TY - JOUR
T1 - Concept Learning in a Probabilistic Language of Thought. How is it possible and what does it presuppose?
AU - Colombo, Matteo
PY - 2023
Y1 - 2023
N2 - Where does a probabilistic language-of-thought (PLoT) come from? How can we learn new concepts based on probabilistic inferences operating on a PLoT? Here, I explore these questions, sketching a traditional circularity objection to LoT and canvassing various approaches to addressing it. I conclude that PLoT-based cognitive architectures can support genuine concept learning; but, currently, it is unclear that they enjoy more explanatory breadth in relation to concept learning than alternative architectures that do not posit any LoT.
AB - Where does a probabilistic language-of-thought (PLoT) come from? How can we learn new concepts based on probabilistic inferences operating on a PLoT? Here, I explore these questions, sketching a traditional circularity objection to LoT and canvassing various approaches to addressing it. I conclude that PLoT-based cognitive architectures can support genuine concept learning; but, currently, it is unclear that they enjoy more explanatory breadth in relation to concept learning than alternative architectures that do not posit any LoT.
U2 - 10.1017/S0140525X23002029
DO - 10.1017/S0140525X23002029
M3 - Comment/Letter to the editor
SN - 0140-525X
VL - 46
JO - Behavioral and Brain Sciences
JF - Behavioral and Brain Sciences
M1 - 271
ER -