Examining Explanatory Biases in Young Children's Biological Reasoning

Social organism theories of the past have defined human societies as “biological organisms”, similar to animals or plants. This present work draws from the recent technological breakthroughs in both biology and astronomy to define the worldwide human society as a “multizoa organism”, i.e. an organism made of many animals. The paper then puts forth the idea that as a multizoa organism, human society is subject to some of the same biological processes that apply to other organisms, such as the natural cycles of growth, feeding and reproduction, the principles of evolution through natural selection, and the dangers of evolutionary pressures. Finally, it argues that war can be understood as a multizoa disease that decreases the chances of a society to survive in its environment and reproduce, thus providing a purely biological reasoning against the use of warfare.

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Simulating a Computational Biological Model, Rather Than Reading, Elicits Changes in Brain Activity during Biological Reasoning

CBE—Life Sciences Education ◽

10.1187/cbe.19-11-0237 ◽

2020 ◽

Vol 19 (3) ◽

pp. ar45

Author(s):

Caron A. C. Clark ◽

Tomáš Helikar ◽

Joseph Dauer

Keyword(s):

Brain Activity ◽

Brain Regions ◽

Biological Model ◽

Reading Accuracy ◽

Model Based ◽

Increased Activity ◽

Biological Reasoning

Undergraduates who computationally simulated a biological model showed increased activity in occipital and parietal brain regions when later reasoning about that model relative to students who learned through reading. Accuracy in model-based reasoning correlated with prefrontal brain activity.

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Deriving Population Growth Models by Growing Fruit Fly Colonies

The American Biology Teacher ◽

10.1525/abt.2016.78.3.221 ◽

2016 ◽

Vol 78 (3) ◽

pp. 221-225 ◽

Cited By ~ 1

Author(s):

Aaron J. Heaps ◽

Tyler D. Dawson ◽

Jace C. Briggs ◽

Megan A. Hansen ◽

Jamie L. Jensen

Keyword(s):

Population Growth ◽

Growth Models ◽

Mathematical Reasoning ◽

Fruit Fly ◽

Limiting Factors ◽

Logistic Growth ◽

Attitudinal Data ◽

Growth Equations ◽

Population Growth Models ◽

Biological Reasoning

Population growth presents a unique opportunity to make the connection between mathematical and biological reasoning. The objective of this article is to introduce a method of teaching population growth that allows students to utilize mathematical reasoning to derive population growth models from authentic populations through active learning and firsthand experiences. To accomplish this, we designed a lab in which students grow and count populations of Drosophila over the course of 12 weeks, modifying abiotic and biotic limiting factors. Using the data, students derive exponential and logistic growth equations, through mathematical reasoning patterns that allow them to understand the purpose of these models, and hypothesize relationships between various factors and population growth. We gathered student attitudinal data and found that students perceived the lab as more effective, better at preparing them for lecture, and more engaging than the previous lab used. Through this active and inquiry-based method of teaching, students are more involved and engaged in both mathematical and biological reasoning processes.

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Diagrams in biology

The Knowledge Engineering Review ◽

10.1017/s0269888913000246 ◽

2013 ◽

Vol 28 (3) ◽

pp. 273-286 ◽

Cited By ~ 9

Author(s):

Laura Perini

Keyword(s):

Visual Representations ◽

Visual Appearance ◽

Biological Reasoning

AbstractBiologists depend on visual representations, and their use of diagrams has drawn the attention of philosophers, historians, and sociologists interested in understanding how these images are involved in biological reasoning. These studies, however, proceed from identification of diagrams on the basis of their spare visual appearance, and do not draw on a foundational theory of the nature of diagrams as representations. This approach has limited the extent to which we understand how these diagrams are involved in biological reasoning. In this paper, I characterize three different kinds of figures among those previously identified as diagrams. The features that make these figures distinctive as representational types, furthermore, illuminate the ways in which they are involved in biological reasoning.

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Is the most representative skunk the average or the stinkiest? Developmental changes in representations of biological categories

10.31234/osf.io/mch63 ◽

2018 ◽

Author(s):

Emily Foster-Hanson ◽

Marjorie Rhodes

Keyword(s):

Young Children ◽

Cognitive Process ◽

Extreme Values ◽

Developmental Change ◽

Conceptual Structure ◽

Developmental Changes ◽

The World ◽

Characteristic Features ◽

Biological Reasoning

People often think of categories in terms of their most representative examples (e.g., robin for BIRD). Thus, determining which exemplars are most representative is a fundamental cognitive process that shapes how people use concepts to navigate the world. The present studies (N = 669; ages 5 years – adulthood) revealed developmental change in this important component of cognition. Studies 1-2 found that young children view exemplars with extreme values of characteristic features (e.g., the very fastest cheetah) as most representative of familiar biological categories; the tendency to view average exemplars in this manner (e.g., the average-speeded cheetah) emerged slowly across age. Study 3 examined the mechanisms underlying these judgments, and found that participants of all ages viewed extreme exemplars as representative of novel animal categories when they learned that the variable features fulfilled category-specific adaptive needs, but not otherwise. Implications for developmental changes in conceptual structure and biological reasoning are discussed.

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