TY - JOUR
T1 - Meta-learned models as tools to test theories of cognitive development
AU - Nussenbaum, Kate
AU - Hartley, Catherine A.
PY - 2024/9/23
Y1 - 2024/9/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85204761178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204761178&partnerID=8YFLogxK
U2 - 10.1017/S0140525X24000281
DO - 10.1017/S0140525X24000281
M3 - Article
C2 - 39311529
AN - SCOPUS:85204761178
SN - 0140-525X
VL - 47
SP - e157
JO - The Behavioral and brain sciences
JF - The Behavioral and brain sciences
ER -