Evidence map›Paper›PMID 36439063›Full record

ArticleOpen mind : discoveries in cognitive science2022

Neural Networks Track the Logical Complexity of Boolean Concepts.

Fausto Carcassi, Jakub Szymanik

Abstract read
In one paragraph

Article in Open mind : discoveries in cognitive science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Fausto CarcassiSeminar für Sprachwissenschaft, Tübingen, Germany.ORCID https://orcid.org/0000-0001-6843-1737
Jakub SzymanikInstitute for Logic, Language, and Computation, Universiteit van Amsterdam, Amsterdam, Netherlands.ORCID https://orcid.org/0000-0002-6145-6322

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The language of thought hypothesis and connectionism provide two main accounts of category acquisition in the cognitive sciences. However, it is unclear to what extent their predictions agree. In this article, we tackle this problem by comparing the two accounts with respect to a common set of predictions about the effort required to acquire categories. We find that the two accounts produce similar predictions in the domain of Boolean categorization, however, with substantial variation depending on the operators in the language of thought.

Indexed as

artificial neural networksBoolean complexitycategory acquisition

Identifiers

PMID36439063
PMCPMC9692055

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.