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A fundamental skill needed to learn to program is code tracing, which involves simulating the steps a computer takes when executing a program to predict how it will behave and what output it. While code tracing helps students understand and write programs, students find this activity challenging. However, to date, little work exists on how to best support novice programmers in this activity. To address this gap, the high-level goal of our research is to investigates the utility of peer-tutoring for learning to trace programs. In our prior work, we conducted a preliminary case study exploring qualitative features of peer tutoring interactions. As the next step, we conducted an experimental study with two conditions: code tracing in a peer tutoring context vs. code-tracing alone. We present results from this study, focusing on quantitative results related to learning outcomes in each condition.

CogSci 2022

Investigating the utility of peer tutoring for learning the skill of code tracing

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Laboratory experiments have shown that the first causal explanation people hear “imprints” on the mind. New causal explanations become combined with the first, rather than replace it. The COVID-19 pandemic presented a real-world version of these experiments. The initial causal model communicated by scientific publications focused on how the virus spread via contact with surfaces. However, by July 2020 the science was clearly showing that aerosol transmission was more important, yet there has still been a greater focus on cleaning surfaces than ventilation since then. The current studies show how the surface model and aerosol model coexist in people’s minds, are associated with risk perceptions, and drives preferences for pandemic control strategies. In addition, the studies shows that a tutorial based on a deeper explanation of aerosol transmission focused on the density of virus particles in the air helps causal model updating.

Causal imprinting in the pandemic: the persistence of the surface-model of Sars CoV2 transmission

Research has shown that visual diagrams facilitate people’s understanding of and communication about abstract relations. In addition, the distinction between causal versus associative relations is important in human reasoning However, previous research has not directly compared how humans represent these two types of relations through visual diagrams. The current study examined whether causal and associative relations differ with respect to how people cognitively represent and interpret them in a spatial context using diagrams. We found that participants perceived relatedness of causal relationships to be stronger than that of associative relationships. This difference was reflected in their drawing of diagrams. Participants connected variables that shared a causal relationship with a shorter line than they did with variables that shared an associative relationship. The results shed light on the difference between causal and associative relations, and suggest new directions for future research to explore the spatial component of causal reasoning.


Causal versus Associative Relations: Do Humans Perceive and Represent Them Differently?

Bayesian Causal Inference (BCI) models multisensory perception as inference about two causal structures: either the sensory data stems from several separate causes or from a common cause. If the sensory evidence supports a common cause, the multimodal sensory input is integrated into a single percept. BCI has been applied to the rubber hand illusion, in which the subject integrates tactile stimulation with seen brush strokes on a rubber hand and experiences said hand as their own. A model of body ownership is relevant for virtual reality design, especially for strengthening avatar embodiment. In previous work, we have criticized the applied BCI model for its extraordinarily wide priors. Here we investigate whether the priors can be narrowed by increasing the sensory evidence. We try to accomplish this by extending the model across time. Preliminary results look encouraging, but further research, especially psychophysical experiments to inform the parameter choices, is needed.


Extending the Bayesian Causal Inference of Body Ownership Modell Across Time

When judging the relative difficulty of impossible actions within the context of a magical world like that of Harry Potter, individuals honor real-world causal principles (e.g., assuming that heavier objects would be harder to levitate than lighter ones even though levitation itself is impossible; Shtulman & Morgan, 2017). We examined whether this effect persists when events are presented outside of this context. U.S. (Studies 1 and 2) and Chinese (Study 2) adults were asked to rate the relative difficulty of two impossible events that varied according to an irrelevant causal principle in one of three contexts: present science, future science, or magical. Though Chinese and U.S. adults honored irrelevant causal principles to a similar degree across the three contexts, Chinese adults’ confidence in their judgments varied by context. Additionally, individual differences in cognitive reflection (U.S.) and fantasy engagement (Chinese) related to judgments. Findings indicate that adults honor irrelevant causal constraints when reasoning about the impossible across multiple contexts, though subtle differences exist at both the cultural and individual level.


Cross-Cultural Sensitivity to Context when Reasoning about the Impossible

An outstanding issue in cognitive science is whether the computational principles that apply to causal reasoning also guide the way that participants encode the relations among events in memory. The outcome density effect is a behavioral pattern in causal reasoning in which participants’ causal ratings linearly decrease as the base rate of the effect also decreases, even while contingency remains 0. It is key evidence for Bayesian models of causal reasoning as it reflects decreasing uncertainty. We queried whether it may also, separately, affect memory for events. We measured both recall and causal ratings in a causal learning task to test whether the outcome density manipulation affects causal judgment, recall, or both. We replicated the outcome density effect on causal judgment, lending support to Bayesian models, but found that memory instead exhibited a U-shaped relationship with base rate, and therefore, causal judgment and memory had divergent signatures.

Patterns of Causal Judgements Diverge from Patterns of Recall: a Test of the Outcome Density Effect

Children are capable of using the causal properties of objects to determine category membership. Can they also recognize causal system categories, or abstract patterns of causation that apply across phenomena (Rottman et al., 2012)? For example, do children recognize that one causal chain (e.g. the transmission of salmonella across species) shares the same underlying structure as a chain in another domain (e.g. heat transfer between objects)? If so, recognizing that superficially distinct events share the same underlying causal system may facilitate knowledge transfer. In Study 1, 6- to 7-year-olds and adults, but not 4 to 5-year-olds, are capable of identifying simple machines that share the same causal structure. In ongoing work, we control for low-level perceptual similarities and explore whether adults and children recognize similar causal structures among unique mechanical systems. We also consider whether their recognition of causal systems can be used to support knowledge transfer.

Children’s Recognition of Shared Causal Structure in Mechanical Systems

Humans, even from infancy, are capable of unsupervised (“sta- tistical”) learning of linguistic information. However, it re- mains unclear which of the myriad algorithms for unsuper- vised learning captures human abilities. This matters because unsupervised learning algorithms vary greatly in how much can be learned how quickly. Thus, which algorithm(s) humans use may place a strong bound on how much of language can ac- tually be learned in an unsupervised fashion. As a step towards more precisely characterizing human unsupervised learning capabilities, we quantitatively synthesize the literature on adult unsupervised (“statistical”) word segmentation. Unfortunately, most confidence intervals were very large, and few moderators were found to be significant. These findings are consistent with prior work suggesting low power and precision in the litera- ture. Constraining theory will require more, higher-powered studies.

Evaluating unsupervised word segmentation in adults: a meta-analysis

A common result in novel word generalization is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. We investigated which type of items four-, five- and six-year-old children would choose as referents in a free-choice novel noun generalization task. We manipulated the generalization items availability at test (i.e., generalization stimuli introduced sequentially or simultaneously). We also manipulated the semantic distance between items. In a signal detection theory framework, results showed that a simultaneous presentation of generalization items improves children’s sensitivity and helps them use a neutral strategy to generalize. Conceptual distance at learning also affects generalization performance. We discuss the cognitive constraints that both types of presentation bring into the task, and how distance might impede or favor conceptual alignments.

How Stimuli Availability Effects Novel Noun Generalization in a Free-Choice Design.

Early language learning relies on statistical regularities that exist across timescales in infants’ lives. Two types of these statistical regularities are the routine activities that make up their day, such as mealtime and play, and the real-time repeated behaviors that make up the moment-by-moment dynamics of those routines. These two types of regularities are different in nature and are embedded at two different temporal scales, which led to divergent research in the literature – those who collect long-form recordings and observations of at-home behavior and those who use eye trackers and micro-level analyses to quantify real-time behavior in laboratories. The goal of present paper is to jointly examine and connect the statistical regularities at these two timescales. Towards this goal, we brought wearable eye trackers to English- and Spanish-speaking families’ homes to record parent and toddler visual attention during daily routines. We transcribed parent speech during object play and mealtime and coded toddler visual attention during naming moments. We found that parents and toddlers jointly interacted with the unique vocabularies of the two activities. Although naming and attention were more coordinated during object play, mealtime still afforded opportunities for high-quality naming moments. Our results lay the building blocks for connecting these two lines of research and demonstrate the feasibility of at-home data collection with eye trackers.

Visual attention and language exposure during everyday activities: an at-home study of early word learning using wearable eye trackers

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

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