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Found 20 papers, 9 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).
Predicting the Future with Simple World Models
Abstracting the dynamics of the environment with simple models can have several benefits.
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.
Visual cognition in multimodal large language models
A chief goal of artificial intelligence is to build machines that think like people.
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.
Playing repeated games with Large Language Models
In a large set of two players-two strategies games, we find that LLMs are particularly good at games where valuing their own self-interest pays off, like the iterated Prisoner's Dilemma family.
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
These findings demonstrate that LLMs are capable of taking on diverse roles and that this in-context impersonation can be used to uncover their hidden strengths and biases.
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.
Learning Parsimonious Dynamics for Generalization in Reinforcement Learning
We argue that two principles can help bridge this gap: latent learning and parsimonious dynamics.
Model-based Reinforcement Learning
reinforcement-learning
+1
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.
Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking
As learning progresses, a hierarchy of chunk representation is acquired by chunking previously learned representations into more complex representations guided by sequential dependence.
Probing the Compositionality of Intuitive Functions
How do people learn about complex functional structure?
Better safe than sorry: Risky function exploitation through safe optimization
Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations.
