Learning to Balance a Beam: The Effect of Instability

Learning to Balance a Beam: The Effect of Instability David Pfeiffer (pfeiffdd@ucmail.uc.edu) Department of Psychology, University of Cincinnati Cincinnati, OH 45221-0376, USA Heidi Kloos (heidi.kloos@uc.edu) Department of Psychology, University of Cincinnati Cincinnati, OH 45221-0376, USA Daniel P. Bullard (bullardp@mail.uc.edu) School of Education, University of Cincinnati Cincinnati OH 45221-0022, USA on general attentional mechanism—have generated a large number of empirical findings, often pitting postulated knowledge against statistical contingencies (e.g., Kuhl, 2000). However, neither of them can fully account for the existing data, as we explain next. Knowledge-rich approaches—those that attribute meaningful performance to mental structures of some sort— fall short when performance changes as a function of miniscule changes in the task context. For example, how could a knowledge-based account explain that the A-not-B error disappears when the infant‘s posture is briefly changed prior to a B trial (e.g., Smith & Thelen, 2003)? Or how could it explain that toddlers pay attention to the solidity of objects when the object is fully visible, but not when it is hidden (e.g., Keen, 2003)? Of course one could always postulate a new representation, schema, or belief for each unique performance. Children might have implicit knowledge of solidity, for example, but lack explicit knowledge of this concept. However, this solution is neither parsimonious nor predictive and testable. On the other hand, knowledge-lean approaches—those that attribute meaningful performance to the mind‘s ability to extract statistical regularities—fall short in cases in which children blatantly ignore even the most straight-forward evidence. In particular, children appear blind to statistical contingencies when they have an expectation that conflicts with the statistical contingencies. For example, both children and adults sometimes mistakenly expect two variables to be correlated, leading to the phenomenon of illusory correlations (e.g., Chapman & Chapman, 1969). Or children fail to learn how two variables correlate if that correlation does not match with their larger frame of references (e.g., Kloos, 2007). The current approach takes a step back and acknowledges that performance is based on both a child‘s specific expectations and a child‘s general ability to track statistical contingencies. In fact, there might be much more that matters in the immediate task context, including a child‘s idiosyncratic history and readiness, details of the verbal Abstract When presented with a problem-solving task, children sometimes fail to discern relevant pieces of information. Instead, they base their judgment on irrelevant information, sometimes ignoring corrective feedback. How could experience highlight relevant information? Using insights from complexity science, the current paper tests the usefulness of adding instability, or noise, to a child‘s experience. The idea is that an appropriate amount of instability flattens the attractor space of mistaken performance, allowing children to explore aspects of the environment perhaps considered irrelevant. To test this idea, we asked children between 4 and 9 years of age to place beams on a fulcrum where they would balance. Instability was conceptualized using beams for which the weight distribution was difficult to discern. While 4- to 5-year-olds and adults were unaffected by the noise manipulation, possibly for different reasons, 7- to 9-year-olds balanced the beams better when instability was at an intermediate level. Keywords: cognitive development; science learning; torque; Introduction How does a child know what to pay attention to? For example, how does a child know that a new word pertains to the overall shape of an object rather than the color of it, or the shape of some part of it? Or how does a child know that two events are causally related? Answers to such questions fall in one of two categories: (1) they assume that specific top-down knowledge allows the child to sort messy variation into relevant and irrelevant information 1 , or (2) they assume that general attentional processes allow the child to decipher rich statistical data 2 . Both types of approaches—the focus on specific knowledge, or the focus Example references include Gopnik et al., 2001; Gelman & Markman, 1986; Carpenter, Call & Tomasello, 2005; Murphy & Medin, 1985; Song, Onishi, Baillargeon & Fisher, 2008; Hamlin, Newman & Wynn, 2009. Example references include Sloutsky, 2003; Mix, Huttenlocher & Levine, 2002; Smith & Samuelson, 2006; Gomez, 2002; Saffran, Aslin & Newport, 1996.