Brainstorming suggests that generating ideas in groups can direct one's thoughts into areas that might be difficult to reach or consider, resulting in better performance than individuals working separately. However, while intuitively appealing, studies have repeatedly found no evidence for such synergy. Here, we reinvestigate group synergy in a task with simulated agents. We use state-of-the-art language embeddings to generate close or far-off semantic associates to people's ideas. This design allows us to adopt a sampling for inference perspective, where individuals produce sample ideas from a generative process (Sanborn & Chater, 2016). We hypothesize that suggestions can allow participants to create samples from previously not considered concepts, and additionally, knowledge about the diversity of solutions can spur more broad idea generation. Our work helps inform broader questions about when and why groups can expect to produce better performance than individuals.

Decision frames influence how people act. These frames and the resulting decisions can be changed by manipulating how a problem is described. Here, we ask if people themselves can induce frame changes when thinking about a problem and how these frame changes affect decision-making and choice satisfaction. In our experiment, participants (N > 700) generated as many options as they would like for day to day scenarios as choosing a costume for a party or finding a gift for a friend. Then, participants selected one of the options they generated and reported their choice satisfaction. We found that choice satisfaction was higher when the option selected was more semantically dissimilar to the rest of the option set. We argue that this suggests that participants use a novel strategy to facilitate decision-making: Participants aimed to construct decision frames by generating options sets with a uniquely dissimilar option, which facilitated choice and increased satisfaction.

Elicitation methods, such as asking people to produce the deciles of a distribution, are standard practices in policy or applied statistics. However, these approaches often only capture a rough outline of what people know. We investigated whether tasks in which participants generate random sequences of items can be used to elicit people’s implicit beliefs about the distribution of these items. Because it remains unclear if, and at what level of detail, people represent distributions, we applied both decile elicitation and random generation tasks to uncover the kinds of environmental statistics investigated by Griffiths and Tenenbaum (2006). We found that random generation is competitive with decile elicitation in predicting participants’ expectations. Both random generation and decile elicitation revealed that people know the rough shapes of environmental distributions. Random generation, however, goes beyond decile elicitation in establishing the novel finding that people are aware of fine details of environmental distributions

Uncovering how categories develop through childhood is crucial for cognitive science. However, even for simple domains, categories can be complex, making it challenging to access them experimentally, especially in developmental studies. Markov-Chain Monte Carlo with People (MCMCp) is a statistically-based procedure that allows us to elicit category members from participants' implicit categories. However, due to the complexity of the paradigm, MCMCp has been limited to experiments with adult populations. Here, we develop and validate a child-friendly method for applying MCMCp, producing the first MCMCp experiment to elicit category examples from children. Comparing fruit category members for five-year-olds and seven-year-olds, we find generally consistent representative fruits and a developmental progression of initially broad and overlapping fruit categories to more differentiated distributions.

Recent work suggests that people produce random sequences by tapping into a general ability to derive probabilistic inferences through sampling an internal belief distribution. In contrast, previous work has proposed that participants monitor the production of inherently deterministic sequences, changing strategy when randomness appears low. Experiments exploring this process have highlighted the role of task requirements (e.g., production pace, cognitive load), finding that randomness decreased when demands increased. However, assuming random generation stems from sampling for inference, increasing production rates might affect sequential dependencies and the overall sample distribution independently. For example, increased production rates might result in more predictable sequences but leave the overall distribution of the sample intact. Here, we assessed the effects of production rates on sequential statistics and overall distributional properties in a preregistered experiment in which participants produced people's lifespans. We discuss our results in terms of computational models of sampling for inference.

Many models of cognition assume that people can generate independent samples, yet people fail to do so in random generation tasks. One prominent explanation for this behavior is that people use learned schemas. Instead, we propose that deviations from randomness arise from people sampling locally rather than independently. To test these explanations, we teach people one- and two-dimensional arrangements of syllables and ask them to generate random sequences from them. Although our results reproduce characteristic features of human random generation, such as a preference for adjacent items and an avoidance of repetitions, we also find an effect of dimensionality on the patterns people produce. Furthermore, model comparisons revealed that local sampling accounted better for participants' sequences than a schema account. Finally, evaluating the importance of each models' constituents, we show that the local sampling model proposed new states based on its current trajectory, rather than an inhibition-of-return-like principle.

Capturing the structure of human conceptual knowledge is a challenging but fundamental task. The most prominent approach, Multidimensional Scaling (MDS), usually requires many similarity judgments, which leads to long experiments, and only provides a representation of a fixed set of stimuli. In contrast, we present a more flexible method that can generalize to novel stimuli. This method uses a child-friendly task that allows researchers to uncover the development of categories with fewer participant judgments. We evaluate this approach on simulated data and find that it can accurately reveal representations even when trained on data generated by groups that categorize differently. We then analyze data from the World Color Survey and find that we can recover language-specific color organization. Finally, we use the method in a novel developmental experiment and find age-dependent differences in how fruit categories are structured. These results suggest that our method is widely applicable in developmental tasks.

Multidimensional scaling (MDS) has provided insight into the structure of human perception and conceptual knowledge. However, MDS usually requires participants to produce large numbers of similarity judgments, leading to prohibitively long experiments for most developmental research. Here we propose a method that combines simple grouping tasks with recent neural network models to uncover participants’ psychological spaces. We validate the method on simulated data and find that it can uncover the true structure even when given heterogeneous groupings. We then apply the method to data from the World Color Survey and find that it can uncover language-specific color organization. Finally, we apply the method to a novel developmental experiment and find age-dependent differences in conceptual spaces.

Uncovering how categories develop through childhood is crucial for cognitive science. However, even for simple domains, categories can be complex, making it challenging to access them experimentally. Here, we use an experimental method that allows us to produce exemplars from children’s implicit categories (Markov Chain Monte Carlo with People, MCMCP). Instead of querying pre-specified materials, MCMCP adaptively selects which stimuli to present, allowing us to focus on the most informative exemplars. We use MCMCP to uncover age-dependent differences in the category organization of fruits in a pre-registered online experiment. Comparing five-year-olds, seven-year-olds, and adults, we find a developmental progression of initially broad and overlapping fruit categories to more differentiated distributions. Furthermore, we find that across age groups, apple and grape categories exhibit bi-modal color distributions. We discuss these results in the context of theories of randomness reduction over the lifespan and shape biases in category induction.