To be efficient, both active learners and teachers need to be able to judge the relative usefulness of a piece of information for themselves or for their students, respectively. The current study assessed whether experience of active learning facilitates subsequent teaching from imperfect knowledge. Following a visual category learning task, dyads (N=40) of active and yoked passive learners taught (imagined) naive learners how to categorize the same visual stimuli by providing them with a small number of self-generated examples. Active learners narrowed down the possible categorization boundaries more than yoked learners. However, the active learning advantage was modest and limited to categories that were more difficult to learn and, overall, teachers were overly conservative, providing the least ambiguous category examples.

Good teachers and good active learners share the ability to generate samples (examples or queries, respectively) that are informative in light of current knowledge. In line with this, the current experiment found that active learners outperformed yoked passive learners in a subsequent category teaching task. The learning task was replicated from Markant and Gurekis (2014) and included a manipulation of category structure (Rule-based or Information-Integration). Participants (N = 40 dyads) first learned how to categorize stimuli defined along two continuous perceptual features, and their subjective classification boundaries were inferred from categorization tests. During teaching, participants generated a small, fixed number of examples to teach the categorization boundary to an imagined learner. Improvements in teaching due to active learning went beyond what could be explained by better categorization performance prior to teaching, and example selection was modulated by participants’ degree of uncertainty about the boundary to-be-taught.

Who we choose to learn from is influenced by the relative confidence of potential informants. More confident advisers are preferred based on an assumption that confidence is a good indicator of accuracy. However, oftentimes, accuracy and confidence are not calibrated, either due to strategic manipulations of confidence or unintentional failures of metacognition. When accuracy information is readily available, people are additionally vigilant to the calibration of informants, penalizing incorrect, yet confident advisers (Tenney et al., 2007). The current experiment tested whether participants can leverage inferences about two advisers' calibration profiles to make optimal trial-by-trial decisions. We predicted that choice of advisers reflects relative differences in the advisers' probability of being correct given their stated confidence (recalibrated confidence), as opposed to stated confidence differences. The prediction was not supported by data, but calibration had a modulating effect on choices, as more confident advisers were more influential only when they were also calibrated.

In collaborative tasks, humans can make better joint decisions by aggregating individual information in proportion to their communicated confidence (Bahrami et al., 2010). However, if people blindly rely on their partner’s confidence expressions, they could easily reach suboptimal solutions when their collaborator's confidence judgments are not calibrated to their performance, but for instance exhibit an overconfidence bias. Given that calibrated advisers are rated as more credible (Sah et al., 2013), we propose that prior experience with a collaborator will lead to a recalibration of their confidence judgements before incorporating their advice. In an online experiment, participants first viewed two other fictitious participants, one calibrated and one biased, perform a categorization task. Following this, participants completed a similar task by taking advice from just one of the two previously observed advisers on a given trial. We tested whether participants chose the adviser who had the trial-by-trial highest expressed or recalibrated confidence.

Young children, like adults, conform to group consensus. However, it is unclear when children develop more sophisticated intuitions about how the composition of groups, the way in which they acquire and aggregate information, impacts advice quality. This experiment assessed children's developing sensitivity to source independence - that is, do they understand that statistically independent sources of information are more valuable than correlated ones? Children (5-to-11-year-olds, N=106) and teenagers and adults (N=99) played a space exploration game in which they made multiple 2AFC decisions based on the advice of 8 friendly aliens. Across trials and participants, we manipulated consensus (the relative number of advisers endorsing each option) and source diversity (the relative number of independent advisers endorsing each option). Results indicated that children were able to detect the correlations between sources, but their ability to exploit this knowledge was late emerging, likely in the early adolescence years.

Recent studies suggest that children are \emph{ecological active learners} who recognize and exploit the ecology of their learning environment (Ruggeri, 2022). However, when assessed by verbal tasks such as the 20-questions game, systematic search only matures around age 7. The current study examined if even young children can adapt their information-search strategies in a developmentally-appropriate task requiring minimal verbal or conceptual abstraction skills. Three to 7-year-olds (N = 76, M = 5.7 years) played a search game with a structure analogous to the 20-questions game. We manipulated whether children received predecisional cues about the past location of the solution or not, across two search phases, and further varied whether the cues follow a Uniform or Skewed distribution. Children adapted their information-search strategies as predicted: They followed a constraint-seeking strategy in the absence of cues, and only switched to hypothesis-scanning when exposed to the Skewed cues.

There has been little cross-fertilization between research on ac-tive learning and teaching, despite extensive conceptual simi-larities. The current study aims to bridge the gap by show-ing that engaging in active learning can influence subsequentteaching performance. In a one-dimensional boundary teach-ing task, participants who first took the role of an active learnerwent on to become better teachers than participants who didnot. In order to disentangle the effect of active selection ofsamples from their information content, the performance ofactive learners was compared to that of yoked passive learn-ers. While prior passive learning also significantly boostedteaching performance, it did so to a lesser extent. However, inpaired comparisons, teachers with active learning experiencedid not differ significantly from their yoked-passive learningcounterparts. Based on the current results we cannot arguefor a teaching benefit specific to active learning as opposed toa more general improvement caused by experiencing the taskfrom the learner’s perspective. However, we suggest that thisis a promising line of inquiry using more complex learning andteaching tasks.

The optimal perceptual decision making strategy for weighting serially presented information depends on the degreeof sample dependence. Uniform weighting produces optimal estimates from independent samples, but increases in autocorre-lation should be matched by increasing and symmetric overweighting of early and late samples in order to maintain optimalperformance.In the current experiment, participants (N = 30) observed briefly presented sequences of eight dots and were asked to estimatetheir center of mass by dragging the cursor. The autocorrelation of the series was manipulated in two distinct blocks (either 0or .7). Preliminary results show that the weight assignment to uncorrelated inputs did not differ significantly from the optimaluniform allocation. In contrast, in the high-dependence block participants used different weighting profiles - overweightingthe first and/or last samples of the sequence. This suggests that humans flexibly adapt to changes in statistical structure in thepredicted direction of optimality.