PubHawk
April 2019 PMID 30953964

The neural and cognitive architecture for learning from a small sample.

Aurelio Cortese, Benedetto De Martino, Mitsuo Kawato

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Mentions

14
Peak: 4 in May 2024
across 2 sources since Apr 2019

Extended reach

99,462
potential audience

Total likes

17

Video views

6

Mentions per month

Apr 2019 – Jan 2025

Where it's discussed

  • X / Twitter10 posts
  • YouTube4 videos

AI synopsis

What people are saying

Discussions reflect a positive reception, highlighting core ideas such as the brain's ability to reframe problems and generalize from few examples, with phrases like 'key to human intelligence' and 'efficient learning,' which convey a sense of significance. Although engagement varies, many user comments indicate that they see this research as enhancing understanding of cognition and AI, with overall tone balancing curiosity and acknowledgment of its importance.

AgreementModerate agreement

Most discussions acknowledge the significance of the proposed neural and cognitive mechanisms, often highlighting agreement with the idea that human learning involves complex, efficient processes that AI currently struggles to replicate.

InterestHigh interest

Posts show high interest, with many users expressing curiosity about how human intelligence differs from AI and mentioning the potential implications for understanding cognition.

EngagementModerate engagement

Some comments reference specific concepts like brain generalization and problem reframing, indicating moderate depth of engagement, though not deeply technical or analytical.

ImpactModerate impact

The conversations suggest a modest recognition of the article's potential influence, with some posts suggesting it could inform future AI development or cognitive science research.

Abstract in brief

  • The text explains that while AI algorithms are powerful, they are much less efficient than the human brain when it comes to learning from few examples or solving new problems, and explores what makes human intelligence special.
  • It suggests that the brain's ability to generalize is a key factor, but also highlights that the brain can reframe complex problems into simpler, more manageable ones, which aids in learning.
  • The authors propose a model where higher cognitive functions work closely with reinforcement learning to reduce the complexity of problems, making learning more efficient by narrowing down the options the brain needs to consider.
Read the full abstract

Top posts

Most liked on X and Bluesky
Earl K. Miller
@MillerLabMIT · X

The neural and cognitive architecture for learning from a small sample https://t.co/yckBLhlitM

May 23, 2019 · 11 likes · 2 shares · 41,232 followers View post →
hideyuki takahashi
@hideman2009 · X

ATRの川人先生のこの論文が気になる・・・ The neural and cognitive architecture for learning from a small sample https://t.co/DrFeKXyh8K

Apr 24, 2019 · 3 likes · 6,559 followers View post →
hideyuki takahashi
@hideman2009 · X

The neural and cognitive architecture for learning from a small sample https://t.co/WVqZgmOmia

Aug 19, 2021 · 2 likes · 1 share · 6,559 followers View post →
Sanghee Moon
@SangheeMoon · X

The neural and cognitive architecture for learning from a small sample https://t.co/xcek9mI6kt

Apr 5, 2019 · 1 like · 6,626 followers View post →
CLaE
@leafs_s · X

The neural and cognitive architecture for learning from a small sample https://t.co/6yULbzB81M

Jun 5, 2019 · 28,062 followers View post →
Rina Saltman
@rinatie_ceo · X

The neural and cognitive architecture for learning from a small sample - ScienceDirect https://t.co/QjLcDSF8z5

Jan 28, 2025 · 3,560 followers View post →

All mentions

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PASADIZO · May 24, 2024 0 views
Rina Saltman
@rinatie_ceo · X

The neural and cognitive architecture for learning from a small sample - ScienceDirect https://t.co/QjLcDSF8z5

Jan 28, 2025 · 3,560 followers View post →
Yasuhiro Matsushita
@YasuhiroMatz · X

RT @hideman2009: The neural and cognitive architecture for learning from a small sample https://t.co/WVqZgmOmia

Aug 19, 2021 · 1 share · 389 followers View post →
hideyuki takahashi
@hideman2009 · X

The neural and cognitive architecture for learning from a small sample https://t.co/WVqZgmOmia

Aug 19, 2021 · 2 likes · 1 share · 6,559 followers View post →
Rina Saltman
@rinatie_ceo · X

The neural and cognitive architecture for learning from a small sample - ScienceDirect https://t.co/QjLcDSmZkX

Jun 7, 2019 · 3,560 followers View post →
CLaE
@leafs_s · X

The neural and cognitive architecture for learning from a small sample https://t.co/6yULbzB81M

Jun 5, 2019 · 28,062 followers View post →
Mar ía San Día
@innvierna · X

RT @MillerLabMIT: The neural and cognitive architecture for learning from a small sample https://t.co/yckBLhlitM

May 23, 2019 · 2 shares · 370 followers View post →
Earl K. Miller
@MillerLabMIT · X

The neural and cognitive architecture for learning from a small sample https://t.co/yckBLhlitM

May 23, 2019 · 11 likes · 2 shares · 41,232 followers View post →
UCL Discovery
@ucl_discovery · X

Open Access UCL Research: The neural and cognitive architecture for learning from a small sample https://t.co/ddBCQs7cBX

May 17, 2019 · 2,528 followers View post →
hideyuki takahashi
@hideman2009 · X

ATRの川人先生のこの論文が気になる・・・ The neural and cognitive architecture for learning from a small sample https://t.co/DrFeKXyh8K

Apr 24, 2019 · 3 likes · 6,559 followers View post →
Sanghee Moon
@SangheeMoon · X

The neural and cognitive architecture for learning from a small sample https://t.co/xcek9mI6kt

Apr 5, 2019 · 1 like · 6,626 followers View post →