1. Learning from others: Adult and child strategies in assessing conflicting ratings
Falk Lieder1, Zi L. Sim1, Jane C. Hu, Thomas L. Griffiths, and Fei Xu University of California, Berkeley 1These authors contributed equally.
Introduction
Learning from others is critical for beliefs that are neither true nor false (e.g. cultural practices, supernatural concepts, and preferences). For these beliefs, we are likely to encounter conflicting testimonies. How do we determine whom to believe? Previous research has shown that both adults and children consider consensus information, often relying on majority opinion when learning from others’ testimonies (Benedicktus et al., 2010; Corriveau et al., 2009).
However, the mechanism underlying how learners determine consensus remains unclear. What factors do learners take into account, and how do they do so? Two prominent factors in the evaluation of multiple testimonies are overall valence, and the number of testimonials. Making a decision requires weighing both of these factors, but previous studies have only focused on overall valence, or had the two factors confounded in their experimental design.
We thus examine how children and adults reconcile conflicts between these two sources of information. We also investigate whether children use different learning strategies than adults. A difference between child and adult strategies for social learning would suggest that cognitive development includes discovering cognitive strategies, and not just acquiring knowledge and representations.
Materials and Methods
Experiment 1:
Compared children and adults
66 4- to 7-year-olds, and 300 adults
Participants were presented with 18 pairs of ratings, and asked to choose which
option they thought was better.
Trials were designed to be maximally discriminative between pairs of strategies.
Experiment 2:
Evaluated the 5 earlier strategies against 4 alternative models for 160 adults:
1. Difference: choose the option for which the surplus of positive ratings is largest
2. Count- : choose the option with fewer negative ratings
3. Some Positive: choose the only option with positive ratings, else choose at random
4. No Negative: choose the option with no negative ratings, else choose at random
Results of Experiment 2
77.5% of adults used the Bayesian sampling strategy with a neutral prior. We can be more than 99% confident that it is the most frequently used strategy among adults.
Re-analysis of the data under the assumption that each person might use multiple
strategies led to the same conclusions.
Example trial for distinguishing Count+ from Bayesian inference & ratio strategy
Social Learning Strategies
To characterize how we learn from other people’s ratings, we formalize five social learning strategies using probabilistic models, and determine which model best explains each group’s choices. Below, ni, + and ni, - are respectively the number of positive and negative ratings for option i, and w is the Weber fraction.
1. Count+: Under this strategy, the probability of choosing the 1st option is determined by the perceived difference in the number of positive ratings:
2. Ratio: Under this strategy, the choice probability is determined by the perceived difference in the proportions of positive ratings (r1 – r2):
3. Bayesian inference with an optimistic prior
4. Bayesian inference with a neutral prior
From a Bayesian perspective, the goal is to infer whether the probability of a positive experience is higher for option 1 than for option 2 (μ1 > μ2). This inference integrates the ratings with prior knowledge about the prevalence of positive ratings (). According to the optimistic prior, most ratings are positive, whereas the neutral prior is agnostic about this. According to the sampling hypothesis (Bonawitz et al., 2014; Denison et al., 2013), the probability of choosing the 1st option is:
5. Random: This strategy chooses both options with equal probability:
Results of Experiment 1
Adults were more likely than children to choose according to Bayesian inference (neutral prior), p < This strategy was most common for adults.
Children were more likely than adults to use the counting positives strategy (Count+),
p < .03. Among children,
Bayesian inference and Count+
were about equally common.
Conclusions
Both adults and children need to learn from the testimony of others, but they may use different strategies to make sense of them. The choices of adults was consistent with Bayesian inference, while children seem to rely more often on simpler heuristics.
The current results suggest that cognitive strategies for reasoning under uncertainty or the mechanism by which the strategies are selected develop during childhood. The transition to a strategy that approximates Bayesian inference is incomplete at age 6.
Our results also indicate that children use multiple specialized strategies across different domains, rather than relying on a single, universal strategy. In causal inference tasks (e.g. Denison et al., 2013), children reason in ways consistent with Bayesian inference, but they appear not to do so in our social learning task.
Findings are consistent with the overlapping waves theory (Siegler, 1996), illustrating that cognitive development is not limited to the acquisition of knowledge and representations, but also includes discovering cognitive strategies and learning when to use them.
Literature Cited
Benedicktus, R. L., Brady, M. K., Darke, P. R., & Voorhees, C. M. (2010). Conveying Trustworthiness to Online Consumers: Reactions to Consensus, Physical Store Presence, Brand Familiarity, and Generalized Suspicion. Journal of Retailing, 86(4), 310–323.
Corriveau, K. H., Fusaro, M., & Harris, P. L. (2009). Going with the flow preschoolers prefer nondissenters as informants. Psychological Science, 20(3), 372–377.
Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. (2014). Probabilistic models, learning algorithms, and response variability: sampling in cognitive development. Trends in Cognitive Sciences, 18(10), 497–500.
Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (2013). Rational variability in children’s causal inferences: The sampling hypothesis. Cognition, 126(2), 285–300.
Siegler, R. S. (1996). Emerging Minds: The process of change in children’s thinking. New York: Oxford University Press.
Acknowledgments
We thank Meg Bishop, Shirley Chen, Justine Hoch, Chelsea Tuomi, Anamita Guha, Stephanie Jones, Gabi Espinoza, and the Berkeley Early Learning Lab (BELL) for their help in recruitment and collecting the children’s data. We also thank the parents and children for their participation. This work has been submitted to the 37th Annual Meeting of the Cognitive Science Society.
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