Where science starts: Spontaneous experiments in preschoolers’ exploratory play

Cook, Claire; Goodman, Noah D.; Schulz, Laura

doi:10.1016/j.cognition.2011.03.003

Cited by 268 publications

(268 citation statements)

References 34 publications

Supporting

Mentioning

254

Contrasting

Unclassified

Order By: Relevance

“…Moreover, preschoolers are sensitive not only to the relative ambiguity of evidence, but also the potential information gain associated with different interventions. Given ambiguous evidence, children selectively perform interventions that isolate competing candidate causes and maximize information gain [48]; see also [49]) (Figure 3). …”

Section: Opinionmentioning

confidence: 99%

“…These results suggest that preschoolers distinguish potentially informative and uninformative interventions and selectively perform interventions that isolate variables and maximize information gain. Reproduced, with permission, from [48]. one of three non-pedagogical conditions: an interrupted condition (identical to the pedagogical condition, except that the experimenter was interrupted immediately after the demonstration), an accidental demonstration ('Whoops, look at that.…”

Section: Opinionmentioning

confidence: 99%

See 1 more Smart Citation

The origins of inquiry: inductive inference and exploration in early childhood

Schulz

2012

Trends in Cognitive Sciences

163

147

View full text Add to dashboard Cite

Section: Opinionmentioning

confidence: 99%

Section: Opinionmentioning

confidence: 99%

The origins of inquiry: inductive inference and exploration in early childhood

Schulz

2012

Trends in Cognitive Sciences

163

147

View full text Add to dashboard Cite

“…Understanding the informativeness of an intervention for a given scenario is critical for causal learning more generally. For example, Cook, Goodman, & Schulz (2011) showed how preschoolers test causal factors independently, which is often more informative than testing them simultaneously, and Wu and Cheng (1999; see also Cheng, 1997) found that college students believe ceiling and floor effects to be uninformative for scenarios like the Atemporal Network and the Mixed Network in Fig. 1 (see also Kushnir, Wellman, & Gelman, 2008).…”

Section: Learning Causal Direction From Interventionsmentioning

confidence: 99%

Children Use Temporal Cues to Learn Causal Directionality

2013

View full text Add to dashboard Cite

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real-world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.

show abstract

“…Following our discussion of some of the theoretical requirements of an information processing system and the evolutionary constraints it faces, and the increasing evidence that -at least in humans -exploration is not random (Gibson, 1988;Cook et al, 2011), we propose that exploration is composed of structured behavioural strategies supported by specific sensory and motor predispositions. Here we discuss some of the features we could investigate in testing this proposal in animals.…”

Section: How Do Animals Fulfil the Requirements Of Exploration?mentioning

confidence: 98%

How to build an information gathering and processing system: Lessons from naturally and artificially intelligent systems

Chappell

Demery

Arriola-Rios

et al. 2012

Behavioural Processes

View full text Add to dashboard Cite

Imagine a situation in which you had to design a physical agent that could collect information from its environment, then store and process that information to help it respond appropriately to novel situations. What kinds of information should it attend to? How should the information be represented so as to allow efficient use and re-use? What kinds of constraints and trade-offs would there be? There are no unique answers. In this paper, we discuss some of the ways in which the need to be able to address problems of varying kinds and complexity can be met by different information processing systems. We also discuss different ways in which relevant information can be obtained, and how different kinds of information can be processed and used, by both biological organisms and artificial agents. We analyse several constraints and design features, and show how they relate both to biological organisms, and to lessons that can be learned from building artificial systems. Our standpoint overlaps with Karmiloff-Smith (1992) in that we assume that a collection of mechanisms geared to learning and developing in biological environments are available in forms that constrain, but do not determine, what can or will be learnt by individuals.

show abstract

Where science starts: Spontaneous experiments in preschoolers’ exploratory play

Cited by 268 publications

References 34 publications

The origins of inquiry: inductive inference and exploration in early childhood

The origins of inquiry: inductive inference and exploration in early childhood

Children Use Temporal Cues to Learn Causal Directionality

How to build an information gathering and processing system: Lessons from naturally and artificially intelligent systems

Contact Info

Product

Resources

About