Year: 2024
Type: article
Abstract: Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.
Source: Behavioral and Brain Sciences
Authors: Kate Nussenbaum, Catherine A. Hartley
Institutions: Princeton University, New York University
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: green
Award: G398474054
OpenAlex ID: W4402730343