Home > Uncategorized > Weekly QuEST Discussion Topics and News, 3 Oct

Weekly QuEST Discussion Topics and News, 3 Oct

QuEST 3 Oct 2014:

1.)  We want to spend some bandwidth this week reviewing the questions asked by Mike R / Sandy V on formulating qualia.  It will also provide us as a group an opportunity to discuss any fall-out / vectors associated with our colleague Sandy’s dissertation prospectus defense.  Her abstract: Thisprospectus  examines how a computational model of a Qualia Generating Agent (QGA) can be applied to decisionsupport and AI systems for improved deci- sion making. The principal goal of the research is to develop the QGA ina Cognitive Modeling Architecture (CMA) to improve decision making in simulated event-driven environments andprovide a fundamental cognitive  component  for further research in computational modeling of narratives. Thespecific problem focus is to develop a computational formalism that allows a CMA to store, recall and process withagent- centric, context-dependent, conceptual knowledge, i.e., qualia, more in line with human cognition.  Themethodology will  model and simulate Stanovich’s Tripartite Framework, capture stimuli as episodic memory, aggregate and relate episodic memory into conceptual  knowledge and context, model working memory, andsimulate cognitive decoupling and mental simulation in working memory to compute a set of best explanations based on an abductive process.

2.)  Sandy led me to an article ‘ the importance of cognitive architectures: an analysis based on CLARION’ by Ron Sun.  Abstract: Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad, multiple-level, multiple-domain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will be undertaken, and an example cognitive architecture (CLARION) will be described.

3.)  These discussions led us last week to discuss how our view of the Link game can/should play in implementations – we might review those ideas to help the discussion – our previous discussion along this axes led us to talk about the memory competitions and how people create elaborate memory palaces to ‘store’ away the data.

4.)  Those discussions lead us back to what do we expect is the value added for QuEST solutions – What are the axes for improvement in performance for QuEST solutions – is it accuracy or in reduction in computational resources necessary or is it the ability to respond to the unexpected query – For a set of processes that are fragile and designed to provide very specific information – how can a conscious representation take those building blocks and construct a flexible response mechanism to respond to the unexpected query.  To help stimulate thoughts along these lines I reviewed this week a prior discussion we’ve had associated with ‘Good judgments do not require complex cognition’ v2 by Julian N. Marewski • Wolfgang Gaissmaier • Gerd Gigerenzer — in that work they make the point that – Widespread belief in psychology and beyond that complex judgment tasks require complex solutions.  Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true – although the article uses this point to suggest this is the purpose of Type 1 processes (heuristics) I think we can use the same argument to suggest the bit reduced (from a reality perspective) conscious part of the internal representation uses this approach

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