Modelling visual processing via emergence

Leibovitz, D. P. (2012) Modelling visual processing via emergence. [Abstracts of the 2012 CSBBCS annual meeting]. Canadian Journal of Experimental Psychology, 66(4): 308–308. [abstracts doi10.1037/a0029409]

Leibovitz (2012) Modelling visual processing via emergence (CSBBCS)Abstract: A model of low level visual processing is outlined along with a demonstration of the numerous phenomena it unifies. Specifically – filling in, visual memory, image stability, color homogeneity, blind spot, temporal edge detection, eye blink – phenomena that would ordinarily be investigated under different sub fields and with disparate models. The model is based on the interaction between recurrence and eye motion. The model is built using the Emergic Network system, which is a new cognitive modeling system created for this project and others like it. Emergic Networks facilitate the exploration of how recurrent and distributed functions produce functional emergent effects. I will present an overview of the Emergic Network System and the simulation results for each phenomena it models.

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A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture – Animated Test Results

Leibovitz, D. P. (2012) A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture – Animated Test Results. Retrieved September 7, 2015 from http://emergic.upwize.com/?page_id=26.

Abstract: Animated results for the cognitive models within a thesis named “A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture“.

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Understanding each other: Defining a conceptual space for cognitive modeling

West, R. L., & Leibovitz, D. P. (2012). Understanding each other: Defining a conceptual space for cognitive modeling. 34th annual meeting of the Cognitive Science Society (CogSci 2012) (pp. 2535-2539). Sapporo, Japan. [doi10.13140/RG.2.1.2760.1128] (PDF)

West & Leibovitz (2012) Understanding each other- Defining a conceptual space for cognitive modelAbstract: Cognitive modeling is a complex endeavor so it is not surprising that the goals and intentions of modelers are often misunderstood, even by other modelers. To try to clarify this we have attempted to map out the various philosophical and theoretical commitments that one makes when creating a cognitive model or architecture. The goal of this is to avoid misunderstandings between the adherents of different modeling systems and between cognitive modelers and the rest of the scientific community.

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Understanding each other: Defining a conceptual space for cognitive modeling (poster)

West, R. L., & Leibovitz, D. P. (2012). Understanding each other: Defining a conceptual space for cognitive modeling. Poster presented at the 34th annual meeting of the Cognitive Science Society (CogSci 2012). Sapporo, Japan.

Abstract: Cognitive modeling is a complex endeavor so it is not surprising that the goals and intentions of modelers are often misunderstood, even by other modelers. To try to clarify this we have attempted to map out the various philosophical and theoretical commitments that one makes when creating a cognitive model or architecture. The goal of this is to avoid misunderstandings between the adherents of different modeling systems and between cognitive modelers and the rest of the scientific community.

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Modelling visual processing via emergence (Invited Talk)

Leibovitz, D. P. (2012) Modelling visual processing via emergence. Invited talk presented at the 22nd Annual Meeting of the Canadian Society for Brain, Behaviour and Cognitive Science (CSBBCS) in the Computational understanding of Cognition Symposium. pp. 1-43, Queen’s University, Kingston, Ontario, Canada. [doi10.13140/RG.2.1.5141.9368]

Leibovitz (2012) Modelling visual processing via emergence (CSBBCS) (Cover)Abstract: A model of low level visual processing is outlined along with a demonstration of the numerous phenomena it unifies. Specifically – filling in, visual memory, image stability, color homogeneity, blind spot, temporal edge detection, eye blink – phenomena that would ordinarily be investigated under different sub fields and with disparate models. The model is based on the interaction between recurrence and eye motion. The model is built using the Emergic Network system, which is a new cognitive modeling system created for this project and others like it. Emergic Networks facilitate the exploration of how recurrent and distributed functions produce functional emergent effects. I will present an overview of the Emergic Network System and the simulation results for each phenomena it models.

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Emergence of epistemic phenomena (poster)

Leibovitz, D. P. (2012) Emergence of epistemic phenomena. Poster presented at the Institute of Cognitive Science Spring Conference (ICSSC) of Carleton University, pp. 1-12. Ottawa, Ontario, Canada[doi10.13140/RG.2.1.4649.3920] (PDF)

Leibovitz (2012) Emergence of Epistemic Phenomena (ICSSC Poster)Abstract: Q: Are you using the correct level of analysis?

We claim that for the unique requirements of cognition

  1. There is only one micro level of ontology, realization and causal explanation (the systems level)
    1. It is process oriented
    2. It can causally explain all higher level behaviours and phenomena
  2. There are no higher levels of causal explanation
    1. Causality flows among actual ontological parts, not to or from epistemic abstractions Under the standard macro level approach, we further claim that
  3. There are no macro level stimuli, measurements and phenomena – they are epistemic illusions
    1. Merely arbitrary and uninformed patterns of micro-level inputs or outputs between an experimental paradigm and a non-representational cognitive agent

Our claims originate from our unified process model of visual filling-in. We noticed that while the model explains all the phenomena, none of them actually existed. The epistemic phenomena arise from oversimplified and implicit folk-theories. Epistemic phenomena emerge from lack of knowledge, from lack of a Systems level theory.

We show the results – the visual demonstration for a variety of “phenomena”. Your task:

Show me the macro level stimuli, measurement or phenomena!

It is only by getting rid of the macro level of analysis that one can hope to uncover a (micro) systems level and begin to causally unify explanations for cognition.

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Cognitive Re-Use via Emergic Networks (poster)

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), Berlin, Germany. pp. 1-12. [doi10.1037/e557232013-001 (PsychEXTRA); 10.13140/RG.2.1.4218.2884 (content)]

Leibovitz & West (2012) Cognitive Re-Use via Emergic Networks (ICCM Poster)Abstract: In this poster we introduce a new cognitive modeling system called Emergic Networks. The Emergic Network system is designed to facilitate functional, nonlinear decomposition with the aim of understanding how different neural systems can interact to produce specific instances of cognitive functionality. The first part of the paper briefly describes the motivation for the system and the second part briefly describes the system and provides a web location for downloading.

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Cognitive Re-Use via Emergic Networks

Leibovitz, D. P., & West, R. L. (2012) Cognitive Re-Use via Emergic Networks. Proceedings of the 11th International Conference on Cognitive Modeling (ICCM 2012) (pp. 72-73). Berlin, Germany. [doi: 10.13140/RG.2.1.3562.9282 (paper);10.1037/e557102013-021 (PsycEXTRA)] (pdf)

Leibovitz & West (2012) Cognitive Re-Use via Emergic Networks (ICCM Poster)Abstract: In this paper we introduce a new cognitive modeling system called Emergic Networks. The Emergic Network system is designed to facilitate functional, nonlinear decomposition with the aim of understanding how different neural systems can interact to produce specific instances of cognitive functionality. The first part of the paper briefly describes the motivation for the system and the second part briefly describes the system and provides a web location for downloading.

Emergic Network ExampleLinks:

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