Advanced Spatial Skills and Advance Planning

Journal of Cognition and Development

ISSN: 1524-8372 (Print) 1532-7647 (Online) Journal homepage: http://www.tandfonline.com/loi/hjcd20

Advanced Spatial Skills and Advance Planning: Components of 6-Year-Olds' Navigational Map Use Elisabeth Hollister Sandberg & Janellen Huttenlocher To cite this article: Elisabeth Hollister Sandberg & Janellen Huttenlocher (2001) Advanced Spatial Skills and Advance Planning: Components of 6-Year-Olds' Navigational Map Use, Journal of Cognition and Development, 2:1, 51-70, DOI: 10.1207/S15327647JCD0201_3 To link to this article: http://dx.doi.org/10.1207/S15327647JCD0201_3

Published online: 13 Nov 2009.

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JOURNAL OF COGNITION AND DEVELOPMENT, 2(1), 51–70 Copyright © 2001, Lawrence Erlbaum Associates, Inc.

Advanced Spatial Skills and Advance Planning: Components of 6-Year-Olds’ Navigational Map Use

Elisabeth Hollister Sandberg and Janellen Huttenlocher Department of Psychology University of Chicago

Two domains of cognitive development that have captured significant research attention are children’s spatial cognition and children’s planning skills. Children’s ability to use maps for navigational purposes is a task in which these domains necessarily intersect. This study was designed to examine 6-year-olds’ complex route formation and reorientation skills. Thirty-six kindergartners navigated through a large-scale environment using a map. Participants were required to plan their own routes to endpoints designated only on the maps. Success at navigation, as well as route length and types of error, were assessed. The results indicate that kindergartners are able to use maps to plan and execute routes, and that they demonstrate advance planning skills by reliably selecting optimally efficient routes.

The development of spatial cognition has been the topic of much recent research interest. Research efforts have been initiated to discover how children of different ages perceive, encode, represent, manipulate, and act on spatial information. Concomitantly, the development of planning skills has also received a good deal of attention. Investigators have asked what components of this complex skill are present in childhood, what planning abilities are evidenced at different ages, and how these skills might be related to academic and “real-life” achievement. One ecologically

Requests for reprints should be sent to Elisabeth Hollister Sandberg who is now at the Department of Psychology, Suffolk University, 41 Temple St., Boston, MA 02114. E-mail: [email protected]


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SANDBERG AND HUTTENLOCHER

and academically relevant task in which the domains of spatial cognition and planning necessarily intersect is the development of children’s ability to use maps. Previous studies of young children’s ability to use maps have begun to elucidate a rich and highly convoluted skill acquisition process. These studies have provided a foundation for understanding the basic skill components and when they might emerge. The various uses to which maps are put can be roughly classified into academic and navigational categories. Academic map use involves the sort of map tasks that are taught, at least in rudimentary form, in early grade school, such as map deciphering skills. Navigational map use is the practical purpose of maps, that is, using maps to find things and to get places. Competent performance at map reading, map interpretation, and navigation using maps all require different sets of skills. Within the academic realm of map use, we can classify the basic skills into map reading and map interpretation. Map reading is what one does to answer the questions of, “What river runs through France?” and “Where is Elm Street?” To successfully read a map, one must have a grasp of the system of conventional symbols and an ability to comprehend the symbol system unique to the map in question. Map interpretation involves going beyond the information immediately available from the map to make inferences or to draw conclusions. Map interpretation necessitates more advanced or abstract knowledge (Boardman, 1983), such as combining pieces of legend information or using scale to calculate areas. For example, one could use relief, proportional areas, and rainfall information to hypothesize about a region’s agricultural prowess. Some grasp of academic map skills is undoubtedly a prerequisite for using maps to navigate, and there is a significant body of literature that indicates very young children (preschool and kindergarten) have acquired basic map-reading skills. Preschoolers, in fact, seem to appreciate that two-dimensional maps represent three-dimensional space (Atkins, 1981). There is also some evidence that young children are able to interpret aerial views (Blaut & Stea, 1971; Downs & Liben, 1987). There is little question that they can automatically interpret or easily be taught the meaning of iconic map symbols (Atkins, 1981; Blaut & Stea, 1971; Boardman, 1983; Presson, 1982; Scholnick, Fein, & Campbell, 1990). On the other hand, prekindergarten children do not seem to be able to comprehend abstract locational notions, such as city and county. Similarly, a full grasp of relative length and scale is not thought to emerge until sometime after kindergarten (Atkins, 1981; Liben, Moore, & Golbeck, 1982). Using maps to navigate requires skills above and beyond understanding and manipulating the symbol systems of cartography (Board, 1978; Levine, 1982). Within the realm of navigation, maps are generally put to two purposes: place finding and way finding. To find a place, the map user must (a) understand the representational capacity of the map, (b) recognize the symbols used in the representation, (c) know how to orient the map to the environment, (d) know how


SIX-YEAR-OLDS’ NAVIGATIONAL MAP USE

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to find one’s own location, and (e) know how to translate the information gleaned from the map into environmental information that can be acted on. There are a number of studies that show children as young as 4 years of age, and sometimes even 3 years of age, are able to locate a place using a map (for review, see Blades & Spencer, 1994; Freundschuh, 1990). When presented with a treasure map, children as young as 3 years of age are able to locate the object in the room concealing a treasure based on map information. They understand the purpose of the map and are able to utilize it to some extent, but they have a limited appreciation of the spatial correspondence between the map–model and reality and tend to focus on a single dimension of the information (e.g., Blades & Spencer, 1986, 1987b, 1994; Scholnick et al., 1990). Many 4-year-olds are also able to locate themselves on a map (Blades & Spencer, 1987b) but do so with similar attention to only one piece of representational information. Huttenlocher, Newcombe, and Vasilyeva (1999) found that 3-year-olds will use a map to find a small object in a bounded homogenous space (a disk buried in a long rectangular sandbox). This indicates that with reduced task demands, 3-year-olds are able to utilize the metric spatial information present on a map (rather than landmarks), at least along a single dimension. Using maps to plan or follow a route requires skills in addition to those required for place finding. Although locating a target and recognizing its correspondence with the environment are sufficient for place finding, route planning requires construction of a series of directional actions to take the participant from his or her present location to the desired destination. Route following additionally obliges the traveler not only to perform these planned actions, but also to orient the map at the start and continually update that orientation (physically or mentally) during his or her progress through the environment. Though in most real-life cases they occur together sequentially, route planning (or construction) and route following (or implementation) are two separate components of navigational map use. Most of the developmental research has focused largely on route following. Sometime during the fourth year of life, children acquire the ability to follow simple routes when the map is aligned with the environment (Blades & Spencer, 1986, 1987a; Uttal & Wellman, 1989) and can be trained to initially orient the map to the environment before attempting to follow a marked route (although, unaided, they ignore orientation factors). Between ages 5 and 6, children begin to spontaneously notice and correct misalignments between a map and the world (Presson, 1982). Route-planning behavior has been studied by Blades and Spencer (1986, 1987b). They found that by age 4½ years, children will use maps to avoid obstacles in a large-scale maze (Blades & Spencer, 1986). Their study involved having children navigate through a large maze containing obstacles. The obstacles could not be seen in advance of taking a “wrong” turn so the children had to use their map to select the free path. This study provides evidence of using maps in deliberate goaldirected behavior but does not address children’s capacity for reorientation, self-


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correction, or sequential planning. As the maze consisted of three loops with a right–left choice at the start of each loop, participants could make a single-action decision that would enable them to navigate around the first loop of the maze and find themselves oriented exactly as they were at the start (albeit at an updated location), ready to make another single-action decision. This is not to say that it is not possible that some of the children treated the map as a conceptual whole and planned a continuous route, but there is no way to deduce this from the data. Additionally, by virtue of the maze having identically oriented paths leading to the choice point at the start of each loop, participants were not required to adjust the alignment of the map with respect to themselves or the environment, only to update their position (linearly forward) at the completion of each loop. Initial alignment (static orientation) of the map, the map user, and the environment is acknowledged to be critical for successful map use by young children and appears to be a factor even kindergartners are capable of considering (Presson, 1982). However, little research has taken account of the fact that, for any route with turns in which the child carries the map with him or her, the map (or mental representation thereof) must be reoriented every time the child changes direction. Thus, reorientation is a continuous, dynamic process. Lehnung, Leplow, Friege, Herzog, and Ferstle (1998), using a place-finding task, found that 5-year-olds use an orientation strategy dependent on local, proximal cues (akin to reliance on adjacent landmarks), whereas 10-year-olds are able to utilize more spatially complex distal cues. Rutland, Custance, and Campbell (1993) studied the effect of physical reorientation on memory for hiding places and found that having to turn upon entering a simple maze significantly decreased 3- and 4-year-olds’ place-finding performance. This study, however, does not assess the continual reorientation associated with common route following. Freundschuh (1990), in a route-following task, found that 5½-year-old children were able to update their orientation properly when turned 180° from the starting orientation. His task, however, was arranged such that the child could view the entire environment at once (something that seems to facilitate performance in general; see Blades & Spencer, 1986), and the environment and map contained salient and distinctive landmarks. Recognizing the correspondence between landmarks on the map and landmarks in the environment is an established skill at this age and, thus, participants could use landmarks to update position—a very different task from keeping track of their own displacements and reorienting accordingly. Our goal in this study was to examine children’s ability to use simple maps to construct and navigate routes in a large-scale environment. Because reorientation abilities seem to emerge between ages 5 and 6, we selected kindergartners as the sample for our study. We endeavored to avoid the potentially confounding demands of some of the earlier studies in this area and tried to focus on simple, purely spatial navigation. Thus, we provided no salient landmarks, gave no verbal feedback or correction, and did not require children to engage in any other task while



a

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b

FIGURE 1 Figure 1a: Map of the testing environment with a simple start–end point configuration. Arrow indicates orientation of participant at the start of the trial. Dotted line represents the most efficient route and was not present on the map given to participants. Maps used in trials were printed on legal-size paper with a scale of .25 in. = 3 ft. Figure 1b: Map of the testing environment with a complex start–end point configuration. Arrow indicates orientation of participant at the start of the trial. Dotted line represents the most efficient route and was not present on the map given to participants.

navigating. In our task, kindergartners were given maps depicting a large system of interconnecting hallways. The child’s present position was indicated on the map by a dot and the intended goal by a star. Children were asked to walk to the spot in the environment that was marked by the star on the map. This particular system of hallways formed several “loops” (see Figure 1), giving rise to multiple routes that could successfully connect any start-point and end-point. Thus, children had navigational choices, more than one of which would yield a successful navigation to the goal. Our task, thus, constitutes an “open problem” (Gauvain, 1992) in which there are multiple acceptable paths, but only one optimal one. This provided us with the unique opportunity to directly assess children’s recognition of a map’s representational capacity, their navigational planning skills, their scale translation skills, and their capacity for position updating and reorientation.

METHOD Participants Participants for this study were 36 kindergartners from the University of Chicago Laboratory School. Participants ranged in age from 5 years, 4 months to 6 years, 11 months with a mean age of 6 years, 0.5 months. A total of 17 girls and 19 boys participated. The population of the school was predominantly middle class and racially mixed.

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Materials Stimuli consisted of 16 laminated, legal-size maps. All of the maps depicted the same space—a system of hallways on the second floor of the high school adjacent to the school attended by the participants. We ascertained in advance that our participant population had little or no experience navigating through the hallways in question. A ¼ in.: 3 ft (.7 cm: 1 m) scale map was drawn of the hallway system, and eight different start–end point configurations of varying complexity were constructed. A configuration was defined as being the shortest possible path between a given start-point and end-point. Configurations varied in complexity according to the number steps (as in operations—not walking steps) required to successfully navigate from start-point to end-point. Complexity was operationally defined as the number of steps and, imbedded within those steps, the number of choices required to execute the simplest route. The eight different complexity constructions are detailed in Table 1. One of our simplest configurations, depicted in Figure 1a, was a three-step, two-choice route: Step 1: Turn Right or Left prior to locomoting? [choice] Step 2: Turn Left or continue Straight? [choice] Step 3: Designate end-point One of our most complex configurations, depicted in Figure 1b, was a six-step, four-choice route: Step 1: Turn Right or Left prior to locomoting? [choice] Step 2: Turn Right or continue Straight? [choice] Step 3: Turn Right or Left? [choice] Step 4: Turn Left Step 5: Turn Left or continue Straight? [choice] Step 6: Designate end-point Each item had a different start-point and end-point. These points were marked on the map with a blue dot (start-point) and a gold star (end-point). Routes were not marked. A second set of stimuli representing mirror images of the originals were created to counterbalance any potential right–left biases in initial turn decisions. To later examine practice effects and to explore whether practice effects were sensitive to the complexity of the task, trials were administered in two fixed orders. In Condition 1, participants received the trials in ascending level of complexity (i.e., least complex to most complex). In Condition 2, participants first received a simple trial and then the remaining seven trials in descending level of complexity (i.e., most complex to least complex). Condition 2 began with a simple trial to fa-


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TABLE 1 Trial Complexity Breakdown


Complexity Level 1 2 3 4 5 6 7 8 aShown

Steps

Choices

2 3 3 4 4 5 5 6

1 1 2a 2 3 3 4 4b

in Figure 1a. bShown in Figure 1b.

cilitate the instruction-giving process and to promote task adherence. Although this initial trial could be considered a “warm-up,” it was administered in exactly the same manner as the other trials. Procedure Participants were tested individually by two experimenters. Participants were told that they were going to go on a pretend treasure hunt and were taken to the testing site in the adjacent high school. One experimenter then showed the participant a map and pointed out the relevant features. The dyad stood at one end of a long hallway, looking down it, while the experimenter explained that “this long hallway” on the map (pointing to the appropriate hallway) was “this long hallway” (gesturing down the real hallway). This was repeated for the other long hallway. The experimenter also explained the symbol on the map that represented the stairs, pointed out the four short connecting hallways and a set of open double doors, and explained that none of the classroom doorways appeared on the map because they were not allowed to go in them. The experimenter then took the participant to the start-point of the first trial. Both the experimenter and the participant faced in a predetermined direction, and the experimenter aligned the map with the environment in front of the participant. The experimenter pointed to the blue dot on the map and said, “This blue dot marks the spot where we are standing right now.” Then, pointing to the gold star on the map, the experimenter said, “This gold star marks the place where we want to go. That’s where the pretend treasure is. Do you think you can show me how to get there?” All of the children answered affirmatively to this query. The experimenter then said, “The hard part about this game is that we are hunting for pretend treasure that we won’t be able to see. So when we get to the right place, you won’t see the treasure or any special marks. You’ll just have to decide when we are in the right


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place.” Then the child was given the map, still oriented correctly with the environment, and the experimenter said, “You go to the right place and I’ll follow you. Stop when you think you are at the spot marked by the star.” The experimenter followed a few feet behind the child, not giving any indication of the correctness of the navigation attempt or guiding the child in any way. The second experimenter, naive to the designated end-point, followed behind the first, tracing the child’s path on a blank map of the environment. When the child stopped, the first experimenter asked, “Is this the spot?” When an affirmative response was given, the first experimenter visually verified the path end-point on the second experimenter’s tracing of the participant’s route. There were no cases of disagreement between the experimenters regarding the path end-point location. Participants were not told whether they had correctly reached the end-point. The participant was then taken to the start-point of the next trial and the procedure was repeated. Only minimal instructions were required on subsequent trials, but the experimenter made certain to always point out that the blue dot represented the dyad’s current position and the gold star represented the goal. Children easily grasped the task and after a brief perusal of each stimulus map, children launched into navigation without prompting. At the conclusion of the last trial, children were praised and taken back to their classroom. Most participants required about 10 min to complete all eight trials. Item sets were alternated with every other participant.

RESULTS Navigational Success Of initial primary interest was whether participants were generally able to successfully navigate from the start-point to the end-point. Each trial was given a success score of either 0 (failure) or 1 (success) based on whether the participant had or had not reached the designated goal, respectively. Because our scoring procedures did not allow for extremely accurate scale recordings of the child’s end-point designation, we allowed for some error in the recording technique as well as for some error in the participants’ translation of scale. Thus, to receive a success score of 1 on a particular trial, the child would have had to stop within ± 5 ft of the designated target. A second coder rescored half of the path-tracing maps for navigational success. Intercoder agreement was 100%. The number of successes was summed for each participant and divided by the number of trials to find the success rate for each participant. The mean success rate across all participants was .49. To compare this observed success rate with chance, we adopted a conservative estimate of chance based on the length of the navigable field. Because a successful navigation was one that ended within 5 ft on either side of the goal, there was, for every trail, a range of 10 linear ft within which any re-


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TABLE 2 Performance for Younger and Older Groups Younger (< 72 months)


Success rate Efficiency rate

Older (≥ 72 months)

M

SD

n

M

SD

n

.39 .90

.25 .12

19 19

.60 .90

.29 .13

17 17

sponse would have been correct. The total linear feet of the navigable path in the testing environment was approximately 485 ft. The ratio of the acceptable response range to the total path, then, is .02. A stopping place selected at random from the environment has a 2% probability of being correct by chance. Across eight trials then, the probability of achieving at least one successful navigation by chance is .16. Viewed another way, only 16% of participants (6 children) would be expected to achieve at least one successful navigation by chance. In fact, 100% of the participants had a success rate of at least 1 in 8. A chi-square analysis of the observed success rates compared with rates expected by chance was highly significant (α = .05 in this and all subsequent analyses), χ2(1, N = 36) = 174.04, p < .0001. We believed the range in the ages of our participants (19 months) allowed for some basic assessment of age-related changes in performance. A small but significant Pearson product—moment correlation was found between age (in months) and success rate, r = .35, p < .05. A median split for age was employed for the purposes of all further analyses (Mdn = 72 months). Nineteen participants were in the younger group, and 17 were in the older group. A 2 × 2 × 2 analysis of variance (ANOVA) was performed with participant success rate as the outcome variable and with gender, condition, and age group as the predictor variables. No main effect was found for gender, F(1, 28) = .03, MSE = .07, ns; or for condition, F(1, 28) = .45, ns. There was a significant main effect of age group, F(1, 28) = 4.84, p < .05, and a significant interaction between age group and condition, F(1, 28) = 9.18, p < .01. See Table 2 for a breakdown of performance by age and Figure 2 for a plot of mean performances by condition and age category. Further exploration of this interaction revealed no significant difference between the older children (M = .48, SD = .26) and the younger children (M = .43, SD = .29) in Condition 1 (in which stimuli are ordered from least to most complex), t(15) = .34, ns, nor in the performance of the younger group in Condition 1 (M = .48, SD =.26) versus the younger group in Condition 2 (M = .29, SD = .22), t(17) = 1.65, ns. Children in the older age group did perform significantly better in Condition 2 (M = .73, SD = .23) than in Condition 1, t(15) = 2.34, p < .05, and significantly better than younger children in Condition 2, t(17) = 4.17, p < .001. In other words, participants under the age of 6 years of age demonstrate similar performance levels regardless of


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FIGURE 2 Success rate for each condition broken down by age group. Older children in Condition 2 perform significantly better than older children in Condition 1 and than younger children in either condition.

condition, whereas older participants are performing at the same level as the younger participants in Condition 1, but when trials are ordered from most to least complex (Condition 2), their overall performance improves significantly. In sum, the overall rate of success in navigating to designated goals was well above what would be expected by chance. Gender and experimental condition had no primary effects on navigational success rates. Age group was found to be a main predictor of navigational success, and a significant interaction between Age Group × Condition was observed, which seems to indicate that perhaps older participants are able to take advantage of the early exposure to a large part of the navigable environment (only the most complex trials required participants to traverse the complete space) and are able to use the information gleaned from their early forays to aid in their navigation in later trials. Older participants may be creating a more global spatial representation or capitalizing on derived familiarity, thereby gleaning the “big picture” from exposure to complex early trials. Alternatively, the performance of older participants may have been enhanced by starting with a challenging trial, but we have no evidence that they, in fact, found the more complex trials to be more challenging. Practice Effects To investigate whether performance improved with practice during the administration of the eight trials, data were collapsed across participants and conditions. There was no significant correlation between success rate and trial number. Figure 3 depicts the relation between trial number and performance. Division of the data by



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condition yielded comparable results. In other words, regardless of the complexity ordering, performance was not found to improve across trials. This finding need not be at odds with the proposed explanation for the interaction between Age × Condition described previously. There, we found that older children performed better overall in the condition that began with the most complex items and proposed that they might derive benefit from early exposure to the entire space. It was not the case, however, that their performance steadily improved over the course of the session (as would be expected with a practice effect). Rather, we propose the older participants were gaining an immediate (effective as of the first trial) representational advantage, rather than following an across-trials learning curve. Route Efficiency Another main question was whether participants who successfully navigated to the intended goal did so by using the most efficient route available. Across all trails, successful and not, an efficient route to the participant indicated end-point was taken 88% of the time. Efficient routes were defined as those that represented the shortest possible linear path from the start-point to the end-point. In some trials, there was more than one possible optimal route, and in all trials countless nonoptimal possibilities existed. Thus, if routes are chosen at random, one is much more likely to be inefficient than efficient. Nonetheless, if a conservative 50% chance is assigned to being either efficient or inefficient, we would expect 18 participants to have an efficiency rate of at least four in eight. In fact, 35 of the 36 par-

FIGURE 3

Overall success rate for each trial. No significant practice effects were found.


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FIGURE 4 Overall success rate for each level of configurational complexity. No significant effects of complexity level on performance were found.

ticipants had efficiency rates grater than 50%. The observed efficient rate was significantly greater than expected by chance, χ2(1, N = 36) = 30.25, p < .005. Across the 141 trials that received a success score of 1, the shortest route to the designated end-point was taken in 125, or 89% of the trails. Even for failed trials, an efficient route was usually taken to an incorrect end-point. For the 147 failed navigations, 127, or 86%, were efficient routes to the participant designated endpoint (8 trails could not be classified with respect to efficiency as the participant never designated an end-point). This indicates that the participants who did not reach the intended goal were not failing because of “task prolonging” or excessive navigational behaviors that lead to confusion and disorientation. A 2 × 2 × 2 ANOVA with efficiency rate as the outcome variable and with gender, condition and age group as the predictor variables revealed no significant main effects, F(1, 28) = 1.21, .23, and .19, respectively, MSE = .02; and no significant interactions. In sum, the vast majority of both the successful and unsuccessful navigations were performed using an efficient, or optimal, route from the start-point to the participant designated end-point. Efficiency in navigation was not effected by age, gender, or experimental condition. Route Complexity We hypothesized that more complex configurations would be more difficult and, thus, would have lower success rates. Figure 4 shows mean success rates for each complexity level. No significant correlation was found. Nor was a significant correlation found between efficiency rates and complexity level (Figure 5). In sum, the



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FIGURE 5 Overall navigational efficiency rate for each level of configurational complexity. No significant effects of complexity level on efficiency were found.

complexity of the configuration does not appear to have a significant effect on navigational success or on route efficiency. More complex configurations were just as likely to be navigated correctly and efficiently as less complex configurations. Analysis of Unsuccessful Trials Next we concerned ourselves with the trials on which participants did not succeed. When participants fail, at what point do they go wrong? We considered only those failed routes that were judged to be efficient routes (i.e., did not involve backtracking or looping). The vast majority of the failed trials fell into this category: 127 out of the 147 failed trials, or 86%. For each item, every participant’s “move” on each step of the route was compared with the correct “move” based on the ideal route alternative. For example, one of the simplest items first required the participants to choose to start off to the right, then to continue straight past a potential turn off, and then to stop shortly past the turn point (see Figure 1a). This trial has three steps, and two choices. Step 1: Turn Right Step 2: Continue Straight Step 3: Stop

Choice 1: Right or Left? Choice 2: Straight or Right?

For every step in which the participant’s move matched the correct move, a score of 1 was given for that step. By calculating the mean score for every step, by item, we are able to determine at what point, or points, participants are going


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astray. Across the 127 efficient failed trials, 71% at least began with the correct first choice. This is well removed from what would be expected by chance, χ2(1, N = 127) = 11.98, p < .001. Of those trials with the first step completed correctly, 61% had a correct completion of the second step, and correct completion of the third step occurred in only 38% of those. Neither of these percentages differs significantly from chance. The fact that unsuccessful navigators were not random in their first locomotor decision, but that subsequent performance drops off to chance, strongly suggests that children are not failing due to an inability to understand the map and use it as a representational tool. Were this the case, we would expect random navigation from the outset of the failed trail. Instead, participants are initially making map-informed navigational choices but appear to lose their way after beginning navigation. Whether the failed trials represent instances in which children planned to navigate to a nondesignated endpoint, or instances in which children became misaligned or disoriented after initiating locomotion remains an open question, although the latter explanation seems more likely in light of participants’ typical zeal for the designated goal-directed task. We believe that most failed navigation attempts do not indicate map-use failure in the most basic sense, but rather an inability to continually correct the map–person–environment alignment after initial locomotion, resulting in fairly random subsequent navigation.

DISCUSSION Researchers have long known that young children are able to use maps in primitive ways under simple conditions. However, real-life map use is far removed from the conditions under which children have largely been tested. Practical map use requires not only an appreciation of the representational capacity of the map, but also the ability to establish one’s location on the map, identify the location of the intended goal, plan sequences of navigational moves, realign the map with the environment or to keep mental tabs on successive bodily rotations, and “undo” (make corrections when the plan has either been improperly formed or improperly executed). This study attempted to capture some of these factors in a large-scale navigational task. Specifically, we asked whether kindergarten-aged children are able to successfully navigate between given start-point and end-points without benefit of route indication or guided corrections. In doing so, we were serendipitously rewarded with evidence regarding spatial planning skills. Spatial Skills At the most basic level, we discovered that kindergarten-aged children are quite skilled at using simple maps to independently navigate through a large-scale, real-



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istic environment. Previous research had indicated that children of this age do know what a map is and what to do with it, but had left open the question of their facility with complex independent route following. The data from our study show that children, although not perfect navigators, are clearly able to construct and execute complex routes—a skill that requires not only a comprehension of the map’s representational capacity, but also a capacity for reorientation. As the child moves through the environment, choosing directions and turning corners, he or she must continually update not only his or her environmental position as it correlates with the map, but also his or her directionality. In addition, the child must attend to the alignment of the map with respect to the environment, so that this directionality can be added to the equation when the next direction choice needs to be made. Because we find that the vast majority of failed navigations seem to be a result of confusion after an initial correct start, reorientation and updating skills are clearly still developing. Nonetheless, considerable skills are in evidence. Whether the navigational activities are plotted in advance (e.g., I will turn right are the first corner, then left at the second) or in action (e.g., now I must turn left) does not change the significance of their ability to take into account the factors of self, map, and environment in a fluid way. Amazingly enough, these mental mappings take place with little overt evidence—no consistent map turning or body direction maintenance was observed in our study. Although developmental effects were observed (children in the top half of the age range performed significantly better than children in the lower half), participants’ navigational skills are clearly not in their nascent stage. One would expect a newly emerging or newly acquired skill to be initially evidenced under only simple task requirements. We varied the length and complexity of potential routes across trials, ranging from very simple (e.g., turn right, walk a bit, and stop) to highly convoluted (five different choices to make in the shortest route alternative). We found no effect of configuration complexity on children’s performance. Failed trials and errors were not reliably associated with the more complex configurations. In fact, the performance of older children appears to benefit from early exposure to complex trials, suggesting that perhaps they were immediately able to capitalize on the more global information about the environment (afforded only through more complex trials) in ways that children under six were not.

Planning Skills Route construction, which is necessary if one is to move from Point A to Point B, requires planning: the act of deliberately engaging in a series of nonrandom acts that ultimately achieve a goal (Friedman, Scholnick, & Cocking, 1987; Hayes-Roth & Hayes-Roth, 1979; Hughes, 1988; Scholnick & Friedman, 1987). Current conceptions of planning have evolved away from the most basic definition of “any goal-di-


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rected activity” (Wellman, Fabricius, & Sophian, 1985) to embrace the notion that planning can only take place when there are “alternatives for reaching or accomplishing the goal” (Haith, 1997, p. 25). Contemplating options and making decisions before acting, then, are the most essential cognitive prerequisites for planning: Representational capacity and motor control are absolutely required (Cocking & Copple, 1987; Haith, 1997). When actual planning behaviors (e.g., collecting items in a model grocery store or solving a Tower of Hanoi problem) are investigated, the list of cognitive components grows. A problem must be represented and a goal state must be selected. The individual must decide to plan—planning is a deliberate, voluntary act—and then must create a plan. That plan must then be implemented, and the outcome must be monitored (Friedman & Scholnick, 1997; Friedman et al., 1987; Scholnick & Friedman, 1987; Scholnick, Friedman, & Wallner-Allen, 1997; Szepkouski, Gauvain, & Carberry, 1994). Of course, plans can vary greatly in their complexity and planning to reach any goal that is two or more steps removed from the initial state can occur in different ways. One could plan a complete course of action, or engage in hill climbing (continually trying to move from one’s present state to a state that is closer to the goal). Or, as other researchers have construed it, one can plan in advance or one can plan in action (Gardner & Rogoff, 1990; Gauvain, 1992; Rogoff, Gauvain, & Gardner, 1987). In this study, we have a task that asks a child to move from Point A to Point B when multiple paths can be taken. We find that the child can arrive at Point B. The problem has been represented, the options have been considered, choices have been made, and progress has been monitored. How, exactly, are these planning skills being used? Are children constructing routes in a step-by-step manner as they navigate or are they constructing their route in its entirely before locomoting? The two methods are radically different in terms of cognitive sophistication and unless preparatory activities are observed or a verbal announcement of the complete intended course of action is made (Ellis & Siegler, 1997; Haith, 1997), it is decidedly difficult to know whether a series of executed actions was planned in advance of execution, or was constructed step-by-step in the midst of action. This study was not specifically designed to tease apart these possibilities, but the performance of our participants surprisingly resolved the ambiguity for us. Our window into the heart of planning is granted not by the task itself, but by the tendency of participants to complete the task via efficient routes. If children routinely and reliably select an efficient route from a number of route alternatives, what does this imply? First, it implies an awareness of route length. Second, it implies some motivation for selecting the shortest route. Both of these implications shed light on the cognitive underpinnings of our participants’ performance. To reliably move from a start-point to an end-point via the shortest possible path requires that the various path options be compared in advance of locomoting. For our task, there were always multiple path options of varying lengths that would result in



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goal achievement. Furthermore, because our stimuli did not delineate specific paths, route options had to be constructed before they could be assessed for efficiency. To determine an efficient route, participants must be making estimations and comparisons of relative path lengths. In some cases, the route alternatives differs by a very small distance margin, and in nearly all of the trials, estimation of route length requires the summation of perpendicular “legs.” Given that most participants contemplated the map for mere seconds before proceeding to reliably navigate the most efficient path, their capacity for distance assessment clearly allows for quick estimation and comparison. Additionally, because no instructions about efficiency were given and there was no implicit constraint of the task that required planning of any sort, our observation was of spontaneous advance planning—evidence of recognizing plan utility in the absence of plan necessity.1 What we have indirectly discovered here is that kindergartners not only have the capacity for creating an advance plan without direct instruction, but also seem to demonstrate knowledge of the utility of efficiency. We did not instruct children to find “the best” or “the fastest” way; nor were there any environmental constraints on route choice. Children were deciding, quite of their own accord, to take efficient routes, thereby implying some understanding of the value of efficiency. Advance planning that is not necessary for task completion is not something one would expect from participants in our age group. In a study of route construction in a pictorial maze, Ellis and Siegler (1997) found that even third graders will plan only when required for effective performance. They posited that “because planning takes time and effort, it would not ordinarily be the first strategy chosen when solving easy problems” (p. 191). They later go on to say that greater levels of planning behavior will be evidenced as children’s understanding of a task grows. For our task, this suggests either that the route assessment is not a particularly “effortful” part of the task (that their distance estimation skills are so facile as to be automatic), or that children are actually well developed in their understanding of the role of strategy in spatial navigation (that their planning knowledge is quite developed). It is also interesting to note that, unlike the popular grocery store task or pictorial maze task, children in our task could not perceive the entire task environment directly. In our study, plans had to be constructed using information from the map. This plan then needed to be translated into actions scaled to the environment. Monitoring the execution of the plan would require further translations back and forth

1On the other hand, not taking the most efficient route does not prove the definitive absence of advance planning, so negative evidence is inconclusive in this system. Although failure to follow an efficient route may indicate a lack of advance planning, it may also alternatively indicate that the navigator is unaware of the utility of shortest routes, may be seeking to deliberately prolong the task (a form of advance planning!), or may be unable to make the distance comparisons required for shortest route determinations.


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between the map (or a mental representation thereof) and the environment. To form a plan “representationally” and then apply it to a novel environment seems to approach the idea of planning in the abstract—a skill that might not be expected to emerge until adolescence (DeLisi, 1987; Piaget, 1963). In sum, this study shows that kindergarten children can use simple maps to plan and execute complex routes in large spaces when no other task demands are included. Half of all navigational attempts reveal the ability of 6-year-olds to monitor displacements and reorientations between self, map, and environment. Successful navigations were nearly always efficient, demonstrating that kindergartners, at some level, appreciate the utility of efficient navigation and are able to spontaneously construct an advance plan to achieve efficient navigation. Such planning requires comparisons of the lengths of potential routes, and the observed efficiency rates indicate that these children do make rapid comparisons of route length. These results show that kindergarten-aged children can use maps for largescale navigation and suggest that young children’s planning skills may be more sophisticated that was previously believed.

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