Articolo in rivista, 2016, ENG, 10.1371/journal.pcbi.1004864
Donnarumma, Francesco; Maisto, Domenico; Pezzulo, Giovanni
CNR; CNR
How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.
PLoS computational biology 12 (4)
Problem solving, Probability distribution, Cognition, Behavior, Algorithms, Decision making, Learning, Perception
Pezzulo Giovanni, Maisto Domenico, Donnarumma Francesco
ICAR – Istituto di calcolo e reti ad alte prestazioni, ISTC – Istituto di scienze e tecnologie della cognizione
ID: 361310
Year: 2016
Type: Articolo in rivista
Creation: 2016-11-24 19:39:30.000
Last update: 2021-04-23 17:23:36.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1371/journal.pcbi.1004864
URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004864
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:361310
DOI: 10.1371/journal.pcbi.1004864
ISI Web of Science (WOS): 000376584400043