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Computational Psychiatry for Computers.

Eric Schulz¹, Peter Dayan^1,2

¹Max Planck Institute for Biological Cybernetics, Tübingen, Baden-Württemberg, Germany.

|December 9, 2020

Summary

This summary is machine-generated.

Computational psychiatry analyzes how we navigate a complex world to understand mental dysfunction. This study explores if these computational principles can reveal flaws in artificial intelligence systems.

Keywords:

Computer Science Human-Computer Interaction Psychology

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Area of Science:

Computational psychiatry
Artificial intelligence
Cognitive science

Background:

Computational psychiatry uses multi-level analyses to understand mental dysfunction.
Key areas include environmental adaptivity and heuristic processing.
Artificial computational devices are increasingly used to navigate complex environments.

Purpose of the Study:

To examine how computational psychiatry principles can be applied to study artificial computational devices.
To identify potential flaws in AI systems by analyzing their computational processes.
To bridge the gap between understanding human cognition and developing robust AI.

Main Methods:

Analysis of computational psychiatry frameworks.
Comparative study of human and artificial computational processes.
Identification of common failure modes in complex environmental navigation.

Main Results:

Computational psychiatry concepts offer a novel lens for AI flaw detection.
Environmental adaptivity and heuristics are critical areas for AI evaluation.
Potential for AI to exhibit analogous flaws to human cognitive biases.

Conclusions:

Computational psychiatry provides valuable insights into AI limitations.
Understanding human computational challenges can inform AI development.
Further research is needed to fully leverage computational psychiatry for AI safety and efficacy.