The Development of Casual Explanatory Reasoning.

Young children actively seek to understand the world around them; they construct causal explanations for how and why things happen. The early-developing capacity for causal explanatory reasoning raises several questions: How do children assemble causal-explanatory systems of knowledge? What motivates children to construct causal explanations? What can the kinds of events that trigger causal explanatory reasoning tell us about the function of children’s explanations? In a series of studies with preschool children, contrastive outcomes were used as an experimental paradigm for studying the kinds of events that provoke children’s causal explanations. In Study 1 (N=48, age range 3,2 to 5,6) and Study 2 (N=32, age range 3,0 to 4,11), in order to investigate two competing hypotheses about the function of children’s explanations, events that were inconsistent with children’s prior knowledge were simultaneously contrasted with events that were consistent with children’s prior knowledge. Results suggest that inconsistent outcomes are an especially powerful trigger for children’s explanations, and that children provide explanations for inconsistent outcomes that refer to underlying, internal causal properties, overriding perceptual appearances. Study 3(N=28 children, age range 3,1 to 5,2; N=16 adults) specifically targeted state-change and negative outcomes as additional kinds of explanatory triggers, within a knowledge-rich context (illness). In Study 3, preschool children’s causal reasoning about illness was investigated, specifically, their explanations for preventing illness versus curing illness. Results indicate that state-change and negative outcomes provoke children’s causal explanations. As predicted, illness prevention provokes explanations less often than illness cure or treatment. In sum, data provide evidence for the interplay of three distinct, but interrelated biases that guide children’s causal explanatory reasoning. The data also provide insight into the function of children’s explanations and empirical evidence for the kinds of events that motivate children to construct explanations.Ph.D.PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60717/1/chlegare_1.pd

Developing explanations : Student reasoning about science concepts during claims-evidence inquiry lessons

Recent science education reforms have placed a large emphasis on inquiry-based teaching strategies as an effective way of improving conceptual understanding of science principles, comprehension of the nature of scientific inquiry, and development of the abilities for inquiry (NRC, 1996). To better understand the relationship between inquiry-based instruction and student learning, this study examined the nature of student reasoning about science concepts during Claims-Evidence Inquiry lessons. The Claims-Evidence approach to inquiry teaching was chosen as the context for this study, because it focuses student investigations on specific scientific concepts. It uses a deductive approach to question generation, in which scientific claims are used as springboards for student investigations (Gummer, 2002; Thompson, 2003; Briley, 2003). This study found that the Claims-Evidence Inquiry model provides a framework for encouraging student reasoning about science concepts by providing supports for the development of explanations. Students were encouraged to develop explanations and consider how science concepts related to their investigations. A number of instructional factors appeared to influence students’ development of explanations during Claims-Evidence inquiry. These included explicitly encouraging explanations, clarifying the connection between the claim and the investigation, the presentation of the claim, the nature of the claim, the development of science concepts, the design of the task, and the development of inquiry skills. Students were found to engage in discourse related to explanations during all four phases of the inquiry; forming a question or hypothesis, designing an investigation, collecting and presenting data, and analyzing results. Most of the verbal discourse related to explanations occurred when students were reasoning about hypotheses and most of the written discourse related to explanations occurred when students were reasoning about hypotheses and results. This analysis also identified three primary types of explanations utilized by students: analogical explanations, systems-based explanations, and concept-focused explanations. Analysis of the reasoning used in written explanations for results highlighted issues related to the application of the science concepts, explicit links between variables in the investigation and the science concepts, and the nature of the causal reasoning used in explaining results

Inferential Dependencies in Causal Inference: A Comparison of Belief-Distribution and Associative Approaches

Causal evidence is often ambiguous, and ambiguous evidence often gives rise to inferential dependencies, where learning whether one cue causes an effect leads the reasoner to make inferences about whether other cues cause the effect. There are 2 main approaches to explaining inferential dependencies. One approach, adopted by Bayesian and propositional models, distributes belief across multiple explanations, thereby representing ambiguity explicitly. The other approach, adopted by many associative models, posits within-compound associations-associations that form between potential causes-that, together with associations between causes and effects, support inferences about related cues. Although these fundamentally different approaches explain many of the same results in the causal literature, they can be distinguished, theoretically and experimentally. We present an analysis of the differences between these approaches and, through a series of experiments, demonstrate that models that distribute belief across multiple explanations provide a better characterization of human causal reasoning than models that adopt the associative approach

Explaining away, augmentation, and the assumption of independence

In reasoning about situations in which several causes lead to a common effect, a much studied and yet still not well-understood inference is that of explaining away. Assuming that the causes contribute independently to the effect, if we learn that the effect is present, then this increases the probability that one or more of the causes are present. But if we then learn that a particular cause is present, this cause “explains” the presence of the effect, and the probabilities of the other causes decrease again. People tend to show this explaining away effect in their probability judgments, but to a lesser extent than predicted by the causal structure of the situation. We investigated further the conditions under which explaining away is observed. Participants estimated the probability of a cause, given the presence or the absence of another cause, for situations in which the effect was either present or absent, and the evidence about the effect was either certain or uncertain. Responses were compared to predictions obtained using Bayesian network modeling as well as a sensitivity analysis of the size of normative changes in probability under different information conditions. One of the conditions investigated: when there is certainty that the effect is absent, is special because under the assumption of causal independence, the probabilities of the causes remain invariant, that is, there is no normative explaining away or augmentation. This condition is therefore especially diagnostic of people’s reasoning about common-effect structures. The findings suggest that, alongside earlier explanations brought forward in the literature, explaining away may occur less often when the causes are assumed to interact in their contribution to the effect, and when the normative size of the probability change is not large enough to be subjectively meaningful. Further, people struggled when given evidence against negative evidence, resembling a double negation effect

Scientific Argumentation as a Foundation for the Design of Inquiry-Based Science Instruction

Despite the attention that inquiry has received in science education research and policy, a coherent means for implementing inquiry in the classroom has been missing [1]. In recent research, scientific argumentation has received increasing attention for its role in science and in science education [2]. In this article, we propose that organizing a unit of instruction around building a scientific argument can bring inquiry practices together in the classroom in a coherent way. We outline a framework for argumentation, focusing on arguments that are central to science—arguments for the best explanation. We then use this framework as the basis for a set of design principles for developing a sequence of inquiry-based learning activities that support students in the construction of a scientific argument. We show that careful analysis of the argument that students are expected to build provides designers with a foundation for selecting resources and designing supports for scientific inquiry. Furthermore, we show that creating multiple opportunities for students to critique and refine their explanations through evidence-based argumentation fosters opportunities for critical thinking, while building science knowledge and knowledge of the nature of science

Beliefs as inner causes: the (lack of) evidence

Many psychologists studying lay belief attribution and behavior explanation cite Donald Davidson in support of their assumption that people construe beliefs as inner causes. But Davidson’s influential argument is unsound; there are no objective grounds for the intuition that the folk construe beliefs as inner causes that produce behavior. Indeed, recent experimental work by Ian Apperly, Bertram Malle, Henry Wellman, and Tania Lombrozo provides an empirical framework that accords well with Gilbert Ryle’s alternative thesis that the folk construe beliefs as patterns of living that contextualize behavior

Attributions as Behavior Explanations: Toward a New Theory

Attribution theory has played a major role in social-psychological research. Unfortunately, the term attribution is ambiguous. According to one meaning, forming an attribution is making a dispositional (trait) inference from behavior; according to another meaning, forming an attribution is giving an explanation (especially of behavior). The focus of this paper is on the latter phenomenon of behavior explanations. In particular, I discuss a new theory of explanation that provides an alternative to classic attribution theory as it dominates the textbooks and handbooks—which is typically as a version of Kelley’s (1967) model of attribution as covariation detection. I begin with a brief critique of this theory and, out of this critique, develop a list of requirements that an improved theory has to meet. I then introduce the new theory, report empirical data in its support, and apply it to a number of psychological phenomena. I finally conclude with an assessment of how much progress we have made in understanding behavior explanations and what has yet to be learned

How do medical researchers make causal inferences?

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera