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It is clear, however, that the relationship between false-belief understanding in infancy and executive functions including working memory and inhibitory control should be further investigated. The present study replicated Thoermer et al.��s (2012) finding that 18-month-olds as a group do not perform above chance level when anticipating an agent��s action based on the agent��s false belief. Southgate et al. (2007) demonstrated that the majority of 25-month-olds pass this test which shows that between 18 and 25 months of age infants develop the ability to anticipate others�� action based on the others�� false beliefs. The fact that children who passed the false-belief test at 18 months of age were more likely to pass a standard false-belief test at 48 months of age indicates that infants at 18 months of age do not perform at selleck inhibitor random, but that some 18-month-olds are sensitive to another��s false belief. In the present study, we showed this successful performance in 18-month-olds is not predicted by distraction or memory interference as measured by the modified A-not-B task. In sum, the present study revealed no relationship between performance in a false-belief task using an anticipatory looking paradigm and a modified version of the A-not-B task even after controlling for cognitive development. Accordingly, this study finds no evidence in support of a relationship between working memory and false-belief understanding in infancy. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Footnotes 1A similar result was obtained when applying the original coding criterion of Southgate et al. (2007), who coded an anticipatory look to one location if the infant fixated on the AOI for more than 20 ms.""Twenty years ago, Gigerenzer and Hoffrage (1995) demonstrated that Bayesian inferences can be improved without instructing participants how to solve such Bayesian tasks. By providing the relevant information not in terms of probabilities, percentages, or relative frequencies, as it is usually done, but in terms of natural frequencies, the percentage of correct (i.e., Bayesian) inferences tripled, specifically, from 16 to 46%. What is a Bayesian inference task and what are natural frequencies? Consider the following example: The Skiwell Manufacturing Company gets material from two suppliers. Supplier A��s materials make up for 30% of what is used, with supplier B providing the rest. Past records indicate that 15% of supplier A��s materials are defective and 10% of B��s material are defective.