Philosophy)

Posted on September 26, 2008 by admin

Leibovitz, D. P. (2008) Plants, Cognition, Time (& Philosophy). Talk presented at Carleton University, pp. 1-28, Ottawa, Canada. [doi: 10.13140/RG.2.1.2470.3209]

Abstract: When plants are viewed under various time and spatial scales, their behaviour can appear quite intelligent. This presentation simply aims at questioning some of the basic terminology used by Philosophers of Mind, and Cognitive Scientists. The goal of the presentation is not to answer the following questions, but to stimulate discussion and reflection.

Can plants process information? Do they compute/calculate? Make decisions? Can plants think?
Do they have goals, plans, intentions, memories? Can they know? Do they represent information? How?
Do they attend, sense, perceive, feel? Can they be conscious?
Can they learn? Anticipate?
Can they communicate?
Do they have free will?

What do we mean by all the aforementioned terms, and how do we clarify them so that plants are once again relegated to simple stimulus-response systems?

The parting thought is in showing that a trivial stimulus-response system is Turing Complete, so perhaps pointing to individual plant processes and showing that each one alone is a stimulus-response portion might miss the overall system-wide intelligence…

Links:

This talk as PDF (static, 3.64 MB)
This talk as PPT (animated, 3.76 MB)
Presented to FYSM 1400 “Cognition: A Scientific Exploration of the Mind” on September 26, 2008 (Instructed by Robert L. West).
Presented to x on November 1, 2011 (Instructed by Robert L. West).
Presented to Introduction to Cognitive Psychology (Psych 2700A) on October 17, 2018 (Instructed by Robert L. West).
@academia.edu
@frontiersin.org
@mendeley.com
@researchgate.net
@scholar.google.ca

Emergic Network (EN)

Posted on August 18, 2008 by admin

The Emergic Network (or EN) is an “artificial neural” network architecture that abandons traditional neural oversimplifications and facilitates an Emergic Approach to design that harnesses emergence by explicitly encoding the interactions among multiple flows of information.

Note: that while an Emergic Network unit can correspond to an actual neuron, the Emergic Network is not a network of neurons, and each unit can correspond to an arbitrary domain of analysis, as low as quantum mechanics if desired, up to social groupings. That is why “neural” is in quotes. Indeed a single unit is Turing complete and could simulate an entire artificial neural network.

The Emergic Network architecture, is described and housed within Wikimergic.

Related Publications:

Leibovitz, D. P. (2013). A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture. (Order No. NR94549, Carleton University (Canada)). ProQuest Dissertations and Theses, pp. xxxii-459. Retrieved from http://search.proquest.com/docview/1437103134?accountid=9894. (1437103134). [doi: 10.13140/RG.2.1.2681.6482]
Leibovitz, D. P., & West, R. L. (2012) Cognitive Re-Use via Emergic Networks. In N. Rußwinkel, U. Drewitz, & H. van Rijn (Eds.), Proceedings of the 11th International Conference on Cognitive Modeling (ICCM 2012), pp. 72–73. [doi: 10.13140/RG.2.1.3562.9282]
Leibovitz, D. P., & West, R. L. (2012). Cognitive Re-Use via Emergic Networks. Poster presented at the 11th International Conference on Cognitive Modeling (ICCM 2012), pp. 1-12. Berlin, Germany. [doi: 10.13140/RG.2.1.4218.2884]
Leibovitz, D. P. (2011) Emergic Network. Published as open sourced code. Retrieved September 7, 2015 from http://emergic.upwize.com/?page_id=6.

See also:

Emergic Approach (constrains the design and use of the Emergic Network)
Emergic Cognitive Model (is an Emergic Network)
software (for an Emergic Network)
Wikimergic (describes and houses the Emergic Network)

Emergic Cognitive System

Posted on February 29, 2008 by admin

The Emergic Cognitive System (or ECS) is a system for embodying a developmental cognitive model in a virtual and dynamic agent that is situated in a dynamic environment using simulated real-time for non-representational information processing. This allows a zero parameter model to be tested with a wide variety of experimental paradigms covering a large contextual domain.