Wikimergic

Wikimergic is home of the Emergic Approach, and is a top-level WikiSilo to articulate its impact to the science of cognition.

Emergic Approach

The Emergic Approach is an epistemology for doing science over complex dynamic systems with massive feedback and parallelism. It is based on Emergic Metaphysics, and is housed within Wikimergic

Emergic Metaphysics

Emergic Metaphysics is an analytic "process philosophy" simplified for cognition but based on modern Physics - it is the only notation that can deal with change under feedback cycles. It is the basis for the Emergic Approach housed within Wikimergic.

WikiSilo

WikiSilo is a minimalist epistemology for theoretical exploration and a discipline for  progressing science. It is a radical reduction of the richer epistemology found within Wikimergic.

Emergic Cognitive Model

The Emergic Cognitive Model is a unified cognitive model (currently for visual processing) based on the Emergic Network computational architecture for cognition.

Emergic Network

An Emergic Network is a computational architecture for cognition harnessing massive feedback (recurrence) and parallelism. It is the basis for the Emergic Cognitive Model - a unified cognitive model (currently for visual processing).

SGOMS

SGOMS is an adaptation of GOMS modeling that is meant to be used in complex sociotechnical systems

Evolution & Altruism

This project looks at whether altruism, the urge to help others while ignoring the cost to yourself, should be included as a fundamental brain mechanism, based on evolutionary arguments and simulations. This question has important implications for modeling how helping behaviors occur.

Constrained Scaling

Constrained scaling is a psychophysics scaling technique based on a dynamic model of how self reports about perceived magnitudes are generated

Emotional ACT-R

The goal of this project is to create and test an emotional module for the ACT-R cognitive architecture. The module, which runs in Python ACT-R, simulates the role of the amygdala in cognitive activity. Currently we are testing it using the Iowa Gambling Task

Dynamic Spreading Activation

This project examines the idea that spreading activation decays over time. The main use of this model, which was created in Python ACT-R, has been to model anaphor resolution

Holographic Memory

This project looks at modeling memory with a holographic system. So far the model has been applied to the fan effect and human game playing. 

Game Playing

This project involves understanding human game playing in terms of detecting sequential dependencies (as opposed to understanding it in terms of probabilities, which is commonly the case). Models have been created and tested using neural networks and ACT-R

CCM Suite

The CCM suite is a powerful set of tools for creating cognitive models, situating them in a simulated environment, and rigorously analyzing the results

Python ACT-R

Python ACT-R is a re-implementation and re-conceptualization of the ACT-R architecture in the Python language. It has the same cognitive functionality as Lisp ACT-R but it does not have Lisp ACT-R's perceptual and motor modules. Instead, Python ACT-R has a system for building modules by re-implementing the production system mechanism and the semantic network mechanism used to model procedural memory and declarative memory in ACT-R theory. Therefore, Python ACT-R is driven by multiple production systems operating in parallel. This is different from Lisp ACT-R, but will produce the same behavior as long as only one production system is used for modeling procedural memory.