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Environmental-Scale Map Use in Middle Childhood: Links to Spatial Skills, Strategies, and Gender

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Title: Environmental-Scale Map Use in Middle Childhood: Links to Spatial Skills, Strategies, and Gender
Language: English
Authors: Liben, Lynn S., Myers, Lauren J., Christensen, Adam E.
Source: Child Development. Nov-Dec 2013 84(6):2047-2063.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
Peer Reviewed: Y
Page Count: 17
Publication Date: 2013
Document Type: Journal Articles
Reports - Research
Descriptors: Children, Map Skills, Spatial Ability, Gender Differences
DOI: 10.1111/cdev.12090
ISSN: 0009-3920
Abstract: Researchers have shown that young children solve mapping tasks in small spaces, but have rarely tested children's performance in large, unfamiliar environments. In the current research, children (9-10 years; N = 40) explored an unfamiliar campus and marked flags' locations on a map. As hypothesized, better performance was predicted by higher spatial-test scores, greater spontaneous use of map-space coordinating strategies, and participant sex (favoring boys). Data supported some but not all hypotheses about the roles of specific spatial skills for mapping performance. Data patterns were similar on a computer mapping task that displayed environmental-scale videos of walks through a park. Patterns of children's mapping errors suggested both idiosyncratic and common mapping strategies that should be addressed in future research and educational interventions.
Abstractor: As Provided
Entry Date: 2014
Accession Number: EJ1025368
Database: ERIC
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  Value: <anid>AN0091914632;cdv01nov.13;2018Jul11.08:27;v2.2.500</anid> <title id="AN0091914632-1">Environmental-Scale Map Use in Middle Childhood: Links to Spatial Skills, Strategies, and Gender. </title> <p>Researchers have shown that young children solve mapping tasks in small spaces, but have rarely tested children's performance in large, unfamiliar environments. In the current research, children (9–10 years; N = 40) explored an unfamiliar campus and marked flags' locations on a map. As hypothesized, better performance was predicted by higher spatial‐test scores, greater spontaneous use of map–space coordinating strategies, and participant sex (favoring boys). Data supported some but not all hypotheses about the roles of specific spatial skills for mapping performance. Data patterns were similar on a computer mapping task that displayed environmental‐scale videos of walks through a park. Patterns of children's mapping errors suggested both idiosyncratic and common mapping strategies that should be addressed in future research and educational interventions.</p> <p>Symbolic artifacts have a central role in human cognition and communication. Among the oldest and most pervasive of these are maps (Harley & Woodward, [<reflink idref="bib11" id="ref1">11</reflink>] ), which provide tools to conceptualize environments in ways that extend human perception and cognition (Downs, [<reflink idref="bib6" id="ref2">6</reflink>] ). Some understanding of simple maps emerges early in life. For example, investigators have found that 3‐ to 4‐year‐old children can use a location indicated on a small rectangular map to find an analogous location in a larger sandbox (Huttenlocher, Newcombe, & Vasilyeva, [<reflink idref="bib15" id="ref3">15</reflink>] ), 3‐year‐olds can use a location shown next to the smallest angle of an isosceles triangle on a map to lead them to an analogous location next to a larger triangle in a larger room (Vasilyeva & Bowers, [<reflink idref="bib44" id="ref4">44</reflink>] ), and kindergarten children distinguish mapped locations abutting right versus acute angles (Spelke, Gilmore, & McCarthy, [<reflink idref="bib39" id="ref5">39</reflink>] ).</p> <p>Preschoolers also perform well with representations of more complex spaces when there are unique referent–symbol pairs, as when a single blue chair is represented by a single blue symbol (DeLoache, [<reflink idref="bib5" id="ref6">5</reflink>] ). They encounter more difficulty, however, when there are multiple pairs of the same kind or when target locations are in undifferentiated areas (e.g., Blades & Spencer, [<reflink idref="bib1" id="ref7">1</reflink>] ; Bluestein & Acredolo, [<reflink idref="bib2" id="ref8">2</reflink>] ; Liben & Yekel, [<reflink idref="bib28" id="ref9">28</reflink>] ). By 6 or so, children can use maps to navigate through unfamiliar, but geometrically simple hallways or sparsely furnished laboratory rooms (Sandberg & Huttenlocher, [<reflink idref="bib38" id="ref10">38</reflink>] ; Uttal & Wellman, [<reflink idref="bib43" id="ref11">43</reflink>] ).</p> <p>Maps beyond the controlled world of psychological research, however, rarely represent environments as simple as sandboxes or small triangles, and are rarely consulted to navigate through rooms, regularized corridors, or playgrounds. Indeed, scholars working within the field of geography—a discipline that places maps at its center—have emphasized the importance of studying other scales of space. A useful taxonomy offered by Montello ([<reflink idref="bib30" id="ref12">30</reflink>] ) distinguishes among spaces at four scales: figural—smaller than the body and experienced and understood without body movement (e.g., a desk‐sized array of objects); vista—larger than the body, but visible from a single vantage point (e.g., a room); environmental—larger than the body and requiring multiple forays through the space to see and understand (e.g., a neighborhood); and finally, geographic—so much larger than the human body that they cannot be understood even by multiple forays into the space and thus require representations to be known (e.g., Europe).</p> <p>Most developmental map research has focused on figural‐ or at most vista‐scale spaces, as illustrated by the research cited earlier. Some map researchers have used environmental‐scale spaces, but have commonly employed iconic representations (e.g., large‐scale photographs) of familiar environments such as the child's school or home neighborhood (e.g., Davies & Uttal, [<reflink idref="bib4" id="ref13">4</reflink>] ; Plester, Richards, Blades, & Spencer, [<reflink idref="bib34" id="ref14">34</reflink>] ). Less is known about performance on tasks in which children must relate a large, surrounding, unfamiliar environment to a map, the way that maps are most commonly used in real life (as when a tourist uses a map to navigate through an unfamiliar city).</p> <p>The work described here begins from the assumption that studying mapping behaviors in figural‐ or vista‐scale spaces or in highly familiar environmental‐scale spaces cannot be fully informative about the ecological use of maps in unfamiliar environmental‐scale spaces. The current research was thus designed to extend the study of children's mapping performance to the latter context. In addition to expanding the database on children's map use, the work was designed to study the impact of three participant variables on mapping performance.</p> <p>First, based on conceptual and empirical work on the role of spatial cognition in mapping (e.g., Hegarty & Waller, [<reflink idref="bib14" id="ref15">14</reflink>] ; Liben, Kastens, & Stevenson, [<reflink idref="bib25" id="ref16">25</reflink>] ; Uttal, [<reflink idref="bib41" id="ref17">41</reflink>] ; Vasilyeva & Lourenco, [<reflink idref="bib45" id="ref18">45</reflink>] ), we examined mapping in relation to individual differences in spatial skills.</p> <p>Second, drawing from the literature on the importance of metacognition (e.g., Bransford, Brown, & Cocking, [<reflink idref="bib3" id="ref19">3</reflink>] ) and embodied cognition (Rieser, Lockman, & Nelson, [<reflink idref="bib37" id="ref20">37</reflink>] ), we examined mapping in relation to the child's spontaneous use of strategies that function to coordinate map, space, and self. Third, given sex differences in spatial cognition (Halpern, [<reflink idref="bib9" id="ref21">9</reflink>] ), we examined mapping in relation to participant sex.</p> <p>In the course of collecting data to study the role of these three participant variables for performance on an ecological mapping task (Goal 1), we also designed our work to learn more about children's performance on a computer mapping task (Goal 2). Increasingly, such tasks are being used in mapping research (e.g., Hamilton, Kodituwakku, Sutherland, & Savage, [<reflink idref="bib10" id="ref22">10</reflink>] ; Hegarty, Montello, Richardson, Ishikawa, & Lovelace, [<reflink idref="bib12" id="ref23">12</reflink>] ). More information is needed about whether these tasks draw on skills like those used in outdoor mapping tasks and about whether performance on computer mapping tasks can be informative about environmental‐scale mapping. Finally, we designed our work to examine the qualities of children's errors as a means of gaining insight into the bases of children's confusions (goal 3).</p> <p>Children were asked to explore an unfamiliar area of a college campus and to place stickers on a map to show locations of flags found in the field site. Children also completed a computer mapping task in which they used mouse clicks to control their (videotaped) walk through a park to reach a target location and to determine where they had been “dropped” in the park. Children then completed a battery of paper‐and‐pencil spatial tests. We conducted our work with 9‐ to 10‐year‐old children because children of this age typically understand the general symbolic and spatial links between a map and environment (Myers & Liben, [<reflink idref="bib31" id="ref24">31</reflink>] ), and are long past the age at which they could be expected to excel in using simple maps of figural‐ and vista‐scale spaces like those used in earlier research with young children. In the remainder of this Introduction, we overview our goals and hypotheses in more detail.</p> <hd id="AN0091914632-2">Mapping Performance in Relation to Participant Variables</hd> <hd id="AN0091914632-3">Spatial skills</hd> <p>To ensure that we sampled spatial skills broadly, we drew on Linn and Petersen's ([<reflink idref="bib29" id="ref25">29</reflink>] ) conceptual and meta‐analysis of the development of spatial cognition in which they identified and defined three major categories of spatial skills: spatial perception, the ability to determine spatial orientation in relation to one's own body even in the face of distracting information about orientation from other referents; mental rotation, skill in imagining the way two‐ or three‐dimensional (2D; 3D) objects, figures, or arrays look as they are moved through space; and spatial visualization, skill in solving problems by using multistep, verbal, and visual solution strategies to manipulate spatially presented information. We selected one test that Linn and Petersen had identified as falling within each category: a water‐level test (WLT) for spatial perception, a 2D mental rotation test (MRT) for mental rotation, and a paper folding test (PFT) for spatial visualization.</p> <p>We also administered a hidden pictures test (HPT) similar to the Embedded Figures Test of field dependence (Witkin & Goodenough, [<reflink idref="bib47" id="ref26">47</reflink>] ), included because field tasks may draw on skill in perceiving components embedded within visually complex environments (Kastens & Ishikawa, [<reflink idref="bib18" id="ref27">18</reflink>] ). Finally, we used a test that taps a variant of mental rotation. In most mental rotation tests, respondents imagine themselves stationary while objects move through imagined space in front of them. This process may be distinguished from the reciprocal one of imagining oneself moving through space to view objects or vistas from different perspectives (Hegarty & Waller, [<reflink idref="bib14" id="ref28">14</reflink>] ; Huttenlocher & Presson, [<reflink idref="bib16" id="ref29">16</reflink>] ; Newcombe, [<reflink idref="bib32" id="ref30">32</reflink>] ). Given the user's movement through the environment during mapping tasks, we judged it important to include a test of perspective‐taking skill, and thus administered a vantage‐point test (VPT) in addition to the MRT. All spatial tests are described more fully in the Method section.</p> <p>We hypothesized that performance on the spatial battery, taken as a whole, would predict performance on the mapping tasks. Furthermore, we hypothesized that the WLT in particular would account for unique variance on the mapping tasks because both kinds of tasks require appreciation of alternative frames of reference. That is, to represent liquid's invariant horizontality on the WLT, one must go beyond the immediate frame of reference of the tilted container's sides and attend to the frame of reference offered by the larger environment (e.g., floor, horizon) or body (gravitational upright); for map use, one must recognize and coordinate frames of reference provided by self, space, and map (Liben & Downs, [<reflink idref="bib24" id="ref31">24</reflink>] ).</p> <p>For both conceptual and empirical reasons we also expected that performance on the PFT would be uniquely predictive of performance on the outdoor mapping task. The conceptual reason is that the outdoor task can (like the PFT) be solved by diverse, multistep processes (e.g., mental rotation, physical rotation of the map, use of landmarks). The empirical reason is that past work has shown a link between PFT and outdoor mapping success in adults (Liben, Myers, & Christensen, [<reflink idref="bib26" id="ref32">26</reflink>] ; Liben, Myers, & Kastens, [<reflink idref="bib27" id="ref33">27</reflink>] ). Finally, MRT scores were expected to be uniquely predictive for the computer but not outdoor mapping task because only the former precludes physical rotation of the map, which therefore places a premium on mental rotation.</p> <hd id="AN0091914632-4">Map‐use strategies</hd> <p>A second participant variable examined in relation to environmental mapping was the child's use of actions that physically coordinate the map and space in some way. Prior research has shown that both children and adults perform significantly worse when they are forced to complete location or direction tasks with unaligned maps (Bluestein & Acredolo, [<reflink idref="bib2" id="ref34">2</reflink>] ; Levine, [<reflink idref="bib21" id="ref35">21</reflink>] ; Liben & Downs, [<reflink idref="bib24" id="ref36">24</reflink>] ; Presson, [<reflink idref="bib36" id="ref37">36</reflink>] ) and that adults who spontaneously align the map perform better (Liben et al., [<reflink idref="bib26" id="ref38">26</reflink>] ). Thus, on the outdoor task, we recorded children's spontaneous use of coordinating actions (rotating the map, tracing a route on the map, and aligning the map), hypothesizing that children who made greater use of such strategies would perform significantly better on the outdoor mapping task.</p> <hd id="AN0091914632-5">Participant sex</hd> <p>For both mapping tasks, the final participant variable we examined was gender because many studies have reported a male advantage on spatial and navigation tasks (Lawton, [<reflink idref="bib20" id="ref39">20</reflink>] ; Linn & Petersen, [<reflink idref="bib29" id="ref40">29</reflink>] ; Voyer, Voyer, & Bryden, [<reflink idref="bib46" id="ref41">46</reflink>] ). Although we expected to find that spatial skills and strategies would be the key predictors of mapping performance, we hypothesized that if a further sex difference were found, it would favor boys.</p> <hd id="AN0091914632-6">Examination of Computer Mapping Performance</hd> <p>The second goal of our research was to add to the small but growing literature examining performance on both ecological and simulated mapping tasks, important because of the increasing use of computer platforms in research on mapping (e.g., Hamilton et al., [<reflink idref="bib10" id="ref42">10</reflink>] ; Hegarty et al., [<reflink idref="bib12" id="ref43">12</reflink>] ; Liben et al., [<reflink idref="bib27" id="ref44">27</reflink>] ). Although 2D images on a small screen might generally be classified as figural‐scale spaces in the Montello ([<reflink idref="bib30" id="ref45">30</reflink>] ) taxonomy described earlier, the images used here were dynamic eye‐level views similar to those experienced when walking through a large environment. Thus, they may function more like environmental‐scale than figural‐scale spaces. In addition to studying whether computer mapping performance could be predicted by the spatial skill and gender variables examined for the outdoor task, we also tested whether performance on the computer mapping task could predict performance on the outdoor mapping task better than, or above and beyond, prediction provided by spatial tests alone. Indoor tasks that successfully predict outdoor mapping performance would have practical utility for mapping research and for educational research that seeks to evaluate curriculum efficacy.</p> <hd id="AN0091914632-7">Examination of Error Patterns on the Outdoor Task</hd> <p>Our third goal was to gain insight into what might underlie children's errors on the outdoor mapping task. One potential means of discovering children's reasoning is to ask children directly to explain their thought processes as they work. However, past research shows that explicitly asking children to explain their reasoning changes their mapping performance dramatically (Kastens & Liben, [<reflink idref="bib19" id="ref46">19</reflink>] ). Thus, we instead attempted to infer what children may have been thinking by looking for patterns in individual children's maps of all flag locations and in composite maps that assembled all children's responses to each individual flag.</p> <hd id="AN0091914632-8">Method</hd> <hd id="AN0091914632-9">Participants</hd> <p>Participants, recruited via a database informing families of research opportunities, were 19 boys, 9.00–10.50 years, M (SD) = 9.68 (0.54) years, and 21 girls, 9.00–10.50 years, M (SD) = 9.68 (0.49) years. The sample was representative of the local (but not national) population. Most were middle class with at least some college education. Of the 80% of families providing demographic data, 90% were European American, 3% Asian, and 7% biracial.</p> <hd id="AN0091914632-10">Procedure</hd> <hd id="AN0091914632-11">Overview</hd> <p>Families were greeted at the psychology building where parental consent and child assent were obtained. A female tester then escorted the child via a fixed route to a nearby field area for the outdoor task and later returned indoors for the computer mapping task and the spatial tests. All children were highly engaged in the sessions, which lasted about 2 hr.</p> <hd id="AN0091914632-12">Outdoor mapping task</hd> <p>The field area (Figure [NaN] a) was a dormitory quadrangle, approximately.32 km ×.27 km, with few distracting pedestrians and little noise, and no internal roads posing traffic dangers. The area is complex, with both symmetrical and asymmetrical features, much vegetation, and two major levels connected by a staircase.</p> <p>The tester explained that the activities were to “help us understand what kinds of map tasks children find hard or easy.” The tester told children that eight flags had been placed in the area and pointed to the visible yellow flag as an example. Children were told they would receive colored stickers matched to the flags' colors and were then told:</p> <p>Your job is to go to each flag, and then put a sticker of the same color on the map to show where each flag is located. You should put the stickers on the map as carefully as you can so they will show as exactly as possible where each flag is located.</p> <p>Children were told that there was no time limit, that they were free to walk around to find the flags but not to enter buildings or cross streets. They were also alerted to the fact that the experimenter would be following them and taking notes to remember what they did.</p> <p>The tester then led the child to the location where the You Are Here (YAH) map was introduced (Figure [NaN] b), gave the child a strip of stickers and the YAH map, and explained that “the ‘X’ shows where we are standing right now.” The map was handed to the child casually so it was out of alignment with the space; no instructions about alignment or facing direction were given. Children then explored the area and used stickers to show flags' locations (Figure [NaN] c). Any child asking for help or reassurance was reminded that we were trying to learn what mapping tasks are easier or harder so it was important for children to work on their own.</p> <p>Stickers were round, 5 mm in diameter, representing about 5 m. The experimenter recorded the order in which flags were visited and the use of three strategies: rotating (turning the map while walking), tracing (running a finger along a route on the map while walking), and aligning (holding the map so that it was aligned with the environment while at the flag).</p> <p>Two independent coders recorded X and Y coordinates of each sticker; 100% of the 1,280 data points were measured within 4 mm; 99% were within 2 mm. The first coder's data were used for a bidimensional regression analysis (Friedman & Kohler, [<reflink idref="bib8" id="ref47">8</reflink>] ), which yields a distortion index (DI), standardized on a scale of 0–100, indicating how well the configuration of the child's eight stickers matches (is isomorphic with) the correct configuration (Figure [NaN] d). Because DI reflects the proportion of unexplained variance, lower DIs indicate better performance. Maps were also scored with a number‐correct measure used in prior research (e.g., Liben et al., [<reflink idref="bib26" id="ref48">26</reflink>] ). The scores were highly correlated, r(<reflink idref="bib40" id="ref49">40</reflink>) = .88, p < .01, and thus results are reported for DI only.</p> <hd id="AN0091914632-13">Computer mapping</hd> <p>The computer mapping task included three problems derived from software included in the Where Are We? (WAW?) map skills curriculum (Kastens, [<reflink idref="bib17" id="ref50">17</reflink>] ). The software displays videotaped eye‐level views of a walk through a park. Videos are controlled by clicking on arrows that either point straight up (triggering video of what would be seen if one walked straight ahead) or left or right (triggering videos of what would be seen if one were to turn, respectively, 90º left or right). The first problem was from the Are We There Yet? (AWTY?) mode: Children were given their starting position and initial heading direction and asked to use the arrow keys to navigate through the park to reach a target destination. Two problems were from the Lost! mode: After being “dropped” somewhere in the park, children tried to figure out their location by exploring the park via arrow clicks. Up to 8 min were allowed for each problem. Performance was indexed by the total number of mouse clicks because fewer clicks indicate more efficient travel (e.g., not retracing one's steps).</p> <hd id="AN0091914632-14">Spatial test battery</hd> <p>The water level test (WLT; Liben, [<reflink idref="bib22" id="ref51">22</reflink>] ) included drawings of six empty tilted bottles; children were asked to draw a line inside each to show where the liquid would be if the bottle were about half full. Scores were the number of lines drawn within 10° of horizontal.</p> <p>The hidden pictures test (HPT; Liben et al., [<reflink idref="bib26" id="ref52">26</reflink>] ) asked children to outline a simple figure (house shape) within each of 13 complex figures (e.g., a beehive). The score was the number of figures in the correct size and orientation outlined correctly within 4 min.</p> <p>The paper folding test (PFT; Ekstrom, French, & Harman, [<reflink idref="bib7" id="ref53">7</reflink>] ) presented drawings that showed a piece of paper undergoing successive folds and then showed one or more holes punched through the layers. For each of the 20 items, children were asked to select which of five drawings showed how the paper would look once again unfolded. Because this test was designed for older respondents, a concrete sample item was added to instructions (i.e., a piece of paper, the paper folded, the folded paper punched, and then the paper unfolded). The score was the number of items answered correctly within 4 min, minus one fourth the number of items marked incorrectly (to correct for chance).</p> <p>The mental rotation test (MRT, modified from Thurstone & Thurstone, [<reflink idref="bib40" id="ref54">40</reflink>] ) included 21 items, each showing a 2D model figure followed by five drawings. Children were given 7 min to circle which of the drawings showed the model rotated in the plane, leaving unmarked any that were flipped over as well as rotated. The score was the number of rotated drawings marked (correctly) minus the number of rotated‐and‐flipped drawings marked (incorrectly).</p> <p>The vantage‐point test (VPT) presented oblique maps of small campus areas. For each of the 5 items, children were asked to pick which of five photographs had been taken by a photographer standing at a position indicated by an arrow on the map. The score was the number of correct selections.</p> <hd id="AN0091914632-15">Results</hd> <hd id="AN0091914632-16">Preliminary Analyses</hd> <p>Although no age differences were expected given the narrow age range, we first examined performance on all measures by age. The only significant correlation of age was with PFT score, r(<reflink idref="bib38" id="ref55">38</reflink>) = .38, p = .015, which did not contribute uniquely to any of the regression models reported later. Thus, for simplicity, age was omitted from the analyses reported below.</p> <hd id="AN0091914632-17">Overview</hd> <p>Results are presented in three sections. The first describes data on the outdoor mapping task and presents analyses linking participant variables to mapping performance. The second describes parallel findings for the computer task, and examines the relation between computer and outdoor mapping tasks. The third describes error patterns evident in individual children's responses and in composites of all children's responses to each individual flag.</p> <hd id="AN0091914632-18">Outdoor Mapping Task</hd> <hd id="AN0091914632-19">Measures of task success</hd> <p>The key measure of success on the outdoor mapping task was DI, which indexes how much the child's configuration distorts the correct one (meaning that lower scores indicate better performance). DIs ranged between 6.63 and 97.73, thus covering almost the entire gamut of the measure. Averaging over children, M (SD) of DI was 37.08 (25.29). DIs differed significantly by gender, with girls showing greater distortion than boys, Ms (SDs), respectively, 45.16 (27.72) versus 28.16 (19.31), F(<reflink idref="bib1" id="ref56">1</reflink>, 39) = 4.96, p = .032, η<subs>p</subs><sups>2</sups> = .115.</p> <p>Two additional parameters generated by the bidimensional regressions characterize children's behavior. One is the scale parameter in which perfect scaling, contraction, and expansion are indicated, respectively, by scores of 1.00, < 1.00, and > 1.00 (although because locations were distributed over most of the page there was little opportunity for expansion). Scale parameters ranged from 0.21 to 1.13, M (SD) = .82 (.27), and did not differ for girls versus boys:.77 (.30) versus.88 (.23), F(<reflink idref="bib1" id="ref57">1</reflink>, 39) = 1.51 p = .226. Absolute scale errors averaged.21 (.25), again, not differing in girls versus boys:.26 (.28) versus.15 (.21), F(<reflink idref="bib1" id="ref58">1</reflink>, 39) = 1.87 p = .180.</p> <p>A second parameter, referred to as theta (θ), represents how much the child's configuration is rotated from correct, with positive values indicating counterclockwise rotations. Averaging over children, the mean of θ was 2.89°, showing virtually no mean directional bias. The absolute values of rotational errors were larger, 19.73° (30.63°), not differing significantly in girls versus boys: 23.72° (35. 11°) versus 15.32° (24.97°), F(<reflink idref="bib1" id="ref59">1</reflink>, 39) < 1.</p> <hd id="AN0091914632-20">Individual difference variables</hd> <p>Participant variables included spatial skills, strategy use, and participant sex. The Ms (SDs) for spatial tests were as follows: WLT, 2.8 (2.1); HPT, 4.5 (3.0); PFT, 2.5 (2.5); MRT, 10.6 (15.2); VPT, 2.4 (1.4). Analyses of variance showed no significant differences between girls' and boys' scores on any spatial measure. Strategy use varied widely, with some children never using a particular strategy and others using it for every flag. The mean (SD) number of flags on which rotating, tracing, and aligning strategies were used were, respectively, 1.9 (2.3); 2.6 (2.4); and 2.8 (2.1). A multivariate analysis of variance (MANOVA) using children's three scores as the dependent measures and participant sex as the between‐subjects factor revealed no significant differences in strategy use by sex.</p> <p>Correlations between pairs of measures are shown in Table [NaN] . Data pooled over gender show significant associations within and across types of variables; data divided by sex suggest that spatial skills are less tightly integrated in girls than boys whereas strategies are more consistent in girls than boys. Analyses bearing on further interrelations among measures are discussed elsewhere in the article.</p> <p>Pairwise Correlations for All Measures Pooled and Divided by Participant Sex</p> <p> <ephtml> <table><tr><th align="left" /><th align="left">All children</th><th align="left">Divided by sex</th></tr><tr><th align="left">Spatial test battery</th><th align="left">Strategies</th><th align="left">Mapping</th><th align="left">Spatial test battery</th><th align="left">Strategies</th><th align="left">Mapping</th></tr><tr><th align="left">WLT</th><th align="left">HPT</th><th align="left">PFT</th><th align="left">MRT</th><th align="left">VPT</th><th align="left">ROT</th><th align="left">TRA</th><th align="left">ALN</th><th align="left">DI</th><th align="left">CLKS</th><th align="left">WLT</th><th align="left">HPT</th><th align="left">PFT</th><th align="left">MRT</th><th align="left">VPT</th><th align="left">ROT</th><th align="left">TRA</th><th align="left">ALN</th><th align="left">DI</th><th align="left">CLKS</th></tr><tr><td align="left">WLT</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">—</td><td align="left">−.03</td><td align="left">.44</td><td align="left">.15</td><td align="left">.16</td><td align="left">.41</td><td align="left">.10</td><td align="left">.44</td><td align="left">−.31</td><td align="left">−.48</td></tr><tr><td align="left">HPT</td><td align="left">.17</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">.40</td><td align="left">—</td><td align="left">.34</td><td align="left">.40</td><td align="left">.06</td><td align="left">.15</td><td align="left">.31</td><td align="left">.35</td><td align="left">−.17</td><td align="left">−.10</td></tr><tr><td align="left">PFT</td><td align="left">.30</td><td align="left">.25</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">.14</td><td align="left">.13</td><td align="left">—</td><td align="left">.16</td><td align="left">−.07</td><td align="left">.10</td><td align="left">−.20</td><td align="left">−.02</td><td align="left">.07</td><td align="left">−.17</td></tr><tr><td align="left">MRT</td><td align="left">.21</td><td align="left">.38</td><td align="left">.33</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">.30</td><td align="left">.37</td><td align="left">.53</td><td align="left">—</td><td align="left">.31</td><td align="left">.32</td><td align="left">.35</td><td align="left">.25</td><td align="left">−.16</td><td align="left">−.14</td></tr><tr><td align="left">VPT</td><td align="left">.15</td><td align="left">.24</td><td align="left">.07</td><td align="left">.39</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">.18</td><td align="left">.48</td><td align="left">.19</td><td align="left">.45</td><td align="left">—</td><td align="left">.11</td><td align="left">.22</td><td align="left">−.02</td><td align="left">−.31</td><td align="left">−.30</td></tr><tr><td align="left">Rotate</td><td align="left">.24</td><td align="left">−.07</td><td align="left">.04</td><td align="left">.15</td><td align="left">−.01</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">−.08</td><td align="left">−.47</td><td align="left">.03</td><td align="left">−.05</td><td align="left">−.09</td><td align="left">—</td><td align="left">.35</td><td align="left">.60</td><td align="left">−.58</td><td align="left">na</td></tr><tr><td align="left">Trace</td><td align="left">.11</td><td align="left">−.02</td><td align="left">−.08</td><td align="left">.13</td><td align="left">.03</td><td align="left">.21</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left" /><td align="left">.12</td><td align="left">−.35</td><td align="left">.06</td><td align="left">−.07</td><td align="left">−.14</td><td align="left">−.01</td><td align="left">—</td><td align="left">.49</td><td align="left">−.69</td><td align="left">na</td></tr><tr><td align="left">Align</td><td align="left">.42</td><td align="left">.22</td><td align="left">.08</td><td align="left">.30</td><td align="left">.12</td><td align="left">.33</td><td align="left">.28</td><td align="left">—</td><td align="left" /><td align="left" /><td align="left">.44</td><td align="left">.06</td><td align="left">.14</td><td align="left">.33</td><td align="left">.21</td><td align="left">.06</td><td align="left">.11</td><td align="left">—</td><td align="left">−.62</td><td align="left">na</td></tr><tr><td align="left">DI (log)</td><td align="left">−.42</td><td align="left">−.15</td><td align="left">−.07</td><td align="left">−.29</td><td align="left">−.37</td><td align="left">−.42</td><td align="left">−.29</td><td align="left">−.61</td><td align="left">—</td><td align="left" /><td align="left">−.66</td><td align="left">−.14</td><td align="left">−.18</td><td align="left">−.40</td><td align="left">−.36</td><td align="left">−.44</td><td align="left">.10</td><td align="left">−.54</td><td align="left">—</td><td align="left">.36</td></tr><tr><td align="left">Clicks</td><td align="left">−.40</td><td align="left">−.09</td><td align="left">−.12</td><td align="left">−.20</td><td align="left">−.40</td><td align="left">na</td><td align="left">na</td><td align="left">na</td><td align="left">.55</td><td align="left">—</td><td align="left">−.44</td><td align="left">−.08</td><td align="left">.02</td><td align="left">−.18</td><td align="left">−.44</td><td align="left">na</td><td align="left">na</td><td align="left">na</td><td align="left">.67</td><td align="left">—</td></tr></table> </ephtml> Note</p> <p>1  = p < .05 two tailed; bolded = p < .05 one tailed. Lower distortion index (DI) scores and fewer clicks indicate better performance. na = not applicable. WLT = water level test; HPT = hidden pictures test; PFT = paper folding test; MRT = mental rotation test; VPT = vantage‐point test; ROT = rotate; TRA = trace; ALN = align; CLKS = clicks.</p> <p>2 Girls (n = 21) above diagonal; boys (n = 19) below diagonal.</p> <hd id="AN0091914632-21">Mapping performance in relation to participant variables</hd> <p>Hierarchical multiple regressions were used to examine mapping performance in relation to participant variables. We had anticipated using DI from the bidimensional regression as the dependent measure, but the residual plot revealed a systematic pattern of nonlinearity and nonconstant variance. DI was thus transformed using a natural log function, yielding a residual plot showing linearity, constant variance, and little evidence of curvature.</p> <p>Spatial scores (WLT, HPT, PFT, MRT, VPT) were entered on Step 1, strategy scores (rotating, tracing, alignment) on Step 2, and sex on Step 3. A Sex × WLT interaction term was added in Step 4. (WLT was selected given its high correlation with DI and its appearance as a unique predictor in Step 1; sample size precluded use of multiple interaction terms.)</p> <p>A hierarchical regression showed that, as predicted, the spatial test battery accounted for significant variance at the first level of the model, F(<reflink idref="bib5" id="ref60">5</reflink>, 34) = 2.78, p = .033, with WLT emerging as the only significant predictor (see Table [NaN] for standardized betas and probability levels for individual variables by step). Prediction was significantly improved by adding strategy use on Step 2 and then again by adding participant sex on Step 3. The addition of the Sex × WLT interaction term in Step 4 resulted in only a marginal increase in R<sups>2</sups>. The final model was significant, F(<reflink idref="bib10" id="ref61">10</reflink>, 29) = 5.65, p < .001, R<sups>2</sups><subs>adjusted</subs> = .54.</p> <p>Hierarchical Multiple Regression Analyses Predicting Distortion (log DI) on Outdoor Mapping Task</p> <p> <ephtml> <table><tr><th align="left" /><th align="left">Step 1</th><th align="left">Step 2</th><th align="left">Step 3</th><th align="left">Step 4</th></tr><tr><th align="left">R<sup>2</sup><sub>adjusted</sub> (p)</th><th align="left">.19 (.033)</th><th align="left">.43 (.001)</th><th align="left">.50 (.001)</th><th align="left">.54 (.001)</th></tr><tr><th align="left">ΔR<sup>2</sup> (p<sub>Δ</sub>)</th><th align="left">.29 (.033)</th><th align="left">.26 (.003)</th><th align="left">.06 (.034)</th><th align="left">.05 (.053)</th></tr><tr><th align="left">Predictor</th><th align="left">β</th><th align="left">p</th><th align="left">β</th><th align="left">p</th><th align="left">β</th><th align="left">p</th><th align="left">β</th><th align="left">p</th></tr><tr><td align="left">WLT</td><td align="left" char=".">−.38</td><td align="left">.018</td><td align="left">.01</td><td align="left">.965</td><td align="left">−.21</td><td align="left">.140</td><td align="left">.04</td><td align="left">.846</td></tr><tr><td align="left">HPT</td><td align="left" char=".">.01</td><td align="left">.965</td><td align="left">.01</td><td align="left">.973</td><td align="left">−.05</td><td align="left">.702</td><td align="left">.00</td><td align="left">.981</td></tr><tr><td align="left">PFT</td><td align="left" char=".">.11</td><td align="left">.490</td><td align="left">.02</td><td align="left">.905</td><td align="left">.08</td><td align="left">.568</td><td align="left">.01</td><td align="left">.928</td></tr><tr><td align="left">MRT</td><td align="left" char=".">−.14</td><td align="left">.415</td><td align="left">.03</td><td align="left">.823</td><td align="left">.04</td><td align="left">.761</td><td align="left">.08</td><td align="left">.579</td></tr><tr><td align="left">VPT</td><td align="left" char=".">−.27</td><td align="left">.104</td><td align="left">−.31</td><td align="left">.026</td><td align="left">−.25</td><td align="left">.060</td><td align="left">−.27</td><td align="left">.033</td></tr><tr><td align="left">Rotate</td><td align="left" /><td align="left" /><td align="left">−.24</td><td align="left">.081</td><td align="left">−.34</td><td align="left">.015</td><td align="left">−.41</td><td align="left">.004</td></tr><tr><td align="left">Trace</td><td align="left" /><td align="left" /><td align="left">−.10</td><td align="left">.446</td><td align="left">−.12</td><td align="left">.314</td><td align="left">−.12</td><td align="left">.329</td></tr><tr><td align="left">Align</td><td align="left" /><td align="left" /><td align="left">−.42</td><td align="left">.007</td><td align="left">−.29</td><td align="left">.060</td><td align="left">−.30</td><td align="left">.047</td></tr><tr><td align="left">Sex</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">−.30</td><td align="left">.034</td><td align="left">.02</td><td align="left">.935</td></tr><tr><td align="left">Sex × WLT</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">−.44</td><td align="left">.053</td></tr></table> </ephtml> Note</p> <ulist> <item>3  = p < .05; bolded = p < .10. Lower distortion index (DI) scores indicate better performance. WLT = water level test; HPT = hidden pictures test; PFT = paper folding test; MRT = mental rotation test; VPT = vantage‐point test.</item> <item>4 As shown, rotate, trace, and align strategies were entered on Step 2. However, because alignment credit was given only if the strategy resulted in a correctly aligned map, alignment scores actually reflect both spontaneous strategy use and successful implementation, thus arguably in part a test of mapping performance. Thus, as a more conservative approach we reran the regression with only rotating and tracing strategies entered on Step 2. Findings remained unchanged except that the contribution of the rotation strategy in Step 2 changed from marginal (p = .081) to significant (p = .030).</item> </ulist> <p>Although there was thus only a weak suggestion that prediction patterns differed by gender, and recognizing that additional analyses on a divided sample could be only exploratory, we repeated the hierarchical regressions separately for each gender. Among boys, the pattern was similar to that seen in the full sample: DI was significantly predicted by the spatial battery, F(<reflink idref="bib5" id="ref62">5</reflink>, 13) = 3.94, p = .021, R<sups>2</sups><subs>adjusted</subs> = .45, and WLT was the only significant individual predictor. At Step 2, prediction was improved by entering strategies, ∆R<sups>2</sups> = .23, p<subs>∆</subs> = .029, with rotation as the significant strategy predictor. Among girls, the pattern differed: DI was not significantly predicted by spatial scores, F(<reflink idref="bib5" id="ref63">5</reflink>, 15) = 1.13, p = .386, R<sups>2</sups><subs>adjusted</subs> = .03. Entering strategies on Step 2 significantly improved prediction, ∆R<sups>2</sups> = .46, p<subs>∆</subs> = .006, with tracing as the only significant predictor. (Detailed statistics for both regressions are provided in online supporting information Table S1.)</p> <hd id="AN0091914632-22">Computer Mapping Task</hd> <hd id="AN0091914632-23">Measures of task success</hd> <p>Performance also ranged widely on the computer mapping task. Some children solved the problems quickly and seemingly effortlessly. Most dramatic was one child's performance on the first Lost! problem. He looked at the video display and instantly identified his location on the map. Another 15 children solved this problem in under a minute. On the second Lost! problem, 12 participants solved the problem within half the allotted time, whereas 17 children were unable to do so before time expired. By the end of the allotted 8 min, the AWTY?, Lost 1, and Lost 2 problems remained unsolved by, respectively, 28%, 15%, and 42% of the children. By implication, there was also variability with respect to the number of problems that individual children successfully solved within the time limit: Eighteen percent of children solved only one problem, 50% solved two, and 32% solved all three. The distributions across these three categories differed for girls versus boys, respectively, 24%, 67%, and 9% versus 10%, 32%, and 58%, χ<sups>2</sups>(<reflink idref="bib2" id="ref64">2</reflink>, N = 40) = 7.96, p = .019 (Yates corrected).</p> <p>Total number of mouse clicks ranged between 14 and 67, M (SD) = 33.90 (13.69). Even this large range is artificially truncated because as noted earlier, some children failed to solve the problems within the time limits. The number of problems completed was significantly correlated with fewer mouse clicks, r(<reflink idref="bib38" id="ref65">38</reflink>) = −.78, p < .001. On average, girls used significantly more mouse clicks than boys, 38.10 (11.51) versus 29.26 (14.69), F(<reflink idref="bib1" id="ref66">1</reflink>, 39) = 4.53, p = .040, η<subs>p</subs><sups>2</sups> = .106.</p> <hd id="AN0091914632-24">Individual difference variables</hd> <p>Data on individual difference variables relevant for the computer task appear in Table [NaN] ; analyses relating these data to performance are discussed next.</p> <hd id="AN0091914632-25">Mapping performance in relation to participant variables</hd> <p>First we tested predictors of performance on the computer mapping task and examined whether results were similar to those observed on the environmental task. The number of mouse clicks served as the dependent measure in a hierarchical regression in which spatial‐test scores were entered on Step 1, sex on Step 2, and Sex × WLT on Step 3. As predicted, the spatial battery accounted for significant variance on Step 1; prediction was significantly improved by entering participant sex on Step 2 but not increased further by the interaction term on Step 3 (see Table [NaN] ). The final model was significant, F(<reflink idref="bib7" id="ref67">7</reflink>, 32) = 2.70, p = .025, R<sups>2</sups><subs>adjusted</subs> = .23. Thus, as seen by comparing Tables [NaN] and [NaN] , predictors for both mapping tasks were similar (i.e., WLT, VPT, and gender).</p> <p>Hierarchical Multiple Regression Analyses Predicting Number of Mouse Clicks on Computer Mapping Task</p> <p> <ephtml> <table><tr><th align="left" /><th align="left">Step 1</th><th align="left">Step 2</th><th align="left">Step 3</th></tr><tr><th align="left">R<sup>2</sup><sub>adjusted</sub> (p)</th><th align="left">.18 (.035)</th><th align="left">.26 (.013)</th><th align="left">.23 (.025)</th></tr><tr><th align="left">ΔR<sup>2</sup> (pΔ)</th><th align="left">.29 (.035)</th><th align="left">.08 (.045)</th><th align="left">.00 (.765)</th></tr><tr><th align="left">Predictor</th><th align="left">β</th><th align="left">p</th><th align="left">β</th><th align="left">p</th><th align="left">β</th><th align="left">p</th></tr><tr><td align="left">WLT</td><td align="left">−.36</td><td align="left">.025</td><td align="left">−.41</td><td align="left">.010</td><td align="left">−.36</td><td align="left">.086</td></tr><tr><td align="left">HPT</td><td align="left">.06</td><td align="left">.691</td><td align="left">.04</td><td align="left">.797</td><td align="left">.05</td><td align="left">.749</td></tr><tr><td align="left">PFT</td><td align="left">.00</td><td align="left">.988</td><td align="left">.05</td><td align="left">.729</td><td align="left">.04</td><td align="left">.795</td></tr><tr><td align="left">MRT</td><td align="left">−.00</td><td align="left">.976</td><td align="left">.01</td><td align="left">.953</td><td align="left">.01</td><td align="left">.937</td></tr><tr><td align="left">VPT</td><td align="left">−.36</td><td align="left">.029</td><td align="left">−.30</td><td align="left">.065</td><td align="left">−.30</td><td align="left">.066</td></tr><tr><td align="left">Sex</td><td align="left" /><td align="left" /><td align="left">−.30</td><td align="left">.045</td><td align="left">−.24</td><td align="left">.347</td></tr><tr><td align="left">Sex × WLT</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left">−.08</td><td align="left">.765</td></tr></table> </ephtml> Note</p> <p>5  = p < .05; bolded = p < .10. Fewer mouse clicks indicate better performance. WLT = water level test; HPT = hidden pictures test; PFT = paper folding test; MRT = mental rotation test; VPT = vantage‐point test; ROT = rotate; TRA = trace; ALN = align; CLKS = clicks.</p> <p>Also of interest was whether the computer task was informative about performance on the outdoor mapping task. We addressed this question in two ways. First, to test whether the computer task would improve prediction above and beyond what was afforded by other indoor measures, we conducted a hierarchical regression with DI as the dependent measure, spatial‐test scores as predictors on Step 1, and number of clicks as predictors on Step 2. Results of Step 1 are necessarily identical to those reported earlier in Table [NaN] . Results of Step 2 showed that entering the number of clicks increased prediction significantly, F(<reflink idref="bib1" id="ref68">1</reflink>, 33) = 5.70, ∆R<sups>2</sups> = .104, p<subs>∆</subs> = .023. The only significant predictor on Step 2 was the number of clicks, β = .38, p = .023.</p> <p>As a second way to assess the value of the computer measure, we entered spatial‐test scores, rotating and tracing strategies, and number of clicks into a stepwise regression. Model 1 was significant, F(<reflink idref="bib1" id="ref69">1</reflink>, 38) = 16.42, p < .001, with clicks emerging as the only significant predictor of DI, β = .55, p < .001. In Model 2 the number of clicks remained the best predictor, β = .49, p < .001, with the rotation strategy emerging as a second significant predictor, β = −.34, p = .013. The final model was significant, F(<reflink idref="bib2" id="ref70">2</reflink>, 37) = 12.88, p < .001. (An identical pattern emerges if the aligning strategy is included as well [see note in Table [NaN] ], except that alignment replaces rotation as the significant predictor in Model 2.)</p> <hd id="AN0091914632-26">Error Patterns</hd> <hd id="AN0091914632-27">Individual children's maps</hd> <p>Our first approach to exploring errors was to examine the full set of sticker placements made by each participant. Each child's map was first categorized by two independent coders as either generally correct or strikingly incorrect (i.e., bearing little obvious relation to the correct sticker configuration). Coders' divisions were identical, with 24 maps placed in the first category and 16 in the second. Bidimensional regressions showed that maps in the former category had significantly and dramatically better scores than those in the latter. The Ms (SDs) were, respectively, for DI, 19.55 (10.64) versus 63.38 (16.14), F(<reflink idref="bib1" id="ref71">1</reflink>, 39) = 107.59, p < .001, η<subs>p</subs><sups>2</sups> = .739; for absolute scale,.05 (.06) versus.44 (.25), F(<reflink idref="bib1" id="ref72">1</reflink>, 39) = 56.63, p < .001, η<subs>p</subs><sups>2</sups> = .598; and for absolute rotation, 2.53° (2.38°) versus 45.52° (35.32°), F(<reflink idref="bib1" id="ref73">1</reflink>, 39) = 35.78, p < .001, η<subs>p</subs><sups>2</sups> = .485. Girls' maps were more likely to fall into the strikingly incorrect category, χ<sups>2</sups>(<reflink idref="bib1" id="ref74">1</reflink>, N = 40) = 2.82, p = .044, η<subs>p</subs><sups>2</sups> = .266.</p> <p>A prototypical example of the maps categorized as generally correct is shown in Figure [NaN] a. As evident from comparing this map to Figure [NaN] d, this girl's placements were almost perfect, with only tiny metric errors (e.g., the light blue flag is slightly too far along the path). Other maps in this category typically had an additional (or more extreme) metric error or two, reversed left and right positions of two flags found within the same area (e.g., reversing white and orange stickers), or placed a sticker or two in a nearby, symbolically similar but incorrect area (e.g., placing the black sticker at the path intersection directly below the correct one).</p> <p>The maps categorized as strikingly incorrect are more difficult to illustrate via a single map because each looked unique. Thus, two random examples are provided. Figure [NaN] b suggests a child who has done reasonably well in using a piece of paper to record relative distances and angles among the flags. However, this child seems to have ignored the way that the piece of paper was designed as a spatial representation (map) of the surrounding environment, a problem reflected in the value ofθ (see figure caption). Figure [NaN] c suggests a child who has understood the orientation of the map in relation to the environment, but has not heeded the scale relation between them, a problem reflected in the scale parameter (see figure caption). Although the inaccuracies of the 16 maps appear to differ dramatically at the surface level, what they appear to have in common at a deeper level is a failure to establish or maintain the basic isomorphic spatial link (orientation and/or scale) between map and space.</p> <hd id="AN0091914632-28">Composites by flag</hd> <p>We also explored error qualities by creating composite maps of all children's responses to each individual flag. Three major qualities varied across flags: (a) distance from field‐site entry and thus from receipt of the YAH map, (b) when in the sequence a flag was encountered (which varied across children; see online supporting information Table S2), and (c) what environmental and map properties were available to pinpoint the particular flag location. Although this study was not designed to test the impact of these variables by systematic manipulation (e.g., by varying the point of entry to the field site), examining performance by individual flags is useful for generating hypotheses about children's response strategies.</p> <p>To illustrate, Figure [NaN] shows composites of responses to the red and orange flags, divided by participant sex in light of significant sex differences in DI scores reported earlier. These two flags differed on all three location qualities. The red flag was close to entry and YAH locations, was typically visited first, and could be pinpointed within the area near the “X” by a unique configuration of buildings and paths. In contrast, the orange flag was down a flight of stairs and distant from the entry point, was typically visited late in the sequence, and was located on a grassy area near a diagonal path similar to a number of others nearby.</p> <p>The red flag composite shows that all stickers were in the correct general area, suggesting that children realized that they had not walked far from where they had just been shown their YAH location. Girls' and boys' composites, though, show different patterns. Despite representing their proximity to the YAH location, many girls (n = 8) apparently either did not think to monitor (or misunderstood) their heading direction; of these, most (n = 6) also erred by identifying a location that incorrectly implied a turn off the initial path. In contrast, only one boy placed a sticker in the incorrect direction from the YAH location and another one committed a symbol‐type error.</p> <p>Responses on the orange flag were far more diverse. By the point at which children were placing the orange sticker, many had apparently lost track of their general location. Almost all children appear to have realized that they were quite far from the location at which they entered the site and received the map, but some did not seem to realize just how far (e.g., two girls correctly identified a grassy area by a diagonal path, but placed their stickers only about half the distance from the starting point as they should have). Also indicative of metric errors is the dispersion of placements on the long diagonal path extending from either side of the correct location. Both boys and girls appear to be making errors within the same type of symbol (several erroneous answers are placed on other diagonal paths), and both evidenced some confusion about left versus right. The orange flag also elicited more symbol‐type errors, with some children placing stickers on or abutting a building rather than on an open grassy area.</p> <p>Composites for remaining flags (see the Appendix) suggest that gross errors of symbol type (e.g., stickers on symbols for buildings rather than on grass or paths) are rare. More common are symmetry errors (e.g., see clusters of responses mirroring correct locations for green, orange, and white flags) and metric errors, particularly likely when a nearby area on the map is of the same symbol type (e.g., the large grassy area surrounding the correct location of the white flag).</p> <hd id="AN0091914632-29">Discussion</hd> <p>As anticipated, mapping scores varied widely, allowing us to test hypotheses about the role of participant variables (Goal 1). The prediction concerning spatial skills in general was supported: Regressions showed that the spatial battery predicted scores on both mapping tasks.</p> <p>Predictions about specific spatial tests received more mixed support. First, consistent with our hypotheses, WLT scores accounted for unique variance on both mapping tasks. This finding is compatible with the initial conceptual argument that the WLT taps at least an implicit understanding of alternative frames of reference. That is, to perform well on the WLT, the child must discount the proximal frame of reference of the tilted bottle's oblique sides and attend instead to a stable, distal frame of reference (e.g., the horizon or gravitational upright). In mapping tasks, the child must likewise take into account the frames of reference offered by the map, environment, and self. In addition, if—as Piaget and Inhelder ([<reflink idref="bib33" id="ref75">33</reflink>] ) argued in developing the WLT in the first place—the task taps the child's construction of Cartesian coordinate axes, children doing well on the WLT should be more likely to use two axes to pinpoint a given flag location, and thus should be better able to mark precise locations.</p> <p>Also as expected, better performance on the VPT predicted better performance on both mapping tasks. The data thus support the idea proposed in the Introduction that being able to imagine how vistas look from different positions is valuable for linking one's own position and heading direction to a map. In the current work, understanding self‐location is needed on the outdoor task because children are asked to record the flag's location while standing at the flag (e.g., see Figure [NaN] c); it is needed on the computer task because children must know their own location and heading to decide which way to go to reach (or to interpret) a particular location.</p> <p>However, contrary to expectations based on earlier findings with adults (Liben et al., [<reflink idref="bib27" id="ref76">27</reflink>] ; Liben et al., [<reflink idref="bib26" id="ref77">26</reflink>] ) and on the conceptual link between Linn and Petersen's ([<reflink idref="bib29" id="ref78">29</reflink>] ) definition of spatial visualization and the way the outdoor mapping task might be solved (i.e., multistep; subject to multiple processes and solution strategies), PFT scores did not predict performance on the outdoor mapping task. One possible explanation is that because the PFT was designed for older respondents it may be invalid for children this young. Another is that the PFT may not represent a cohesive category of spatial skills, a suggestion made by Linn and Petersen in their initial meta‐analysis and raised again recently by Uttal et al. ([<reflink idref="bib42" id="ref79">42</reflink>] ) who noted that the category lacks specificity.</p> <p>Also contrary to expectations, MRT scores did not predict computer mapping task scores despite the map's fixed position on the computer screen. Perhaps the lack of an association means that the mapping task and MRT engage different mental rotation strategies, with the former invoking mentally rotation of an entire stimulus (the map) and the latter invoking rotation of separate components of the figure (a strategy Linn & Petersen, [<reflink idref="bib29" id="ref80">29</reflink>] , suggested might be operating in the 2D mental rotation task used here). Before attempting to test this or other potential explanations, it would be important to first test whether the finding itself is replicable. Although the data from the MRT thus failed to support the hypothesis about scores on the computer task, the contrast in performance between the MRT and VPT did support the more general suggestion that mental rotation and perspective‐taking skills are distinct, thus reinforcing the advantage of including both kinds of measures in mapping research.</p> <p>With respect to the last spatial measure, HPT, there was no support for the suggestion that skill in disembedding would predict mapping performance. Perhaps requiring children to find a geometric form in a consistent size and orientation does not tap the kind of disembedding skill needed to discern features that vary in size or orientation in complex environments. Furthermore, the mapping tasks used here demanded the recognition of only gross features (e.g., buildings and paths in the environment and their symbols on the map) rather than subtle ones (e.g., geological variations) needed for other kinds of field tasks (Kastens & Ishikawa, [<reflink idref="bib18" id="ref81">18</reflink>] ).</p> <p>The data linking spatial‐test scores to mapping performance also bear on the question posed in the current investigation concerning the relation between outdoor and computer mapping tasks (Goal 2). Hierarchical regressions showed similar patterns of prediction by WLT, VPT, and gender for performance on both mapping tasks, suggesting that the computer task calls upon skills similar to those used in the more ecological outdoor mapping task. Also supporting the suggestion that the computer task might be used as a convenient proxy for outdoor mapping tasks was the finding from the stepwise regression that performance on the computer mapping task was the single strongest predictor of performance on the outdoor mapping task.</p> <p>The contribution of the participant variable of strategy use was investigated on the outdoor mapping task. Consistent with our hypothesis that participants who spontaneously acted to establish map–space–self correspondences would perform better on the mapping task, strategy use predicted DI scores. Interestingly, once strategy use was entered into the regression, the WLT lost its predictive power (see Table [NaN] ). This finding is compatible with the suggestion made earlier that WLT scores reflect the child's insight into the need to coordinate or select from alternative frames of reference, an insight tapped even more directly by the strategy‐use variable.</p> <p>The third participant variable examined was gender. Based on long‐standing sex differences found in spatial performance (e.g., Halpern, [<reflink idref="bib9" id="ref82">9</reflink>] ), we predicted an overall sex difference in performance on mapping tasks, expecting that much of this difference would be accounted for by sex differences in spatial skills. Unexpectedly, we did not find a male advantage on the spatial tests themselves. Perhaps this finding reflects a general diminution of spatial sex differences over historical time, a possibility that has received some support in a meta‐analysis by Voyer et al. ([<reflink idref="bib46" id="ref83">46</reflink>] ). Of further interest with respect to sex differences was the finding that the hierarchical regression showed a marginal effect of the Sex × WLT interaction on the outdoor mapping task DI score. Although follow‐up analyses must be treated with caution given that the interaction was marginal and the sample size was small once divided by gender, there is a suggestion that spatial skills may be more robust predictors of mapping success for boys.</p> <p>One possible reason that spatial skills might be less predictive for girls is that more girls may failed to have understood or maintained the basic correspondence between map and space. Absent this understanding, spatial skills that support precision in sticker placements (e.g., determining the precise metric location along a path or determining if a flag should be to the right or left of a courtyard area) would be irrelevant. Consistent with this interpretation is the finding that girls' maps were overrepresented among the maps judged as strikingly incorrect.</p> <p>Taken together, the various findings on sex differences imply the need to identify additional factors that are simultaneously relevant to the use of maps and differentially distributed in girls and boys. One potential factor is children's prior experience with maps. Because no data on map experience were collected, we cannot test the role of this factor, but research with adults has shown that individual differences in self‐reported way‐finding experiences and styles do predict performance on various mapping tasks (e.g., Hegarty, Richardson, Montello, Lovelace, & Subbiah, [<reflink idref="bib13" id="ref84">13</reflink>] ; Liben et al., [<reflink idref="bib26" id="ref85">26</reflink>] ). If future studies were to find that girls report less map experience than do boys even by age 10, it might help to explain the current sex differences on the mapping tasks, and, simultaneously, would suggest the value of educational interventions that foster girls' mapping opportunities at an early age.</p> <p>At the broadest level, the findings from the current research demonstrate that although even very young children have been shown to be able to use spatial and representational correspondences in decoding aspects of maps of figural‐ and vista‐scale spaces, many children in elementary school grades remain challenged by maps of unfamiliar, environmental‐scale spaces. Why are these larger spaces more challenging?</p> <p>In a laboratory room there are usually easily identifiable and relatively few objects (e.g., a chair, couch, and table) represented on the map by an equally identifiable and limited set of symbols (e.g., drawings or photographs of each object), distributed over the paper in isomorphic positions. Room boundaries (walls) are represented by outlines on the map, and cues to orientation abound (e.g., long vs. short walls and symbols for one or more distinct objects).</p> <p>An outside space such as the site used in this study contrasts on many dimensions. The range of objects is virtually limitless, and any given category (e.g., vegetation, lamp posts, pathways, large concrete planters, security telephones, staircases) might or might not be indicated on the map. Furthermore, even if mapped, an object category might be represented by generic or specific symbols. For example, a college map might use generic symbols for vegetation to indicate something about the general area, for example, showing potential locations for an outdoor class or sunbathing. A map of the same campus prepared for a botany class might include highly differentiated vegetation symbols to indicate exact locations of specific kinds of trees, flowering bushes, grasses, and so on. Thus, users of environmental‐scale maps must avoid assumptions about what is mapped; users must be prepared to identify what classes of objects are symbolized and at what degree of specificity.</p> <p>In addition, the natural environment provides no explicit boundary lines, and thus the user is also challenged to understand what portion of the surrounding navigable environment is represented within the map boundaries. Map orientation may also be more difficult to discern in outdoor environments. In the current field site, for example, there is considerable symmetry in the layout of buildings and paths that makes it difficult to distinguish viewing direction. Furthermore, those asymmetries that are available may be invisible from within the space due to foliage, the opacity of buildings, physical distance, and varying elevations. Illustratively, the open versus closed ends of the quadrangle that look so different on the map (cf. the bottom vs. top of Figure [NaN] d) cannot be seen from the entry site, the YAH orientation site, or from most individual flag locations. When one can see the entire map but not the entire environment, it is also difficult to appreciate their scale relation.</p> <p>Given the challenges of larger spaces, it is not surprising that the data show that well after children readily interpret locations on vista‐scale mapping tasks (e.g., Huttenlocher et al., [<reflink idref="bib15" id="ref86">15</reflink>] ; Spelke et al., [<reflink idref="bib39" id="ref87">39</reflink>] ; Vasilyeva & Bowers, [<reflink idref="bib44" id="ref88">44</reflink>] ) and detect correspondence between a scale model and a room (DeLoache, [<reflink idref="bib5" id="ref89">5</reflink>] ), they still make errors on environmental‐scale tasks. Individual data records suggest that a significant number of children were deeply confused about symbols and basic environment–map correspondence (see Figures [NaN] b and [NaN] c). Composite maps suggest that some children have not yet mastered an understanding of vantage point, with errors commonly involving reflections around an axis of symmetry (e.g., orange stickers erroneously placed on a path opposite the correct one; white stickers placed on the opposing grassy area; see Figure [NaN] and the Appendix). These mirror‐image errors suggest that children are using multiple landmarks to identify categorical locations (Plumert, Hund, & Recker, [<reflink idref="bib35" id="ref90">35</reflink>] ), but failing to understand the direction from which the map or space is being viewed. Likewise, many children appear to have not yet mastered proportion, scale, or the use of more than one axis to identify a location, all skills associated with a mature understanding of measurement: They place stickers in the correct region and on the correct kind of symbol, but are imprecise (as in orange stickers on the correct diagonal path, but placed too far along).</p> <p>The data from these composite maps cannot provide definitive explanations of the error patterns within or across flags because, as noted earlier, this study does not allow us to tease apart the roles of specific flag locations, location of field‐site entry, and sequence of flag visits. To do so would require new research that systematically manipulates these factors. Whatever insights such future research may provide, the data in hand already imply that despite the studies mentioned in the Introduction demonstrating some remarkable mapping successes in very young children, it is not necessarily a trivial matter to import map skills from figural‐ or vista‐scale spaces to environmental‐scale spaces.</p> <p>The finding that the computer task engaged similar spatial skills to the outdoor mapping task is encouraging from both a research and educational perspective because it suggests that map‐skill assessment and instruction might be profitably pursued inside the classroom environment using materials like those found in the WAW? curriculum. It is particularly useful to find that negotiating a large space via videotape seems to present challenges that are more like environmental‐scale challenges than figural‐scale challenges.</p> <p>There are, however, limitations in WAW? software both as a research tool and as a teaching tool. For example, WAW? permits users to select the direction, but neither the length nor speed of video walks, and not all routes can be followed. Turns are limited to 90° left and right, and viewing angle is always straight ahead at adult eye level. The software presents a map and videos of only one particular space, thereby providing only a single type of environment (a particular park, rather than, say, an urban grid system, forest, or mountain region). Furthermore, except for people who happen to live near the depicted park, research and instructional activities could not involve connecting the physical and videotaped environments.</p> <p>Many of these limitations can be overcome with newer technologies. For example, Google Maps or Google Earth might be used to expand (or test) children's understanding of the connection between maps and eye‐level views of environments by developing exercises that involve toggling between maps and street views. With the rapid expansion of Google Street View to more and more locations, such exercises could be individualized to the locale, thereby permitting assessments and curriculum activities that link simulated and physical spaces.</p> <p>Being able to link a map to the real physical environment is an important life skill in and of itself, but it is also likely to enhance the user's skill in interpreting spatial‐graphic representations more generally, for example, interpreting cross‐sectional diagrams of geological formations, drawings or computer visualizations of human anatomy, and diagrams of molecular structures. Map skills can enhance spatial skills, just as spatial skills can enhance map skills (Liben, [<reflink idref="bib23" id="ref91">23</reflink>] ; Uttal, [<reflink idref="bib41" id="ref92">41</reflink>] ). Furthermore, as Montello ([<reflink idref="bib30" id="ref93">30</reflink>] ) has argued, without an understanding of the way symbolic artifacts represent physical space, we could not come to know geographic space. In short, maps are not simply artifacts for psychological research; they are tools for moving around in and understanding the immediate environment and the distant world. Studying how children come to produce and decode maps in relation to physical reality is thus important not only for developmental science and spatial education but also for building a citizenry that has the cognitive resources to make wise decisions about issues such as land use and global warming that will affect humankind for centuries to come.</p> <hd id="AN0091914632-30">Appendix</hd> <p>Composite maps of children's sticker placements by flag. Open circles show girls' responses; filled circles show boys' responses; numbers indicate multiple responses at identical locations; correct locations are shown in Figure [NaN] d.</p> <ref id="AN0091914632-31"> <title>Footnotes</title> <blist> <bibl id="bib1" idref="ref7" type="bt">1</bibl> <bibtext>The authors express their sincere thanks to Lacey Hilliard and Danielle Russell for helping to collect data in the summer heat; members of the Penn State Cognitive and Social Development Lab for data coding and entry; Lloyd Rhoades of Penn State's Physical Plant for facilitating the placement of flags on campus; Kim Kastens for providing the Where Are We? software; Roger Downs, Nora Newcombe, and an anonymous reviewer for insightful comments on earlier versions of this manuscript; and to the participating children and parents for their time and enthusiasm. We offer special thanks to Stefania Vescio‐Franz for her willingness to be a pilot participant in our research, and for permission to include her photograph in Figure . Portions of this work were presented at the biennial meetings of the Society for Research in Child Development in Toronto, 2011. Partial financial support for the research described here was provided through the National Science Foundation through Grants REC04‐11686 and ESI‐01‐01758, although the opinions presented are those of the authors, and no endorsement by the NSF should be inferred. </bibtext> </blist> </ref> <ref id="AN0091914632-32"> <title>References</title> <blist> <bibtext>Blades, M., & Spencer, C. ( 1994 ). The development of children's ability to use spatial representations. In H. W. Reese (Ed.), Advances in child development and behavior (Vol. 25, pp. 157 – 199 ). New York : Academic Press. </bibtext> </blist> <blist> <bibl id="bib2" idref="ref8" type="bt">2</bibl> <bibtext>Bluestein, N., & Acredolo, L. 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New York : International Universities Press. </bibtext> </blist> </ref> <p>Graph: (a) Test site with the mapped area outlined in red. (b) Location at which children were given the You Are Here ( YAH ) map. (c) A participant in the process of placing her green sticker on the map. (d) Response map depicting the field area at approximately 1:1,000. The blue star shows the location at which children entered the test area; the red “X” indicates the YAH location at which children received the map. Colored circles show the flags' locations.</p> <p>Graph: Illustrative children's maps described in text. Data for each map are as follows: sex (F = female, M = male); age in months; distortion index ( DI ); scale parameter ( SP ); rotation parameter ( RP ). (a) F; 124; DI : 8.77; SP : 0.99; RP : −0.84°; (b) M; 111; DI : 34.95; SP : 0.73; RP : −49.52°; and (c) F; 110; DI : 49.02; SP : 0.50; RP : +4.45°. Correct locations are shown in Figure  d.</p> <p>Graph: Composite maps of children's sticker placements for the red and orange flags, divided by participant sex. Numbers indicate multiple stickers placed at exactly the same location (see Figure  d for correct locations).</p> <p>Graph: Table S1. Hierarchical Multiple Regression Analyses Predicting Distortion (log DI) on the Outdoor Mapping Task Separately for Girls and Boys. Table S2. Information About the Order in Which Children Visited Flags.</p> <aug> <p>By Lynn S. Liben; Lauren J. Myers; Adam E. Christensen and Corinne A. Bower</p> </aug>
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  Data: Environmental-Scale Map Use in Middle Childhood: Links to Spatial Skills, Strategies, and Gender
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  Data: <searchLink fieldCode="SO" term="%22Child+Development%22"><i>Child Development</i></searchLink>. Nov-Dec 2013 84(6):2047-2063.
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
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  Data: 17
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  Data: <searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Map+Skills%22">Map Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+Ability%22">Spatial Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink>
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  Data: 10.1111/cdev.12090
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  Data: 0009-3920
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  Data: Researchers have shown that young children solve mapping tasks in small spaces, but have rarely tested children's performance in large, unfamiliar environments. In the current research, children (9-10 years; N = 40) explored an unfamiliar campus and marked flags' locations on a map. As hypothesized, better performance was predicted by higher spatial-test scores, greater spontaneous use of map-space coordinating strategies, and participant sex (favoring boys). Data supported some but not all hypotheses about the roles of specific spatial skills for mapping performance. Data patterns were similar on a computer mapping task that displayed environmental-scale videos of walks through a park. Patterns of children's mapping errors suggested both idiosyncratic and common mapping strategies that should be addressed in future research and educational interventions.
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  Data: 2014
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  Data: EJ1025368
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        Value: 10.1111/cdev.12090
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        PageCount: 17
        StartPage: 2047
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      – SubjectFull: Children
        Type: general
      – SubjectFull: Map Skills
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      – SubjectFull: Spatial Ability
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      – SubjectFull: Gender Differences
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      – TitleFull: Environmental-Scale Map Use in Middle Childhood: Links to Spatial Skills, Strategies, and Gender
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            NameFull: Myers, Lauren J.
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            NameFull: Christensen, Adam E.
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