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Generalization as diffusion: human function learning on graphs

https://doi.org/10.1101/538934
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/j.2333-8504.1963.tb00958.xopenalex-snapshot
FUNCTIONAL LEARNING: THE LEARNING OF CONTINUOUS FUNCTIONAL MAPPINGS RELATING STIMULUS AND RESPONSE CONTINUA
inferredcites →https://doi.org/10.1016/0030-5073(74)90002-6openalex-snapshot
Hypotheses about relations between scaled variables in the learning of probabilistic inference tasks
inferredcites →https://doi.org/10.1016/j.cogpsych.2017.11.002openalex-snapshot
Compositional Inductive Biases in Function Learning
inferredcites →https://doi.org/10.1016/j.cogpsych.2019.101261openalex-snapshot
Finding structure in multi-armed bandits
inferredcites →https://doi.org/10.1016/j.jmp.2018.03.001openalex-snapshot
A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions
inferredcites →https://doi.org/10.1037//0278-7393.17.5.811openalex-snapshot
Function learning: Induction of continuous stimulusesponse relations.
inferredcites →https://doi.org/10.1037//0278-7393.23.4.968openalex-snapshot
Extrapolation: The sine qua non for abstraction in function learning.
inferredcites →https://doi.org/10.1037/0033-295x.111.4.1072openalex-snapshot
Population of Linear Experts: Knowledge Partitioning and Function Learning.
inferredcites →https://doi.org/10.1037/0278-7393.17.5.811openalex-snapshot
Function learning: Induction of continuous stimulus-response relations.
inferredcites →https://doi.org/10.1037/0278-7393.32.5.1019openalex-snapshot
Why people underestimate y when extrapolating in linear functions.
inferredcites →https://doi.org/10.1038/nn.4650openalex-snapshot
The hippocampus as a predictive map
inferredcites →https://doi.org/10.1038/s41562-017-0180-8openalex-snapshot
The successor representation in human reinforcement learning
inferredcites →https://doi.org/10.1038/s41562-018-0467-4openalex-snapshot
Generalization guides human exploration in vast decision spaces
inferredcites →https://doi.org/10.1073/pnas.1809787115openalex-snapshot
Measurement invariance explains the universal law of generalization for psychological perception
inferredcites →https://doi.org/10.1101/258665openalex-snapshot
Connecting conceptual and spatial search via a model of generalization
inferredcites →https://doi.org/10.1101/449470openalex-snapshot
Predicting the Future with Multi-scale Successor Representations
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1126/science.3629243openalex-snapshot
Toward a Universal Law of Generalization for Psychological Science
inferredcites →https://doi.org/10.1126/science.aaq1118openalex-snapshot
Efficient coding explains the universal law of generalization in human perception
inferredcites →https://doi.org/10.1162/neco.1993.5.4.613openalex-snapshot
Improving Generalization for Temporal Difference Learning: The Successor Representation
inferredcites →https://doi.org/10.1523/jneurosci.0151-18.2018openalex-snapshot
The Successor Representation: Its Computational Logic and Neural Substrates
inferredcites →https://doi.org/10.31222/osf.io/v94h6openalex-snapshot
bridgesampling: An R Package for Estimating Normalizing Constants
inferredcites →https://doi.org/10.3758/bf03194066openalex-snapshot
Iterated learning: Intergenerational knowledge transmission reveals inductive biases
inferredcites →https://doi.org/10.3758/bf03196347openalex-snapshot
The conceptual basis of function learning and extrapolation: Comparison of rule-based and associative-based models
inferredcites →https://doi.org/10.3758/s13423-015-0808-5openalex-snapshot
A rational model of function learning
inferredcites →https://doi.org/10.48550/arxiv.1710.11089openalex-snapshot
Eigenoption Discovery through the Deep Successor Representation
inferredcites →https://doi.org/10.7551/mitpress/3206.001.0001openalex-snapshot
Gaussian Processes for Machine Learning
inferredcites →https://doi.org/10.7551/mitpress/3577.003.0008openalex-snapshot
Concepts and Categories
inferredcites →https://doi.org/10.7551/mitpress/4071.003.0015openalex-snapshot
Learning Functional Relations Based on Experience With Input-Output Pairs by Humans and Artificial Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/hbm.25988openalex-snapshot
Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T
inferred← citeshttps://doi.org/10.1007/s42113-020-00091-xopenalex-snapshot
Inference and Search on Graph-Structured Spaces
inferred← citeshttps://doi.org/10.1101/2021.02.16.431506openalex-snapshot
A mixture of generative models strategy helps humans generalize across tasks
inferred← citeshttps://doi.org/10.1111/cogs.13262openalex-snapshot
Learning to Learn Functions
inferred← citeshttps://doi.org/10.32470/ccn.2019.1041-0openalex-snapshot
Searching for rewards in graph-structured spaces
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