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  1. Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    Listening to music activates representations of movement and social agents. Why? We ask whether high-level causal reasoning about how music was generated can lead people to link musical sounds with animate agents. To test this, we asked whether people (N=60) make flexible inferences about whether an agent caused musical sounds, integrating information from the sounds’ timing and from the visual context in which it was produced. Using a 2x2 within-subject design, we found evidence of causal reasoning: In a context where producing a musical sequence would require self-propelled movement, people inferred that an agent had been present causing the sounds.more »When the context provided an alternative possible explanation, this ‘explained away’ the agent, reducing the tendency to infer an agent was present for the same acoustic stimuli. People can use causal reasoning to infer whether an agent produced musical sounds, suggesting that high-level cognition can link music with social concepts.« less
    Free, publicly-accessible full text available July 26, 2022
  2. Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    Artifacts – the objects we own, make, and choose – provide a source of rich social information. Adults use people’s artifacts to judge others’ traits, interests, and social affiliations. Here we show that 4-year-old children (N=32) infer others’ shared interests from their artifacts. When asked who had the same interests as a target character, children chose the character with a conceptually similar object to the target’s – an object used for the same activity – over a character with a perceptually similar object. When asked which person had the same arbitrary property (bedtime, birthday, or middle name), children did notmore »systematically select either character, and most often reported that they did not know. Adults (N=32) made similar inferences, but differed in their tendency to use artifacts to infer friendships. Overall, by age 4, children show a sophisticated ability to make selective, warranted inferences about others’ interests based solely on their artifacts.« less
    Free, publicly-accessible full text available July 26, 2022
  3. Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by computing similarities between structured representations of analogs? We address this question by comparing human performance on visual analogies created using images of familiar three-dimensional objects (cars and their subregions) with the performance of alternative computational models. Human reasoners achieved above-chance accuracy for all problem types, but made more errors in several conditions (e.g., when relevant subregions were occluded). We compared human performance to that of two recent deep learning models (Siamesemore »Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations. The compositional model based on part representations, but not the deep learning models, generated qualitative performance similar to that of human reasoners.« less
  4. Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    Competence and morality are two of the most important dimensions in social evaluation. Recent studies have suggested the primacy of morality, showing that information about immorality of an ordinary target person decreases evaluation of their competence. We examined the effect of moral taint on multiple non-moral judgments: ratings of the competence, accomplishment, and contribution of fictitious professionals who were described as highly successful in various fields. Moral taint significantly decreased participants’ non-moral social evaluations of professionals regardless of their field. Mediation analyses showed that the negative impact of immoral character on competence judgments is more strongly mediated by the decreasemore »in participants’ psychological involvement with the target, rather than a decrease in perceived social intelligence of the target. These findings suggest that motivation to distance oneself from immoral others plays a critical role in the revision of social evaluations.« less
  5. Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    The QAnon conspiracy posits that Satan-worshiping Democrats operate a covert child sex-trafficking operation, which Donald Trump is destined to expose and annihilate. Emblematic of the ease with which political misconceptions can spread through social media, QAnon originated in late 2017 and rapidly grew to shape the political beliefs of millions. To illuminate the process by which a conspiracy theory spreads, we report two computational studies examining the social network structure and semantic content of tweets produced by users central to the early QAnon network on Twitter. Using data mined in the summer of 2018, we examined over 800,000 tweets aboutmore »QAnon made by about 100,000 users. The majority of users disseminated rather than produced information, serving to create an online echochamber. Users appeared to hold a simplistic mental model in which political events are viewed as a struggle between antithetical forces—both observed and unobserved—of Good and Evil.« less
  6. Fitch, T ; Lamm, C ; Leder, H ; Tessmar, K (Ed.)
    Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants’ judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. How- ever, participants had considerable difficulty controllingmore »for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn’t affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA.« less
  7. Fitch, T ; Lamm, C ; Leder, H ; Tessmar, K (Ed.)
    Delays between causes and effects are commonly found in cause-effect relationships in real life. However, previous studies have only investigated delays on the order of seconds. In the current study we tested whether people can learn a cause- effect relation with hour long delays. The delays between the cause and effect were either 0, 3, 9, or 21 hours, and the study lasted 16 days. Surprisingly, we found that participants were able to learn the causal relation about equally as well in all four conditions. These findings demonstrate a remarkable ability to accurately learn causal relations in a realistic timeframemore »that has never been tested before.« less
  8. Fitch., T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    We make frequent decisions about how to manage our health, yet do so with information that is highly complex or received piecemeal. Causal models can provide guidance about how components of a complex system interact, yet models that provide a complete causal story may be more complex than people can reason about. Prior work has provided mixed insights into our ability to make decisions with causal models, showing that people can use them in novel domains but that they may impede decisions in familiar ones. We examine how tailoring causal information to the question at hand may aid decision making,more »using simple diagrams with only the relevant causal paths (Experiment 1) or those paths highlighted within a complex causal model (Experiment 2). We find that diagrams tailored to a choice improve decision accuracy over complex diagrams or prior knowledge, providing new evidence for how causal models can aid decisions.« less
  9. Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)
    Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance andmore »the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance.« less