Microgenetic Method

Cognitive variability ultimately depends on people’s constructing novel ways of thinking even when we already possess reasonable alternatives. If we did not supplement existing approaches with new ones, there would be no variation in the approaches we use to solve a single problem, nor any need to choose adaptively among the alternatives. But how do we generate new approaches, and what leads us to do so when existing approaches already are adequate? This is the focus of the present chapter. Despite the obvious importance of constructing new ways of thinking, we know little about how the process occurs. For every study focusing on how children generate new approaches, there must be a hundred that focus on how the thinking of different-age children differs. Both conceptual and methodological considerations have contributed to this imbalance. Conceptually, the staircase approaches that have been dominant for the last 30 years have depicted generation of new ways of thinking as a rare event, too rare to be studied systematically. The outcomes of the generative process could be observed, as in the studies of age-related differences in reasoning, but not the generative process itself. Methodologically, standard developmental approaches, whether cross-sectional or longitudinal, are not well suited to studying the process of change. Developmental psychologists’ reliance on such approaches in part reflects the conceptual blinders imposed by the staircase conceptions of development. If generation of new approaches is a rare event, then these methods might be the best possible. Expedience also plays a role; cross-sectional methods in particular are cheaper and easier to use than methods more specifically designed to get at the process of change. Habit also plays a role; the standard methods are so prevalent, and have been for so long, that they almost inevitably are the first methods that come to mind when thinking about how to study a developmental issue. However, they are not the only methods for studying development, nor the ones best suited to studying change processes. An alternative approach that seems especially promising for studying change is the microgenetic method.

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The Dynamic Display of Process: Historical Development and Contemporary Uses of the Microgenetic Method

Human Development ◽

10.1159/000273062 ◽

1986 ◽

Vol 29 (5) ◽

pp. 252-263 ◽

Cited By ~ 26

Author(s):

Liza Catán

Keyword(s):

Historical Development ◽

Dynamic Display ◽

Microgenetic Method

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The Use of the Microgenetic Method with Children with Disabilities: Discovering Competence

Early Education and Development ◽

10.1207/s15566935eed0904_3 ◽

1998 ◽

Vol 9 (4) ◽

pp. 357-373 ◽

Cited By ~ 9

Author(s):

Kathryn L. Fletcher ◽

Lisa F. Huffman ◽

Norman W. Bray ◽

Lisa A. Grupe

Keyword(s):

Children With Disabilities ◽

Microgenetic Method

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Win-Stay, Lose-Sample: A Simple Sequential Algorithm for Approximating Bayesian Inference

10.31219/osf.io/pm4au ◽

2018 ◽

Author(s):

Elizabeth Bonawitz ◽

Stephanie Denison ◽

Alison Gopnik ◽

Tom Griffiths

Keyword(s):

Bayesian Inference ◽

Practical Method ◽

Causal Learning ◽

Sequential Algorithm ◽

Learning Tasks ◽

Approximate Bayesian ◽

Bayesian Models Of Cognition ◽

Microgenetic Method ◽

New Evidence ◽

Approximate Bayesian Inference

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm “Win-Stay, Lose-Sample”, inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a “mini-microgenetic method”, investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people’s judgments.

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