Search Results for author:
Found 14 papers, 8 papers with code
CogBench: a large language model walks into a psychology lab
Large language models (LLMs) have significantly advanced the field of artificial intelligence.
In-context learning agents are asymmetric belief updaters
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology.
Ecologically rational meta-learned inference explains human category learning
We tackle the second challenge by deriving rational agents adapted to these tasks using the framework of meta-learning, leading to a class of models called ecologically rational meta-learned inference (ERMI).
How should the advent of large language models affect the practice of science?
For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate.
The Acquisition of Physical Knowledge in Generative Neural Networks
As children grow older, they develop an intuitive understanding of the physical processes around them.
Language Aligned Visual Representations Predict Human Behavior in Naturalistic Learning Tasks
Humans possess the ability to identify and generalize relevant features of natural objects, which aids them in various situations.
Turning large language models into cognitive models
We find that -- after finetuning them on data from psychological experiments -- these models offer accurate representations of human behavior, even outperforming traditional cognitive models in two decision-making domains.
Inducing anxiety in large language models increases exploration and bias
We focus on the Generative Pre-Trained Transformer 3. 5 and subject it to tasks commonly studied in psychiatry.
Meta-Learned Models of Cognition
Meta-learning is a framework for learning learning algorithms through repeated interactions with an environment as opposed to designing them by hand.
Stochastic Gradient Descent Captures How Children Learn About Physics
We find that the model's learning trajectory captures the developmental trajectories of children, thereby providing support to the idea of development as stochastic optimization.
Using cognitive psychology to understand GPT-3
We study GPT-3, a recent large language model, using tools from cognitive psychology.
Modeling Human Exploration Through Resource-Rational Reinforcement Learning
Equipping artificial agents with useful exploration mechanisms remains a challenge to this day.
Where Do Human Heuristics Come From?
Here we approach this discrepancy from the perspective of bounded rationality and our goal is to provide a justification for such seemingly sub-optimal strategies.
Emulating Human Developmental Stages with Bayesian Neural Networks
We compare the acquisition of knowledge in humans and machines.
