Interpreting the internal structure of a connectionist model of the balance scale task.

Abstract. One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides a detailed example of the synthetic approach by showing how the construction (and subsequent analysis) of a connectionist network can be used to contribute to a theory of how humans solve Piaget's classic balance scale task

Osteoblast mineralization requires β1 integrin/ICAP-1–dependent fibronectin deposition

ICAP-1 prevents recruitment of kindlin-2 to β1 integrin to control dynamics of fibrillar adhesion sites, fibronectin deposition, and osteoblast mineralization during bone formation

Technology as a disruptive agent: Intergenerational perspectives

YesThis study explores how British South Asian parents perceive their children’s technology consumption through their collectivist lenses and interdependent values. The findings for this qualitative study indicate that second and third generation South Asian parents acknowledge the benefits of children’s technology use; but largely perceive technology as a disruptive agent, whereby children are becoming isolated and increasingly independent within the household. The analysis aims to understand how parents view their children’s relationship with others as a result of technology consumption. Accordingly, this paper proposes an extension of the Construal of self conceptualisation and contributes a Techno-construal matrix that establishes a dyadic connection between technology consumption and cultural values. Overall, the study reveals that children display less inter-reliance and conformance typically associated with collectivist cultures, resulting from their technology use. Consequently, parents interpret their children’s shift from interdependence to more independence as a disruptive and unsettling phenomenon within the household

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Teaching the control-of-variables strategy: A meta-analysis

A core component of scientific inquiry is the ability to evaluate evidence generated from controlled experiments and then to relate that evidence to a hypothesis or theory. The control-of-variables strategy (CVS) is foundational for school science and scientific literacy, but it does not routinely develop without practice or instruction. This meta-analysis summarizes the findings from 72 intervention studies at least partly designed to increase students\u27 CVS skills. By using the method of robust meta-regression for dealing with multiple effect sizes from single studies, and by excluding outliers, we estimated a mean effect size of g = 0.61 (95% CI = 0.53–0.69). Our moderator analyses focused on design features, student characteristics, instruction characteristics, and assessment features. Only two instruction characteristics – the use of cognitive conflict and the use of demonstrations – were significantly related to student achievement. Furthermore, the format of the assessment instrument was identified as a major source of variability between study outcomes. Implications for teaching and learning science process skills and future research are discussed

The development of scientific reasoning skills

The purpose of this article is to provide an introduction to the growing body of research on the development of scientific reasoning skills. The focus is on the reasoning and problem-solving strategies involved in experimentation and evidence evaluation. Research on strategy use in science has undergone considerable development in the last decade. Early research focused on knowledge-lean tasks or on tasks in which subjects were instructed to disregard prior knowledge. The purpose of this article is to provide a general introduction to the body of research in cognitive and developmental psychology conducted under the labels ''scientific reasoning,'' ''scientific discovery,'' and ''scientific thinking.'' There are three main reasons for reviewing this literature. First, within the last decade, there was a call to develop a distinct ''psychology of science' Correspondence and reprint requests should be addressed to Corinne Zimmerman, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260. E-mail: czimmϩ@pitt.edu. 99 0273-2297/00 $35.00 Copyright © 2000 by Academic Press All rights of reproduction in any form reserved. 100 CORINNE ZIMMERMAN science educators also have been interested in children's understanding of both scientific concepts and the scientific enterprise (e.g., This intersection with science education provides the third rationale for this review. In a recent Handbook of child psychology chapter, DEVELOPMENT OF SCIENTIFIC REASONING 101 in science, but reviews only a handful of studies that exist on the development of domain-general scientific reasoning skills. To bridge this gap, my third goal is to provide a review of this literature for both audiences and to demonstrate how developmental research on scientific reasoning can be used to inform science educators. The plan of the article is as follows. In the first section, I will provide a general overview of the two main approaches to the study of scientific thinking: one focused on the development of conceptual knowledge in particular scientific domains and a second focused on the reasoning and problemsolving strategies involved in hypothesis generation, experimental design, and evidence evaluation. Both approaches will be introduced to distinguish two different connotations of ''scientific reasoning,'' but it is the second line of research that is the primary focus of this review. Next, I will introduce In the second section, I will review major empirical findings using the SDDS model as a framework. The first subsection will include a brief review of research that has been focused on experimentation skills. In the second subsection, I will review the research on evidence evaluation skills. The last subsection will include a discussion of self-directed experimentation studies. In these integrative investigations of scientific reasoning, participants actively engage in all aspects of the scientific discovery process so that researchers can track the development of conceptual knowledge and reasoning strategies. I will conclude this subsection with some generalizations about development of skills and individual differences. In the third and final section of the paper, I will provide a general summary around the points outlined above. Based on this review of the skills and strategies of scientific reasoning, I suggest the idea that science may best be taught as both an academic skill and a content domain. APPROACHES AND FRAMEWORK The most general goal of scientific investigation is to extend our knowledge of the world. ''Science'' is a term that has been used to describe both a body of knowledge and the activities that gave rise to that knowledge. In parallel, psychologists have been interested in both the product, or individuals' knowledge about scientific concepts, and the processes or activities that foster that knowledge acquisition. Science also involves both the discovery of regularities, laws, or generalizations (in the form of hypotheses or theories) and the confirmation of those hypotheses (also referred to as justification or verification). That is, there has been interest in both the inductive processes involved in the generation of hypotheses and the deductive processes 102 CORINNE ZIMMERMAN used in the testing of hypotheses. 2 Scientific investigation broadly defined includes numerous procedural and conceptual activities such as asking questions, hypothesizing, designing experiments, using apparatus, observing, measuring, predicting, recording and interpreting data, evaluating evidence, performing statistical calculations, making inferences, and formulating theories or models 3 The Domain-Specific Approach: Conceptual Knowledge about Science One approach to studying the development of scientific reasoning has involved investigating the concepts that children and adults hold about phenomena in various content domains in science, such as biology (e.g., Carey, 1985; DEVELOPMENT OF SCIENTIFIC REASONING 103 or instruction. Historically, this approach has roots in the pioneering work of Piaget, who was interested in the development of various concepts such as time, number, space, movement, and velocity (e.g., In the domain-specific approach, a typical scientific reasoning task consists of questions or problems that require participants to use their conceptual knowledge of a particular scientific phenomenon. For example, children were asked to answer questions about the earth such as ''Is there an edge to the earth?'' and ''Can you fall off the edge? '' (Vosniadou & Brewer, 1992, p. 553). In the domain of genetic inheritance, participants were asked to reason about issues such as the origins of anatomical and behavioral differences in species by answering questions such as ''How does it come about that people have different color of eyes/hair?'' and ''How did the differences between horses and cows originate? '' (Samarapungavan & Wiers, 1997, p.174). In the domain of physics, individuals were instructed to draw the path of a ball as it exits a curved tube In the previous examples, participants were using their current conceptual understanding (or ''misconceptions'') to generate a solution to the task. They were not required to evaluate evidence, make observations, or conduct experiments to verify their solutions or answers. 4 As there are several different domains of science, and numerous concepts of interest within each (e.g., within the domain of physics alone, different researchers have studied the concepts of gravity, motion, velocity, balance, heat/temperature, electricity, and force, to name a few), a thorough discussion of these literatures is not appropriate given the scope of this review. Reviews and collections of work on domain-specific concepts can be found in Carey (1985), CORINNE ZIMMERMAN The Domain-General Approach: Introduction and Background A second approach that has been taken to understand the development of scientific thinking has involved a focus on domain-general reasoning and problem-solving strategies that are involved in the discovery and modification of theories about categorical or causal relationships. These strategies include the general skills implicated in experimental design and evidence evaluation, where the focus is on the cognitive skills and strategies that transcend the particular content domain to which they are being applied. In their review of research on the acquisition of intellectual skills, Voss, Riley, and Carretero (1995) classified scientific reasoning as a ''general intellectual skill.'' Scientific thinking as defined in this approach involves the application of the methods or principles of scientific inquiry to reasoning or problemsolving situations This approach has historical roots in experimental psychology, in the body of research on reasoning and problem solving (e.g., In addition to examining the deductive components of scientific inference, Initial investigations with adults focused on a what seemed to be a perva-DEVELOPMENT OF SCIENTIFIC REASONING 105 sive ''confirmation bias'' that existed, even among scientists (e.g., Given Studies using the 2-4-6 task with children are rare, however. This dearth of developmental studies could be due, in part, to Integration of Concepts and Strategies: A Framework for the Scientific Discovery Process The two contrasting approaches outlined represent different conceptualizations about what the development of scientific reasoning involves. In some respects, the different approaches reflect a lack of agreement concerning which type of acquisition (i.e., concepts or strategies) is more important for accounting for developmental differences in scientific reasoning Background The SDDS model was influenced by the work and assumptions of Simon and his colleagues (e.g., Scientific ''reasoning'' is the most common label for the research approaches outlined thus far. A careful examination of what is involved in scientific inquiry should reveal that it involves aspects of both problem solving and reasoning (Copi, 1986, Chap. 14; One of the main generalizations about problem-solving processes is the use of heuristic searches (e.g., The SDDS Model Klahr and With respect to searching hypothesis space, Klahr and Dunbar (1988) noted the difference between ''evoking'' and ''inducing'' a hypothesis. The key difference is that in some situations, one can use prior knowledge in order to constrain the search of hypothesis space, while in other situations, one must make some observations (via experimentation) before constructing an initial hypothesis. The latter scenario relies more heavily on inductive reasoning, while the former may rely on memory retrieval. One implication of this distinction is that the search through experiment space may or may not be constrained by a hypothesis. Initial search through the space of experiments may be done in the service of generating observations. In order to test a hypothesis, once induced, the search process involves finding an experiment that can discriminate among rival hypotheses. The search through these 108 CORINNE ZIMMERMAN two spaces requires different representations of the problem and may require different heuristics for moving about the problem spaces. The first two cognitive processes of scientific discovery involve a coordinated, heuristic search. The third process of the SDDS model involves evidence evaluation. This process was initially described as the decision made on the basis of the cumulative evidence, that is, the decision to accept, reject, or modify the current hypothesis. Initially, Klahr and Dunbar emphasized the ''dual search'' nature of scientific discovery, while the evidence evaluation process was somewhat neglected in the overall discovery process. In more recent descriptions, Klahr has elaborated upon the evidence evaluation process, indicating that it involves a comparison of results obtained through experimentation with the predictions derived from the current hypothesis Klahr and Dunbar's original description of the model highlighted the dual search coordination, but updated descriptions acknowledge that scientific discovery tasks depend upon the coordination and integration of all three components The SDDS framework captures the complexity and the cyclical nature of the process of scientific discovery. The framework incorporates many component processes that previously had been studied in isolation. Summary Scientific discovery is a complex activity that requires the coordination of several higher level cognitive skills, including heuristic search through problem spaces, inductive reasoning, and deductive logic. The main goal of scientific investigation is the acquisition of knowledge in the form of hypotheses that can serve as generalizations or explanations (i.e., theories). Psychologists have investigated the development of scientific concepts and the development of strategies involved in the discovery and verification of hypotheses. In initial studies of scientific thinking, researchers examined these component processes in isolation or in the absence of meaningful content (e.g., the 2-4-6 task). MAJOR EMPIRICAL FINDINGS Thus far I have described only in very general terms the main approaches to studying scientific reasoning and an attempt to integrate the major components of scientific activity into a single framework. In this section I will 110 CORINNE ZIMMERMAN describe the main findings or generalizations that can be made about human performance and development on simulated discovery tasks. 5 Initial attempts to study scientific reasoning began with investigations that followed a ''divide-and-conquer'' approach by focusing on particular cognitive components, as represented by the cells in Research on Experimentation Skills Experimentation is an ill-defined problem for most children and adults DEVELOPMENT OF SCIENTIFIC REASONING 111 teristics common to experimentation across content domains. At a minimum, one must recognize that the process of experimentation involves generating observations that will serve as evidence that will be related to hypotheses. Ideally, experimentation should produce evidence or observations that are interpretable in order to make the process of evidence evaluation uncomplicated. One aspect of experimentation skill is to isolate variables in such a way as to rule out competing hypotheses. An alternative hypothesis can take the form of a specific competing hypothesis or the complement of the hypothesis under consideration. In either case, the control of variables and the systematic combinations of variables are particular skills that have been investigated. Early approaches to examining experimentation skills involved minimizing the role of prior knowledge in order to focus on the strategies that participants used. That is, the goal was to examine the domain-general strategies that apply regardless of the content to which they are applied (i.e., cell E in An analogous task is the colorless liquid task used by Tschirgi (1980) looked at one aspect of hypothesis testing in ''natural'' problem situations. Story problems were used in which two or three variables were involved in producing either a good or a bad outcome (e.g., baking a good cake, making a paper airplane) and therefore involved some domain knowledge (i.e., cells B and E of Tschirgi (1980) found that in familiar, everyday problem situations, the type of outcome influenced the strategy for generating an experiment to produce evidence. In all age groups, participants looked for confirmatory evidence when there was a ''positive'' outcome. That is, for positive outcomes, they used a ''Hold One Thing At a Time'' (HOTAT) strategy for manipulating variables (choice a above). They selected disconfirmatory evidence when there was a ''negative'' outcome, using the more valid ''Vary One Thing At a Time'' (VOTAT) strategy (choice b above). The only developmental difference was that the sixth graders and adults (but not second and fourth graders) were aware of the appropriateness of the VOTAT strategy. Tschirgi suggested that the results supported a model of natural inductive logic that develops through everyday problem-solving experience with multivariable situations. That is, individuals base their choice of strategy on empirical foundations (e.g., reproducing positive effects and eliminating negative effects), not logical ones. Sodian, Zaitchik, and Carey (1991) investigated whether children in the early school years understand the difference between testing a hypothesis and reproducing an effect. The tasks used by Over half of the first graders answered the series of questions correctly (with justifications) and 86% of the second graders correctly differentiated between conclusive and inconclusive tests. It is important to point out, however, that the children were provided with the two mutually exclusive and exhaustive hypotheses and, moreover, were provided with two mutually exclusive and exhaustive experiments from which to select In summary, researchers interested in experimentation skills have focused on the production of factorial combinations and the isolation of variables on tasks in which the role of prior knowledge was minimized. An important precursor for success in producing a combinatorial array in the absence of domain-specific knowledge is systematic or rule-governed behavior, which appears to emerge around age 5. An awareness of memory limitations and of the importance of record keeping appears to emerge between the ages of 10 and 13. With respect to the isolation of variables, there is evidence that the goal of the experiment can affect the strategies selected. When the hypothesis to be tested can be construed as involving a positive or negative outcome, second-, fourth-, and sixth-grade children and adults select valid experimental tests when the outcome is negative, but use a less valid strategy when the outcome is positive. What develops is an awareness of the appropriateness of the VOTAT strategy selected in the case of negative outcomes. When domain knowledge can be used to view the outcome as positive, even adults do not appear to have developed an awareness of the inappropriateness 114 CORINNE ZIMMERMAN of the HOTAT strategy. The research reviewed in this section provides evidence that, under conditions in which producing an effect is not at issue, even children in the first grade understand what it means to test a hypothesis by conducting an experiment and, furthermore, that children as young as 6 can differentiate between a conclusive and an inconclusive experiment. Research on Evidence Evaluation Skills The evaluation of evidence as bearing on the tenability of a hypothesis has been of central interest in the work of Kuhn and her colleagues In the evidence evaluation studies to be reviewed, the evidence provided for participants to evaluate is covariation evidence. In the first section, I will provide a general description of what is meant by covariation evidence and generalizations about tasks used in the studies to be reviewed. Then, early studies of rule use in the evaluation of covariation evidence will be described. In the second section I will summarize the landmark work of Covariation Evidence: General Description and Early Research With respect to determining causal relationships, Hume (1758/1988) identified the covariation of perceptually salient events as one potential cue that DEVELOPMENT OF SCIENTIFIC REASONING 115 two events are causally related. Even young children have a tendency to use the covariation of events (antecedent and outcome) as one indicator of causality (e.g., In covariation, there are four possible combinations of the presence and the absence of antecedent (or potential cause) and outcome (see CORINNE ZIMMERMAN In evidence evaluation tasks involving covariation of events, participants are provided with data corresponding to the frequencies in the cells of a 2 ϫ 2 contingency table (i.e., represented in either tabular or pictorial form). The pattern could represent perfect covariation, partial (or imperfect) covariation, or no correlation between the two events. The task may require participants to evaluate a given hypothesis in light of the evidence or to determine which hypothesis the pattern of data support. In either case, the focus is on the inferences that can be made on the basis of the evidence (i.e., in most cases, participants were instructed to disregard prior domain knowledge). Experimental design skills are not of interest. Early work on covariation detection was conducted by Shaklee and her colleagues (e.g., Shaklee and her colleagues found a general trend in the rules used to weigh the evidence in the contingency tables. It was predicted that younger children (Grades 2, 3, and 4) would use the frequency reported in cell A to make a judgment and proceed developmentally to a rule in which they compare frequencies in cells A vs. B. However, the cell A strategy was not common. The most sophisticated strategy that participants seemed to use, even as adults, was to compare the sums-of-diagonals. The conditional probability rule was used only used by a minority of participants, even at the college level. Adults could readily learn this rule, if they were shown how to compare the relevant ratios (see footnote 6). Children in Grades 4 through 8 could be taught to use the sums-of-diagonals rule The Work of Kuhn, Amsel, and O'Loughlin (1988) Kuhn et al. Kuhn et al. 's (1988) general method involved the presentation of covariation data sequentially and cumulatively. Participants were asked a series of questions about what the evidence shows for each of the four variables. Responses were coded as either evidence-based or theory-based. To be coded as evidence-based, a participant's response to the probe questions had to make reference to the patterns of covariation or instances of data presented (i.e., the findings of the scientists). For example, if shown a pattern in which type of cake covaried with getting colds, a participant who noted that the sick children ate chocolate cake and the healthy kids ate carrot cake would be coded as having made an evidence-based response. In contrast, theorybased responses made

The development of scientific thinking skills in elementary and middle school

The goal of this article is to provide an integrative review of research that has been conducted on the development of childrens scientific reasoning. Broadly defined, scientific thinking includes the skills involved in inquiry, experimentation, evidence evaluation, and inference that are done in the service of conceptual change or scientific understanding. Therefore, the focus is on the thinking and reasoning skills that support the formation and modification of concepts and theories about the natural and social world. Recent trends include a focus on definitional, methodological and conceptual issues regarding what is normative and authentic in the context of the science lab and the science classroom, an increased focus on metacognitive and metastrategic skills, and explorations of different types of instructional and practice opportunities that are required for the development, consolidation and subsequent transfer of such skills. (http://www.sciencedirect.com/science/article/pii/S0273229706000797