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Found 12 papers, 1 papers with code
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves.
Are task representations gated in macaque prefrontal cortex?
A recent paper (Flesch et al, 2022) describes behavioural and neural data suggesting that task representations are gated in the prefrontal cortex in both humans and macaques.
Abrupt and spontaneous strategy switches emerge in simple regularised neural networks
Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on.
Fine-tuning language models to find agreement among humans with diverse preferences
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user.
Continual task learning in natural and artificial agents
How do humans and other animals learn new tasks?
Beyond Bayes-optimality: meta-learning what you know you don't know
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals
Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting.
The Good Shepherd: An Oracle Agent for Mechanism Design
Our results show that our mechanisms are able to shepherd the participants strategies towards favorable outcomes, indicating a path for modern institutions to effectively and automatically influence the strategies and behaviors of their constituents.
HCMD-zero: Learning Value Aligned Mechanisms from Data
Our analysis shows HCMD-zero consistently makes the mechanism policy more and more likely to be preferred by human participants over the course of training, and that it results in a mechanism with an interpretable and intuitive policy.
Human-centered mechanism design with Democratic AI
Building artificial intelligence (AI) that aligns with human values is an unsolved problem.
Characterizing emergent representations in a space of candidate learning rules for deep networks
Studies suggesting that representations in deep networks resemble those in biological brains have mostly relied on one specific learning rule: gradient descent, the workhorse behind modern deep learning.
If deep learning is the answer, then what is the question?
In this Perspective, our goal is to offer a roadmap for systems neuroscience research in the age of deep learning.
Neurons and Cognition
