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One hallmark of human reasoning is that we can bring to bear a diverse web of common-sense knowledge in any situation. The vastness of our knowledge poses a challenge for the practical implementation of reasoning systems as well as for our cognitive theories – how do people represent their common-sense knowledge? On the one hand, our best models of sophisticated reasoning are top-down, making use primarily of symbolically-encoded knowledge. On the other, much of our understanding of the statistical properties of our environment may arise in a bottom-up fashion, for example through asso- ciationist learning mechanisms. Indeed, recent advances in AI have enabled the development of billion-parameter language models that can scour for patterns in gigabytes of text from the web, picking up a surprising amount of common-sense knowledge along the way—but they fail to learn the structure of coherent reasoning. We propose combining these approaches, by embedding language-model-backed primitives into a state- of-the-art probabilistic programming language (PPL). On two open-ended reasoning tasks, we show that our PPL models with neural knowledge components characterize the distribution of human responses more accurately than the neural language models alone, raising interesting questions about how people might use language as an interface to common-sense knowledge, and suggesting that building probabilistic models with neural language-model components may be a promising approach for more human-like AI.
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This content will become publicly available on December 31, 2025
Built to Adapt: Mechanisms of Cognitive Flexibility in the Human Brain
Adaptability is a distinguishing feature of the human species: We thrive as hunter-gatherers, farmers, and urbanites. What properties of our brains make us highly adaptable? Here we review neuroscience studies of sensory loss, language acquisition, and cultural skills (reading, mathematics, programming). The evidence supports a flexible specialization account. On the one hand, adaptation is enabled by evolutionarily prepared flexible learning systems, both domain-specific social learning systems (e.g., language) and domain-general systems (frontoparietal reasoning). On the other hand, the functional flexibility of our neural wetware enables us to acquire cognitive capacities not selected for by evolution. Heightened plasticity during a protracted period of development enhances cognitive flexibility. Early in life, local cortical circuits are capable of acquiring a wide range of cognitive capacities. Exuberant cross-network connectivity makes it possible to combine old neural parts in new ways, enabling cognitive flexibility such as language acquisition across modalities (spoken, signed, braille) and cultural skills (math, programming). Together, these features of the human brain make it uniquely adaptable.
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- Award ID(s):
- 2318685
- PAR ID:
- 10611470
- Publisher / Repository:
- Annual Review of Developmental Psychology
- Date Published:
- Journal Name:
- Annual review of developmental psychology
- ISSN:
- 2640-7922
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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