**In adulthood, research has demonstrated that surrounding the spatial location of attentional focus is a suppressive field, resulting from top-down attention promoting the processing of relevant stimuli and inhibiting surrounding distractors (e.g., Hopf et al., 2006). It is not fully known, however, how this phenomenon manifests during development. This is an important question since attention processes are likely even more critical in development because of their potential impact on learning and day-to-day activities. The current study examined whether spatial suppression surrounding the focus of visual attention, a predicted by-product of top-down attentional modulation, is observed in development. A wide age range separated in six incremental age levels was included, allowing for a detailed examination of potential differences in the effect of attention on visual processing across development. Participants between 12 and 27 years of age exhibited spatial suppression surrounding their focus of visual attention. Their accuracy increased as a function of the separation distance between a spatially cued (and attended) target and a second target, suggesting that a ring of suppression surrounded the attended target. Attentional surround suppression was not observed in 8- to 11-years-olds, even with a longer spatial cue presentation time, demonstrating that the lack of the effect at these ages is not due to slowed attentional feedback processes. Our findings demonstrate that top-down attentional processes exhibit functional maturity beginning around 12 years of age with continuing maturation of their expression until 17, which likely impacts education and the diagnosis of visual and cognitive clinical pathologies.**

^{1}process initially localizes the neurons with the largest response at the top layer. All of the connections of the neurons that do not contribute to the winner are inhibited. This strategy of finding the winners, layer by layer, and then pruning away irrelevant connections is applied recursively. The remaining connections can be considered as the pass zone or the spotlight of attentional focus, while the pruned connections form the suppressive surround. Neurally, the sources of top-down attentional signals are hypothesized to be a network of frontoparietal regions (Zanto & Rissman, 2015), including the frontal eye fields (Couperus & Mangun, 2010; Seiss, Driver, & Eimer, 2009), inferior frontal junction (Sylvester, Jack, Corbetta, & Shulman, 2008), superior frontal and angular gyri (Ruff & Driver, 2006), and precuneus (Payne & Allen, 2011).

*n*= 180) were required to detect two red letter character targets (Target 1 and Target 2) from among black letter distractors and report whether the targets were identical (L-L and T-T) or different (L-T or T-L).

*n*= 18), but they were unable to properly complete the task (e.g., could not complete all blocks, could not maintain focus, etc.) and were therefore excluded from the final study.

*n*= 164) were tested on a similar paradigm as in Experiment 1, with the exception of a central cue being presented instead of a spatial cue. This experiment was included to verify whether the results of Experiment 1 were in fact a consequence of spatial attention.

*n*= 57) were tested on a modified version of the Experiment 1 paradigm, where the cue presentation time was doubled. All other task parameters remained the same as the Experiment 1 task. Experiment 3 allowed us to examine whether top-down feedback processes in 8- to 11-year-olds require more time in order to optimize the visual processing of attended stimuli and suppress the processing of surrounding stimuli.

*F*(5, 174) = 2.23,

*p*> 0.05. The main effect of intertarget separation was significant,

*F*(5, 870) = 7.26,

*p*< 0.0001. The interaction of age group and intertarget separation was also significant,

*F*(25, 870) = 1.73,

*p*< 0.05.

*M*= 0.57,

*SD*= 0.11) was significantly lower than at 0.87 (

*M*= 0.59,

*SD*= 0.13), 0.97 (

*M*= 0.64,

*SD*= 0.14), and 1.00 (

*M*= 0.65,

*SD*= 0.13;

*p*< 0.01). Participants' accuracy at 0.50 (

*M*= 0.56,

*SD*= 0.12) was also significantly lower than at 0.87 (

*M*= 0.59,

*SD*= 0.13), 0.97 (

*M*= 0.64,

*SD*= 0.14), and 1.00 (

*M*= 0.65,

*SD*= 0.13;

*p*< 0.01).

*F*(5, 135) = 11.33,

*p*< 0.0001. Bonferroni-corrected post hoc tests revealed that young adults' accuracy was significantly lower at the minimum intertarget separation of 0.26 (

*M*= 0.60,

*SD*= 0.07) compared with separations of 0.71 (

*M*= 0.67,

*SD*= 0.10), 0.87 (

*M*= 0.70,

*SD*= 0.11), 0.97 (

*M*= 0.71,

*SD*= 0.11), and 1.00 (

*M*= 0.72,

*SD*= 0.11;

*p*< 0.001 for 0.26 compared with 0.71 and

*p*< 0.0001 for all other comparisons). Accuracy was also lower at the intertarget separation 0.50 (

*M*= 0.62,

*SD*= 0.11) compared with 0.87 (

*M*= 0.70,

*SD*= 0.11), 0.97 (

*M*= 0.71,

*SD*= 0.11), and 1.00 (

*M*= 0.72,

*SD*= 0.11;

*p*< 0.01).

*F*(5, 162) = 6.20,

*p*< 0.0001, indicating that the null hypothesis of all the slope coefficients being equal to 0 can be rejected. In young adults, accuracy therefore increased as a function of intertarget separation. The

*R*

^{2}statistic of the linear regression model was

*R*

^{2}= 0.16, which as an index of effect size represents a medium effect (Cohen, 1988).

*F*(5, 150) = 9.50,

*p*< 0.0001. Bonferroni-corrected post hoc tests revealed that the 16- to 17-year-olds' accuracy was significantly lower at the minimum intertarget separation of 0.26 (

*M*= 0.58,

*SD*= 0.12) compared with separations of 0.97 (

*M*= 0.69,

*SD*= 0.13) and 1.00 (

*M*= 0.70,

*SD*= 0.12;

*p*< 0.0001). Accuracy was lower at intertarget separation 0.50 (

*M*= 0.59,

*SD*= 0.11) compared with 0.97 (

*M*= 0.69,

*SD*= 0.13) and 1.00 (

*M*= 0.70,

*SD*= 0.12;

*p*< 0.001). Accuracy was also lower at 0.71 (

*M*= 0.59,

*SD*= 0.13) compared with 0.97 (

*M*= 0.69,

*SD*= 0.13) and 1.00 (

*M*= 0.70,

*SD*= 0.12; all

*p*values < 0.001).

*F*(5, 180) = 6.12,

*p*< .0001, indicating that the null hypothesis of all the slope coefficients being equal to 0 can be rejected. In 15- to 16-year-olds, accuracy therefore increased as a function of intertarget separation. The

*R*

^{2}statistic of the linear regression model was

*R*

^{2}= 0.15, which as an index of effect size represents a medium effect (Cohen, 1988).

*F*(5, 120) = 9.32,

*p*< 0.0001. Bonferroni-corrected post hoc tests revealed that participants' accuracy was significantly lower at the minimum intertarget separation of 0.26 (

*M*= 0.59,

*SD*= 0.09) compared with separations of 0.97 (

*M*= 0.72,

*SD*= 0.10) and 1.00 (

*M*= 0.69,

*SD*= 0.09;

*p*< .001). Accuracy was lower at intertarget separation 0.50 (

*M*= 0.57,

*SD*= 0.10) compared with accuracy at 0.97 (

*M*= 0.72,

*SD*= 0.10) and 1.00 (

*M*= 0.69,

*SD*= 0.09;

*p*< 0.05). Accuracy was lower at 0.71 (

*M*= 0.64,

*SD*= 0.11) compared with 0.97 (

*M*= 0.71,

*SD*= 0.10). Accuracy was also lower at 0.87 (

*M*= 0.61,

*SD*= 0.13) compared with 0.97 (

*M*= 0.72,

*SD*= 0.10) and 1.00 (

*M*= 0.69,

*SD*= 0.09;

*p*< 0.05).

*F*(5, 120) = 7.85,

*p*< 0.0001, indicating that the null hypothesis of all the slope coefficients being equal to 0 can be rejected. In 14- to 15-year-olds, accuracy therefore increases as a function of intertarget separation. The

*R*

^{2}statistic of the linear regression model was

*R*

^{2}= 0.25, which as an index of effect size represents a medium to large effect (Cohen, 1988).

*F*(5, 175) = 7.26,

*p*< 0.0001. Bonferroni-corrected post hoc tests revealed that the 12- to 13-year-olds' accuracy was significantly lower at the minimum intertarget separation of 0.26 (

*M*= 0.54,

*SD*= 0.10) compared with separations of 0.97 (

*M*= 0.63,

*SD*= 0.14) and 1.00 (

*M*= 0.65,

*SD*= 0.11;

*p*< 0.001 for both comparisons). Accuracy was lower at intertarget separation 0.50 (

*M*= 0.56,

*SD*= 0.10) compared with of 0.97 (

*M*= 0.63,

*SD*= 0.14) and 1.00 (

*M*= 0.65,

*SD*= 0.11; both at

*p*< 0.001). Accuracy was also lower at 0.71 (

*M*= 0.57,

*SD*= 0.11) compared with 1.00 (

*M*= 0.65,

*SD*= 0.11;

*p*< 0.01).

*F*(5, 210) = 5.27,

*p*< 0.001, indicating that the null hypothesis of all the slope coefficients being equal to 0 can be rejected. In 12- to 13-year-olds, accuracy therefore increased as a function of intertarget separation. The

*R*

^{2}statistic of the linear regression model was

*R*

^{2}= 0.11, which as an index of effect size represents the lower bounds of a medium effect (Cohen, 1988).

^{2}The repeated-measures ANOVA showed no significant main effect of intertarget separation on accuracy,

*F*(5, 140) = 1.81,

*p*> 0.05. The linear regression model was not significant,

*F*(5, 168) = 1.23,

*p*> 0.05, indicating that the null hypothesis of all the slope coefficients being equal to 0 could not be rejected. The

*R*

^{2}statistic of the linear regression model was

*R*

^{2}= 0.04.

^{3}The repeated-measures ANOVA showed no significant main effect of intertarget separation on accuracy,

*F*(5, 150) = 0.58,

*p*> 0.05. The linear regression model was not significant,

*F*(5, 150) = 1.80,

*p*> 0.05, indicating that the null hypothesis of all the slope coefficients being equal to 0 cannot be rejected. The

*R*

^{2}statistic of the linear regression model was

*R*

^{2}= 0.01.

*F*(5, 158) = 2.57,

*p*< 0.05. The main effect of intertarget separation was not significant,

*F*(5, 790) = 1.61,

*p*> 0.05. The interaction of age group and intertarget separation was also not significant,

*F*(25, 790) = 1.73,

*p*> 0.05.

*M*= 0.64,

*SD*= 0.12) performed significantly greater than the 14- to 15-year-olds (

*M*= 0.56,

*SD*= 0.11;

*p*< 0.05). No other age group comparison was significant. Therefore, we believe that the significant difference in accuracy between young adults and 14- to 15-year-olds is likely not developmental in nature and instead sampling error.

*F*(1, 55) = 2.88,

*p*> 0.05, nor intertarget separation,

*F*(5, 275) = 0.19,

*p*> 0.05, were significant. The interaction of age group and intertarget separation was also not significant,

*F*(25, 275) = 0.95,

*p*> 0.05.

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^{1}Winner-take-all is a parallel algorithm that localizes the maximum value of a set (Koch & Ullman, 1985)