Developmental Science 11:5 (2008), pp 644– 649

DOI: 10.1111/j.1467-7687.2008.00712.x


Domain-specific and domain-general changes in children’s development of number comparison Ian D. Holloway and Daniel Ansari Department of Psychology, University of Western Ontario, Canada

Abstract The numerical distance effect (inverse relationship between numerical distance and reaction time in relative number comparison tasks) has frequently been used to characterize the mental representation of number. The size of the distance effect decreases over developmental time. However, it is unclear whether this reduction simply reflects developmental changes in domain-general speed of processing and whether it is specific to numerical compared with non-numerical magnitude. To examine these open questions, we conducted a cross-sectional study with 6-, 7-, and 8-year-old children as well as adult college students. Participants performed comparisons on Arabic numerals, arrays of squares, squares of varying luminance and bars of varying height. To control for general age-related changes in reaction time, a measure of speed of processing was used as a covariate in the analysis. A significant developmental decrease in the distance effect was found across numerical and non-numerical comparison tasks over and above general changes in processing speed. However, this change was not found to differ as a function of format. These data suggest that developmental changes in the distance effect are reflective of changes in a domain-general comparison process, rather than domain-specific developmental changes in number representations. However, analysis of overall reaction times revealed significantly greater developmental changes for numerical relative to non-numerical comparison tasks. These findings highlight the importance of taking multiple measures into account when characterizing developmental changes in numerical magnitude processing. Implications for theories of numerical cognition and its development are discussed.

Introduction Researchers have often examined how quantity is processed and represented by asking individuals to compare the relative numerical magnitude of two numbers. A well replicated effect is the ‘numerical distance effect’ (NDE; Moyer & Landauer, 1967). The NDE is represented by the observation that comparisons between two numbers separated by a numerically small distance such as 2 (e.g. 9 vs. 7) take significantly longer and are more error-prone than comparisons of numbers separated by a larger distance, such as 5 (e.g. 3 vs. 8). The NDE is considered to be an important tool for exploring mental representations of numerical magnitude. The NDE is present in the comparisons of Arabic numerals as well as in the comparison of non-symbolic arrays (Ansari, Dhital & Siong, 2006; Barth, Kanwisher & Spelke, 2003; Buckley & Gillman, 1974) and number words (Ansari, Fugelsang, Dhital & Venkatraman, 2006; Pinel, Dehaene, Riviere & Le Bihan, 2001), indicating that the NDE is not constrained by stimulus format. Thus, the NDE is thought to arise from features of the semantic representation of a quantity (Dehaene, 1996; Dehaene, Dehaene-Lambertz & Cohen, 1998).

In school-aged children, convergent evidence from two studies revealed that the NDE decreases with age (Duncan & McFarland, 1980; Sekuler & Mierkiewicz, 1977). To account for these findings, Sekuler and Mierkiewicz proposed three types of developmental changes in number representation. First, the subjective distance between numerosities on a mental number line could increase over developmental time. Second, variability of the representations could decrease over developmental time. Third, both changes could occur simultaneously. Another possibility, not suggested by Sekuler and Mierkiewicz, is that changes in the NDE are not defined by changes in numerical representation, but reflect age-related changes in the comparison process, which may not be specific to numerical comparisons. A related issue concerns the current absence of investigations examining whether developmental changes in the NDE do or do not exceed those associated with domain-general age-related changes in speed of processing. This leaves open the question of whether the developmental shifts in the NDE reported by Sekuler and Mierkiewicz are primarily the consequence of ontogenetic increases in processing speed rather than changes in number representation.

Address for correspondence: Daniel Ansari, Department of Psychology, University of Western Ontario, Ontario, Canada, N6A 5C2; e-mail: [email protected] © 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

Developmental changes of number comparison

Another, previously unexplored, question concerns the domain-specificity of developmental changes in the NDE. The distance effect is by no means limited to the domain of numbers. Non-numerical magnitude judgments such as line length (Johnson, 1939) and object size (Moyer, 1973) show comparable distance effects (DEs). It has been suggested that magnitude comparison of both numerical and non-numerical stimuli is accomplished using similar mechanisms (Moyer & Landauer, 1967; Walsh, 2003). These considerations raise the question of whether the developmental changes in the NDE reflect domainspecific shifts in numerical representation or domaingeneral developmental changes common to numerical and non-numerical comparative judgments. On the one hand, if numerical and non-numerical stimuli are compared using a domain-general comparison process, one would expect the DE to change similarly across numerical and non-numerical tasks. Alternatively, if changes in the numerical distance effect differ from the changes associated with the non-numerical distance effect, this would yield strong evidence that the developmental shifts in the NDE are a result of domain-specific changes in the underlying representation of numerical magnitude. We addressed these questions by analyzing changes in the DE for both numerical and non-numerical tasks, while controlling for age-related changes in speed of processing. We asked participants to make comparisons between two Arabic numerals, two arrays of squares, two squares of varying luminance, and two bars of varying height.

Method Participants Eighty-seven typically developing children aged 6–8 years were recruited from elementary schools in the Upper Valley region of Vermont and New Hampshire (see Table 1). Additionally, 18 adult participants were recruited from Dartmouth College, New Hampshire. Participants gave informed consent/assent in accordance with the Committee for the Protection of Human Subjects, Dartmouth College. Stimuli All stimuli were created using Adobe PhotoShop software and presented using E-prime software (Psychological


Software Tools, Pittsburgh, PA). Unless noted, all stimuli were black presented on a white background measuring 600 × 800 pixels. All stimuli, with the exception of the non-symbolic condition, were presented equidistant from a fixation dot that appeared between individual trials. Symbolic task Participants were presented with two Arabic numerals measuring 200 pixels in height. Numerals 1–9 were paired to create numerical distances ranging from 1 to 6, with six pairs per distance. These pairs were presented in random order. Each of the 36 pairs was laterally counterbalanced for a total of 72 trials. Non-symbolic task Participants were presented with arrays of black squares. Each non-symbolic trial matched the distance/numberpair parameters of a corresponding symbolic trial. To demarcate which squares belonged to which side of the screen, a vertical black line replaced the fixation dot in the middle of the screen during each trial. Density, individual square size, and total area of each array were systematically varied across trials. Brightness task Participants were presented with two squares (5.5 × 5.5 cm) of varying brightness. For each square, Adobe PhotoShop was used to define the hue and saturation as zero while varying the brightness. The resulting nine brightness levels approximated numerals 1–9 in the symbolic condition and were used to create different stimuli pairs to correspond to each pair in the numerical condition. Starting with 10% brightness (corresponding to numeral 1), each level included an additional 10% increase in brightness. Thus, the fifth level had a brightness level of 50% and the ninth level had a brightness value of 90%. Height task In the height condition, bars of 100 pixel width and varying heights were presented. The smallest bar was 150 pixels high and the bar for each subsequent level of height included an increase of 50 pixels. Thus, the bar at

Table 1 Average age and standardized test scores for the four age groups. Higher scores represent better performance. Achievement scores did not significantly differ between different age groups of children. Adults scored significantly higher than children in math but not reading (M = Mean; SD = Standard Deviation of the scores; m = months) Age group 6 7 8 Adult

N 29 31 27 18

(11 males) (15 males) (13 males) (5 males)

Age in months M = 6 y 7.34 m, SD = 2.65 m M = 7 y 5.00 m, SD = 3.94 m M = 8 y 5.41 m, SD = 3.61 m M = 24 y 9.43 m, SD = 38.44 m

© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd.

Woodcock Johnson Math Calculation Skills M M M M

= = = =

107.52, 104.52, 108.48, 115.28,


= = = =

12.56 12.28 10.92 15.35

Woodcock Johnson Basic Reading Skills M M M M

= = = =

113.07, 110.48, 108.48, 109.57,


= = = =

16.28 10.43 9.84 8.61


Ian Holloway and Daniel Ansari

the second level of height measured 200 pixels and the bar at the ninth level of height measured 550 pixels. The nine resulting bars were used to create stimuli pairs that corresponded to each pair in the numerical conditions. Basic speed of processing condition Participants were presented with a red square on one side of the screen and a black square on the other. Each of these squares measured 150 pixels. Twenty pairs were randomly displayed and laterally counterbalanced. Procedure Children were tested in their school and adults were tested in a laboratory space in the Department of Education at Dartmouth College, using a 15″ touch-screen monitor (Planar Systems, Beaverton, OR) connected to a Dell D810 Laptop computer. Participants were told to make their selections as quickly and accurately as possible. In the symbolic and non-symbolic conditions, participants were instructed to touch the side of the display containing the larger numerosity. In the brightness condition, participants were instructed to choose the darker square. Instructions in the height condition required that subjects touch the side of the display presenting the taller bar. Finally, the goal of the speed of processing task was to touch the side of the screen displaying the red square. Stimuli remained on the screen until participants responded. Between trials, the central fixation dot appeared for 1000 milliseconds. To prevent fatigue, each task included a break after 36 of the 72 trials. Four practice trials were included at the beginning of each condition to ensure that participants understood the requirements. Participants completed the four tasks in one of three different orders. Response latencies and accuracy were recorded for each subject. For the analyses, RTs were removed for trials on which participants made erroneous responses. In addition to the DE measures, children’s and adults’ mathematical skills were assessed using Mathematics Fluency and Calculation subtests of Woodcock Johnson III Tests of Achievement (Woodcock, McGrew & Mather, 2001). Reading skills were tested using the Letter-Word Identification and Word Attack subtests. The subtests were aggregated into the measures of Math Calculation Skills and Basic Reading Skills for each child participant (see Table 1). Achievement scores were not collected from four adults.

Results Reaction time Reaction times (RTs) for responses were analyzed in a Split-plot analysis of variance using Age (four levels) as a between-subjects factor and Task (four levels) and © 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd.

Figure 1 Mean reaction time collapsed across all distances shown for each of the four tasks, separately for each age group.

Distance (six levels) as within-subjects factors. To control for age-related changes in speed of processing, the measure of Basic Reaction Time was used as a covariate for the analysis. Because Mauchly’s Test of Sphericity indicated that the circularity could not be assumed, all of the following F-statistics are adjusted by the GreenhouseGeisser correction. Results revealed a main effect of Task [F(3, 239) = 5.385, p < .01, η2 = .051, Power = .884], a main effect of Distance [F(2, 197) = 5.158, p < .01, η2 = .049, Power = .817], and a main effect of age [F(3, 100) = 28.227, p < .001, η2 = .459, Power = 1.00]. The effect of Distance interacted with Age [F(6, 197) = 3.973, p < .01, η2 = .106, Power = .967]. Further, as illustrated in Figure 1, a significant interaction of Task × Age [F(8, 239) = 5.024, p < .001, η2 = .131, Power = .997] was found. No other significant effects were found in the RT data. Of particular interest, the interaction of Task × Age was not mediated by Distance. This result was equivalent even when the analyses were performed without including Basic RT as a covariate and remained significant even if adults were excluded from the analysis. To explore the locus of the Distance by Age interaction, Bonferroni-corrected (p < .05) pair-wise comparison of means were computed. These indicated that, at all levels of distance, the 6-year-olds differed from the other three groups. Similarly, the adults’ RTs were significantly faster than the RTs of all children. The 7- and 8-year-old children did not differ at any levels of distance. The pairwise comparisons also indicated that across all tasks, the effect of distance decreased with increasing age. Four one-way analyses of variance revealed that RT changed significantly with age for all four comparison tasks (ps < .001). However, as can be seen from the data in Figure 1 there is a striking difference between the sizes of the age-related change in numerical versus non-numerical tasks, which explain the significant Age × Task interaction. Furthermore, Bonferroni-corrected (p < .05) pair-wise comparisons show that within the three groups of children both of the numerical tasks differed from brightness and

Developmental changes of number comparison


Table 2 Average proportion of correct responses of each age group for each task (M = Mean; SE = Standard Error of the Mean) Age group 6 7 8 Adult

Accuracy of symbolic task M M M M

= = = =

.9020, .9041, .9236, .9596,


= = = =

.016 .012 .012 .010

Accuracy of non-symbolic task M M M M

= = = =

.8829, .9174, .9000, .9523,


= = = =

height tasks (in all children the brightness and height tasks were also significantly different from each other). In the adult group, the only significant difference related to task was slower response time associated with the non-symbolic task compared with the height task. Thus, the extent of the developmental decrease in number comparison was much greater than the decrease in nonnumerical comparison. Post-hoc tests revealed that symbolic and non-symbolic tasks did not significantly differ within any age group. Accuracy A Split-plot analysis of variance was used to explore participants’ accuracy (see Table 2). The analysis used Age (four levels) as a between-subjects factor and Task (four levels) and Distance (six levels) as within-subjects factors. Due to violation of the assumption of circularity, all of the following F-statistics were adjusted with the Greenhouse-Geisser correction. We found a main effect of Age [F(3, 102) = 3.873, p < .05, η2 = .102, Power = .811], a main effect of Distance [F(4, 342) = 138.292, p < .001, η2 = .576, Power = 1.00], and a main effect of Task [F(3, 277) = 16.893, p < .001, η2 = .142, Power = 1.00]. An interaction between Task and Distance was also found [F(10, 978) = 23.39, p < .001, η2 = .187, Power = 1.00]. Bonferronicorrected (p < .05) pair-wise comparisons of the interaction revealed that smaller distance (1 and 2) resulted in a higher error rate for numerical vs. non-numerical tasks. This pattern of results is not consistent with a speed– accuracy trade-off as more errors occurred simultaneously with higher RTs. To account for the possibility that only one of the age groups might have been utilizing a speed– accuracy trade-off, we conducted a correlation analysis for each group between individuals’ average RT for each task (collapsed across distance) and their average accuracy for each task. No significant correlations were found.

Discussion Previous research examining the development of number representation has emphasized how numerical processing in infancy and early childhood is qualitatively similar to that of adults and animals (Brannon, 2006; Dehaene et al., 1998; Spelke & Dehaene, 1999; Wynn, 1998). This study, in contrast, is one of relatively few studies addressing © 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd.

.013 .009 .010 .008

Accuracy of brightness task M M M M

= = = =

.9428, .9530, .9653, .9672,


= = = =

.008 .008 .008 .009

Accuracy of bar height task M M M M

= = = =

.9438, .9601, .9606, .9550,


= = = =

.013 .011 .011 .022

how children’s numerical understanding is quantitatively different from that of adults. By controlling for variance in RT accounted for by speed of processing, we verified that the DE changes significantly over developmental time. Further, by examining these ontogenetic changes in the DE using numerical and non-numerical tasks, we were able to investigate the domain-specificity of such changes. The ontogenetic decrease in the effect of distance described above appears to reflect changes in a domaingeneral comparison mechanism over and above agerelated changes in speed of processing. This conclusion is supported by the lack of a significant interaction between Task, Age and Distance. If the age-related shifts in the DE reflected changes specific to number representation, we should have seen evidence that the developing effect of distance is modulated by Task in such a way that the DE for the numerical comparison tasks changes differently from the non-numerical tasks. Instead, the ontogenetic shifts in DE suggest that what is changing over development is children’s proficiency at discrimination between magnitude-related stimuli in general. The finding is not altogether surprising. In the seminal publication on the distance effect, Moyer and Landauer (1967) wrote that, ‘a comparison is then made between these magnitudes in the same way that comparisons are made between physical stimuli such as loudness or length of line’ (p. 1520). What could account for a domain-general comparison process? One possibility is that dimensions that are conceptualized as having a magnitude (e.g. size, number, intensity, time) are mentally represented in very similar ways. Thus, if different types of magnitude are represented in a common way, comparisons between these magnitudes could conceivably be highly similar. Another possible explanation of a domain-general comparison process is that magnitude comparison, either numerical or non-numerical, shares a common neural mechanism. Indeed, recent neuroimaging evidence has shown that the neural correlates underlying numerical comparison at least partially overlap with correlates of other types of magnitude comparison (Cohen Kadosh, Henik, Rubinsten, Mohr, Dori, van de Ven, Zorzi, Hendler, Goebel & Linden, 2005; Pinel, Piazza, Le Bihan & Dehaene, 2004; Walsh, 2003), although other evidence that suggests that numerical and non-numerical comparison tasks modulate distinct brain regions should also be acknowledged (Castelli, Glaser & Butterworth, 2006). Our data converge with existing theory and data that indicate a strong relationship between number and other types of magnitude comparison.


Ian Holloway and Daniel Ansari

It is possible that some of these domain-general changes are related to language development. However, it has been shown that children with SLI perform similarly compared to typically developing children on number comparisons tasks, thus making it more likely that the developmental changes in number comparison reflect non-verbal ontogenetic changes (Donlan & Gourlay, 1999). Our study does not exclude the possibility of domainspecific developmental changes in numerical representation. By examining age-related task differences using overall RT rather than distance, we observed clear evidence that numerical comparison changes to a greater extent than non-numerical comparison over developmental time. The pattern of RT changes illustrates increasing efficiency in numerical comparison with age. This increase could be due to developmental changes in numerical representation. Another explanation is that while elementary-school children’s representations of quantity are comparable to adults’, access to these representations is slower in children. In other words, the differential trajectory of overall RTs in the numerical compared to the non-numerical tasks may reflect age-related increases in automatic access to numerical magnitude representations (Girelli, Lucangeli & Butterworth, 2000; Rubinstein, Henik, Berger & Shahar-Shalev, 2002). Such changes in automatic access may not be captured by the DE, which primarily reflects the comparison component of the tasks. Taken together, our results yield a detailed characterization of the developmental changes underlying number comparison. Specifically, we demonstrate that developmental changes in the DE cannot be reduced to age-related changes in speed of processing. Furthermore, since the changing effect of distance over developmental time was not found to differ between tasks, these developmental changes are likely due to shifts in a domain-general comparison mechanism. We did, however, find that when examining overall changes in RTs, number comparison undergoes greater ontogenetic change than non-numerical comparison. It should be acknowledged that numerical and non-numerical tasks were not matched for average RTs and participants in all groups were faster for the non-numerical compared to the numerical tasks. In future research, the difficulty of numerical and non-numerical tasks should be matched so that stronger conclusions about differences and similarities between numerical and non-numerical comparison can be drawn. Nonetheless, our result suggests important differences between the developmental trajectory underlying the processing of numerical and non-numerical stimuli, which warrants further investigation. Furthermore, it is possible that measures of individual differences in the size of the distance effect may differ between numerical and nonnumerical comparison tasks and this possibility should be investigated in future studies. Finally, future studies should exercise caution in their interpretations of the DE in terms of its relationship to number representation and consider using several measures to better characterize © 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd.

quantitative understanding and its developmental trajectory.

Acknowledgements This work was supported by an NSF Science of Learning Center Grant (SBE-0354400). We would like to thank Gwyn Taylor, Lucia van Eimeren, Ian Lyons, Bibek Dhital, and Nicholas Garcia for assistance in collection and preparation of data for analysis. Finally, we would like to express our sincere gratitude to the administrators, principals, teachers, parents and children of the Lebanon School District and the Windsor Central Supervisory Union for their enthusiastic cooperation in the recruitment and testing of child participants.

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Developmental changes of number comparison

Girelli, L., Lucangeli, D., & Butterworth, B. (2000). The development of automaticity in accessing number magnitude. Journal of Experimental Child Psychology, 76 (2), 104 –122. Johnson, D.M. (1939). Confidence and speed in the two-category judgment. Archives of Psychology, 241, 1–52. Moyer, R.S. (1973). Comparing objects in memory: evidence suggesting an internal psychophysics. Perception and Psychophysics, 13, 180–184. Moyer, R.S., & Landauer, T.K. (1967). Time required for judgements of numerical inequality. Nature, 215 (109), 1519– 1520. Pinel, P., Dehaene, S., Riviere, D., & Le Bihan, D. (2001). Modulation of parietal activation by semantic distance in a number comparison task. Neuroimage, 14 (5), 1013–1026. Pinel, P., Piazza, M., Le Bihan, D., & Dehaene, S. (2004). Distributed and overlapping cerebral representations of number, size, and luminance during comparative judgments. Neuron, 41 (6), 983–993.

© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd.


Rubinstein, O., Henik, A., Berger, A., & Shahar-Shalev, S. (2002). The development of internal representations of magnitude and their association with arabic numerals. Journal of Experimental Child Psychology, 81, 74–92. Sekuler, R., & Mierkiewicz, D. (1977). Children’s judgments of numerical inequality. Child Development, 48, 630–633. Spelke, E., & Dehaene, S. (1999). Biological foundations of numerical thinking: response to T.J. Simon (1999). Trends in Cognitive Sciences, 3 (10), 365–366. Walsh, V. (2003). A theory of magnitude: common cortical metrics of time, space and quantity. Trends in Cognitive Sciences, 7 (11), 483–488. Woodcock, R.W., McGrew, K.S., & Mather, N. (2001). WoodcockJohnson III Tests of Achievement. Itasca, IL: Riverside Publishing. Wynn, K. (1998). Psychological foundations of number: numerical competence in human infants. Trends in Cognitive Sciences, 2, 296–303.

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