Academic journal article Memory & Cognition

Exploring the Knowledge Behind Predictions in Everyday Cognition: An Iterated Learning Study

Academic journal article Memory & Cognition

Exploring the Knowledge Behind Predictions in Everyday Cognition: An Iterated Learning Study

Article excerpt

Published online: 3 April 2015

© Psychonomic Society, Inc. 2015

Abstract Making accurate predictions about events is an important but difficult task. Recent work suggests that people are adept at this task, making predictions that reflect surprisingly accurate knowledge of the distributions of real quantities. Across three experiments, we used an iterated learning procedure to explore the basis of this knowledge: to what extent is domain experience critical to accurate predictions and how accurate are people when faced with unfamiliar domains? In Experiment 1, two groups of participants, one resident in Australia, the other in China, predicted the values of quantities familiar to both (movie run-times), unfamiliar to both (the lengths of Pharaoh reigns), and familiar to one but unfamiliar to the other (cake baking durations and the lengths of Beijing bus routes). While predictions from both groups were reasonably accurate overall, predictions were inaccurate in the selectively unfamiliar domains and, surprisingly, predictions by the China-resident group were also inaccurate for a highly familiar domain: local bus route lengths. Focusing on bus routes, two follow-up experiments with Australia-resident groups clarified the knowledge and strategies that people draw upon, plus important determinants of accurate predictions. For unfamiliar domains, people appear to rely on extrapolating from (not simply directly applying) related knowledge. However, we show that people's predictions are subject to two sources of error: in the estimation of quantities in a familiar domain and extension to plausible values in an unfamiliar domain. We propose that the key to successful predictions is not simply domain experience itself, but explicit experience of relevant quantities.

Keywords Everyday reasoning . Iterated learning . Bayesian inference . Cross-cultural comparison

Survival depends upon making successful predictions about the future. This is hard enough in domains with which we are familiar but often predictions are also required in unfamiliar domains. A prosaic example is offered by the office worker who has a good understanding of how long to wait for the elevator in her building; how long should she wait in another, unfamiliar, building? If three futile minutes have already passed, should she head for the stairs? Given that these kinds of predictions are commonplace, two main questions arise: on what are the predictions based and how accurate are they?

Surprisingly, there is evidence that people can make accurate predictions even in domains for which they might have limited or even no direct experience. Griffiths and Tenenbaum (2006) asked participants to estimate quantities in several different domains that varied in familiarity (though they were collectively referred to as Beveryday phenomena^), such as male life-spans, the baking time of cakes, movie grosses, and the lengths of pharaohs' reigns. Participants were asked to make a single prediction for each domain based on an observed (probe) value of that quantity. For example, they could be asked: given that a man is 39 years old, what is the best estimate of his total life span? Strikingly, responses generally reflected the actual distribution of the relevant quantity (as calculated from publicly available data), and were consistent with an optimal Bayesian updating rule (see also, Griffiths & Tenenbaum, 2011). Therefore, people behave as if they have accurate knowledge of the distributions of both familiar and unfamiliar quantities in the world and can use this knowledge to make sensible predictions.

The results found by Griffiths and Tenenbaum (2006)were extended by Lewandowsky, Griffiths, and Kalish (2009)using an iterated learning procedure (instead of the single-prediction approach). In this procedure, participants make multiple predictions in response to uniformly distributed probe values based on their previous responses. Griffiths and Kalish (2007)hadearlier shown that this procedure converges to participants' subjective distribution of a quantity as long as their responses are independent and consistent with a Bayesian updating rule. …

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