ArticleBrain sciences2018
A Motivational Model of BCI-Controlled Heuristic Search.
Article in Brain sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market.Brain sciences · 2021Article
- Prefrontal Asymmetry BCI Neurofeedback Datasets.Frontiers in neuroscience · 2020Article
- A Computer Mouse Using Blowing Sensors Intended for People with Disabilities.Sensors (Basel, Switzerland) · 2019Article
- Brain⁻Computer Interfaces for Human Augmentation.Brain sciences · 2019Article
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Authors and funding
1 author.
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Abstract
Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain⁻Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user's disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.
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