TY - JOUR
T1 - Structure learning and the Occam's razor principle: A new view of human function acquisition.
AU - Narain, D.
AU - Smeets, J.B.J.
AU - Mamassian, P.
AU - Brenner, E.
AU - van Beers, R.J.
PY - 2014
Y1 - 2014
N2 - We oftenen counter pairs of variables in the world whose mutual relationship can be described by a function. Aftertraining, human responses closely correspond to these functional relationships. Here west Study how humans predict unobserved segments of a function that they have been trained on and we compare how human predictions differ to those made by various function-learning models in the literature. Participants’performance was best predicted by the polynomial functions that generated the observations. Further, participants were able to explicitly report the correct generating function in most cases upon a post-experiment survey. This suggests that humans can abstract functions. To understand how they do so, we modeled human learning using an hierarchical Bayesian framework organized at two levels of abstraction: function learning and parameter learning, and used it to understand the time course of participants’ learning as we surreptitiously changed the generating function over time. This Bayesian model selection framework allowed us to analyze the time course of function learning and parameter learning in relative isolation. We found that participants acquired new functions as they changed and even when parameter learning was not completely accurate, the probability that the correct function was learned remained high. Most importantly, we found that humans selected the simplest-fitting function with the highest probability and that they acquired simpler functions faster than more complex ones. Both aspects of this behavior, extent and rate of selection, present evidence that human function learning obeys the Occam’s razor principle.
AB - We oftenen counter pairs of variables in the world whose mutual relationship can be described by a function. Aftertraining, human responses closely correspond to these functional relationships. Here west Study how humans predict unobserved segments of a function that they have been trained on and we compare how human predictions differ to those made by various function-learning models in the literature. Participants’performance was best predicted by the polynomial functions that generated the observations. Further, participants were able to explicitly report the correct generating function in most cases upon a post-experiment survey. This suggests that humans can abstract functions. To understand how they do so, we modeled human learning using an hierarchical Bayesian framework organized at two levels of abstraction: function learning and parameter learning, and used it to understand the time course of participants’ learning as we surreptitiously changed the generating function over time. This Bayesian model selection framework allowed us to analyze the time course of function learning and parameter learning in relative isolation. We found that participants acquired new functions as they changed and even when parameter learning was not completely accurate, the probability that the correct function was learned remained high. Most importantly, we found that humans selected the simplest-fitting function with the highest probability and that they acquired simpler functions faster than more complex ones. Both aspects of this behavior, extent and rate of selection, present evidence that human function learning obeys the Occam’s razor principle.
U2 - 10.3389/fncom.2014.00121
DO - 10.3389/fncom.2014.00121
M3 - Article
VL - 8
SP - 1
EP - 13
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
SN - 1662-5188
M1 - 121
ER -