Research shows the experience of awe is associated with a variety of benefits ranging from increased well-being and prosocial behavior to enhanced cognition. The adaptive purpose of awe, however, is elusive. In this article, we aim to show that the current framework used to conceptualize awe points towards higher-order cognition as the key adaptive function. This goes against past evolutionary positions that posit social benefits or unidimensional behavioral adaptations. In the second half of the article, we highlight a distinct cognitive (...) advantage of awe. The literature connecting awe and cognition is surveyed and used to develop a view that situates awe as a critical component in the cognitive success of the human species. (shrink)

We need not propose, as Carey does, a radical discontinuity between core cognition, which is responsible for abstract structure, and language and which are responsible for learning and conceptual change. From a probabilistic models view, conceptual structure and learning reflect the same principles, and they are both in place from the beginning.

The ability to recognize and correct errors in one's explanatory understanding is critically important for learning. However, little is known about the mechanisms that determine when and under what circumstances errors are detected and how they are corrected. The present study investigated thought experiments as a potential tool that can reveal errors and trigger belief revision in the service of error correction. Across two experiments, 1149 participants engaged in reasoning about force and motion (a domain with well‐documented misconceptions) in a (...) pre‐training—training—post‐training design. The two experiments manipulated the type of mental model manipulated in the thought experiments (i.e., whether participants reasoned about forces acting on their own bodies vs. on external objects), as well as the level of relational and argumentative reasoning about the outcomes of the thought experiments. The results showed that: (i) thought experiments can serve as a tool to elicit inconsistencies in one's representations; (ii) the level of relational and argumentative reasoning determines the level of belief revision in the service of error correction; and (iii) the type of mental model manipulated in a thought experiment determines its outcome and its potential to initiate belief revision. Thought experiments can serve as a valuable teaching and learning tool, and they can help us better understand the nature of error detection and correction systems. (shrink)

Scientists from Einstein to Sagan have linked emotions like awe with the motivation for scientific inquiry, but no research has tested this possibility. Theoretical and empirical work from affective science, however, suggests that awe might be unique in motivating explanation and exploration of the physical world. We synthesize theories of awe with theories of the cognitive mechanisms related to learning, and offer a generative theoretical framework that can be used to test the effect of this emotion on early science learning.

A central tenet of constructivist models of conceptual development is that children's initial conceptual level constrains how they make sense of new evidence and thus whether exposure to evidence will prompt conceptual change. Yet little experimental evidence directly examines this claim for the case of sustained, fundamental conceptual achievements. The present study combined scaling and experimental microgenetic methods to examine the processes underlying conceptual change in the context of an important conceptual achievement of early childhood—the development of a representational theory (...) of mind. Results from 47 children (M age = 3.7 years) indicate that only children who were conceptually close to understanding false belief at the beginning of the study, and who were experimentally exposed to evidence of people acting on false beliefs, reliably developed representational theories of minds. Combined scaling and microgenetic data revealed how prior conceptual level interacts with experience, thereby providing critical experimental evidence for how conceptual change results from the interplay between conceptions and evidence. (shrink)

My research program proposes that children have representations and learning mechanisms that can be characterized as causal models of the world Bayesian Fundamentalism.”.

A theory of conceptual development must specify the innate representational primitives, must characterize the ways in which the initial state differs from the adult state, and must characterize the processes through which one is transformed into the other. The Origin of Concepts (henceforth TOOC) defends three theses. With respect to the initial state, the innate stock of primitives is not limited to sensory, perceptual, or sensorimotor representations; rather, there are also innate conceptual representations. With respect to developmental change, conceptual development (...) consists of episodes of qualitative change, resulting in systems of representation that are more powerful than, and sometimes incommensurable with, those from which they are built. With respect to a learning mechanism that achieves conceptual discontinuity, I offer Quinian bootstrapping. TOOC concludes with a discussion of how an understanding of conceptual development constrains a theory of concepts. (shrink)

A novel experimental paradigm that measured theory change and confidence in participants' theories was used in three experiments to test the effects of anomalous evidence. Experiment 1 varied the amount of anomalous evidence to see if “dose size” made incremental changes in confidence toward theory change. Experiment 2 varied whether anomalous evidence was convergent or replicating. Experiment 3 varied whether participants were provided with an alternative theory that explained the anomalous evidence. All experiments showed that participants' confidence changes were commensurate (...) with the amount of anomalous evidence presented, and that larger decreases in confidence predicted theory changes. Convergent evidence and the presentation of an alternative theory led to larger confidence change. Convergent evidence also caused more theory changes. Even when people do not change theories, factors pertinent to the evidence and alternative theories decrease their confidence in their current theory and move them incrementally closer to theory change. (shrink)

For centuries, humans have contemplated the minds of gods. Research on religious cognition is spread across sub-disciplines, making it difficult to gain a complete understanding of how people reason about gods' minds. We integrate approaches from cognitive, developmental, and social psychology and neuroscience to illuminate the origins of religious cognition. First, we show that although adults explicitly discriminate supernatural minds from human minds, their implicit responses reveal far less discrimination. Next, we demonstrate that children's religious cognition often matches adults' implicit (...) responses, revealing anthropomorphic notions of God's mind. Together, data from children and adults suggest the intuitive nature of perceiving God's mind as human-like. We then propose three complementary explanations for why anthropomorphism persists in adulthood, suggesting that anthropomorphism may be an instance of the anchoring and adjustment heuristic; a reflection of early testimony; and/or an evolutionary byproduct. (shrink)

Historians of science have pointed to essentialist beliefs about species as major impediments to the discovery of natural selection. The present study investigated whether such beliefs are impediments to learning this concept as well. Participants (43 children aged 4–9 and 34 adults) were asked to judge the variability of various behavioral and anatomical properties across different members of the same species. Adults who accepted within‐species variation—both actual and potential—were significantly more likely to demonstrate a selection‐based understanding of evolution than adults (...) who denied within‐species variation. The latter demonstrated an alternative, incorrect understanding of evolution and produced response patterns that were both quantitatively and qualitatively similar to those produced by preschool‐aged children. Overall, it is argued that psychological essentialism, although a useful bias for drawing species‐wide inductions, leads individuals to devalue within‐species variation and, consequently, to fail to understand natural selection. (shrink)

(2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.

Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We (...) present 4‐ and 5‐year‐olds with two different simplified magnet‐learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence. Following a critical intervention, they learn the correct theory. In the second study, children infer the correct number of categories given no information about the possible causal laws. Children's hypotheses in these tasks are explained as rational inferences within a Bayesian computational framework. (shrink)

Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize (...) from limited observations to predict future actions and their consequences. Our main argument is that none of these inferences (i.e., induction, generalization, explanation, and prediction) can be achieved without causal reasoning. As a case in point we use the popular television series desperate housewives and show how causal Bayes nets are able to explain the inferences made in social contexts. Crucially, causal Bayes nets also allow us to understand why we can infer so much from so little when making sense of a protagonist's behavior. (shrink)

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...) objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (shrink)

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical (...) thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components. (shrink)