How to Grow a Mind: Statistics, Structure, and Abstraction

Joshua B. Tenenbaum; Charles Kemp; Thomas L. Griffiths; Noah D. Goodman

doi:https://doi.org/10.1126/science.1192788

How to Grow a Mind: Statistics, Structure, and Abstraction

2011 · Science · 1,923 citations · 4 from inside this corpus

Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, Noah D. Goodman

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

7 of 7 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Relational inductive biases, deep learning, and graph networks→How to Grow a Mind: Statistics, Structure, and AbstractionLegacy edge — source detail unavailable
Probabilistic machine learning and artificial intelligence→How to Grow a Mind: Statistics, Structure, and AbstractionLegacy edge — source detail unavailable
Causability and explainability of artificial intelligence in medicine→How to Grow a Mind: Statistics, Structure, and AbstractionLegacy edge — source detail unavailable
Bayesian Cognitive Modeling: A Practical Course→How to Grow a Mind: Statistics, Structure, and AbstractionLegacy edge — source detail unavailable
How to Grow a Mind: Statistics, Structure, and Abstraction→Parallel Distributed ProcessingLegacy edge — source detail unavailable
How to Grow a Mind: Statistics, Structure, and Abstraction→Bayesian Data AnalysisLegacy edge — source detail unavailable
How to Grow a Mind: Statistics, Structure, and Abstraction→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable
Probabilistic machine learning and artificial intelligence→Bayesian Data AnalysisLegacy edge — source detail unavailable
Probabilistic machine learning and artificial intelligence→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable
Bayesian Cognitive Modeling: A Practical Course→Information theory, inference, and learning algorithmsLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Parallel Distributed Processing	1986	15,411
Bayesian Data Analysis	1995	13,754
Information theory, inference, and learning algorithms	2004	6,580

What cites it, inside the corpus

Paper	Year	Cited
Relational inductive biases, deep learning, and graph networks	2018	2,406
Probabilistic machine learning and artificial intelligence	2015	2,031
Causability and explainability of artificial intelligence in medicine	2019	1,851
Bayesian Cognitive Modeling: A Practical Course	2014	1,423

Links

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Topics

Bayesian Modeling and Causal Inference	Computer Science
Child and Animal Learning Development	Psychology
Topic Modeling	Computer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

supports4 author record(s) attached.
supports64 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

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