Bayesian reverse-engineering considered as a research strategy for cognitive science

Reverse engineering the cognitive brain.

One of the greatest aspirations of the human mind has been to realize machines that surpass its cognitive intelligence. The rapid expansion in computing power, about to exceed the equivalent of the human brain, has yet to produce such a machine. The article by Neftci et al. in PNAS (1) offers a refreshing and humbling reminder that the brain’s cognition does not arise from exacting digital prec...

Scenarios Research and Cognitive Reframing: Implications for Strategy as Practice

Machine Reading as a Cognitive Science Research Instrument

We describe how we are using natural language techniques to develop systems that can automatically encode a range of input materials for cognitive simulations. We start by summarizing this type of problem, and the components we are using. We then describe three projects that are using this common infrastructure: learning from multimodal materials, modeling decision making in moral dilemmas, and...

Reverse engineering for biologically inspired cognitive architectures: a critical analysis.

Research initiatives on both sides of the Atlantic try to utilize the operational principles of organisms and brains to develop biologically inspired, artificial cognitive systems. This paper describes the standard way bio-inspiration is gained, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood b...

How does Bayesian reverse-engineering work?

Bayesian models of cognition and behavior are particularly promising when they are used in reverse-engineering explanations: explanations that descend from the computational level of analysis to the algorithmic and implementation levels. Unfortunately, it remains unclear exactly how Bayesian models constrain and influence these lower levels of analysis. In this paper, we review and reject two w...