Year: 2014
Type: article
Abstract: Human thought is remarkably flexible: we can think about infinitely many different situations despite uncertainty and novelty. Probabilistic models of cognition (Chater, Tenen-baum, & Yuille, 2006) have been successful at explaining a wide variety of learning and reasoning under uncertainty. They have borrowed tools from statistics and machine learn-ing to explain phenomena from perception (Yuille & Kersten, 2006) to language (Chater & Manning, 2006). Traditional symbolic models (e.g. Newell, Shaw, & Simon, 1958; An-derson & Lebiere, 1998), by contrast, excel at explaining the productivity of thought, which follows from compositional-ity of symbolic representations. Indeed, there has been a gradual move toward more structured probabilistic models (Tenenbaum, Kemp, Griffiths, & Goodman, 2011) that incor-porate aspects of symbolic methods into probabilistic model-
Authors: Noah D. Goodman, Joshua B. Tenenbaum
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2403318356