Developmental Disconnects: Adults and Teachers Deprioritize Mechanistic Content when Introducing Young Children to STEM

How do adults consider explaining science to young children? We examined adult prioritization of different kinds of explanatory science content when teaching young children both in and outside of the classroom. Across five studies, we predicted and found that adults and K-12 teachers deprioritized mechanistic content relative to comparatively superficial content (e.g., labels and functional explanations) when introducing areas of science to young school age children. Beyond perceiving mechanistic explanations to be relatively infrequent in elementary school science curricula, adults appear to perceive mechanistic content as excessively challenging for students, reporting that in-depth, mechanistic content is less important for early elementary school children to learn compared to broader, superficial content. The same misperceptions were found among experienced teachers and lay adults, suggesting a general intuition that science learning should start with relatively superficial content before describing causal relations that produce a scientific phenomenon. These findings contradict widely adopted educational standards emphasizing the importance of in-depth content such as mechanistic explanations. Such in-depth, mechanistic content supports children’s scientific engagement, combats potential misconceptions, and bolsters future learning. Despite this, lay adults and experienced teachers support teaching science to young children in ways that do not fit with children’s learning abilities and interests.

Focus on How it Works: A Mechanistic Emphasis Enhances Elementary Student Learning Outcomes

Biologists, lay adults, and children alike value understandings of how biological entities work, prioritizing these mechanistic explanations in learning choices from at least five years of age and onwards. Despite this, formal education of young children has historically lacked mechanistic content, reserving these types of causal explanations for older students. We explored strategies by which mechanistic explanations may be emphasized to learners, identifying asymmetries between teacher intuitions and the influence of a mechanistic focus on young children’s science learning. In Study 1, we contrasted K-12 teacher intuitions about two types of learning goals—mechanistic or labels—in elementary school biology lessons, assessing general preferences and beliefs about which goal would maximize learning. Teachers preferred labels-focused learning goals when considering first and second grade lessons, but increasingly shifted to mechanistic learning goals for third through fifth grade lessons. In Study 2, children ages 6 to 11 were given either a mechanistic or a labels-focused learning goal prior to watching a video lesson about the heart. In Study 3, children ages 6 to 9 heard either a mechanism-focused or labels-focused description of the small intestine prior to viewing the target heart lesson. For both learning studies, children of all sampled age groups guided to focus on mechanism performed better on a learning assessment than those guided to focus on labels. While teachers believe that younger students benefit more from superficial goals such as labels, we find that mechanistic goals enhance learning even among the youngest children. We discuss implications of initial emphasis of mechanistic science content in early elementary school to boost subsequent learning outcomes and science interest.

Persuasive Features of Scientific Explanations: Explanatory Schemata of Physical and Psychosocial Phenomena

Explanations are central to understanding the causal relationships between entities within the environment. Instead of examining basic heuristics and schemata that inform the acceptance or rejection of scientific explanations, recent studies have predominantly examined complex explanatory models. In the present study, we examined which essential features of explanatory schemata can account for phenomena that are attributed to domain-specific knowledge. In two experiments, participants judged the validity of logical syllogisms and reported confidence in their response. In addition to validity of the explanations, we manipulated whether scientists or people explained an animate or inanimate phenomenon using mechanistic (e.g., force, cause) or intentional explanatory terms (e.g., believes, wants). Results indicate that intentional explanations were generally considered to be less valid than mechanistic explanations and that ‘scientists’ were relatively more reliable sources of information of inanimate phenomena whereas ‘people’ were relatively more reliable sources of information of animate phenomena. Moreover, after controlling for participants’ performance, we found that they expressed greater overconfidence for valid intentional and invalid mechanistic explanations suggesting that the effect of belief-bias is greater in these conditions.

The joint construction of goals and means as a solution for the problem of variability in behavior

The understanding of variability in behavior is extraordinarily difficult because behavior consists of actions that are purposeful processes directed to reach future results and psychological functions can deliberately be adjusted for this. The conventional method used in handling this problem is to make behavior in experiments as similar as possible to processes studied in the natural sciences. It is suggested this allows the revealing of simple mechanisms of behavior that are independent of purpose and deliberation. A sufficient basis of the simple mechanisms should elucidate purposefulness mechanistically. It is implicitly assumed this method defines the simple mechanisms unequivocally. The replication crisis hints this assumption is incorrect. Arocha (2021) suggests purpose is the essential component in understanding behavior. However, Arocha assumes no mechanistic explanations for goal-directed processes, thus restricting the usefulness of his ideas. I suggest the goal and means of an action are constructed jointly through the criterion of minimal construction costs. This mechanistically determines actions.

Habit-change, heightened consciousness, and agential “crises”: impersonal mechanisms, personal agents, and their complex entanglement

The author begins by highlighting Peirce’s claim that every kind of consciousness is more or less like a cognition. He concludes by making a plea for a cognitive semiotics in which both mechanistic explanations and accounts framed in terms of personal agents are necessary for an adequate account of human cognition. The topics of habit-taking and the form of consciousness associated with this process are what link Peirce’s cognitivist approach to consciousness and an inclusive, non-reductionist vision of cognitive semiotics. Impersonal mechanisms play an integral role in even the most sophisticated forms of human cognition. But the self-critical endeavors of personal agents, especially ones susceptible to “crises” such as doubt, play no less an important role.

Understanding ‘How’: Two kinds of mechanistic explanations underlie known explanation preferences

Parallel research programs across decades have developed contrasting accounts of people’s explanation preferences. One perspective emphasizes adults’ and children’s preferences for teleological explanations (i.e., explanations referring to something’s purpose), even in direct contrast with mechanistic (or causal) explanations. The other perspective highlights contexts where people instead seek out mechanistic knowledge and judge it to be particularly valuable. These characterizations of people’s explanation preferences support fundamentally different theories of people’s intrinsic worldviews: People may either be irrational and prone to unscientific explanation, or relatively sophisticated investigators of the world around them. How can these teleo-centric and mech-centric views of explanation preferences be reconciled? Here, we demonstrate that mechanistic explanations are comprised of two significant subtypes of explanation. Etiological mechanisms address how things came to be, whereas constitutive mechanisms address how they currently work. In Experiments 1 and 2, we find that people prefer constitutive mechanisms to etiological mechanisms. In Experiments 3, 4a, and 4b, we also find that constitutive and etiological mechanisms are judged differently against teleological explanations. In general, constitutive mechanisms perform better against teleological explanations than etiological mechanisms. Thus, people’s preferences depend on the type of mechanism involved; they may prefer teleology to one kind of mechanism but not to the other. We discuss implications for the larger debate on explanation preferences.

Beyond Cause: The Development of Clockwork Cognition

Thinking of the world in mechanistic terms—how things work—is both cognitively natural and motivating for humans from the preschool years onward. Mechanisms have distinct structural properties that go far beyond mere causal facts. They typically contain layers of causal clusters and the systematic interactions between those clusters that give rise to the next level up. Following developments in the philosophy of science and studies on children’s questioning behaviors, recent research shows that, from an early age, people appreciate the informational and inductive potential of mechanistic information. People selectively notice and choose mechanistic explanations as especially useful opportunities for learning; but they also soon forget the details of what they encounter. We argue that enduring cognitive abstractions from such details provide powerful ways of accessing and evaluating expertise in other people.

Understanding ‘Why’: How implicit questions shape explanation preferences

‘Why’ questions are semantically ambiguous. A question like “Why is the sky blue?” can be rephrased as either a ‘how’ (“How did the sky get its blue color?”) or a ‘purpose’ question (“What is the purpose of the sky being blue?”). This semantic ambiguity allows us to seek many kinds of information with the same ‘why’ question. As a result, ‘why’ questions have often been used to investigate people’s explanation preferences. From such work, we know that people will often prefer teleological over mechanistic explanations—a tendency that has been linked to many broader theories of human cognition. But are ‘why’ questions pragmatically ambiguous? You may, for instance, have a specific expectation about what “Why is the sky blue?” was really meaning to ask. Here, we show that (a) people have clear, domain-specific expectations about what specific questions are implied by ambiguous ‘why’ questions; (b) people have clear preferences for certain kinds of questions over others; and (c) there is a direct link between implicit questions and explanation preferences. Thus not only is “why” pragmatically unambiguous, but these specific expectations may shape known explanation preferences. To test this view, we finally show that people endorse teleological answers even when they are explicitly non-explanatory. In other words, people may sometimes prefer teleological answers because they interpret ‘why’ questions as ‘purpose’ questions (rather than as ‘how’ questions) and teleological explanations may simply better address these questions. We discuss how understanding ‘why’ may reshape our understanding of people’s explanation preferences and their consequences.

Introduction

This brief introductory chapter begins with the key question to be addressed in the book: why does brain size vary among animal species? It contains a short outline of the book’s contents and establishes the rationale for the examination of the evidence that has been gathered using the comparative method over the past five decades. I explain that the book will be both a review and a critique of the work that has attempted to explain which natural selection pressures led to changes in brain size. This is a focus that, to a large extent, excludes work that addresses mechanistic explanations for brain size.