Cover
Title Page
Copyright
Preface
Contributors
Chapter 1: Cultural Neuroscience of the Developing Brain in Childhood
1. Introduction
2. Theories in Cultural Neuroscience of the Developing Brain
3. Methods in Cultural Neuroscience Research of the Developing Brain
4. Empirical Progress in Cultural Neuroscience of the Developing Brain in Childhood
5. Future Directions in Cultural Neuroscience of the Developing Brain in Childhood
6. Implications of Cultural Neuroscience of the Developing Brain
7. References
Chapter 2: The Role of Culture and Language in the Development of Color Categorization
1. Color Categories and Reasons to Investigate Them
2. Contributions from Developmental Science
3. Unresolved Issues and New Developmental Questions
4. Conclusions
5. References
Chapter 3: How Much Mathematics Is “Hardwired,” If Any at All: Biological Evolution, Development, and the Essential Role of Culture
1. Introduction
2. Nativism in Cognitive Development, Cognitive Neuroscience, and Animal Cognition
3. Quantity‐Related “Hardwired” Capacities? Yes,…Are They Mathematical? No
4. Aren't Number‐to‐Space Mappings “Hardwired”? No
5. What Can We Learn from the History of Mathematics? A Lot
6. Are Results in Experimental Studies on Number‐Line Mappings Consistent with Historical Records? Yes
7. Biocultural Issues for Child Psychology and Developmental Theory: Is Snowboarding “Hardwired”? No, It Is Not
8. References
Chapter 4: Culture, Language, and Emotion: Explorations in Development
1. Introduction
2. Culture as Instantiated through Language
3. Culture as Instantiated through Emotion and Emotion Regulation Strategies
4. Limitations of Both “Cultural” and “Cross‐Cultural” Approaches to the Development of Language and Emotion Regulation
5. Can We Move beyond Metaphors?
6. References
Chapter 5: Cultural Expressions and Neurobiological Underpinnings in Mother–Infant Interactions
1. Introduction
2. Parenting
3. Parenting Practices
4. Autonomic Nervous System
5. Central Nervous System
6. Frontiers
7. Conclusions and Final Thoughts
8. References
Chapter 6: The Cultural Organization of Young Children's Everyday Learning
1. An Uneasy Peace
2. Culture: The “Thorn” in Developmental Theory
3. Human Development: Becoming a Cultural Being
4. Theoretical Potential of an Expanded Model of Development
5. Putting Yucatec Mayan Children's Development into Context
6. Early Social Interactions
7. The Role of Children's Play (and Work)
8. Attentional Stance: Focused versus Open
9. Negotiating a Stable Peace among Developmental Claims
10. References
Chapter 7: Socioemotional Development across Cultures
1. Introduction
2. Conceptual and Methodological Issues in Research on Culture and Human Development
3. The Contextual‐Developmental Perspective: A Theoretical Framework for Cross‐Cultural Research on Socioemotional Development
4. Culture and Shyness‐Inhibition
5. The Display of Shyness‐Inhibition in Chinese and North American Children
6. Parents' and Peers' Attitudes toward Shyness‐Inhibition
7. Shyness‐Inhibition and Adjustment Outcomes
8. Issues and Future Directions
9. References
Chapter 8: Two Senses of Cultural Relativity
1. Introduction
2. Cultural Relativism: An Introduction to Two Senses
3. Verstehen Relativism and Its Application
4. Verstehen Relativism, Warfare, and the Training of Civilized People
5. The Problem of Trade‐offs
6. Child Labor and Child Schooling: Another Likely Trade‐off?
7. Egalitarian Cultural Relativism and Moral Perspectives
8. Strong Cultural Relativism: An Internally Inconsistent Idea
9. Difficulties with Two Psychological Defenses of Strong Relativism
10. Arguments from Benevolent Informed Intentions, and the Problem of Unequal Power
11. Gender Inequality
12. Moral Equality through Neutralization Because of Conformity
13. The Different Causes of Conformity; Conformity as Motivated, Not Inertial
14. Arguments about Effective Functioning
15. Qualifying and Restricting the Arguments
16. References
Author Index
Subject Index
End User License Agreement

List of Illustrations

Chapter 1: Cultural Neuroscience of the Developing Brain in Childhood
1. Figure 1.1 The cultural neuroscience framework.
2. Figure 1.2 Model of cultural neuroscience.
3. Figure 1.3 Cultural influences on medial prefrontal cortex (MPFC) response during self‐judgments. (a) Example of self‐judgment task; (b) MPFC response during culturally congruent self‐judgments; (c) Degree of cultural collectivism predicts MPFC response to culturally congruent self‐judgments.
4. Figure 1.4 Cultural influences on empathic neural response. (a) Example of painful and nonpainful emotional scenes; (b) Other‐focusedness predicts anterior cingulate cortex (ACC) and right anterior insula (right AI) empathic response to group members in Koreans to a greater extent relative to Caucasian Americans.
5. Figure 1.5 Racial identification predicts default mode network response during empathy in African American (AA) and Caucasian American (CA) young adults (a) Example of painful and nonpainful emotional scenes; (b) Racial identification predicts empathic neural response within the default mode network.
6. Figure 1.6 Cultural differences in N2 response during behavioral inhibition in Chinese Canadian and European Canadian children (adapted from Lahat, et al., 2009). (a) Go/No‐Go task performed by children; (b) Mean amplitude response of the N2 waveform in the right (top) and left (bottom) hemisphere during Go and No‐Go trials in Chinese Canadian and European Canadian children.
Chapter 2: The Role of Culture and Language in the Development of Color Categorization
1. Figure 2.1 The mean distribution of basic color terms for the Munsell stimulus array, for English speakers (top) and Berinmo speakers (bottom). Dots in the top figure indicate the chosen best example of each color category (from Heider, 1972). Numbers in the bottom figure indicate number of participants who chose that stimulus as the best example of the color category.
2. Figure 2.2 World Color Survey color naming plots for speakers of nine languages. Each plot shows the modal naming response for speakers of a language that has five basic color terms. The rectangular shape corresponds to the WCS stimulus grid (with Munsell value [lightness] on the y‐axis and hue on the x‐axis). Regions shaded in the same lightness indicate regions of the stimulus grid where stimuli were given the same name. The percentages reported after the language name indicates the percentage match between the boundaries in the modal naming plot and those for the Berinmo language. The figure therefore shows striking similarity in color naming across these languages even though the languages are genetically and geographically distinct.
3. Figure 2.3 Analysis of the distribution of color terms from WCS nonindustrialized languages indicates clustering around particular points in color space. The figure shows the WCS stimulus grid (y = lightness, x = hue, 320 colors within the grid). Outer contour lines show the location of 100 category centroids for WCS languages. Each subsequent inner contour indicates an increment of 100 more. Dots show the location of the centroids for English terms, and these fall near the peaks of the clusters in the WCS distribution.
4. Figure 2.4 Modal grouping maps for four groups of participants (G1: beginning color namers; G2: developing color namers; G3: accurate color namers; adults). The x‐axis refers to Munsell hue codes, the y‐axis to Munsell value (lightness), and each square represents a colored stimulus. The shading of the squares indicates grouping of colors on the grouping task (same shading indicates stimuli grouped together according to modal responses). The numbers in each square correspond to the modal number of participants who allocated the stimulus to that stimulus group. The figure shows the similarity of color grouping irrespective of color naming ability.
5. Figure 2.5 Mean accuracy (+/–1 se) at identifying the colored target when paired with a same‐category (within) or different‐category (between) foil, for toddlers who had learned the names of the colors and had an appropriate category boundary (name boundary) or an inappropriate category boundary (reverse name boundary) and those who had not learned the color names (no name boundary).
6. Figure 2.6 Mean accuracy (+/–1 se) at identifying the colored target when paired with a same‐category (within) or different‐category (between) foil, for blue‐green and blue‐purple stimulus sets, for toddlers who were classified as not knowing (don't know) or knowing (know) the basic terms for the relevant colors.
7. Figure 2.7 The top panel provides infant data from Bornstein et al. (1976). Infants were habituated to a given wavelength (indicated by dots on the figure) and then shown novel wavelengths (indicated by vertical bars). The horizontal connection between dot and bar indicates no difference in mean looking times for those two wavelengths, and a gap indicates that there was a significant difference in looking time. The “infant summary” row indicates the ranges of wavelength that infants treat as equivalent. The bottom panel provides adult color naming functions (percentage of total point value corresponds to frequency of color term use) using naming data from Boynton and Gordon (1965). The correspondence between the infant and the adult data can clearly be seen in the alignment of the location of the significant differences in infant looking data and the crossover points in the adult color naming functions.
8. Figure 2.8 Mean novelty preference (+/−1 se) for novel colors that are from the same‐category (within) or different‐category (between) as the familiar color and where novel‐familiar hue differences were small (near) or large (far). The dashed line indicates the point at which there is no preference for the novel color over the familiar color.
9. Figure 2.9 Grand averaged ERP waveforms elicited during the 1700 ms following stimulus onset (0 ms) for 7‐month‐old infants in response to a frequently presented standard color (dashed line waveform), an infrequent deviant color of a different category from the standard (black line waveform), and an infrequent deviant color from the same category as the standard (gray line waveform). The top plot gives the waveforms for electrode Fz (frontal midline) and the bottom plot for electrode C3 (central left). The category effect can be seen clearly for the Negative Central ERP component (negative peak around 400 ms) at Fz and in the slow waves (1150 ms onward) at C3.
10. Figure 2.10 Eye‐movement latency (log transformation of eye‐movement initiation time in ms) to colored targets in the left or right visual field (L/RVF) on same‐ or different‐category colored backgrounds for toddlers who do (namers) and do not (learners) know the color terms for the colored targets and backgrounds. Error bars are within‐subjects' 90% confidence intervals.
Chapter 3: How Much Mathematics Is “Hardwired,” If Any at All Biological Evolution, Development, and the Essential Role of Culture
1. Figure 3.1 A clay tablet known as YBC 7289 from the Old Babylonian period, from the Yale Babylonian Collection. The tablet, from the first third of the second millennium BC, is one of the few containing drawings. It shows numbers associated with measurements but no depiction of number lines. According to contemporary historians of Mesopotamian mathematics, Old Babylonians did not operate with number‐line concepts despite manifesting sophisticated notions of number and arithmetic.
2. Figure 3.2 The introduction of the number line in 17th‐century Europe. The figure shows the top of page 265 of John Wallis's Treatise of Algebra, published in 1685, chapter 66, “Of Negative Squares and Their Imaginary Roots.” This passage seems to be the first explicit characterization of a number line for operational purposes in the history of mathematics.
3. Figure 3.3 The first page of chapter 1 of John Napier's A Description of the Admirable Table of Logarihmes published in 1616 (English edition), in which he introduces his definition of logarithm via an explicitly depicted number‐to‐line mapping.
4. Figure 3.4 A diagram from page 392 of Descartes's first edition of La Géometrie, published in 1637. Contrary to what has been claimed, Descartes did not introduce the number line in this classic volume through the invention of coordinate systems.
5. Figure 3.5 An illustration of the number line from a manual from the National Council of Teachers of Mathematics (in the United States) intended for children in kindergarten to fifth grade. The manual includes practice questions, such as “If I take a hop of 5 and then a hop of 4, where will I land?”
6. Figure 3.6 College‐level students' responses to a number mapping task, by stimulus modality and reporting condition. Data are mean perceived response intensity (nonspatial reporting) and mean response location on the line (spatial reporting), with corresponding standard errors of the mean. B_log indicates the relevant unstandardized weight (plus/minus standard error) of the logarithmic regressor in a multiple regression analyses with linear and logarithmic predictors. Gray‐scaled graphs indicate a significant p‐value for the corresponding B_log weight (based on a B_log/(standard error) t ratio with df = 7 in each case; black indicates a nonsignificant p‐value).
7. Figure 3.7 Detail from data reported in Dehaene et al. (2008a, 2008b) showing the mean response location on the line segment that participants picked as corresponding to the numerosities 1, 2, and 3—numbers for which Mundurukú speakers have a well‐established lexicon. Data are mean ± standard error of the mean. The top row (a) shows values for 16 American participants; the middle row (b) shows values for 33 Mundurukú participants; and the bottom row (c) shows values for 7 Mundurukú uneducated adults. If participants understand the task and spontaneously map numbers to the line, they should systematically map the lowest stimulus number (“1”) with the response location “1”—that is, with the left end of the line segment presented to them. American participants (a) accurately and systematically mapped “stimulus number 1” with “response location 1.” Mundurukú participants, however, systematically failed to establish this fundamental number–space mapping (b). Crucially, data reported exclusively in the corresponding “Supporting Online Material” (Dehaene et al., 2008b) show that the Mundurukú uneducated adults—the most relevant subgroup for testing the innate mental number line hypothesis—failed to do this in a more dramatic way (c). For words and tones, this group even failed to establish the fundamental property of order. These results suggest that the number‐line intuition is not universal.
8. Figure 3.8 Pointing responses for stimulus numerosities 3 and 6 (dots) during the number‐line task.
9. Figure 3.9 Responses to three different types of number stimuli on the number‐line task. Graphs show proportions of responses on segment sections with corresponding repeated measures ANOVA statistics. In stark contrast with schooled Yupnos (middle column) and California controls (right column), unschooled Yupnos (left column) did not use the extent of the segment when mapping intermediate numbers and numerosities, producing instead a bicategorical mapping without a metric (a distance function). Schooled Yupnos (middle column) did use the line but tended to manifest a bias toward the endpoints.
Chapter 4: Culture, Language, and Emotion: Explorations in Development
1. Figure 4.1 Acoustic signal for kan4kan zhei4‐ge ping2guo3, recorded by a native Mandarin speaker and rendered via FinalCut software.
2. Figure 4.2 Examples of the word apple that a child might hear.
3. Figure 4.3 A schematic representation of infant in evoked response potential experiment (a) that showed mismatch negativity response to native (English) phonetic contrasts versus nonnative (Mandarin) contrasts (b) and a hierarchical linear growth modeling curve of vocabulary growth between 14 and 30 months for infants with +1 and –1 standard deviation to the native contrast (left) versus the nonnative contrast (right) when tested at 7.5 months of age.
Chapter 6: The Cultural Organization of Young Children's Everyday Learning
1. Figure 6.1 Minimize cultural differences.
2. Figure 6.2 Minimize importance of environment.
3. Figure 6.3 Minimize universal outcomes.
4. Figure 6.4 An integrative model of competing claims.

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First Edition

Preface

Someone once said that the important questions in science do not change, only our answers to them do. That is a good thing because when the topic of culture emerged as a potential one for this symposium, I had the feeling that we had done it before. Indeed we had, and not too long ago. In 1999, Ann Masten organized a symposium called Cultural Processes in Child Development. Ann's opening remarks to that volume ended with the hope that the volume would usher in a “cultural renaissance” in developmental science. Organizing another symposium on the topic is a testament to that insight.

The role of culture in human development is undeniable. No child today has to figure out how to control fire, invent the wheel or the alphabet. For at least the last 70,000 years, the advancement of humans has depended on cultural innovation. Every generation gets information critical to its success from the generations that precede it. Yet answers to questions regarding the role of culture in human development seem to vacillate from one extreme to the other—from culture being everything, to cultures being really all the same and so not impacting development in meaningful ways. In my own line of work, questions about the role of language on thought—a line that can be encompassed within the broader umbrella of cultural relativity—have yielded apparently contradicting answers. Empirical work on the topic began with a paper by Roger Brown and Eric Lenneberg published in 1954 showing that colors for which English speakers agreed on a name were better remembered than colors for which speakers did not agree on a name (these were focal hues, or the prototypes of English colors). The stage seemed to be set for work on linguistic (and cultural) relativity—that differences across speakers of different languages would analogously yield differences across speakers of those languages in thought. That view of relativity came to a dead halt in the early 1960s and 1970s when studies showed that: (1) cones in the primate retina differentially absorb hues that corresponded to the English color prototypes—blue, yellow, and green—suggesting a strong physiological basis for color perception; (2) adults whose languages had different color terms categorized colors the same way as English speakers did, suggesting that color perception was universal and language had no effect; and (3) infants—who do not know any words—also categorize colors the same way as do English adults, suggesting that color perception could not be affected by language. What followed might be viewed as the “dark ages” of nativism and universalism of the 1970s and 1980s. I wrote in the early 1990s that color might not have been the best domain in which to look for effects of language on cognition, and started looking for effects in domains that change with development. At about the same time, John Lucy published an argument about why the color work was flawed. The “renaissance” began. Between 1990 and today, there has been an explosion of work on the role of language on cognition. Effects of language have been shown on object perception, categorization, space, number, emotion recognition, and other concepts. But even with all the evidence in support of relativity, significant questions remain. For instance, even though 1954 might seem like a long time ago, it is not long enough to bring about the evolutionary change required to alter the physiological structure of the primate retina. How can the old work showing universal tendencies be reconciled with the more recent work in support of relativity? Significant progress has been made, and chapter 2 of this volume begins to reconcile the evidence on the role of culture (via language) on color categorization specifically.

More generally, the goal of the 38th Minnesota Symposium was to bring together scholars that examine a wide range of cultural factors, from genes to governments, in development. These scholars also represent different stages of academic careers—from recently minted PhDs to scholars who published early seminal papers. The volume begins with an exploration of how culture might have selected for certain genes, which in turn might shape neural functioning (in chapter 1). Chapter 2 moves to the role of culture via language on perception. Chapter 3 reviews the essential role of culture in mathematical thinking. Chapter 4 highlights cross‐cultural differences in the earliest stages of language development and their implications for theories of development. Chapter 5 highlights differences and similarities across cultures in parenting practices. Chapter 6 offers a picture of learning by children who do not go to regular (formal) schools—the way Homo sapiens learned for all but the last few hundred years. Chapter 7 illustrates how social emotional behaviors, such as shyness, can be differentially valued and perceived across different cultures. The final chapter makes explicit different ways of thinking about cultural relativity with examples from different points in history, countries, religions, and governing bodies. All make original contributions to a better understanding of culture and development.

The Minnesota Symposium has always been a joint intellectual exercise, and this one was no exception. I want to thank Stephanie Carlson and Michael Maratsos for their shared enthusiasm for the topic and their efforts with selecting speakers, organizing the event on which this volume is based, and editing the chapters. I also want to thank all who contributed chapters. They are, in the order that their chapters appear in the volume: Joan Y. Chiao, Anna Franklin, Rafael Núñez, Twila Tardif, Marc H. Bornstein, Suzanne Gaskins, Xinyin Chen, and Michael Maratsos. I also want to thank Delores Mordorski for helping to organize the event and Eric Hart for his sharp editorial eye and help with the chapters. We also thank Patricia Rossi and Jeevarekha Babu at Wiley for keeping us on task. And last but certainly not least, I want to thank the graduate students at the Institute of Child Development who help with the event in so many ways, especially Jose Causadias and Sandra Ahumada. The symposium would not have been the same without you.

Maria D. Sera

Minnesota Symposium on Child Psychology

Culture and Developmental Systems

Volume 38

Preface

Contributors