Knowing When Help Is Needed: A Developing Sense of Causal Complexity

Ghosts, Divination, and Magic among the Nuosu: An Ethnographic Examination from Cognitive and Cultural Evolutionary Perspectives

I present a detailed ethnographic study of magic and divination of the Nuosu people in southwest China and offer a cognitive account of the surprising prevalence of these objectively ineffective practices in a society that has ample access to modern technology and mainstream Han culture. I argue that in the belief system of the Nuosu, ghosts, divination, and magical healing rituals form a closely interconnected web that gives sense and meaning to otherwise puzzling practices, and such a belief system is importantly supported and reinforced by individual's everyday experiences. Contemporary Nuosu people overwhelmingly treat these practices as instruments for achieving specific ends and often entertain considerable uncertainty regarding their efficacy, which may be overestimated for a number of reasons, including the following: (1) the intuitive plausibility of divination for ghost identification and exorcist rituals is enhanced by the belief in the existence of ghosts as a result of abductive reasoning, (2) negative instances (divinatory or healing ritual failures) are underreported, and (3) people's misperception of the probability of uncertain events' occurrence often prevents them from realizing that the efficacies of magical/divinatory practices do not outperform chance. I conclude with some comments on the generalizability of the psychological and social mechanisms discussed.

Contributions of causal reasoning to early scientific literacy

Although early causal reasoning has been studied extensively, inconsistency in the tasks used to assess it has clouded our understanding of its structure, development, and relevance to broader developmental outcomes. The current research attempted to bring clarity to these questions by exploring patterns of performance across several commonly used measures of causal reasoning, and their relation to scientific literacy, in a sample of 3- to 5-year-old children from diverse backgrounds (N = 153). A longitudinal confirmatory factor analysis revealed that some measures of causal reasoning (counterfactual reasoning, causal learning, and causal inference), but not all of them (tracking cause-effect associations and resolving confounded evidence), assess a unidimensional factor and that this resulting factor was relatively stable across time. A cross-lagged panel model analysis revealed associations between causal reasoning and scientific literacy across each age tested. Causal reasoning and scientific literacy related to each other concurrently, and each predicted the other in subsequent years. These relations could not be accounted for by children's broader cognitive skills. Implications for early STEM (science, technology, engineering, and math) engagement and success are discussed.

"I know it's complicated": Children detect relevant information about object complexity

Mechanistic complexity is an important property that affects how we interact with and learn from artifacts. Although highly complex artifacts have only recently become part of human material culture, they are ever-present in contemporary life. In previous research, children successfully detected complexity contrasts when given information about the functions of simple and complex objects. However, whether children spontaneously favor relevant information about an object's causal mechanisms and functions when trying to determine an object's complexity remains an open question. In Study 1, 7- to 9-year-olds and adults, but not 5- and 6-year-olds, rated information about relevant actions (e.g., the difficulty in fixing an object) as more helpful than information about irrelevant actions (e.g., the difficulty in spelling an object's name) for making determinations of mechanistic complexity. Only in Study 2, in which the relevance contrasts were extreme, did the youngest age group rate relevant actions as more helpful than irrelevant actions. In Study 3, in which participants rated the complexity of the actions themselves, participants performed differently than in the previous studies, suggesting that children in the prior studies did not misinterpret the study instructions as prompts to rate the actions' complexity. These results suggest that the ability to detect which object properties imply complexity emerges during the early school years. Younger children may be misled by features that are not truly diagnostic of mechanistic complexity, whereas older children more easily disregard such features in favor of relevant information.

The ritual stance does not apply to magic in general

Contrary to the author's proposed classification scheme, I argue that most magical practices are better viewed as “instrumental” rather than “ritualistic.” Much ethnographic and historical evidence shows that magicians and ritual experts often have elaborate causal theories regarding how magic actions lead to the putative outcome, and the “physical/mechanical” versus “supernatural” distinction in causal mechanisms needs serious reconsideration.

Motivated to learn: An account of explanatory satisfaction

Many explanations have a distinctive, positive phenomenology: receiving or generating these explanations feels satisfying. Accordingly, we might expect this feeling of explanatory satisfaction to reinforce and motivate inquiry. Across five studies, we investigate how explanatory satisfaction plays this role: by motivating and reinforcing inquiry quite generally ("brute motivation" account), or by selectively guiding inquiry to support useful learning about the target of explanation ("aligned motivation" account). In Studies 1-2, we find that satisfaction with an explanation is related to several measures of perceived useful learning, and that greater satisfaction in turn predicts stronger curiosity about questions related to the explanation. However, in Studies 2-4, we find only tenuous evidence that satisfaction is related to actual learning, measured objectively through multiple-choice or free recall tests. In Study 4, we additionally show that perceptions of learning fully explain one seemingly specious feature of explanatory preferences studied in prior research: the preference for uninformative "reductive" explanations. Finally, in Study 5, we find that perceived learning is (at least in part) causally responsible for feelings of satisfaction. Together, these results point to what we call the "imperfectly aligned motivation" account: explanatory satisfaction selectively motivates inquiry towards learning explanatory information, but primarily through fallible perceptions of learning. Thus, satisfaction is likely to guide individuals towards lines of inquiry that support perceptions of learning, whether or not individuals actually are learning.

No guts, no glory: underestimating the benefits of providing children with mechanistic details

Previous research shows that children effectively extract and utilize causal information, yet we find that adults doubt children's ability to understand complex mechanisms. Since adults themselves struggle to explain how everyday objects work, why expect more from children? Although remembering details may prove difficult, we argue that exposure to mechanism benefits children via the formation of abstract causal knowledge that supports epistemic evaluation. We tested 240 6-9 year-olds' memory for concrete details and the ability to distinguish expertise before, immediately after, or a week after viewing a video about how combustion engines work. By around age 8, children who saw the video remembered mechanistic details and were better able to detect car-engine experts. Beyond detailed knowledge, the current results suggest that children also acquired an abstracted sense of how systems work that can facilitate epistemic reasoning.

Components and Mechanisms: How Children Talk About Machines in Museum Exhibits

The current investigation examines children's (N = 61; 4- to 8-year old) learning about a novel machine in a local history museum. Parent-child dyads were audio-recorded as they navigated an exhibit that contained a novel artifact: a coffee grinder from the turn of the 20th century. Prior to entering the exhibit, children were randomly assigned to receive an experimental "component" prompt that focused their attention on the machine's internal mechanisms or a control "history" prompt. First, we audio-recorded children and their caregivers while they freely explored the exhibit, and then, we measured children's learning by asking them two questions in a test phase. Children of all ages, regardless of the prompt given, discussed most aspects of the machine, including the whole machine, its parts, and, to a lesser extent, its mechanisms. In the test phase, older children recalled more information than younger children about all aspects of the machine and appeared more knowledgeable to adult coders. Overall, this suggests that children of all ages were motivated to discuss all aspects of a machine, but some scaffolding may be necessary to help the youngest children take full advantage of these learning opportunities. While the prompts did not significantly influence the number of children who discussed the machine's mechanisms, children who received the component prompt were rated as more knowledgeable about the machine in the test phase, suggesting that this prompt influenced what they learned. Implications for visitor experience and exhibit design are discussed.

Concept Appraisal

This paper reports the first empirical investigation of the hypothesis that epistemic appraisals form part of the structure of concepts. To date, studies of concepts have focused on the way concepts encode properties of objects and the way those features are used in categorization and in other cognitive tasks. Philosophical considerations show the importance of also considering how a thinker assesses the epistemic value of beliefs and other cognitive resources and, in particular, concepts. We demonstrate that there are multiple, reliably judged, dimensions of epistemic appraisal of concepts. Four of these dimensions are accounted for by a common underlying factor capturing how well people believe they understand a concept. Further studies show how dimensions of concept appraisal relate to other aspects of concepts. First, they relate directly to the hierarchical organization of concepts, reflecting the increase in specificity from superordinate to basic and subordinate levels. Second, they predict inductive choices in category-based induction. Our results suggest that epistemic appraisals of concepts form a psychologically important yet previously overlooked aspect of the structure of concepts. These findings will be important in understanding why individuals sometimes abandon and replace certain concepts; why social groups do so, for example, during a "scientific revolution"; and how we can facilitate such changes when we engage in deliberate "conceptual engineering" for epistemic, social, and political purposes.

Curious Objects: How Visual Complexity Guides Attention and Engagement

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience-and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons-essentially quantifying the "amount of information" within each shape-and then used this approach to ask new questions about the perception of complexity. Experiments 1-3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors-a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4-5 asked subjects to study serially presented objects in a self-paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects-even when object shape was completely task-irrelevant-suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of "perceptual curiosity" about objects that encourages subsequent attentional engagement.

An Illusion of Self-Sufficiency for Learning About Artifacts in Scaffolded Learners, But Not Observers

Two studies ask whether scaffolded children (n = 243, 5-6 years and 9-10 years) recognize that assistance is needed to learn to use complex artifacts. In Study 1, children were asked to learn to use a toy pantograph. While children recognized the need for assistance for indirect knowledge, 70% of scaffolded children claimed that they would have learned to use the artifact without assistance, even though 0% of children actually succeeded without assistance. In Study 2, this illusion of self-sufficiency was significantly attenuated when observing another learner being scaffolded. Learners may fail to appreciate artifacts' opacity because self-directed exploration can be partially informative, such that learning to use artifacts is typically scaffolded instead of taught explicitly.

The world within: Children are sensitive to internal complexity cues

When reasoning about the mechanisms of complex entities, it is important to consider their internal parts. Previous research has shown that young children view "insides" as critical to how objects function. However, whether children hold specific expectations regarding complex objects' insides remains an open question. Here, children (n = 378) and adults (n = 124) made internal and causal complexity judgments regarding real-world objects. In Study 1, 5- and 6-year-olds, but not 4-year-olds, succeeded at internal complexity judgments and matched complex artifacts with complex insides. All age groups succeeded at causal complexity judgments and identified complex artifacts as causally complex. Study 2 tested whether the internal complexity cues of number/area, diversity, and connections of internal parts conveyed complexity to children. The 5-year-olds were sensitive only to number/area of internal parts as a complexity cue, but the older children and adults were sensitive to all three cues plus number of parts when controlling for area (Study 3). Despite limited exposure to insides, even young school-age children hold detailed and abstract expectations concerning internal complexity.

Content counts: A trait and moral reasoning framework for children's selective social learning

We present evidence that evaluative information plays a major role in children's selective social learning. We demonstrate that social learning patterns differ as a function of whether children are exposed to positively or negatively valenced information (e.g., content; informant characteristics) and that these patterns can be understood in the context of children's schemas for social groups, morality, and trait understanding. We highlight that attention must be given to theoretical ties between social learning and children's trait judgments and moral reasoning to strengthen our understanding of selective trust and account for variations in children's sophistication when they judge potential sources of information. Finally, we suggest revisions to current theoretical frameworks and offer suggestions to move the field forward.

How causal information affects decisions

BACKGROUND

Causality is inherently linked to decision-making, as causes let us better predict the future and intervene to change it by showing which variables have the capacity to affect others. Recent advances in machine learning have made it possible to learn causal models from observational data. While these models have the potential to aid human decisions, it is not yet known whether the output of these algorithms improves decision-making. That is, causal inference methods have been evaluated on their accuracy at uncovering ground truth, but not the utility of such output for human consumption. Simply presenting more information to people may not have the intended effects, particularly when they must combine this information with their existing knowledge and beliefs. While psychological studies have shown that causal models can be used to choose interventions and predict outcomes, that work has not tested structures of the complexity found in machine learning, or how such information is interpreted in the context of existing knowledge.

RESULTS

Through experiments on Amazon Mechanical Turk, we study how people use causal information to make everyday decisions about diet, health, and personal finance. Our first experiment, using decisions about maintaining bodyweight, shows that causal information can actually lead to worse decisions than no information at all. In Experiment 2, we test decisions about diabetes management, where some participants have personal domain experience and others do not. We find that individuals without such experience are aided by causal information, while individuals with experience do worse. Finally, our last two experiments probe how prior experience interacts with causal information. We find that while causal information reduces confidence in individuals with prior experience, it has the opposite effect on those without experience. In Experiment 4 we show that our results are not due to an inability to use causal models, and that they may be due to familiarity with a domain rather than actual knowledge.

CONCLUSION

While causal inference can potentially lead to more informed decisions, we find that more work is needed to make causal models useful for the types of decisions found in daily life.