Cognitive Architecture with Evolutionary Dynamics Solves Insight Problem

Cognitive Architecture with Evolutionary Dynamics Solves Insight Problem

In this paper, we show that a neurally implemented a cognitive architecture with evolutionary dynamics can solve the four-tree problem. Our model, called Darwinian Neurodynamics, assumes that the unconscious mechanism of problem solving during insight tasks is a Darwinian process. It is based on the evolution of patterns that represent candidate solutions to a problem, and are stored and reprod...

Recurrent cerebellar architecture solves the motor-error problem.

Current views of cerebellar function have been heavily influenced by the models of Marr and Albus, who suggested that the climbing fibre input to the cerebellum acts as a teaching signal for motor learning. It is commonly assumed that this teaching signal must be motor error (the difference between actual and correct motor command), but this approach requires complex neural structures to estima...

Solves the Stereo Aperture Problem

If a bar stimulus extends beyond a cell's receptive field, then alterations in binocular disparity parallel to the bar's orientation leave the portion of the stimulus within the cell's receptive field unchanged. This makes it hard for the cell to respond correctly to the bar's disparity. Likening the cell's receptive field to an aperture through which the cell views the world, this issue has be...

Dynamics and constraints in insight problem solving.

This article reports 2 experiments that investigated performance on a novel insight problem, the 8-coin problem. The authors hypothesized that participants would make certain initial moves (strategic moves) that seemed to make progress according to the problem instructions but that nonetheless would guarantee failure to solve the problem. Experiment 1 manipulated the starting state of the probl...

Running Header: Dynamics and Constraints in Insight. Dynamics and Constraints in Insight Problem-solving