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Socio-affective and cognitive predictors of social adaptation in vulnerable contexts

  • Alejandra Neely-Prado ,

    Contributed equally to this work with: Alejandra Neely-Prado, Gorka Navarrete, David Huepe

    Roles Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Center of Social and Cognitive Neuroscience, Universidad Adolfo Ibáñez, Santiago, Chile

  • Gorka Navarrete ,

    Contributed equally to this work with: Alejandra Neely-Prado, Gorka Navarrete, David Huepe

    Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Center of Social and Cognitive Neuroscience, Universidad Adolfo Ibáñez, Santiago, Chile

  • David Huepe

    Contributed equally to this work with: Alejandra Neely-Prado, Gorka Navarrete, David Huepe

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    david.huepe@uai.cl

    Affiliation Center of Social and Cognitive Neuroscience, Universidad Adolfo Ibáñez, Santiago, Chile

Socio-affective and cognitive predictors of social adaptation in vulnerable contexts

  • Alejandra Neely-Prado, 
  • Gorka Navarrete, 
  • David Huepe
PLOS
x

Abstract

People living in vulnerable environments face a harder set of challenges adapting to their context. Nevertheless, an important number of them adapt successfully. However, which cognitive and socio-affective variables are specifically related to these variations in social adaptation in vulnerable contexts has not been fully understood nor directly addressed. Here we evaluated socio-affective variables (anxious attachment style, internal locus of control, self-esteem and stress) and cognitive variables (fluid intelligence, crystallized intelligence, working memory, numeracy, probabilistic reasoning and logical reasoning) to explain variations in social adaptation in a sample of 232 adults living in vulnerable contexts (M = 42.3, SD = 14.9, equal amount of men and women). Our results show that an important amount of variance in social adaptation can be explained by socio-affective variables, principally by self-esteem, while cognitive variables also contributed significantly. As far as we know, this is one of the first steps towards understanding the role of cognitive and socio-affective features on social adaptation. In the long run, this area of research could play an important role on the assignation of resources to ease people’s integration into society. Our data and R analysis scripts can be found at: https://osf.io/egxy5/.

Introduction

Social adaptation has been defined as the capacity to compromise, relate, confront and cooperate with the environment and others, accommodating our mental and behavioral processes [1]. Although defined, this concept has not been frequently addressed as a phenomenon by itself (e.g. [24]). Most studies about social adaptation do not use social adaptation scales that measure this ability directly. Instead, they commonly analyze correlations between variables that might be relevant for social adaptation, such as having a job, intellectual functioning, health-related quality of life, among others [24]. Although some authors show how social adaptation is negatively affected in psychological and psychiatric disorders [5, 6] and in people who had to overcome problems on initial stages of development [710], to the best of our knowledge there is no research on the social adaptation of healthy people living in vulnerable contexts.

To directly assess social adaptation, we used the Social Adaptation Self-Evaluation Scale (SASS) [11]. This scale measures people’s motivation and the implications of their behavior on engaging in social activities, asking about hobbies, family life, work, relationships, intellectual interests, environment managing capacity and perception of self-performance.

Literature has shown evidence that growing up in poverty, predicts difficulties in several dimensions such as emotion and behavior regulation, and academic failure [1214]. Although vulnerability and poverty are not the same, they generally coexist. Poverty is typically defined by a specific SES (Socio Economic Status) threshold [15]. Vulnerability is a more complex construct, that includes a SES threshold, and also other aspects as living in environments characterized by limited access to social security, education, housing, jobs and health. Since 2015, the Chilean Social Development department defines poverty in this multidimensional way that equals the vulnerability concept described above [16]. We will analyze which socio-affective and cognitive variables predict better levels of social adaptation among vulnerable contexts. A secondary goal will be to assess if socio-affective or cognitive features are more relevant for this process.

It seems important to acknowledge that even though we are studying social adaptation among adults, early experiences are in many occasions crucial to the development of socio-affective and cognitive variables [1719]. For example, it has been seen that early experiences with a mother suffering from depression can predict the apparition of eating disorders by the time children meet adolescence [20]. Thus, many of the variables that we included and which we expect to behave as predictors of social adaptation have been seen to develop during life’s early stages [10, 14, 21, 22]. Keeping the above in mind, we made an exhaustive literature search that allowed us to identify several variables that could be significant predictors of social adaptation. We also saw that these variables could be grouped in socio-affective and cognitive features. In the following sections, we will review each variable expected to predict social adaptation in vulnerable contexts in this study, and the justifications for including them in a hierarchical multiple regression analysis. To do so, we will review first the socio-affective variables, and then the cognitive variables.

Socio-affective variables

A persons’ social environment, and social and affective characteristics, are important predictors of several psychological and coping difficulties. More specifically, it has been suggested that socio-affective features give rise to coping strategies that lead to better adjustment and well-being [2326]. Also, when difficulties are seen in socio-affective dimensions of the individual, proneness to psychopathology and psychiatric symptoms tend to arise [2730]. On these studies, self-esteem, stress and attachment are repeatedly mentioned as important aspects that determine vulnerability vs well-adjustment when facing traumatic or anxious situations and life events. Although locus of control is less mentioned, there is an extended amount of literature that suggests it might be of some importance to social adaptation (for references and more details see below in the section “Locus of Control”).

Thus, although there could be several socio-affective variables to consider, we will consider the following ones suggested by the literature, and discuss their possible relationship to social adaptation on the next lines: anxious attachment, locus of control, stress and self-esteem. Although our aim is not to understand the specific mechanisms between socio-affective variables, we would like to clarify the importance they may have on predicting social adaptation in vulnerable contexts, and then offer some underlying mechanisms between the proposed socio-affective and cognitive models.

It has been suggested that the relational and cognitive tendencies that shape our representations of the world are strongly influenced by attachment [31]. And because early social deprivation has been shown to predict higher levels of insecure attachment [8], we believe that insecure attachment, specifically the anxious type, will be over represented in people living in vulnerable contexts and impact negatively in their capacity to adapt. Insecure attachment styles (anxious and avoidant) are associated with difficulties in several functions [25, 3234]. A study revealed that people with a avoidant attachment style, have shown to use curiosity to connect with others and the environment, and although they fear to hurt others feelings, they actively try to gain as much information as possible, which reduces probabilities of isolation [25]. On the other hand and according to the same study, people with anxious attachment style, tend to use curiosity as a way to gain control over their relationships as the same study describes. The latter are now perceived as threats which can lead to isolation. Thus, it has been argued that people with an anxious attachment style have more difficulties than people with avoidant attachment when managing emotions during decision-making or interpreting reality [3540]. In this sense, it is possible that an anxious attachment style could lead to less social adaptation abilities in vulnerable contexts.

Locus of control, meanwhile, refers to the tendency of people to interpret life situations as depending on its own behavior instead of as a result of luck, fate, or the influence of other powerful people or forces [41]. People with this kind of bias may engage differently in tasks and everyday decisions, affecting their capacity to adapt to social situations. Actually, it has been argued that the style of locus of control that is adopted during childhood seems related to educational attainment and health levels during the next stages of development [22]. In a study with recovering cardiac patients spouses, those who had a perception of control (associated to internal locus of control) over the disease, had a better emotional adjustment, less anxiety levels, less hostility and less levels of depression [42]. Also, it has been documented that people with higher levels of self-efficacy living in vulnerable contexts are more prone to use social support systems to improve their social adaptation [43]. Following the lead of these studies we hypothesize that an internal locus of control will be related to higher levels of social adaptation in vulnerable contexts.

Other variable that becomes of interest for the aim of this study is stress. People who live in vulnerable contexts often suffer from constant exposure to high levels of stress, which are thought to be related to social adaptation difficulties [12, 44]. With stress, we refer to the perception each person has of its environment and the amount of threat this implies for the organism’s homeostasis [45]. Stress has its roots in neurobiological mechanisms that regulate how we perceive and respond to our environment and has repercussions on the expression of aggressive and antisocial behavior [46, 47]. Some negative consequences related to high levels of stress have been seen in people with depression, making scholar achievements and functionality more difficult [48]. How the organism copes with stress has consequences in neural circuits, changing the balance between anxiety, memory, mood and decision making [49]. We suggest that high levels of perceived stress could be related to social adaptation difficulties in vulnerable contexts. In the same vein, there is evidence that chronic stress, as occurs in contexts of poverty, leads to reductions in performance in cognitive and interpersonal domains [50, 51].

Finally, when considering individual factors that can affect social adaptation from a socio-affective perspective, self-esteem also seems to play an important role. There has been an increased interest in the relationship between self-esteem and subjective well-being [52], and also its relationship with high anxiety traits that have been shown to lead to chronic health issues [53]. At the same time, social stress has shown to be related to worse self-esteem [54]. Although studies directly relating self-esteem to social adaptation are still missing, high self-esteem has been associated with higher success and well-being in interpersonal, work and health dimensions [55]. Also, low self-esteem has been linked to higher tendencies to depression or anxiety [56]. Thus, it is likely that higher self-esteem would be related to better social adaptation since it seems to have a positive impact on several psychological outcomes.

Cognitive variables

Although there seems to be more interest and evidence on the effects of socio-affective features on coping and well-being (constructs that indirectly address social adaptation characteristics) [57, 58], some studies suggest that cognitive factors seem to support these processes [59], while cognitive factors also appear negatively affected by traumatic and stressful situations and by difficulties on emotion-regulation [6062]. Specifically these studies suggest that aspects like working memory, long-term memory, cognitive control, and others have a role on emotion-regulation that could help coping and resilience mechanisms, while at the same time these individual characteristics face difficulties when living in adverse environments or passing through stressful situations.

As the case of socio-affective variables, there could be several cognitive variables to consider. However, we will take into account ones that may have an association to social adaption according to the literature: working memory, fluid intelligence, crystallized intelligence, numeracy, probabilistic reasoning, and logical reasoning.

Working memory is a widely studied cognitive function [63] that we suggest could help to predict social adaptation in vulerable contexts. There is some evidence that working memory is diminished in people who live in poverty [50], and it seems to be highly relevant for social adaptation because of its connections with cognitive control, behavioral inhibition, intelligence and executive functions [6468]. All variables that have been related to important aspects of social adaptation, as academic success or self-regulation (e.g. [6971]) Although little seems to be known about how working memory affects social adaptation, it has been found that working memory and emotional recognition are negatively related with the ability of people with schizophrenia to solve social problems [72, 73]. Also, it is known that working memory is associated to metacognitive skills [74] while these have proven to be crucial to different aspects of social adaptation on everyday life and in contexts of chronic social stress [7578].

Strongly associated with the above, are fluid intelligence and crystallized intelligence [79, 80]. It has been suggested that these variables could be significant variables that influence social adaptation positively as well, according to an important body of previous findings. Both seem to be tied to educational attainment [81, 82], which in the case of the population included in this research, is generally low. Fluid intelligence is a cognitive capacity that has shown to be very important for several functions such as emotional regulation and theory of mind [21, 83]. Low fluid intelligence has been associated with tendencies to commit physical violence, to be a victim of physical violence, to drug consumption, to worst perception of mental health and to lower self-esteem [84]. In contrast, crystallized intelligence, or vocabulary tests used to measure crystallized intelligence, have been shown to be negatively related with behavioral impulsiveness in adolescents and academic failure [85]. Besides, crystallized intelligence seems to be necessary for concrete figurative reasoning [86], which could impact context interpretations. Although to the best of our knowledge crystallized intelligence has not shown to be directly associated with social adaptation, fluid intelligence has [84]. But because both have to do with how much information we store and our ability to use it in novel and every day situations [87], we believe that both could contribute to social adaptation.

Living in vulnerable contexts implies low access to education, which makes it difficult to achieve numerical or other types of knowledge [8890]. In this sense, numeracy could also have an important role in predicting social adaptation in vulnerable contexts. Specifically, it has been postulated that numeracy could be an important predictor of inequalities regarding health and income because of its central role for decision making [91]. It has been documented that numeracy is negatively associated with people’s proneness to influence their decisions and judgments by emotional events that are irrelevant [92]. Numeracy seems to help people to achieve a better balance between available information and emotions, which also improves probabilistic reasoning in risky situations [93].

Interestingly, probabilistic reasoning is important when understanding our surroundings, enabling us to use novel information to update our knowledge, but also, when solving everyday life problems [94]. Although studies linking probabilistic reasoning and social adaptation are lacking, there is evidence that it affects the way people make judgments and thus, it is not surprising to find it diminished in people with psychiatric disorders [95, 96]. Besides, there is evidence indicating that social learning processes could be a product of this kind of reasoning because of its relevance for decision-making, the evaluation of environmental cues [97] and for thinking about and evaluating the future [98]. We believe that this could transfer to a positive influence of probabilistic reasoning on social adaptation.

Finally logical reasoning, as the capacity to arrive at valid conclusions from given premises [99], may also be related to people’s social adaptation. Reasoning requires filtering emotional information to distinguish relevant from irrelevant information [100] which is also useful in routine decision making. It has been shown that logical reasoning is related to cognitive ability [101], and there is evidence suggesting that cognitive ability predicts better performance in logical reasoning even when conflicting information is presented [102]. As we know, information as presented in daily tasks is not always clear and formally presented, people regularly need to correctly select and process information to make the best decisions according to their goals. We believe logical reasoning should be important for social adaptation because of its impact on information processing and decision making.

Relationship between socio-affective and cognitive variables

Although there is evidence supporting a relationship between the above-mentioned variables and social adaptation, there is another distinction to be made: which group of variables—cognitive or socio-affective—will have greater predictive power on social adaptation.

How cognitive and socio-affective abilities differentiate and connect is still a hotly contested topic [103], and it is difficult to estimate if socio-affective or cognitive variables are more important for social adaptation, since both dimensions are deeply intertwined [104106]. However, ‘high order cognitive functions’ (as the cognitive variables included in this study [107]) have shown to be connected with emotion regulation: emotion regulation affects cognition efficiency and vice-versa [108]. One of the aims of this research was to test the influence of both kind of variables on social adaptation in a hierarchical approach. This type of analysis was chosen to find differences in the weights of each group of variables over social adaptation.

It has been suggested that cognitive control capacity depends on emotions [109] and that emotional tendencies are important for behavior regulation [110]. Thus, it is not surprising to find increasing evidence of cognition and emotion being interdependent, because they share functions across several networks in the brain across domains [104, 111, 112]. Following this reasoning, we will analyze the influence that cognitive variables add to the relationship between socio-affective features and social adaptation. We expected to find (1) that every cognitive and socio-affective variable selected through our careful literature review (see Socio-affective variables and Cognitive variables sections for an exhaustive list) will individually show a significant correlation with social adaptation, (2) that all the selected socio-affective and cognitive variables will significantly predict social adaptation when included in a global model, (3) that the socio-affective model would predict a larger percent of social adaptation variance than the cognitive model and (4) that the cognitive model variables will improve social adaptation prediction beyond the socio-affective variables.

Materials and methods

Ethics statement

Every procedure of this research was approved by Universidad Diego Portales’s ethics committee. Each participant signed an informed consent which contained its rights and research details.

Participants

A group of 232 people aged between 18 and 89 (M = 42.3, SD = 14.9, equal amount of men and women), with 94.7% of the sample between 18 and 65 years old (excluding the older participants from the analysis made no qualitative difference) were recruited by accessibility.

Participants were adults from the communes of La Granja and San Joaquín (Santiago, Chile) who belong to the 40th percentile of the Chilean welfare program (Programa Social Chile Solidario del Ministerio de Desarrollo Social). The Chilean welfare program grants subsidies, bonuses and other facilities to people who are living in a vulnerable situation. For people or families to receive these benefits, they must be registered to a social index card that indicates the percentile of vulnerability of the person or family. To calculate these percentile, their socioeconomic status and the access they have to public services are taken into account. People or families in the 40th percentile are considered as living in a vulnerable situation and adults from this group were included in this study.

Educational level of participants was ranged between “Primary school, not completed” (1) and “Bachelor degree completed or more” (8), but the maximum educational level achieved by people from this sample was of 5, where 1 = “Primary school, not completed” (22,8%), 2 = “Primary school, completed” (15,1%), 3 = “High school, not completed” (30,6%), 4 = “High school completed”(23,7%), 5 = “Technical degree not completed”(7,8%), 6 = “Technical degree completed”, 7 = “Bachelor degree not completed”, 8 = “Bachelor degree completed or more”. Plus, two other categories: 9 = “No studies”, 10 = “I don’t know/ it doesn’t apply”. People were interviewed to be certain they didn’t have any psychiatric or psychological disorder, and then they proceeded to the evaluation session, where they completed a set of computerized tests and questionnaires. This was done in one session of approximately 2 hours duration. Data collection was part of two FONDECYT research projects (N° 1171200, of PI David Huepe and N° 1171035 of PI Gorka Navarrete).

Materials

Participants were requested to answer several tests and questionnaires. We selected a group of constructs that, according to the revised literature, could help to answer our main question about the relevance of cognitive and socio-affective features on social adaptation. Each construct was measured by a single test or questionnaire. Demographic information such as age, education and socioeconomic level (by income) was also collected. We used Cronbach’s Alpha to determine the reliability of the scales.

Social adaptation.

This construct was measured using the Social Adaptation Self evaluation Scale (SASS) [11], a 21 item questionnaire that assesses various aspects of social life, like the capacity of maintaining a job, interpersonal relationships, hobbies, etc. Each item has 4 answering options that are scored from 0 to 3. All items need to be answered, except for items 1 and 2, where only one of them must be answered depending if the person has a job or not. Answering options vary for each item (see example below). The scale has a maximum score of 60 points, where the higher the score the more socially adapted a person is considered to be. We used the Spanish validation of this instrument [113], obtaining an optimal level of reliability (α = 0.8). See for example item 7:

“Is the state of relations with your family:”

0) unsatisfactory, 1) fair, 2) good, 3) very good

Attachment.

We used the Chilean validation of the ECR-RS test to measure participants’ attachment styles [114, 115]. It is a 36 (9 x 4) item questionnaire used to classify people’s attachment style (secure, insecure avoidant or insecure anxious attachment). The same 9 items are used in 4 sections, to assess attachment to the mother, the father, the person’s romantic partner and friends, respectively. Each item has 3 response options: 0 meaning “no”, 1 meaning “yes”, and 2 meaning “does not apply”. Our literature review showed that only anxious attachment styles could be a good predictor of social adaptation, so for this study, we only took into account the anxious dimension of the scale. Specifically, this consists in the mean of responses on items 7, 8 and 9 across the 4 sections. The higher the score, the more anxiously attached each person was considered to be. The mean per subject was calculated summing the responses for all items (0 or 1 in 12 items; 3 items x 4 sections) and then dividing it by the number of items (12). There were no “2” answers, so no special treatment was needed for calculating total scores. In our sample, test reliability was of α = 0.87 for the anxiety dimension.

  1. 7) “I usually worry that I don’t longer matter to this person”
  2. 8) “I fear this person can abandon me.”
  3. 9) “I worry that this person doesn’t care for me as much as I care for him or her.”

0) No, 1) Yes, 2) Does not apply

Self-esteem.

To measure self-esteem, we selected the Rosenberg’s scale [116]. This scale consists in 10 items answered in a Likert scale ranging from 1 (“Totally Disagree”) to 4 (“Totally Agree”). In this case a Chilean validation was used [117]. Scores vary between 10 and 40, higher scores meaning higher self-esteem. Items 3, 5, 8, 9, 10 were reversed (α = 0.81). For example, item 9:

“Sometimes I feel that I am truly useless”

1 Totally Disagree—4 Totally Agree

Locus of control.

As our literature review showed the importance of Internal Locus of control for several variables related to social adaptation, we used the “Internality” dimension of the Chilean validated version of the Levenson questionnaire [118, 119]. This dimension consists of eight items (items 1, 4, 5, 9, 18, 19, 21 and 23). We summed the items from this dimension to achieve an “internality measure” as suggested in the literature [119]. Answers range from 1 (“Totally Agree”) to 6 (“Totally disagree”), so the higher the score in the “Internality” dimension, the more internal the person’s attribution style is considered to be. The test showed an adequate reliability for the “Internality” dimension (α = 0.71). For example, Item 1:

“That I become a leader depends mostly of my own abilities.”

1 Totally Agree—6 Totally disagree

Stress.

To address stress we used the “Stress” dimension of the General Health Questionnaire (GHQ-12) [120]. The GHQ-12 is a 12 item questionnaire aimed at measuring three different dimensions: “Adaptive success” (items 1, 3, 4, 7, 8 and 12), “Self-Esteem” (items 6, 9, 10 and 11), and “Stress” (items 2, 5 and 9). A Spanish validated adaptation was used [120], with answers ranging from 0 to 3 according to the frequency each situation is experienced by the person. Higher the scores on the “Stress” dimension, are considered to reflect higher levels of perceived stress. The “Stress” dimension of the GHQ-12 questionnaire had an adequate reliability (α = 0.74). For example, item 5:

“Felt constantly under strain”

0) No, not at all, 1)Not more than usual, 2)More than usual, 3)Much more than usual

Fluid intelligence.

We selected the matrices sub-test of the WAIS-III [121] test to measure fluid intelligence. For all WAIS-III tasks we used the Spanish version [121]. In this test people have to choose which of the given alternatives completes a pattern shown. For correct answers, one point is given, for incorrect answers, no point is given. There are 26 trials, so 26 is the maximum direct score. We used the standardized scores, which range between 1 and 18. The test showed high reliability for the sample of this study (α = 0.92). For instance, in front of a sequence pattern like:

○ o ○ o ○ ____

What is the correct alternative? 1) ○, 2) ∪, 3) Δ, 4) o, 5) □

Crystallized intelligence.

We used the vocabulary sub-test of the WAIS-III [121] test to measure crystallized intelligence. This subtest consists in 33 words which meaning has to be explained (e.g. item 19 “evolve”). Each answer is classified according to its similarity to the correct definition, specified in the WAIS-III manual. Answers get 0 points when the definition is considered null, 1 point if it is partially correct and 2 points when it is complete. The subtest has a direct score up to 66 points. We used the standardized scores, which range from 1 to 19 points. The higher the score, the higher the persons crystallized intelligence is considered to be (α = 0.84).

Working memory.

We selected the digit sub-test of the WAIS-III [121] test to measure working memory. This subtest consists in 9 series of digits that the person has to repeat in the reversed order (e.g. in item 2: “5-8-2”, people should respond “2-8-5”). The test has 7 span levels (progressively increasing the number of digits), with two trials each. The first span level starts with three-digit sequences, if the person gives a correct answer on the first trial, the next span level is shown, if the person gives an incorrect answer on the first span level, the second trial in the same span level is shown. If the person gives a correct answer in the second trial then it proceeds to the next span level, but if it gives an incorrect answer on the second trial the test finishes in that span level. The person has to give incorrect answers on both trials of one span level to suspend the test. The span level in which the test gets suspended corresponds to the final score.

Numeracy.

To evaluate levels of numeracy we used the Lipkus Numeracy Scale [122]. This test has a total of 11 items where people have to solve simple arithmetic problems, and where the person gets one point for each correct answer. Our research group translated the original instrument through a double-blind method, and it showed adequate reliability levels in this study (α = 0.71). For example, item 1:

“Imagine that we roll a fair, six-sided dice 1,000 times. Out of 1,000 rolls, how many times do you think the dice would come up even: 2, 4, or 6?: ____”

Probabilistic reasoning.

To measure probabilistic reasoning, specific problem solving tasks were designed following other research’s approaches [123126] in which people had to solve probabilistic problems and then make decisions from numerical estimates. The following is an example of a question of this test:

10 out of 1000 50 year old women that take part in a massive screening test have breast cancer

9 of those 10 women with cancer will receive a positive mammogram

20 out of 990 women that don’t have cancer will also receive a positive mammogram

Of the women that receive a positive mammogram, how many would you expect to have cancer?

Answer: __ out of __

Logical reasoning.

We assessed logical reasoning using 23 syllogisms [100, 127129]. In a logical syllogism, two premises and a conclusion are given. The premises are assumed to be true, and the participant has to answer if the conclusion logically follows the premises. The measure of logical reasoning is the sum of accurate responses (α = 0.63 and α = 0.65 after eliminating item 3).

“Every creature that’s born from an egg are lizards.

Some birds are lizards.

Some birds are not born from an egg”.

[TRUE]

Statistics

The group of variables used in this research often have been found to correlate among each other, and/or to social adaptation. To replicate correlations found in previous literature, we collected descriptive information (see Table 1) and then searched for bivariate correlations between variables (see Table 2).

thumbnail
Table 1. Descriptive statistics for our sample on measures of cognitive and socio-affective features and demographic variables.

https://doi.org/10.1371/journal.pone.0218236.t001

Hierarchical multiple regression analysis was used to determine the contribution of each variable to the prediction of social adaptation, and to test if cognitive variables add a significant amount of explained variance to the prediction of socio-affective variables over social adaptation (see Table 3). Hierarchical regression analysis is a specific method for evaluating changes in predictability of one group of independent variables over and above another group of independent variables [130]. Thus, variables are entered in different steps to the regression analysis, testing if variables entered in a second step predict a significant amount of variance over and above those variables entered in a first step in the analysis. This analysis will allow us to observe if socio-affective variables, entered first in the hierarchical regression analysis, predict social adaptation and if there predictability is augmented when cognitive variables are entered in a second stage.

For each step of the statistic analysis we used R [131]. Data and R analysis scripts can be found at: https://osf.io/egxy5/.

Results

Bivariate correlations

Self-esteem (r = .47, p<0.001), stress (r = -.39, p<0.001) and internal locus of control (r = .34, p<0.001) displayed moderate correlations with social adaptation according to Pearson’s r correlation coefficients [132]. Fluid intelligence (r = .30, p<0.001), crystallized intelligence (r = .27, p<0.001), working memory (r = .22, p<0.001), and numeracy (r = .23, p<0.001) showed moderate to weak significant correlations. Though still significant, only a weak correlation was found between anxious attachment (r = -.18, p<0.01) and logical reasoning (r = .15, p<0.05) and social adaptation. Probabilistic reasoning was not significantly related to social adaptation (r = .12, p>0.05). See Table 2 for a detailed account of correlations and p-value thresholds.

Our analysis suggests that higher self-esteem, crystallized intelligence, fluid intelligence, numeracy, working memory, less stress levels and internal locus of control, contribute to social adaptation in vulnerable contexts. In general, socio-affective variables showed a stronger relationship with social adaptation than cognitive variables.

Hierarchical regression

For the hierarchical regression analysis we used socio-affective variables (anxious attachment, self-esteem, stress and internal locus of control) and cognitive variables (fluid intelligence, crystallized intelligence, working memory, numeracy, probabilistic reasoning and logical reasoning) as predictors of social adaptation scores. The analysis revealed that the set of socio-affective variables explain a significant proportion of the social adaptation scores’ variance, adj. R2 = .305, F(4,208) = 24.21, p<.001. Although the set of cognitive variables explained less than the set of socio-affective variables by itself, adj. R2 = .134, F(6, 206) = 6.47, p<.001, they still explained a significant proportion of the variance, when added to the socio-affective variables, ΔR2 = .07, F(6,202) = 3.81, p<.01.

The complete model (socio-affective plus cognitive variables) explained an important amount of variance in social adaptation, adj. R2 = .357, F(10, 202) = 12.76, p<.001. Sample normality was proved using Shapiro-Wilk analysis, W = 0.994, p>.05 and variation inflation factor confirmed that the amount of explained variance was not inflated because of correlation between predictors (VIF < 1.6). The details of the hierarchical regression analysis can be seen in Table 3 and Fig 1.

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Fig 1. Contribution of each group to the dependent variable’s variance.

A) Standardized beta values of each independent variable in predicting social adaptation B) R² values of the Cognitive and Socio-affective models; the complete model that includes the cognitive variables over the socio-affective variables; and the delta R², that shows the contribution of the cognitive model over the socio-affective model.

https://doi.org/10.1371/journal.pone.0218236.g001

Both models (socio-affective and cognitive) explain a significant amount of the variance of social adaptation, with the socio-affective variables contributing on a larger scale to this relationship. Finally, we were able to determine that cognitive variables contributed to predicting social adaptation beyond the socio-affective variables. 31,8% percent of the variance in social adaptation was accounted for by socio-affective variables. Locus of control, stress and self-esteem contributed significantly to the model, but anxious attachment did not. On the other hand, cognitive variables accounted for 7% of the variance of social adaptation after socio-affective were included in the model, but only fluid intelligence and working memory contributed significantly to this relationship. The final model, considering both sets of variables, explained a total of 38,7% of social adaptation variance.

Discussion

Our aim was to understand which psychological aspects help social adaptation in people living in vulnerable contexts. The hypotheses tested were the following: (1) crystallized intelligence, fluid intelligence, working memory, logical reasoning and probabilistic reasoning—as cognitive variables—and self-esteem, locus of control and anxious attachment—as socio-affective variables—correlate significantly with social adaptation; (2) on one hand the socio-affective model, and on the other hand the cognitive model, predict an important amount of social adaptation variance; (3) the socio-affective model included predicts a larger percent of social adaptation variance than the cognitive model included in this study; (4) this cognitive model improves social adaptation prediction beyond that provided by the socio-affective model.

Data analysis showed that the variables that were selected through literature revision do correlate with social adaptation, except for the case of probabilistic reasoning. A floor effect on the test used to measure this variable could explain why it did not show a significant correlation. Also, self-esteem showed the strongest correlation with social adaptation, which goes in line with previous findings that establish that self-esteem relates to subjective well-being and success in different areas such as personal relationships, work and health [52, 55].

The hierarchical regression analysis revealed our model to predict a significant amount of social adaptation variance (38,7%), although socio-affective variables showed to be of greater importance and not all variables contributed to the model’s predictive capacity.

Within socio-affective variables, an unexpected outcome was that anxious attachment did not contribute to the model’s predictive power, which may be a characteristic of this particular sample. This was the only socio-affective variable that did not contribute to the model’s predictability.

The cognitive set of variables we selected also showed to be relevant to predict social adaptation, although none of these variables by itself appeared as a significant predictor when included in the complete model. Crystallized intelligence showed to be a significant predictor in the cognitive model. The high impact of SES on crystallized intelligence [133] could explain why it appeared as a relevant predictor of social adaptation in the cognitive model.

Also, it seems that the contribution of the cognitive model to social adaptation is highly distributed among its variables, which could explain why in the complete model none of the individual cognitive variables is explaining a significant amount of variance by itself. When observing the beta values of working memory, fluid intelligence and crystallized intelligence in the complete model, it’s possible to note that these variables make similar contributions to social adaptation (they also showed stronger bivariate correlations with social adaptation). However, here we show that in a vulnerable population sample, when these cognitive variables and others are integrated into a model, the shared variance between them becomes the predictor of social adaptation. Future work should consider larger samples to better understand the role, for instance, of fluid intelligence and working memory and its relationship with other cognitive and socio-affective variables.

Socio-affective variables showed greater predictive power than cognitive variables, which is consistent with what researchers have found about emotions regulating cognitive abilities [108, 109, 134]. However, the integrative model that includes both socio-affective and cognitive variables showed to predict an important percentage of social adaptation variance, which is also aligned with evidence showing that cognitive and emotional features interplay to cope with the environment [104, 111, 112].

We studied how emotion and cognition help to shape social adaptation in vulnerable populations. Even though most studies on poverty emphasize its negative effects, our findings show a healthy amount of variability in several socio-affective and cognitive traits related to social adaptation. This suggests that there are traits that help people to cope when living in a vulnerable context. High levels of self-esteem showed to be an outstanding predictor, together with stress and internal locus of control. The finding that socio-affective variables play a major role in social adaptation could be good news for policy making and educational institutions, given that these can be meaningfully improved [135138]. This contrasts with the limited or null transference of cognitive abilities training to real-world domains [139142].

Nonetheless, most of the cognitive measures in our sample were on the low end of the distribution, so a relevant inquiry that should be made in the future, is if the presence of higher cognitive abilities could increase their predictive power on social adaptation to levels similar of those of socio-affective variables.

Our findings seem to be aligned with the idea that emotions play an important role for the generation and promotion of cognition, and that both are interdependent [143] in responding to the environment. Predictive power shown by cognitive variables are compatible with literature suggesting that cognitive features are of great importance to different aspects of adaptation [144, 145], we can see that cognitive variables explain less variance of social adaptation once we controlled by socio-affective variables, which suggests that both sets of variables share some common features. However, further research is needed to gain better understanding on the relationship between cognition and affection, and how they can influence core capacities such as social adaptation.

Conclusion

As far as we know, this is the first study showing that cognitive (working memory and fluid intelligence) and socio-affective (self-esteem, stress and locus of control) variables predict social adaptation among adults living in vulnerable contexts. High self-esteem showed the be the strongest predictor of social adaptation, while low levels of stress, internal locus of control and high levels of fluid intelligence and working memory were also critical. Specifically, 31,8% of differences in social adaptation were accounted by stress, internal locus of control, and self-esteem, while 7% depended on working memory and fluid intelligence. Because this line of research has not been deeply addressed, further research is still needed. Nonetheless, these findings suggest that public policies could consider self-esteem, locus of control and perceived stress as relevant areas to intervene, and that cognitive and socio-affective variables should be considered as mechanisms that work together to produce behavior.

Limitations

Because we used self-reported questionnaires to measure socio-affective variables and social adaptation, we collected people’s self-perception. Inevitably, this could affect the outcomes with well-known bias such as desirability. Also, as people living in vulnerable environments generally have low levels of education, it’s possible some of the participants didn’t fully understand some of the items (like probabilistic reasoning that showed floor effects). Nevertheless, we systematically observed that the majority of correlations and effects were consistent with the hypothesis proposed in this study. Although, to lessen these limitations we used reliability analyses, with generally good results.

Finally, we did not have a control group to compare if our findings are specific of people living in vulnerable contexts or if they could be extrapolated to the general population. However, Latin America has a great amount of population living under this multidimensional poverty, although this situation has shown some improvement in the last decade [146, 147]. Within Latin America, Chile is a country with an important amount of vulnerable population as well [16], and for this reason, understanding social adaptation mechanisms in this group is interesting in itself. Also, it is possible to speculate that the subgroup of people with high social adaptation in our study could have similar cognitive and socio-affective characteristics than the general population’s social adaptation levels. For example, the average scores in self-esteem for the subgroup with high social adaptation, are similar to the general population. The Rosenberg’s Self-Esteem scale [117] has a mean of 32.47 in the Chilean general population, while a mean of 30.85 was obtained in a study including people from 53 nations [148]. In this study, those who obtained a “perfectly adapted” score (SASS total score > 48 points) [11], reached a mean of 30.7 in the Rosenberg’s Self-Esteem scale. Nevertheless, future research should be developed to assess this outstanding issue.

Acknowledgments

This work was supported by grants from Comisión Nacional de Investigación Científica y Tecnológica (CONICYT/FONDECYT Regular N° 1171035 to Gorka Navarrete and N° 1171200 to David Huepe). The funders had no role in the decision to publish, or preparation of the manuscript.

References

  1. 1. Samadi M, Sohrabi N. Mediating Role of the Social Problem Solving for Family Process, Family Content, and Adjustment. Procedia—Social and Behavioral Sciences. 2016;217:1185–1188.
  2. 2. Galletta D, Califano AI, Vangone N, De Simone S, Mastrola AM, Suarato V. The weight of borderline intellectual functioning on the capacity of integration and social adaptation of patients with borderline personality disorder. European Psychiatry. 2016;33.
  3. 3. Ravens-Sieberer U, Gosch A, Rajmil L, Erhart M, Bruil J, Power M, et al. The KIDSCREEN-52 quality of life measure for children and adolescents: Psychometric results from a cross-cultural survey in 13 European countries. Value in Health. 2008;11(4):645–658. pmid:18179669
  4. 4. Root AE, Wimsatt M, Rubin KH, Bigler ED, Dennis M, Gerhardt CA, et al. Children with Traumatic Brain Injury: Associations Between Parenting and Social Adjustment. Journal of Applied Developmental Psychology. 2016;42:1–7. pmid:26726276
  5. 5. Bartels SJ, Pratt S. Psychosocial Rehabilitation and Quality of Life for Older Adults with Serious Mental Illness: Recent Findings and Future Research Directions. Curr Opin Psychiatry. 2009;22(4):381–385. pmid:19417666
  6. 6. Grau N, Rubio-Abadal E, Usall J, Barajas A, Butjosa A, Dolz M, et al. Influence of cognition, premorbid adjustment and psychotic symptoms on psycho-social functioning in first-episode psychosis. Psychiatry Research. 2016;242:157–162. pmid:27280526
  7. 7. Bergmann S, Schlesier-Michel A, Wendt V, Grube M, Keitel-Korndörfer A, Gausche R, et al. Maternal Weight Predicts Children’s Psychosocial Development via Parenting Stress and Emotional Availability. Frontiers in Psychology. 2016;7(August):1–16.
  8. 8. Escobar MJ, Rivera-Rei A, Decety J, Huepe D, Cardona JF, Canales-Johnson A, et al. Attachment Patterns Trigger Differential Neural Signature of Emotional Processing in Adolescents. PLoS ONE. 2013;8(8).
  9. 9. Fondacaro M, Dunkle ME, Pathak MK. Procedural justice in resolving family disputes: A psychosocial analysis of individual and family functioning in late adolescence. Journal of Youth and Adolescence. 1998;27(1):101–119.
  10. 10. Masten AS, Coatsworth JD. The development of competence in favorable and unfavorable environments. Lessons from research on successful children. The American psychologist. 1998;53(2):205–220. pmid:9491748
  11. 11. Bosc M, Dubini A, Polin V. Development and validation of a social functioning scale, the social adaptation self-evaluation scale. European Neuropsychopharmacology. 1997;7(SUPPL. 1):S57–S70. pmid:9169311
  12. 12. Kim P, Evans GW, Angstadt M, Ho SS, Sripada CS, Swain JE, et al. Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Sciences. 2013;110(46):18442–18447.
  13. 13. Yoshikawa H, Aber JL, Beardslee WR. The effects of poverty on the mental, emotional, and behavioral health of children and youth: Implications for prevention. American Psychologist. 2012;67(4):272–284. pmid:22583341
  14. 14. Duncan GJ, Brooks-Gunn J. Family poverty, welfare reform, and child development. Child development. 2013;71(1):188–196.
  15. 15. Evans GW, English K. The Environment of Poverty: Multiple Stressor Exposure, Psychophysiological Stress, and; 2002. 4. Available from: https://www.jstor.org/stable/pdf/3696282.pdf?casa_token=BHn2Rn-EwqoAAAAA:tNwdjpZ0DddrXwQyNvHT9G3KACVW9avwF2I9ZjGdAZcHOaqti3RKghDj8n7tjKAtIwHHg9-3JmyDzzP9ETQa8SGrGU8y6_Lvg5rZNLvlVqPqhF6LLXklGA.
  16. 16. Ministerio de Desarrollo Social. Informe de desarrollo social 2015; 2015. Available from: http://www.ministeriodesarrollosocial.gob.cl/pdf/upload/IDS2.pdf.
  17. 17. Capobianco M, Cerniglia L. Early language development in preterm children without neurological damage: a longitudinal study. F1000Research. 2017;6:2169. pmid:29445449
  18. 18. Bartolomeo L, Cerniglia L, Capobianco M. Empatia e Teoria della Mente: una review narrativa su differenze e convergenze concettuali alla luce delle recenti scoperte neurobiologiche. Ricerche Di Psicologia. 2018;41(1):19–54.
  19. 19. Capobianco M, Cerniglia L. Case Report: Evaluation strategies and cognitive intervention: the case of a monovular twin child affected by selective mutism. F1000Research. 2018;7:221. pmid:29568498
  20. 20. Cimino S, Cerniglia L, Dentale F, Capobianco M, Tambelli R. Maternal Symptoms of Depression and Paranoid Ideation can be Predictive of the Onset of Eating Disorders in Early Adolescents Offspring: A Nine-year Longitudinal Study. International Journal of Psychology and Psychological Therapy. 2018;18
  21. 21. Ibanez A, Huepe D, Gempp R, Gutiérrez V, Rivera-Rei A, Toledo MI. Empathy, fluid intelligence as predictors of theory of mind. Personality and Individual Differences. 2013;54(5):616–621.
  22. 22. Oi K, Alwin DF. Children’s Sense of Control as a Determinant of Adult Health: Causation, Mediation, and Spuriousness. Journal of Health and Social Behavior. 2017; p. 1–19
  23. 23. Dumont M, Provost MA. Resilience in Adolescents: Protective Role of Social Support, Coping Strategies, Self-Esteem, and Social Activities on Experience of Stress and Depression; 1999. 3. Available from: https://link.springer.com/content/pdf/10.1023/A:1021637011732.pdf.
  24. 24. Prati G, Pietrantoni L. Optimism, Social Support, and Coping Strategies As Factors Contributing to Posttraumatic Growth: A Meta-Analysis. Journal of Loss and Trauma. 2009;14(5):364–388.
  25. 25. Mikulincer M. Adult attachment style and information processing: individual differences in curiosity and cognitive closure. Journal of personality and social psychology. 1997;72(5):1217–1230. pmid:9150591
  26. 26. Brougham RR, Zail CM, Mendoza CM, Miller JR, Brougham RR, Zail CM, et al. Stress, Sex Differences, and Coping Strategies Among College Students. Curr Psychol. 2009;28:85–97
  27. 27. Laceulle OM, Veenstra R, Ormel J. Stress View project Alcohol View project. Development and Psychopathology. 2019;31(1).
  28. 28. Haller SPW, Cohen Kadosh K, Scerif G, Lau JYF. Social anxiety disorder in adolescence: How developmental cognitive neuroscience findings may shape understanding and interventions for psychopathology. Developmental Cognitive Neuroscience. 2015;13:11–20. pmid:25818181
  29. 29. Montagna A, Nosarti C. Socio-Emotional Development Following Very Preterm Birth: Pathways to Psychopathology. Frontiers in Psychology. 2016;7(February). pmid:26903895
  30. 30. Mclaughlin KA, Lambert HK, Opin C, Author P. Child Trauma Exposure and Psychopathology: Mechanisms of Risk and Resilience HHS Public Access Author manuscript. Current Opinion in Psychology. 2017;14:29–34.
  31. 31. Sheinbaum T, Kwapil TR, Ballespí S, Mitjavila M, Chun CA, Silvia PJ, et al. Attachment style predicts affect, cognitive appraisals, and social functioning in daily life. Frontiers in Psychology. 2015;6(296):1–10.
  32. 32. Bowlby J. Attachment and loss: volume 1. attachment. (Trad cast: El apego y la pérdida-1. El apego. Barcelona; 1998.
  33. 33. Graci ME, Fivush R. Narrative meaning making, attachment, and psychological growth and stress. Journal of Social and Personal Relationships. 2017;34(4):486–509.
  34. 34. Johnstone M, Feeney JA. Individual differences in responses to workplace stress: the contribution of attachment theory. Journal of Applied Social Psychology. 2015;45(7):412–424.
  35. 35. Pereg D, Mikulincer M. Attachment Style and the Regulation of Negative Affect: Exploring Individual Differences in Mood Congruency Effects on Memory and Judgment. Personality and Social Psychology Bulletin. 2004;30(1):67–80. pmid:15030644
  36. 36. Darke S. Anxiety and working memory capacity. Cognition and emotion. 1988;2(2):145–154.
  37. 37. Shackman AJ, Sarinopoulos I, Maxwell JS, Pizzagalli DA, Lavric A, Davidson RJ. Anxiety selectively disrupts visuospatial working memory. Emotion. 2006;6(1):40–61. pmid:16637749
  38. 38. Sorg BA, Whitney P. The effect of trait anxiety and situational stress on working memory capacity. Journal of Research in Personality. 1992;26(3):235–241.
  39. 39. Jaremka L, Glaser R, Loving T, Malarkey W, Stowell J, Kiecolt-Glaser J. Attachment Anxiety is Linked to Alterations in Cortisol Production and Cellular Immunity. Psychol Sci. 2014;24(3):1–13.
  40. 40. Quirin M, Pruessner JC, Kuhl J. HPA system regulation and adult attachment anxiety: Individual differences in reactive and awakening cortisol. Psychoneuroendocrinology. 2008;33(5):581–590. pmid:18329180
  41. 41. Strickland BR. Internal-External control expectancies: From contingency to creativity. American Psychologist. 1989;44(1):1–12.
  42. 42. Moser DK, Dracup K. Impact of cardiopulmonary resuscitation training on perceived control in spouses of recovering cardiac patients. Research in nursing & health. 2000;23(4):270–278.
  43. 43. Green BL, Rodgers A. Determinants of Social Support Among Low-Income Mothers: A Longitudinal Analysis. American Journal of Community Psychology. 2001;29(3):419–441. pmid:11469115
  44. 44. Evans GW, Gonnella C, Marcynyszyn LA, Gentile L, Salpekar N. The role of chaos in poverty and children’s socioemotional adjustment; 2005. Available from: https://www.human.cornell.edu/sites/default/files/dea/research/The_role_of_chaos_in_poverty_and_children.pdf.
  45. 45. Duval F, González F, Rabia H. Neurobiología del estrés Neurobiology of stress. Rev Chil Neuro-Psiquiat. 2010;48(4):307–318.
  46. 46. Böhnke R, Bertsch K, Kruk MR, Naumann E. The relationship between basal and acute HPA axis activity and aggressive behavior in adults. Journal of Neural Transmission. 2010;117(5):629–637. pmid:20333417
  47. 47. Hawes DJ, Brennan J, Dadds MR. Cortisol, callous-unemotional traits, and pathways to antisocial behavior. Current Opinion in Psychiatry. 2009;22(4):357–362. pmid:19455037
  48. 48. DuBois D, Felner R, Brand S, Adan A, Evans E. A prospective study of life stress, social support, and adaptation in early adolescence. Child Development. 1992;63(3):542–557. pmid:1600821
  49. 49. Mcewen BS. Correction for McEwen, Brain on stress: How the social environment gets under the skin. Proceedings of the National Academy of Sciences. 2013;110(4):2–1561.
  50. 50. Evans GW, Schamberg MA. Childhood poverty, chronic stress, and adult working memory. Proceedings of the National Academy of Sciences. 2009;106(16):6545–6549.
  51. 51. Evans GW, Kim P. Multiple risk exposure as a potential explanatory mechanism for the socioeconomic status-health gradient. Annals of the New York Academy of Sciences. 2010;1186:174–189. pmid:20201873
  52. 52. Pu J, Hou H, Ma R. The Mediating Effects of Self-Esteem and Trait Anxiety Mediate on the Impact of Locus of Control on Subjective Well-Being. Current Psychology. 2017;36(1):167–173.
  53. 53. Shimura A, Takaesu Y, Nakai Y, Murakoshi A, Ono Y, Matsumoto Y, et al. Childhood parental bonding affects adulthood trait anxiety through self-esteem. Comprehensive Psychiatry. 2017;74:15–20. pmid:28086151
  54. 54. Kesting ML, Bredenpohl M, Klenke J, Westermann S, Lincoln TM. The impact of social stress on self-esteem and paranoid ideation. Journal of behavior therapy and experimental psychiatry. 2013;44(1):122–128. pmid:22960709
  55. 55. Orth U, Robins RW. The development of self-esteem. Current Directions in Psychological Science. 2014;23(5):381–387.
  56. 56. Sowislo JF, Orth U. Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin. 2013;139(1):213–240. pmid:22730921
  57. 57. Meng X, Fleury MJ, Xiang YT, Li M, D’arcy Carl. Resilience and protective factors among people with a history of child maltreatment: a systematic review. Social Psychiatry and Psychiatric Epidemiology. 2018;53:453–475. pmid:29349479
  58. 58. Castellanos-Ryan N, Briere FN, O’Leary-Barrett M, Banaschewski T, Bokde A, Bromberg U, et al. The structure of psychopathology in adolescence and its common personality and cognitive correlates. Journal of Abnormal Psychology. 2016;125(8):1039–1052. pmid:27819466
  59. 59. Hoorelbeke K, Marchetti I, De Schryver M, Koster EHW, Dunantlaan H. The interplay between cognitive risk and resilience factors in remitted depression: A network analysis. Journal of Affective Disorders. 2016;195:96–104. pmid:26878206
  60. 60. Krugers HJ, Joëls M. Long-lasting Consequences of Early Life Stress on Brain Structure, Emotion and Cognition. Springer, Berlin, Heidelberg; 2014. p. 81–92. Available from: http://link.springer.com/10.1007/7854_2014_289.
  61. 61. McEwen BS, Sapolsky RM. Stress and cognitive function. Current Opinion in Neurobiology. 1995;5(2):205–216. pmid:7620309
  62. 62. Mikulincer M, Shaver PR, Pereg D. P1: ZBU Motivation and Emotion [me] pp911-moem-468763 Attachment Theory and Affect Regulation: The Dynamics, Development, and Cognitive Consequences of Attachment-Related Strategies 1; 2003. 2. Available from: https://link.springer.com/content/pdf/10.1023/A:1024515519160.pdf.
  63. 63. Baddeley A. Working memory: Looking back and looking forward. Nature Reviews Neuroscience. 2003;4(10):829–839. pmid:14523382
  64. 64. Bogg T, Finn PR. A Self-Regulatory Model of Behavioral Disinhibition in Late Adolescence: Integrating Personality Traits, Externalizing Psychopathology, and Cognitive Capacity. Journal of personality. 2010;78(2):441–470. pmid:20433626
  65. 65. Colom R, Abad FJ, Quiroga MA, Shih PC, Flores-M , Endoza C. Working memory and intelligence are highly related constructs, but why? Intelligence. 2008;36:584–606.
  66. 66. Primi R. Complexity of geometric inductive reasoning tasks Contribution to the understanding of fluid intelligence. Intelligence. 2001;30:41–70.
  67. 67. Riley MR, Constantinidis C. Role of prefrontal persistent activity in working memory. Frontiers in systems neuroscience. 2015;9. pmid:26778980
  68. 68. D’esposito M, Postle BR. The cognitive neuroscience of working memory. Annual review of psychology. 2015;66:115–142. pmid:25251486
  69. 69. Becker DR, Miao A, Duncan R, McClelland MM. Behavioral self-regulation and executive function both predict visuomotor skills and early academic achievement. Early Childhood Research Quarterly. 2014;29(4):411–424.
  70. 70. Holmes AJ, Hollinshead MO, Roffman JL, Smoller JW, Buckner RL. Individual Differences in Cognitive Control Circuit Anatomy Link Sensation Seeking, Impulsivity, and Substance Use. Journal of Neuroscience. 2016;36(14):4038–4049. pmid:27053210
  71. 71. Kyllonen PC, Stephens DL. Cognitive abilities as determinants of success in acquiring logic skill. Learning and Individual Differences. 1990;2(2):129–160.
  72. 72. Huang J, Tan Sp, Walsh SC, Spriggens LK, Neumann DL, Shum DHK, et al. Working memory dysfunctions predict social problem solving skills in schizophrenia. Psychiatry research. 2014;220(1):96–101. pmid:25110314
  73. 73. Tripoli G, Loi E, Sartorio C, La Cascia C, Seminerio F, Sideli L, et al. Working Memory, Jumping to Conclusions and Emotion Recognition: a Possible Link in First Episode Psychosis (Fep). European Psychiatry. 2015;30:918.
  74. 74. Baird B, Smallwood J, Gorgolewski KJ, Margulies DS. Medial and Lateral Networks in Anterior Prefrontal Cortex Support Metacognitive Ability for Memory and Perception. Journal of Neuroscience. 2013;33(42):16657–16665. pmid:24133268
  75. 75. Ladegaard N, Lysaker P, Larsen E, Videbech P. A comparison of capacities for social cognition and metacognition in first episode and prolonged depression Nicolai. Pychiatry Research. 2014;229:1052–1054.
  76. 76. Rose Barlow M, Goldsmith RE. Childhood trauma and active mental processes: Dissociation and metacognition influence control of negative thoughts. Journal of Child and Adolescent Trauma. 2014;7(2):131–140.
  77. 77. Spada MM, Nikčević AV, Moneta GB, Wells A. Metacognition, perceived stress, and negative emotion. Personality and Individual Differences. 2008;44(5):1172–1181.
  78. 78. Yilmaz AE, Gençöz T, Wells A. The temporal precedence of metacognition in the development of anxiety and depression symptoms in the context of life-stress: A prospective study. Journal of Anxiety Disorders. 2011;25(3):389–396. pmid:21144700
  79. 79. Packiam Alloway T, Alloway RG. Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology. 2010;106:20–29.
  80. 80. Chuderski A. When are fluid intelligence and working memory isomorphic and when are they not? Intelligence. 2013;41(4):244–262.
  81. 81. Kaufman AS, Kaufman JC, Liu X, Johnson CK. How do educational attainment and gender relate to fluid intelligence, crystallized intelligence, and academic skills at ages 22-90 years? Archives of Clinical Neuropsychology. 2009;24(2):153–163. pmid:19185449
  82. 82. Rindermann H, Flores-Mendoza C, Mansur-Alves M. Reciprocal effects between fluid and crystallized intelligence and their dependence on parents’ socioeconomic status and education. Learning and Individual Differences. 2010;20(5):544–548.
  83. 83. Opitz PC, Lee IA, Gross JJ, Urry HL. Fluid cognitive ability is a resource for successful emotion regulation in older and younger adults. Frontiers in Psychology. 2014;5(JUN):1–13.
  84. 84. Huepe D, Roca M, Salas N, Canales-Johnson A, Rivera-Rei A, Zamorano L, et al. Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation. PLoS ONE. 2011;6(9):e24858. pmid:21957464
  85. 85. Vigil-Colet A, Morales-Vives F. How impulsivity is related to intelligence and academic achievement. The Spanish journal of psychology. 2005;8(2):199–204. pmid:16255386
  86. 86. Schulze D, Beauducel A, Brocke B. Semantically meaningful and abstract figural reasoning in the context of fluid and crystallized intelligence. Intelligence. 2005;33(2):143–159.
  87. 87. Horn JL, Cattell RB. Age differences in primary mental ability factors. Journal of Gerontology. 1966;21(2):210–220. pmid:5930515
  88. 88. Carlson BA. ¿Que Nos Enseñan Las Escuelas Sobre La Educacion De Los Niños Pobres En Chile? Revista de la CEPAL 72. 2000;72:165–184.
  89. 89. Engle P, Black M. The effect of poverty on child development and educational outcomes. Annals of the New York Academy of …. 2008;1136:243–256.
  90. 90. Lacunza AB. Procesamiento cognitivo y déficit nutricional de niños en contextos de pobreza. Psicología y Salud. 2013;20(1):77–88.
  91. 91. Peters E. Beyond comprehension: The role of numeracy in judgments and decisions. Current Directions in Psychological Science. 2012;21(1):31–35.
  92. 92. Peters E, Hibbard J, Slovic P, Dieckmann N. Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Affairs. 2007;26(3):741–748. pmid:17485752
  93. 93. Petrova DG, Van der Pligt J, Garcia-Retamero R. Feeling the numbers: On the interplay between risk, affect, and numeracy. Journal of Behavioral Decision Making. 2014;27(3):191–199.
  94. 94. Perfors A, Tenenbaum JB, Griffiths TL, Xu F. A tutorial introduction to Bayesian models of cognitive development. Cognition. 2011;120(3):302–321. pmid:21269608
  95. 95. Fear CF, Healy D. Probabilistic reasoning in obsessive-compulsive and delusional disorders. Psychological medicine. 1997;27(1):199–208. pmid:9122300
  96. 96. Tubau E, Aguilar-Lleyda D, Johnson ED. Reasoning and choice in the Monty Hall Dilemma (MHD): implications for improving Bayesian reasoning; 2015. Available from: http://journal.frontiersin.org/article/10.3389/fpsyg.2015.00353.
  97. 97. Perreault C, Moya C, Boyd R. A Bayesian approach to the evolution of social learning. Evolution and Human Behavior. 2012;33(5):449–459.
  98. 98. Griffiths TL, Tenenbaum JB. Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent. Journal of Experimental Psychology: General. 2011;140(4):725–743.
  99. 99. Johnson-Laird PN, Khemlani SS, Goodwin GP. Logic, probability, and human reasoning. Trends in Cognitive Sciences. 2015;19(4):201–214. pmid:25770779
  100. 100. Goel V, Navarrete G, Noveck IA, Prado J. Editorial: The Reasoning Brain: The Interplay between Cognitive Neuroscience and Theories of Reasoning. Frontiers in Human Neuroscience. 2017;10(3):427–457.
  101. 101. Kokis JV, Macpherson R, Toplak ME, West RF, Stanovich KE. Heuristic and analytic processing: Age trends and associations with cognitive ability and cognitive styles. Journal of Experimental Child Psychology. 2002;83(1):26–52. pmid:12379417
  102. 102. Newstead SE, Handley SJ, Harley C, Wright H, Farrelly D. Individual differences in deductive reasoning. The Quarterly Journal of Experimental Psychology Section A. 2004;57(1):33–60.
  103. 103. Oatley K, Johnson-Laird PN. Cognitive approaches to emotions. Trends in cognitive sciences. 2014;18(3):134–140. pmid:24389368
  104. 104. Pessoa L. On the relationship between emotion and cognition. Nature reviews Neuroscience. 2008;9(2):148–58. pmid:18209732
  105. 105. Izard CE. Basic emotions, relations among emotions, and emotion cognition relations. Psychological Review. 1992;99(3):561–565. pmid:1502277
  106. 106. Schulkin J, Thompson BL, Rosen JB. Demythologizing the emotions: Adaptation, cognition, and visceral representations of emotion in the nervous system. Brain and Cognition. 2003;52(1):15–23. pmid:12812800
  107. 107. Paz-Alonso PM, Bunge SA, Ghetti S. Emergence of Higher Cognitive FunctionsReorganization of Large-Scale Brain Networks During Childhood And Adolescence; 2013. Available from: file://www.oxfordhandbooks.com/10.1093/oxfordhb/9780199935291.001.0001/oxfordhb-9780199935291-e-003.
  108. 108. Blanchette I, Richards A. The influence of affect on higher level cognition: A review of research on interpretation, judgement, decision making and reasoning. Cognition & Emotion. 2010;24(4):37–41.
  109. 109. Inzlicht M, Bartholow BD, Hirsh JB. Emotional foundations of cognitive control. Trends in cognitive sciences. 2015;19(3):126–132. pmid:25659515
  110. 110. Campos JJ, Sorce JF, Emde RN, Svejda M. Emotions as behavior regulators: Social referencing in infancy. Emotions in early development. 2013; p. 57.
  111. 111. Ochsner KN, Silvers JA, Buhle JT. Review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences. 2012;1(24):1–35.
  112. 112. Ochsner KN, Gross JJ. The cognitive control of emotion. Trends in Cognitive Sciences. 2005;9(5):242–249. pmid:15866151
  113. 113. Bobes J, Gonzalez MP, Bascaran MT, Corominas A, Adan A, Sanchez J, et al. Validation of the Spanish version of the social adaptation scale in depressive patients. Actas espanolas de psiquiatria. 1999;27(2):71–80. pmid:10380180
  114. 114. Fraley RC, Heffernan ME, Vicary AM, Brumbaugh CC. The experiences in close relationships—Relationship Structures Questionnaire: A method for assessing attachment orientations across relationships. Psychological Assessment. 2011;23(3):615–625. pmid:21443364
  115. 115. Tay Karapas K, Guzmán González M, Barrera Silva J, Lovys Tirado D, Olmos Córdova A. Propiedades psicométricas de la escala de experiencias en relaciones cercanas-estructuras de relaciones (ECR-RS) para medir apego en adolescentes chilenos. Salud & Sociedad. 2015;6(1):78–93.
  116. 116. Swami V, von Nordheim L, Barron D. Self-esteem mediates the relationship between connectedness to nature and body appreciation in women, but not men. Body Image. 2016;16:41–44. pmid:26644328
  117. 117. Rojas-Barahona CA, Zegers PB, Förster M CE. La escala de autoestima de Rosenberg: ValidaciÃ\textthreesuperiorn para Chile en una muestra de jÃ\textthreesuperiorvenes adultos, adultos y adultos mayores. Revista médica de Chile. 2009;137:791–800.
  118. 118. Manso-Pinto JF, Ruggieri-Vega EA. Perceived locus of control among chilean university students. Journal of Social Psychology. 1985;125(6):783–785.
  119. 119. Levenson H. DIFFERENTIATING AMONG INTERNALITY, POWERFUL OTHERS, AND CHANCE. In: Research with the Locus of Control Construct. vol. 1; 1981. p. 15–63. Available from: http://linkinghub.elsevier.com/retrieve/pii/B9780124432017500063.
  120. 120. Sánchez López MP, Dresch V. The 12-Item General Health Questionnaire (GHQ-12): Reliability, external validity and factor structure in the Spanish population. Psicothema. 2008;20(4):839–843.
  121. 121. Wechsler D. Escala de Inteligencia de Wechsler para adultos—III. Manual técnico. Madrid: TEA Ediciones; 1999.
  122. 122. Lipkus IM, Samsa G, Rimer BK. General performance on a numeracy scale among highly educated samples. Medical decision making: an international journal of the Society for Medical Decision Making. 2001;21(1):37–44.
  123. 123. Gigerenzer G, Hoffrage U. How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review. 1995;102(4):684–704.
  124. 124. Kahneman D, Tversky A. On the psychology of prediction. Psychological review. 1973;80(4):237–251.
  125. 125. McNair SJ, Feeney A. When does information about causal structure improve statistical reasoning? The Quarterly Journal of Experimental Psychology. 2014;67(4):625–645. pmid:23931633
  126. 126. Lesage E, Navarrete G, De Neys W. Evolutionary modules and Bayesian facilitation: The role of general cognitive resources. Thinking & Reasoning. 2013;19(1):27–53.
  127. 127. Chater N, Oaksford M. The probability heuristics model of syllogistic reasoning. Cognitive psychology. 1999;38(2):191–258. pmid:10090803
  128. 128. Goel V. Anatomy of deductive reasoning. Trends in Cognitive Sciences. 2007;11(10):435–441. pmid:17913567
  129. 129. Reverberi C, Bonatti LL, Frackowiak RSJJ, Paulesu E, Cherubini P, Macaluso E. Large scale brain activations predict reasoning profiles. NeuroImage. 2012;59(2):1752–64. pmid:21888981
  130. 130. Petrocelli JV. Hierachial Multiple Regression in Couselling Research: Common Problems and Possible Remedies. Journal of Couselling Psychology. 2003;36(April):9–22.
  131. 131. Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychological science. 2007;18(3):233–239. pmid:17444920
  132. 132. Hernández-Sampieri R, Fernández-Collado C, Baptista-Lucio P. Analisis de los datos cuantitativos. 4th ed. Iztapalapa: McGraw Hill; 2006.
  133. 133. Schipolowski S, Wilhelm O, Schroeders U. On the nature of crystallized intelligence: the relationship between verbal ability and factual knowledge. Intelligence. 2014;46:156–168.
  134. 134. Leventhal H, Scherer K. The relationship of emotion to cognition: A functional approach to a semantic controversy. Cognition and emotion. 1987;1(1):3–28.
  135. 135. Garaigordobil M, Martínez-Valderrey V. The Effectiveness of Cyberprogram 2.0 on Conflict Resolution Strategies and Self-Esteem. Journal of Adolescent Health. 2015;57:229–234. pmid:26206445
  136. 136. Anderson E, Cochrane A, Golding J, Nowicki S. Locus of control as a modifiable risk factor for cognitive function in midlife. Aging. 2018;10(7):1542–1555. pmid:30001219
  137. 137. Guo S, Wu Q, Smokowski PR, Bacallao M, Evans CBR, Cotter KL. A Longitudinal Evaluation of the Positive Action Program in a Low-Income, Racially Diverse, Rural County: Effects on Self-Esteem, School Hassles, Aggression, and Internalizing Symptoms. Journal of Youth and Adolescence. 2015;44(12):2337–2358. pmid:26411991
  138. 138. Edwards L. A Mindfulness and Health Promotion Program to Decrease The Perception of Stress and Burnout in Psychiatric Mental Health Nurses Who Provide Driect Patient Care. University of Southern Mississippi; 2015.
  139. 139. Melby-Lervåg M, Hulme C. Is working memory training effective? A meta-analytic review. Developmental psychology. 2013;49(2):270–291. pmid:22612437
  140. 140. Foroughi CK, Monfort SS, Paczynski M, McKnight PE, Greenwood PM. Placebo effects in cognitive training. Proceedings of the National Academy of Sciences. 2016;113(27):7470–7474.
  141. 141. Hayes TR, Petrov AA, Sederberg PB. Do We Really Become Smarter When Our Fluid-Intelligence Test Scores Improve? Intelligence. 2015;48:1–14. pmid:25395695
  142. 142. Sala G, Gobet F. Does far transfer exist? Negative evidence from chess, music, and working memory training. Current Directions in Psychological Science. 2017;26(6):515–520. pmid:29276344
  143. 143. Storbeck J, Clore GL. On the interdependence of cognition and emotion. Cognition & emotion. 2007;21(6):1212–1237.
  144. 144. Evans JSBT. Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition. Annual Review of Psychology. 2008;59(1):255–278. pmid:18154502
  145. 145. Zaval L, Li Y, Johnson EJ, Weber EU. Chapter 8—Complementary Contributions of Fluid and Crystallized Intelligence to Decision Making Across the Life Span; 2015.
  146. 146. Vakis R, Rigolini J, Lucchetti L. Left Behind: Chronic Poverty in Latin America and the Caribbean, Overview; 2015.
  147. 147. Santos ME, Villatoro P. A Multidimensional Poverty Index for Latin America. Review of Income and Wealth. 2018;64(1):52–82.
  148. 148. Schmitt DP, Allik J. Simultaneous administration of the Rosenberg self-esteem scale in 53 nations: Exploring the universal and culture-specific features of global self-esteem. Journal of Personality and Social Psychology. 2005;89(4):623–642. pmid:16287423