A sample of healthy Argentinean children aged 3–5 years participated in the SIP, a longitudinal study implemented between years 2002 and 2004 in three kindergartens in the city of Buenos Aires, selected by applying the conglomerate sample method. The program was an experimental, randomized and controlled study with the main goal to train cognitive performance in preschool children from UBN homes (Colombo and Lipina, 2005; Lipina et al., 2012). Seven hundred and forty five preschool children were authorized to participate. We have verified an attrition rate around 15% per year. Each year new cohorts were enrolled, forming different study groups with 1 and 2 years of intervention. For the present article only data from children with 1 year of intervention between the years 2002 and 2004 were analyzed, because of the small sample size for 2 years of intervention, which did not allow executing the planned analytical procedures. Informed consents were obtained from parents/caregivers, and ethical approval was obtained from the Ethical Review Committee. The study was conducted according to APA's ethical standards, and international and national children rights laws.

Before the beginning of the program, we recruited and trained a group of college students (“trainers” from now on) from the school of psychology and education. During the same period, we informed the parents of children attending the selected institutions about the program activities and we asked them to sign written consents to include their children into the program. After that, from April to July in each year, we evaluated children's cognitive performance (Time 1, baseline) (see section Cognitive Measures), and parents attended individual interviews to give socioeconomic, sociodemographic and child health history information. Then, four intervention modules were implemented from July to November (see above). After the intervention, all children had a final cognitive assessment (Time 2, post-intervention) with the same battery of tasks used at Time 1. We provided trainers with an intervention procedure guide and supervised them daily. Trainers had to complete a form describing the implemented activities, which we revised daily to suggest adjustments. Trainers were blind to the hypotheses of the study. Activities for the control and intervention groups were organized in different days of the week and all trainers were reassigned to different schools for the final phase (post-intervention cognitive assessment).

The program included the following four modules:

(1) The individual cognitive training module consisted of different exercises demanding cognitive control, with increasing complexity. Activities involved some of the tasks used for the pre- and post-assessment, using different trials (Figure 1). Exercises were implemented during the school day as an extracurricular activity. Two schemes were applied: once a week (a total of 16 sessions), or twice a week (a total of 32 sessions) during 16 weeks in 1 year. Activities followed a scheme previously designed considering the cognitive demand and the time available for the session (30/40 min), and were implemented by one trainer (adults/children ratio = 1 per 1), who was the same for each child.

Each training session was structured in four consecutive steps: (1) assessment of children's motivational state with a Likert scale including the following constructs: willing/not willing to collaborate, extroversion/introversion, talkative/quiet, active/passive, impulsive/thoughtful, trustful/distrustful; (2) introduction of novel materials for the activity and task instructions; (3) evaluation of instruction comprehension with pretest exercises; and (4) activities: blocks of different exercises or trials. Only when children were adequately motivated and pretests were properly solved, trainers continued with the next step, otherwise, the activity was scheduled for a new day in the same week. With respect to the fourth step, for each activity children were asked to solve different exercises organized in blocks of 5–10 trials (two blocks of exercises per session). After each block of exercises was completed, trainers evaluated children's performance, and determined the complexity level for the second block. When child efficiency in the first block of exercises reached at least 80%, the trainers increased the complexity level for the next block; otherwise, after trainers' intervention (according to the child's difficulties, considering the problem solving scheme), the second block of exercises presented new trials with the same level of difficulty. Performance on the last block of exercises determined the initial level of difficulty in the next session (Figure 2).

The theoretical framework of the cognitive training module was based on the problem-solving framework proposed by Zelazo et al. (1997). It involves four temporally and functionally distinct steps and substeps: problem representation, planning, execution, and evaluation (detection and correction). To solve a problem, first it is necessary to create or restructure the problem representation, including its possible solutions. Another component considered in the cognitive training module was the dynamic testing approach proposed by Sternberg and Grigorenko (2002). This form of testing proposes giving the children some kind of feedback in order to help them improve their scores, which in turn is based on Vigotsky's conception of the proximal development zone. Finally, two other components of the cognitive training module were the inclusion of challenge activities or trials, and repeated practice (Diamond, 2012).

Based on the results of the SIP, the same group of researchers designed a new CTP. In the year 2005, this program was implemented in the city of Salta in the context of a quasi-experimental prospective design (Segretin et al., 2007a,b; Lipina et al., 2012). Specifically, the aims of the program involved fostering cognitive development in preschoolers from UBN and Satisfied Basic Needs (SBN) homes with a reduction in the adult to child ratio (more children per adult) compared to the previous experience (SIP) (1/15 vs. 1/1, respectively). For this longitudinal study, a sample of 382 healthy Argentinean children aged 3–5 years were recruited from official childcare centers in the city of Salta in Argentina (Secretary of Children and Family from the Government of the Province of Salta) applying a conglomerate sample method. The rate of attrition was 15%. Informed consents were obtained from parents/caregivers, and ethical approval was obtained from the Ethical Review Committee. The study was conducted according to APA's ethical standards, and international and national children rights laws.

In this program, we recruited and trained a group of trainers, and we informed parents of children attending the selected institutions about the program activities and asked them to sign written consents to include their children into the program. After that, from April to July, we evaluated children's cognitive performance (Time 1) (see section Cognitive Measures), and parents attended individual interviews to give socioeconomic, sociodemographic and child health history information. Then, 4-year-old children were randomly assigned to an individual or group modality of cognitive training. Three- and 5-year-old children were all assigned to the group modality. The reasons for such design were: (1) authorities did not allow the research team to generate a control group for ethical reasons (i.e., they considered that all children had to receive the same activities, and that the government was not a research agency aimed at supporting research practices); and (2) authorities required to reduce the number of human resources for the execution of the program (i.e., individual training modality requires more trainers). For the present study, only children assigned to the group modality of training were analyzed (n = 333). The rest of the children (49) were trained with the individual modality, which was similar to the one implemented in the SIP (Colombo and Lipina, 2005; Lipina and Segretin, 2006; Martelli et al., 2007), to have a comparative training group (these children are not considered further in this paper) (Segretin et al., submitted for publication). Then, from July to November two intervention modules were implemented (see above), and after that, all children were administered a final cognitive assessment (Time 2) with the same battery of tasks used at Time 1. Like in the previous program, trainers' work in each phase was supervised daily during the year, and they were provided a procedure guide. Also, trainers had to complete a form for each activity, which were reviewed daily by supervisors.

In the CTP two modules were implemented, and put together with other activities developed by the government agency:

(1) The cognitive training module consisted of different activities demanding cognitive control, with increasing complexity. Activities for both training modalities were designed with a group of pedagogues (in this program activities differed from the basal and post-training cognitive assessment), and were implemented in weekly sessions (with two different activities within each session) during the school day, as an extracurricular classroom activity, for 16 weeks. As previously mentioned, for the present work only children in the group modality of training were considered in the analysis. Training groups were organized based on age and the maximum number of children necessary to form each group (from 10 to 25 children). Activities followed a scheme previously designed considering the cognitive demands and the time available for each session (30/40 min), and were implemented by two trainers (adults/children ratio = 1 per 10/15) (Figure 3).

The theoretical framework for the cognitive training module was the same one that was used in the SIP program. Also, the CTP applied the same session structure as the previous program (see Figure 2), but adapted to the group modality of training. That is, activities were solved with the participation of all children in the group, and only when 80% of children successfully solved 80% of trials in one block of exercises was the complexity level increased.

Additionally, the government agency provided counseling for parents and adults working in the childcare centers. Researchers had no access to the information regarding these interventions.

In both programs data were collected during the school year (March to November) in a private interview with parents. A Socioeconomic Scale (NES) was used to evaluate parents' education and occupation levels, overcrowding, housing and sanitation conditions, to identify indicators of UBN (Boltvinik, 1995). Scores were assigned directly to mothers and fathers for educational and occupational backgrounds; however, only the higher score was considered for the total scores. For housing conditions, scores were assigned based on type of dwelling, floor, water, bathroom, ceiling, external walls, and home property. The Life Stressors and Social Resources Inventory (LISRES inventory) (Mikulic, 1999) was used to identify life stressors and social resources in the family. Specifically, this inventory measures sets of stressors and resources by administering two scales: (1) stressors that includes physical health dimensions (29 items), housing/neighborhood (22 items), finance (9 items), work (15 items), family (13 items), children (15 items), extended family (15 items), friends and social activities, and negative life events (12 items); and (2) resources that includes: finance (9 items), work (15 items), family (13 items), children (15 items), extended family (15 items), friends and social activities (15 items), and positive life events (10 items). The total score for each scale was calculated by adding scores obtained in each set of items, which were then transformed into T-scores (mean = 50, standard deviation = 10). Additionally, a set of questions concerning child peri- and post-natal health conditions was included in the interviews. Finally, in the SIP the Hamilton Scale (Hamilton, 1959, 1960) was employed to consider important aspects of mothers' mental health involved in self-regulation development (Buss et al., 2011). The scale consists of 14 items related to signs and symptoms of anxiety (7 items) and depression (7 items), which measures the intensity and frequency of such behaviors. The sum of the specific items for each type of sign results in a total score for depression and another for anxiety. There are no cut-points to distinguish subjects with and without anxiety or depression, so the results should be interpreted as a quantification of the intensity.

We evaluated cognitive performance in the pre- and post-training phases (Time 1 and Time 2), with a set of tasks administered by trainers in two sessions of about 40 min. Children were tested individually at their schools, in a quiet testing room. Testing was scheduled at times reported by teachers not to interfere with regular meals and activities. Examiners were blind to the objectives of the study and the composition of the groups.

In both programs, the following four tasks were used:

The Selective Attention task—a manual adaptation of the computerized version of a subscale of the NEPSY battery (Korkman et al., 1998)—was used to evaluate attentional control. A set of sheets of paper with 25 pictures and one or more targets on each one was used. The child was required to identify and point to all the drawings that were identical to the target. Levels of difficulty (from 1 to 10) were determined according to the number of targets and the similarity between the target and the distractor drawings. Trials were administered until the child made more than 3 errors and/or omissions in three consecutive sheets. Scores represent the proportion of correct responses.

In addition, in the SIP, the Stroop-like Day-Night task was administered to assess inhibitory control processes (Gerstadt et al., 1994). The task consisted of 16 trials in which children were asked to say the opposite of what they saw in a series of cards. When a picture of a sun was presented, they had to say “night,” and when the picture showed a moon, they had to say “day.” A total score was computed as the sum of correct responses divided by the total number of trials.

A PCA was executed for variables selected from the Socioeconomic Status Scale and the LISRES inventory (see section Socioeconomic, Life, and Health Condition Measures) (PCA with a Promax rotation). The criteria used for the selection of the final PCA model were Eigenvalues over 1.00; Kaiser Coefficients over 0.6, total value of the commonalities over 10 and value of the commonalities for each variable over 0.4. The application of this procedure resulted in the identification of six factors (Table 5): Factor 1 (Household economic status) involves economic and housing stressors, and economic resources; Factor 2 (Family context) concerns couple and child stressors, negative life events, and couple resources; Factor 3 (Socioeconomic status) comprises parental education and parental occupation level, housing conditions, and overcrowding; Factor 4 (Social resources) involves child, family, and social resources; Factor 5 (Ties support) concerns social and family stressors; and Factor 6 (Life events) involves positive life events. All these factors were incorporated into the data set, and for all of them, higher scores refer to better environmental conditions.

We performed a Pearson Correlation analysis including all potential predictors (socio-environmental factors derived from the PCA, and other variables not included in the PCA: demographic information, child health records and training exposure information) to identify variables with significant and high associations (Pearson coefficient over 0.5, p < 0.05). In those cases, only one of the correlated variables was selected for the subsequent steps—the selection was made based on the reliability of measures. The degree of association among independent and dependent variables was separately analyzed. For both, dependent and independent variables results showed no significant associations between variables (Tables 6, 7).

Regarding the methodological approaches to analyze how ecological factors (i.e., micro- and mesosystemic) affect development, one of the traditional methods is the analysis of variance for repeated measures. During the past decade, a number of analytical models that overcome some disadvantages of the previous models (e.g., the ability to handle missing data) have been implemented for this type of analysis. These models are known as general linear mixed models (GLMM) (Long and Pellegrini, 2003; Singer and Willett, 2003; Arnau and Balluerka, 2004; Ferrer et al., 2004; Arnau and Bono, 2008; Seltman, 2009). Based on that, we conducted a sequence of mixed model analyses to identify significant predictors associated with higher levels of cognitive performance pre-intervention and with more improvement in cognitive performance from pre- to post-intervention.

We first conducted mixed model analyses with a basal predictor (time) and the interaction between time and group (intervention and control), in order to identify differences at baseline performance and trajectories (training impact) between both groups. Results showed a significant effect of time (Attention: B = 0.916, p < 0.0001; Working memory: B = 1.076, p < 0.0001; Inhibitory control: B = 0.396, p = 0.0004; Flexibility: B = 0.899, p < 0.0001; and Planning: B = 1.219, p < 0.0001), which means that all children (intervention and control) increased their baseline performance on all tasks. Additionally, results evidenced significant effects of group for most of the dependent variables (Attention: B = −0.493, p = 0.00004; Working memory: B = −0.590, p < 0.0001; Flexibility: B = −0.569, p = 0.0069; and Planning: B = −0.750, p < 0.0001), which means that children in the intervention group demonstrated improved performance after training compared to children in the control group. In addition, no significant differences at baseline were identified between groups.

Second, independent variables were grouped into four blocks of information: (1) living conditions at home, life stressors, and social resources (including the six factors derived from the PCA analysis); (2) demographic information (child age and gender, and maternal stress for physical health problems); (3) child health (health records); and (4) training exposure (frequency of sessions and group). Analyses were executed separately for each block. The interactions between independent variables with time and group (intervention or control) were included in the models, in order to identify differences between both groups at baseline performance and cognitive trajectories after training.

Before the next step, we tested the missing completely at random (MARC) assumption for the independent variables included in the blocks, and the cognitive performance variables. The assumption was verified for the independent variables (X² = 22.85, p = 0.196), but not for the dependent cognitive variables. However, we did not input cognitive data based on the notion that doing this could alter the slope of the trajectories.

For each block, mixed model analyses were executed several times, removing the non-significant variables each time. This procedure was repeated until only significant variables were included for each block for each given cognitive outcome (dependent variable). The purpose of this was to reduce the number of independent variables to generate a final model of prediction to detect significant variables associated with cognitive performance. In general, results from this step showed significant socio-environmental predictors for each dependent variable, and overall, they evidenced a similar pattern, and yet also some differences, between the cognitive control processes and programs. A summary of results from these analyses is available from the authors upon request.

We combined the significant variables detected from each block in the previous step and included them in a final model of predictors. Similarly to what we explained above, we executed mixed model analyses several times, removing every time the non-significant predictors. At the end of this procedure, we identified a set of significant predictors (final model). This step was also executed for each dependent variable.

Each step was performed to ensure that the final model adequately reflected predictors associated with levels of pre-intervention cognitive performance and with improvement in cognitive performance from pre- to post-intervention. For the number of comparisons (attention = 4, workingmemory = 5, inhibitorycontrol = 2, flexibility = 6, planning = 4), the Bonferroni correction was used for a 0.05 level of significance (the final values of p were: attention = 0.0125, workingmemory = 0.01, inhibitorycontrol = 0.025, flexibility = 0.0083, planning = 0.0125).

We selected predictors for a final model for each program based on the results from previous steps. Table 8 includes the final parameter estimates for each cognitive process, and show significant predictors of cognitive performance at Time 1 (baseline) and of intervention trajectories (difference between Time 2 and Time 1). It is important to point out that for the final models there was a reduction in the number of participants due to the lack of information for all the predictors for some children.

In the final model for Attention (Model = time, group, age, time^*group; Pseudo R² = 0.0522; n = 329), the main effect of time shows that children from both groups, on average, significantly increased their basal performance around one standard deviation after training (B = 0.930; p < 0.0001). In addition, results show that children in the intervention group had higher performance after training than children in the control group (B = −0.477, p = 0.0005). Results also show effects of child age on Time 1 (B = 0.564; p < 0.0001). This result indicates that performance at baseline was higher for older children.

In the final model for Working memory [Model = time, group, age, social resources (factor 4), time^*social resources (factor 4); Pseudo R² = 0.2055; n = 138], the main effect of time shows that children from both groups increased their basal performance around one standard deviation (B = 0.810; p < 0.0001). In this final model the interaction between time and group was not included (due to dropping out of the model as non-significant; thus, differences between groups in the cognitive trajectory were not evaluated). Despite that, it is important to consider that results from previous analytical steps (prior to the inclusion of the predictors) show significant differences in cognitive trajectories between groups (i.e., more improvement in the intervention group).

With respect to the prediction of trajectories, results show that for each point on the social resources score (which means the perception of more resources associated with family, children and friends) children increased 0.202 points between pre and post-intervention performance in this task (p = 0.0303). Results also show effects of child age (B = 0.540; p < 0.0001) and group (B = −0.398; p = 0.0042) at Time 1. This pattern of results suggests that performance on Time 1 was higher for older children, and that children in the control group had, on average, lower baseline performance than children assigned to the intervention group.

For the Inhibitory control variable, results from the final model (Model = time, age, Pseudo R² = 0.0203; n = 382) show main effects of the two variables included in the model. With respect to the effect of time, results suggest that children in both groups, on average, increased their initial performance after the intervention (B = 0.334; p < 0.0001). As in the previous case, in this model the variable group was not included. Also, results from previous steps showed non-significant differences in cognitive trajectories between groups for this task (i.e., both groups had similar change in performance from pre- to post-test).

Regarding the effect of age (B = 0.348; p < 0.0001), results suggest that older children had higher scores on this task. Moreover, our results show that none of the socio-environmental variables were related to the change in inhibitory control from pre- to post-intervention assessment.

In the final model for Flexibility (Model = time, group, age, frequency of sessions, time^*group, time^*age; Pseudo R² = 0.1728; n = 329), the main effect of time shows that children in both groups increased their initial performance around two standard deviations (B = −1.920; p < 0.0001). In addition, results show that children in the intervention group had a higher increase in their performance after training than the control group (B = −0.550; p = 0.0046). Likewise, change in flexibility after training was also associated with child age (B = −0.423; p = 0.0002), which suggests that older children had smaller increases in their performance after training. Results also show that older children had higher scores at baseline (B = 0.516; p < 0.0001), and children who were involved in the intervention with a frequency of one session per week had 0.577 lower scores than children who were involved in the intervention with a frequency of two times per week (p < 0.0001).

Finally, in the model for Planning (Model = time, group, age, time^*group; Pseudo R² = 0.1355; n = 329), the main effect of time shows that children from both groups increased their initial performance around one standard deviation (B = 1.192; p < 0.0001). Results also suggest significant effects of group on cognitive trajectories after training, such that children in the intervention group had higher scores after the intervention than children in the control group (B = 0.720; p < 0.0001). Also, our results indicate effects of child age (B = 0.667; p < 0.0001) on Time 1 performance, which suggest that scores tended to be higher for older children.

First, a PCA was executed for variables from the Socioeconomic Status Scale and the LISRES inventory. Same criteria used for the SIP were considered for this program, and seven components were identified (see Table 12): Factor 1 (Housing conditions) involves housing conditions, overcrowding, and housing stressors; Factor 2 (Economic status) contains economic stressors and economic resources variables; Factor 3 (Ties support) comprises family stressors, negative life events and child stressors; Factor 4 (Social aspects of health) concerns social stressors, physical health, and social benefit reception; Factor 5 (Social resources) involves family, child and social resources variables; Factor 6 (Family composition) comprises family composition and parental occupation level; and Factor 7 (Positive events) concerns the variable positive life events. All these factors were incorporated into the data set, and for all of them, higher scores refer to better environmental conditions.

We performed a Pearson Correlation analysis including socio-environmental factors derived from the PCA and other variables (demographic information, child health records, and training exposure information). For the independent variables, results show a pair of variables with a moderately high degree of association: Low weight at birth and Premature (r = 0.53, p < 0.0001) (Table 13). Based on these results, low weight at birth was selected for the block analysis, as it can result from preterm birth or intrauterine growth restriction, or a combination of both (Shah and Ohlsson, 2002). Results show no significant correlations for the dependent variables (Table 14).

With the purpose of identifying significant predictors for both, basal cognitive performance and cognitive performance change between pre- to post-intervention, we conducted a sequence of mixed model analyses. We first ran a model with a basal predictor (time). Results showed significant estimates for all variables (Attention: B = 0.4596, p < 0.0001; Working memory: B = 0.3816, p < 0.0001; Flexibility: B = 0.3458, p < 0.0001; Planning: B = 0.5745, p < 0.0001), which means that children improved their performance on all these tasks following the group modality of cognitive training.

Second, independent variables were grouped into four blocks of information: (1) Living conditions at home, life stressors and social resources (including the seven factors derived from the PCA analysis); (2) demographic information (child age and gender, parental education and mother age group); (3) child health (low weight at birth, neurological disorders, perinatal disorders); and (4) training exposure (training exposure group). Analyses were executed separately for each block. The model included the interaction between each variable with time (see section Study Design, Participants, and Procedures).

Before the next step, the MARC assumption was tested for the independent variables included in the blocks, and the cognitive performance variables. The assumption was verified for all variables (independent variables: X² = 4.46, p = 0.216; basal cognitive performance variables: X² = 0.931, p = 0.818; post-intervention cognitive performance variables: X² = 0.513, p = 0.916).

For each block, we executed mixed model analyses several times, removing the non-significant variables each time. In general, results from this step showed significant socio-environmental predictors for each dependent variable, and overall, they evidenced a similar pattern, and yet also some differences between cognitive processes. As was mentioned for the SIP, the summary of results of these analyses is available upon request.

Significant variables from the previous step were combined and included in a final model of prediction (also for this step, analyses were executed several times removing every time the non-significant predictors). At the end of this procedure we identified a set of significant predictors (final model). For the number of comparisons (attention = 7, workingmemory = 3, flexibility = 5, planning = 6), the Bonferroni correction was used for a 0.05 level of significance (the final values of p were: attention = 0.00714, workingmemory = 0.01667, flexibility = 0.01, planning = 0.0083).

Table 15 includes the final parameter estimates for each cognitive process, and shows significant predictors of cognitive performance at Time 1 (baseline) and of intervention trajectories (difference between Time 2 and Time 1).

In the final model for Attention (Model = time, housing conditions, family composition, child age, training exposure, housing conditions^*time, training exposure^*time; Pseudo R² = 0.0953, n = 188), the main effect of time (after controlling for the other variables in the model) became non-significant (B = 0.064; p = 0.7463). However, results suggest significant effects of housing conditions (B = 0.191; p = 0.003) and marginally significant effects of training exposure (middle exposure: B = 0.4952; p = 0.0217) on cognitive trajectories. This also suggests that changes in performance from pre- to post-assessment enhanced with increasing scores on housing conditions (higher scores in this factor indicate better housing conditions, less perception of stress associated with housing, and less overcrowding conditions), and with a middle exposure to training in comparison to a low exposure. Results also show main effects of family composition (B = 0.1791; p = 0.0006) and child age (B = 0.6067; p < 0.0001) at Time 1. This pattern of results suggests that performance on the attention task was enhanced with increasing scores on family composition (higher scores for this factor indicate better parental occupation backgrounds and the presence of two parents in the home), and for older children.

In the final model for Working memory (Model = time, ties support and child age; Pseudo R² = 0.0817, n = 215), the main effect of time shows that children, on average, significantly increased their basal working memory performance 0.40 of a standard deviation from pre- to post-test performance (p < 0.0001). None of the socio-environmental variables were related to performance change in working memory between assessments. Results also show effects of ties support (B = 0.1199; p = 0.0113) and child age (B = 0.5992; p < 0.0001) on Time 1 performance. This pattern of results indicates that performance in working memory at Time 1 was higher in older children and with increasing scores on the ties support factor (higher scores for this factor are associated with the perception of less stress associated with the family and the children, and with less negative life events).

In the final model for Flexibility (Model = time, housing conditions, family composition, child age, and training exposure; Pseudo R² = −0.0098, n = 188), the main effect of time shows that children, on average, significantly increased their basal performance by 0.3641 points after training (p < 0.0001). None of the socio-environmental variables were related to the performance change in flexibility from pre- to post-assessment. Results also suggest significant effects of family composition (B = 0.1717; p = 0.0016), child age (B = 0.5790; p < 0.0001), and marginally significant effects of housing conditions (B = 0.1386; p = 0.0113) and training exposure (middle exposure: B = 0.3329, p = 0.0402; high exposure: B = 0.4304, p = 0.0209) at Time 1. This pattern of results indicates that performance at Time 1 was higher for older children; with increasing scores on housing conditions (higher scores on this factor indicate better housing conditions, less perception of stress associated with housing, and less overcrowding conditions) and family composition (higher scores for this factor indicate better parental occupation backgrounds and the presence of two parents at home); and with high or middle exposure to training activities.

In the final model for Planning (Model = time, family composition, child age, training exposure, family composition^*time, training exposure^*time; Pseudo R² = 0.0105, n = 188), the main effect of time (after controlling for the other variables in the model) became non-significant (B = 0.2634; p = 0.1613). However, results suggest significant effects of family composition (B = 0.1495; p = 0.0130) and marginally significant effects of training exposure (high exposure: B = 0.5643; p = 0.0131) on cognitive trajectories. These results suggest that change between pre- and post-training performances increases with increasing scores on family composition (higher scores for this factor indicate better parental occupation backgrounds and the presence of two parents at home); and with high exposure to training activities. Results also show main effects of child age (B = 0.7653; p < 0.0001) on Time 1, which suggests that baseline performance in the planning task was higher in older children.