We describe a surprising developmental pattern we found in studies involving three different kinds of problems and age ranges. Younger learners are better than older ones at learning unusual abstract causal principles from evidence. We explore two factors that might contribute to this counterintuitive result. The first is that as our knowledge grows, we become less open to new ideas. The second is that younger minds and brains are intrinsically more flexible and exploratory, although they are also less efficient as a result.
GopnikA. (2012). (See References). An accessible short review of recent work on probabilistic models of cognitive development.
2.
GopnikA.WellmanH. M. (2012). (See References). An extensive, thorough, accessible review of the theoretical and empirical work on Bayesian causal models as a constructivist account of cognitive development, which includes a gentle nontechnical instructional tutorial explaining how the models actually work and discussion of the idea of search and developmental change.
3.
KushnirT.XuF. (Eds.). (2012). (See References). An edited volume including chapters by a wide variety of researchers applying probabilistic models to a very wide range of problems and domains.
4.
LucasC. G.BridgersS.GriffithsT. L.GopnikA. (2014). (See References). The source of much of the empirical work described here, including several more experiments and an explanation and formal model relevant to the annealing ideas.
5.
TenenbaumJ. B.KempC.GriffithsT. L.GoodmanN. D. (2011). (See References). A general and accessible review of probabilistic models and Bayesian inference in cognitive science.
6.
BonawitzE.DenisonS.GriffithsT.GopnikA. (2014). Probabilistic models, learning algorithms, response variability: Sampling in cognitive development. Trends in Cognitive Sciences. doi:10.1016/j.tics.2014.06.006
7.
BuchsbaumD.BridgersS.WeisbergD. S.GopnikA. (2012). The power of possibility: Causal learning, counterfactual reasoning, and pretend play. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 2202–2212. doi:10.1098/rstb.2012.0122
8.
ChengP. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.
9.
ChrysikouE. G.HamiltonR.CoslettH.DattaA.BiksonM.Thompson-SchillS. (2013). Noninvasive transcranial direct stimulation over left prefrontal cortex facilitates cognitive flexibility in tool use. Cognitive Neuroscience, 4, 81–89.
10.
DefeyterM. A.GermanT. (2003). Acquiring an understanding of design: Evidence from children’s insight problem solving. Cognition, 89, 133–155.
11.
DenisonS.BonawitzE.GopnikA.GriffithsT. L. (2013). Rational variability in children’s causal inferences: The Sampling Hypothesis. Cognition, 126, 285–300. doi:10.1016/j.cognition.2012.10.010
12.
GentnerD. (2010). Bootstrapping the mind: Analogical processes and symbol systems. Cognitive Science, 34, 752–775. doi:10.1111/j.1551-6709.2010.01114.x
13.
GopnikA. (2012). Scientific thinking in young children: Theoretical advances, empirical research, and policy implications. Science, 337, 1623–1627. doi:10.1126/science.1223416
14.
GopnikA.GlymourC.SobelD. M.SchulzL. E.KushnirT.DanksD. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3–32. doi:10.1037/0033-295X.111.1.3
15.
GopnikA.MeltzoffA. N. (1997). Words, thoughts, and theories. Cambridge, MA: MIT Press.
16.
GopnikA.SchulzL. (2007). Causal learning: Psychology, philosophy, and computation. New York, NY: Oxford University Press.
17.
GopnikA.TenenbaumJ. B. (2007). Bayesian networks, Bayesian learning and cognitive development. Developmental Science, 10, 281–287. doi:10.1111/j.1467-7687.2007.00584.x
18.
GopnikA.WellmanH. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin, 138, 1085–1108. doi:10.1037/a0028044 1085-1108
19.
GriffithsT. L.TenenbaumJ. B. (2007). Two proposals for causal grammars. In GopnikA.SchulzL. (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford, England: Oxford University Press.
KelleyH. H. (1967). Attribution theory in social psychology. In LevineD. (Ed.), Nebraska symposium on motivation (Vol. 15, pp. 192–238). Lincoln: University of Nebraska Press.
KirkpatrickS.GelattC. D.VecchiM. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.
24.
KuhlP. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience, 5, 831–843.
25.
KushnirT.XuF. (Eds.). (2012). Rational constructivism in cognitive development (Vol. 43). New York, NY: Academic Press.
26.
LucasC. G.BridgersS.GriffithsT. L.GopnikA. (2014). When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships. Cognition, 131, 284–299. doi:0.1016/j.cognition.2013.12.010
27.
PennD. C.HolyoakK. J.PovinelliD. J. (2008). Darwin’s mistake: Explaining the discontinuity between human and nonhuman minds. Behavioral & Brain Sciences, 31, 109–130. doi:10.1017/S0140525X08003543
28.
SeiverE.GopnikA.GoodmanN. D. (2013). Did she jump because she was the big sister or because the trampoline was safe? Causal inference and the development of social attribution. Child Development, 84, 443–454. doi:10.1111/j.1467-8624.2012.01865.x
29.
TenenbaumJ. B.KempC.GriffithsT. L.GoodmanN. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285. doi:10.1126/science.1192788
30.
Thompson-SchillS. L.RamscarM.ChrysikouE. G. (2009). Cognition without control: When a little frontal lobe goes a long way. Current Directions in Psychological Science, 18, 259–263. doi:10.1111/j.1467-8721.2009.01648.x
31.
WalkerC. M.GopnikA. (2013, October). Infants infer higher-order relational principles in causal learning. Paper presented at the Cognitive Development Society Meeting, Memphis, TN.
WellmanH. M.GelmanS. A. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337–375. doi:10.1146/annurev.ps.43.020192.002005
34.
WerkerJ. F.YeungH. H.YoshidaK. (2012). How do infants become experts at native speech perception?Current Directions in Psychological Science, 21, 221–226.
