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Related Experiment Video

Updated: Sep 22, 2025

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
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Hybrid social learning in human-algorithm cultural transmission.

L Brinkmann1, D Gezerli1, K V Kleist1

  • 1Center for Humans and Machines, Max Planck Institute for Human Development, Lentzeallee 94, Berlin 14195, Germany.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

Algorithms can enhance human problem-solving, but this benefit is temporary. Human biases limit the long-term preservation of algorithmic strategies in cultural transmission.

Keywords:
cultural evolutionhuman–machine collaborationsocial learningtransmission chain

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Area of Science:

  • Cultural Evolution
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Humans are adept social learners, with biases influencing cultural transmission.
  • The emergence of advanced algorithms introduces a novel form of cultural transmission: algorithm-to-human.

Purpose of the Study:

  • To investigate if algorithms with biases complementary to humans can improve performance in a planning task.
  • To determine if humans transmit algorithmic behaviors to other humans in a cultural learning context.

Main Methods:

  • A large-scale behavioral study involving human participants and algorithmic agents.
  • An agent-based simulation to model cultural transmission chains.
  • Controlled planning task to assess performance and behavioral transmission.

Main Results:

  • Algorithms significantly boosted the performance of participants who directly interacted with them.
  • Performance gains diminished rapidly in subsequent participants further down the transmission chain.
  • Human biases appeared to impede the sustained preservation of algorithmic solutions.

Conclusions:

  • Algorithmic input can temporarily enhance human problem-solving capabilities.
  • Human cultural transmission mechanisms may filter out or alter beneficial algorithmic strategies.
  • Further research is needed to understand the long-term impact of algorithmic influence on human culture.