Keywords

Introduction

It is now a common knowledge among scientists that most of the published research in the world’s leading scientific journals on human behavior and cognition is primarily based on samples that represent little of the global diversity. These research studies use mainly WEIRD populations (W = western; E = educated; I = industrialized; R = rich; and D = democratic) (Henrich et al., 2010; Rad et al., 2018). This would already be a misrepresentation of the world population in scientific databases, but there is yet another aggravating factor: studies conducted in populations that are not representative of the global context tend to be extremely generalized, with researchers assuming that their WEIRD findings are universal (Henrich et al., 2010). Moreover, considering that studies in cognitive science carried out in large universities take place primarily with undergraduates of this discipline, it is also possible to notice a wide experimentation with college students who, categorically speaking, represent an almost irrelevant portion of the global population spectrum. Thus, in many cases, when they set out to study the human mind and human behavior, many scientists are actually presenting results related to a small portion of an already peculiar WEIRD population (Henrich et al., 2010). It is no stranger to the notion that much of the psychology and neuroscience of the last century actually represents how this group thinks, behaves, learns, and remembers, stipulating an unattainable threshold for the true mix of ethnic, contextual, environmental, nutritional, and psychological conditions of the real world. With this, in order to truly understand the human vicissitudes, it is pertinent to have works in non-WEIRD populations, which present relevant data to the most diverse groups and, thus, can contribute to a greater understanding of individuals in their various contexts.

An example of this proposal can be the study by Begus et al. (2016), which assessed the working memory of infants aged 12–16 months in rural Africa, specifically in Gambia, one of the smallest countries on the continent. This study’s results showed that the neural activity when infants observe objects being hidden for 3 seconds compared to 6 seconds is different, and it is possible to assume that this activation reflects the babies holding in their minds the representation of objects as naturally occurs in child development, but when these results are compared to British babies, it is possible to see a difference between the two samples, this result implies a significant difference between the two populations, and such difference can be related to the socioeconomic context present in the development of these groups, also suggesting that neurodevelopment in at-risk and impoverished regions is less prominent (Katus et al., 2020). Furthermore, similar studies in marginalized groups in India have also shown impaired performance (Wijeakumar et al., 2019). Considering this example, it is possible to assume that results considering only children born in the United Kingdom would invariably be biased and, more broadly, could not be functional for cognitive educational practices applied to populations in extreme poverty contexts, but when one takes only this WEIRD sample as representative of so-called ideal child development, any and all disadvantaged contexts will a priori fall short. Therefore, a qualitatively different look at the contextual differences implies an understanding of the developmental potentials in certain situations and, with this, a perspective that allows for more effective practices in an educational environment, as an example.

Growing up in a family with a low socioeconomic status can be associated with a wide range of negative consequences throughout life, affecting cognition and physical and mental health (Hackman et al., 2010; McEwen & Gianaros, 2010; Ursache & Noble, 2016), but it is still not known exactly which elements influence development in the face of socioeconomic risk factors, and even studies with different socioeconomic perspectives need to be observed in relation to context. As an aggravating factor, situations of violence and chronic stress can happen during childhood and are inexorably present in less developed countries with high social inequality. Nevertheless, even in industrialized countries, the gap found between individual income is considerable, influencing developmental stimulation due to the social inequalities (Farah, 2018). Studies that only consider individuals within the same country, yet still in a particular portion of it, similarly limit the very representativeness of their data.

In view of what has been presented so far, in this chapter we will discuss the development of attention, relating it to academic performance in children and adolescents from non-WEIRD populations. As a primary cognitive function, attention is one of the most vulnerable mechanisms to adversities in early life and, despite this, it is involved in a number of fundamental mental skills needed for various daily applications, including those related to academic performance (Blair & Raver, 2016; Posner et al., 2006). In view of such importance, we will examine how this cognitive function can be impacted in various non-WEIRD contexts, aiming at an understanding that may allow future educators and researchers to better prepare their practice in these contexts, considering populations that live and develop in emerging countries, with diverse ethnicities and/or unique social contexts, but no less important, such as countries with volatile political scenarios, few educational resources, and/or extreme poverty.

Conceptualization of Attention

According to William James (1890),

Every one knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains [p. 404] of thought. (p. 256).

However, despite being a process known by everyone (at least in James’ view), attention is difficult to characterize. As a prerequisite for satisfactory performance in many cognitive and behavioral tasks, attention has a complex functioning and is not related to a single brain region, neither can it be characterized by a singular functioning—contrary to the assumption of much of the common sense, attention is not a unitary entity, but one that is divided into different domains (Cohen, 1993; Fiebelkorn & Kastner, 2020; Parasuraman, 1998; Posner et al., 2019).

Attention involves the allocation of mental resources, that is, the selective directing of these resources toward sensory stimuli, actions, or cognition, such as memory and thoughts (Cohen, 1993; Raz & Buhle, 2006). Thus, this cognitive function, despite its limited capacity—an aspect that is also sometimes ignored by popular knowledge—allows for the careful use of cognitive resources (Cohen, 1993; Krauzlis et al., 2018; Treisman, 2004). One of the most cited and discussed attention models in the literature is the one proposed by Posner and Petersen (1990), which differentiates at least three attentional systems: (1) initiation and maintenance of awareness and vigilance—important for identifying and responding to stimuli; (2) orientation to the appropriate sensory stimulus to process (i.e., the so-called target stimulus); (3) executive control to identify and focus on the stimulus, i.e., to volitionally control responses to stimuli (Petersen & Posner, 2012). Although these are considered independent attentional networks, as well as functionally and neuroanatomically distinct, they still operate in close relation for the intentional regulation of behavior and cognition (Fan et al., 2002; Posner & Boies, 1971; Raz & Buhle, 2006). In this sense, executive attention has also been related to concepts such as emotional regulation and self-control, i.e., behavioral self-regulation (Rueda et al., 2005). Posner’s proposed model is widely used in experimental models, serving as a basis for understanding neurobiological aspects of attention and providing clinical applications. However, there are other taxonomies, which will also be discussed below.

Since the 1980’s, an effort has been made to develop behavioral tests to assess the different components of attention. As mentioned previously, several authors have emphasized that attention is not a unitary construct but consists of distinct and often complementary mechanisms; hence, some operational definitions of attention have been established. Attention can also be classified as selective, divided, and concentrated, and although there is no consensus in the literature about this taxonomy, everyone agrees that it involves multi-components (see Fig. 1), different processes that can even influence each other and cooperate together for cognitive or behavioral actions (Parasuraman, 1998; Raz & Buhle, 2006).

Fig. 1
Two images of conceptualization of attention. A. A schematic of different types of attention comprise of selective, divided and susteined attention. B. Two boxes namely exogenous and endogenous attention with two upward and downward arrows between the boxes.

(a) Different types of attention; (b) two mental processes related to the attention function

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The definition of selective attention is intertwined with the definition of attention itself. It is a system responsible for selection and inhibition: activating cognitive resources and prioritizing the processing of only one (or some) stimulus while voluntarily ignoring others available at the moment (Cohen, 1993; Krauzlis et al., 2018; Pashler, 1998). Therefore, this attentional system has the function of selective orientation, being influenced by stimulus characteristics such as novelty and relevance and by internal factors such as motivation (see Fig. 2). The existence of this phenomenon in other animals—such as amphibians, fish, and birds—indicates its highly adaptive function, evolutionary importance, and complexity (Krauzlis et al., 2018).

Fig. 2
A schematic for different types of attention are as follows. 1.Selective attention comprises motivation, competing stimuli, saturation, external stimulation, internal changes. 2. Divided attention comprises type of task involved, automation and training. 3. Susteined attention comprises fatigue, task time, digression & monotony motivation and declining resources.

Factors that affect different types of attention

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On the other hand, sustained attention is characterized by the ability to maintain attentional focus for a prolonged period on a specific, monotonous task (such as responding to a stimulus that arises infrequently and unpredictably), but which requires fast and accurate responses to stimuli (Fortenbaugh et al., 2017; Raz & Bohle, 2006). Thus, an important differentiation of this type of attention from the others is the task duration, and although some authors have proposed specific delineation of this duration, there is no consensus regarding its extent (Fortenbaugh et al., 2017) (Fig. 1a). There are different hypotheses for performance impairment in this type of task over time, such as (1) decline in attentional resources over time; (2) its repetitive and/or monotonous aspect, which would lead to task disengagement; (3) mind-wandering, which is experienced as time goes by while performing the task, consuming attentional resources that were initially focused on the activity in progress; and (4) motivation or reward involved in the task (Helton et al., 2005; Helton & Russel, 2015; Manly et al., 1999; Parasuraman et al., 2009; Robertson et al., 1997; Smallwood & Schooler, 2006; Stuus et al., 1995) (see Fig. 2).

This concept, sustained attention, is often used as a synonym for vigilance; other authors do not consider them synonymous, but rather that it depends on the task. For example, sustained attention tasks require a continuous response over a long period of time. An example is Conners’ Continuous Performance Test (CCPT), where the subject must respond to every letter that appears on a computer screen, except for the letter X, and is thus used to obtain quantitative information regarding an individual’s ability to sustain attention over time (Conners et al., 2018). Vigilance tasks, on the other hand, have been considered those that require responses to rare and non-predictive events, for example, radar operators waiting for a nonfrequent signal. In these situations, the quality of attention is fragile and declines over time—this is called vigilance decrement. Vigilance decrement is defined as a gradual decline in the detection rate of target signals over time, or an increase in the response time to detect something. In other words, vigilance decrement also depends on perceptual and decision-making factors because the key is to determine whether vigilance declines due to a loss in perceptual sensitivity to detect the signal, or whether it is due to changes in decision criteria, which has been studied as signal detection theory (Parasuraman, 1998).

However, it is not always possible to perform one task at a time, so attention is continuously subject to being divided between a variety of stimuli and activities. Thus, divided attention is characterized by the sharing of attentional resources between two or more stimuli (or tasks) due to the infeasibility of parallel processing (Parasuraman, 1998) (Fig. 1a). In these situations, more than one stimulus is relevant, and the organism must respond concurrently to them; thus, cognitive resources are shared between different spatial locations, features of an object, different targets, or sensory stimuli (Braun, 1998; Cohen, 1993) (Fig. 2). Divided attention requires more control than selective attention because it is more complex and requires more voluntary allocation of attentional resources and executive control (Hahn et al., 2008).

There is also a discussion about the possibility of another subtype of attention: alternating attention. In this modality, instead of the simultaneous processing of stimuli (or tasks), there would be an alternation of attentional resources, that is, the individual would disengage from one stimulus to engage in another (Fiebelkorner & Kastner, 2020). Although they are considered different subsystems by some authors (Hirst et al., 1980), there is no consensus in the literature in this regard, and studies consider it to be the same subtype of attention, involved in the so-called “dual-task” (Franz, 2012; Hahn et al., 2008; Parasumaran, 1998). Many authors still research the topic within the construct of executive functions under the name of alternation (shifting) (Miyake & Friedman, 2012; Miyake et al., 2000; Petersen & Posner, 2012). When executing two or more tasks simultaneously—as has possibly been observed at some point by everyone—there may be interference of an activity in another, an event that will depend on the intrinsic characteristics of each task, including the automatization of one of them, and, according to Shneider and Shiffrin (1977), it is only possible to execute two actions if one has undergone the process of automatization (Hirst et al., 1980).

Mental processes can be controlled or automatic (Fig. 2), despite some controversies about this dual division (for a review, see Melnikoff & Bargh, 2018). Controlled processes are intentional, involve active control by the subject, and have more limited capacity; thus, they can be easily modified (Moors, 2016; Schneider & Shiffrin, 1977). In contrast, automatic processes are rapid, involuntarily triggered, modifying controlled actions that are in progress, directing the attentional focus, and independent of the subject’s active control (Moors, 2016; Schneider & Shiffrin, 1977). Some tasks involve automatic processes from their initial execution, while others may become automated after successive training, and even complex tasks may undergo the automatization process and then become actions performed involuntarily, without effort (Bago & De Neys, 2017; Pennycook, 2017; Schneider & Shiffrin, 1977; Spelke et al., 1976). Once automatized, such processes become difficult to suppress, ignore, modify, or the ongoing actions interrupted (Moors, 2016; Schneider & Shiffrin, 1977). Such processes are also called endogenous and exogenous attentional modalities, or top-down and bottom-up, respectively. The former (top-down) can also be called voluntary attention, is triggered at a slower rate, but is maintained for longer periods of time than exogenous (bottom-up, or involuntary) attention. On the other hand, involuntary attention ensures more prompt allocation of resources and is maintained for a shorter period of time and in a less flexible manner than endogenous attention (Bowling et al., 2019; Chica et al., 2013).

One can notice, then, that despite different classifications, several mechanisms are acting together to perform a certain task. For example, a student during a class should direct his attentional resources to the subject taught by the teacher, while ignoring competing stimuli (for example, the conversation of a classmate next to them, which requires them to self-regulate their behavior). This is a scenario that involves endogenous attention, that is, top-down processing—which will require effort, voluntary direction. Considering the length of a regular class, the student must maintain this attentional focus, and sometimes divide (or alternate) it with the task of writing what the teacher says, for example. Also, according to the assumptions elucidated above, for a student who is already proficient in writing, this divided task will be less demanding—after all, the writing process has already been automatized. Hence, one can observe the heterogeneity involved in this phenomenon, which is often not treated with due complexity, being disregarded, for example, the social variability inherent to its development, which will be discussed below.

Childhood and Adolescence Development in Non-WEIRD Contexts and Academic Achievement

Over the years, the development of attention has been studied from different points of view, either to understand other cognitive processes such as memory, perception, regulation of attention, and behavior related to brain development. In the clinical population, there is also an important impact of these studies, where the predictive value of early attention problems for future dysfunctions in both attentional processes and other areas of development has been sought to be understood (van de Weijer-Bergsma et al., 2008). One factor to be considered is that temporal dissociations of attention processes are evident throughout development (Amso & Scerif, 2015), a model known as attentional networks that divides attention into networks of functions consisting of alertness, orientation, and executive control, which seem less independent in childhood (Rueda et al., 2004; Suades-González et al., 2017), as already explained in the previous topic.

The development of attentional orientation is important because efficient attentional orientation to environmental stimuli is essential in activities of daily living and is a prerequisite for the acquisition of skills such as reading comprehension, operational memory, and executive control. The period between 6 and 10 years old is crucial for the development of endogenous attention and inhibitory control, although the preschool period is also important (Leclercq & Siéroff, 2013).

Reynolds and Romano (2016) point out that the general arousal/attention system shows significant developmental changes throughout early childhood, characterized by gains in both the magnitude and duration of sustained attention periods, and these developmental gains in sustained attention are related to improved performance on working memory tasks. Studies of infants in Western countries have shown that the basic attentional capacities (orienting, anticipating, and processing) emerge gradually during the first year (Pyykkö et al., 2019). With regard to selective attention, there is a prolonged development during childhood, with studies pointing out that younger children fail to filter out irrelevant information, distributing their attention between what is relevant and what is not, which results in processing more information than necessary and therefore less efficiently (Plebanek & Sloutsky, 2019). At school age, the influences of attention on the maintenance of working memory are less efficient in 6- and 11-year-old children in comparison to older adults and adolescents (Amso & Scerif, 2015). The abilities to select among competing stimuli and to preferentially process more relevant information are essentially available in very young children, but the speed and efficiency of these behaviors improve with development (Stevens & Bavelier, 2012), and so there are functional changes in selective attention that occur during childhood and adolescence.

For most developmental theories, maturation of selective and sustained attention processes occurs during adolescence (Smith et al., 2011), but there is a paucity of studies in the period between 12 and 20 years old. For many authors, attention would not constitute a simple construct with a fixed ontogenetic trajectory, but a multidimensional construct with different developmental trajectories in its various components (Plude et al., 1994). Another relevant factor to be considered in studies of attention development in adolescence is the pubertal stage, a criterion of physiological/sexual maturity with neuroendocrine events that influence cognition and that explain this developmental trajectory better than chronological age (Steinberg, 2005; Zanini et al., 2021). Furthermore, the scarcity of systematic research in other cultural contexts is a major impediment to theoretical progress in this area (Nielsen et al., 2017), as cultural differences may in part reflect differences in socioeconomic status (Zanini et al., 2021), which has been strongly supported by many researchers (Nisbett & Masuda, 2003).

In a literature review study, van de Weijer-Bergsma et al. (2008) showed that prematurity seems to be related to lower efficiency of attentional orientation in infants (first year of life), and problems with sustained attention become more evident at preschool age as age increases. Other environmental factors such as maternal IQ, educational level, and socioeconomic status did not show consistent results and the authors point out the lack of studies in this age group (infants and preschool children). Regarding cross-cultural studies, Köster et al. (2018) evaluated 144 5-year-old children from three different cultural contexts (urban area of Germany, rural area of Cameroon Republic, urban area of Japan) to investigate different aspects of children’s visual attention processes. The results showed that children’s visual attention in rural Cameroon differs from both urban contexts, characterized by a high object focus across tasks. According to the authors, these differences may be based on different cultural and environmental learning experiences, such as children’s basic familiarity with materials and stimuli, rather than culturally transmitted attentional styles. In this same line of studies, Jurkat et al. (2020) evaluated the visual attention of different age groups from middle-class urban families in Germany and from a rural area in the Republic of Cameroon. The results were similar; i.e., differences in visual attention between cultures were contingent on the familiarity of the corresponding culture-specific stimuli, thus suggesting that the familiarity of a stimulus strongly affects individuals’ visual attention, meaning that stimulus familiarity needs to be considered when investigating culture-specific differences in attention styles.

Pyykkö et al. (2019) analyzed the development of visual attention abilities in a sample of infants in rural Malawi, focusing on three aspects: (a) visual search, (b) anticipatory responses, and (c) allocation of attention by measures known to be sensitive for assessing the development of visual attention processes. The authors showed that compared to studies in Western populations there was a similar pattern of group-level results across tasks; they also showed that there are changes in most of these measures between 7 and 9 months. Individual variations in infants’ attention abilities were moderately stable across tests, but not related to socioeconomic factors such as prematurity, nutritional status, and psychosocial stress. First, it must be considered that socioeconomic status corresponds to a complex set of social and economic factors such as educational level, income, living conditions, and family purchasing power, which should be considered together in studies that analyze this variable in cross-cultural studies (Zanini et al., 2021). Second, Pyykkö et al. (2019) consider that infants’ early attention abilities may emerge relatively independently of variations in the early environment; the development of these functions is more dependent on “optimal” physical growth and environmental support or stimulation. A recent paper assessed selective attention in 4-year-old children from low and high socioeconomic backgrounds over the course of a year. This study used an auditory selective attention paradigm, in which children listened to two different stories at the same time to check their ability to retain attention on only one of them. The results of both the behavioral test and electroencephalography measurements showed that the development of selective attention is influenced by economic background; that is, individuals from groups with high socioeconomic standards showed more efficient responses to stimuli than their peers of the same age but from a poorer socioeconomic background (Hampton Wray et al., 2017).

Because of this, it is important to analyze studies that are related to academic performance, as this is determined by a variety of factors, including educational opportunity, socioeconomic status, social skills, personality traits, and cognitive abilities (Stevens & Bavelier, 2012). These authors reviewed studies that evaluated the relationship between auditory attention and aspects relevant to academic performance, such as language, literacy, and mathematics. They found that deficits in selective auditory attention are related to language processing difficulties, that selective attention may be important for establishing the neural circuits important for efficient reading, and finally that there is a relationship between attention and math skills, mediated by the effect of selective attention on working memory. However, most of the studies reviewed were conducted in a WEIRD population. Due to this, the study by Alavi et al. (2019) stands out, which evaluated typically developing Malaysian children, aged 7–12 years, to determine whether attention or impulse control could predict overall academic performance, as well as gender difference. Teacher-returned questionnaires were used, which showed that the higher the teacher’s perception of attention and impulse control skills, the better the academic performance. Girls were rated with higher levels of attention and impulse control than boys; the gender of the children did not moderate the relationships between attention or impulse control and academic performance or impulse control. This reinforces previous studies, i.e., that the data is similar when studying typically developing children from far eastern countries living in Malaysia compared to western children.

According to Resett (2021), there was an increase in the levels of schooling achievement by children and adolescents throughout the twentieth century in industrialized and post-industrialized nations. This is a psychosocial variable of great relevance, and academic performance is linked to educational institutions, i.e., an estimated measure of skills, aptitudes, or knowledge that a student learned as a result of a formal instructional process, and that these educational institutions may vary in countries with non-WEIRD populations. Resset (2021) examined whether the attention performance of children and adolescents would be a predictor of school performance, and for this, they evaluated 155 children and adolescents (9–15 years old) from a province in Argentina. It was observed that the reaction time and the number of correct answers in the D2 test, which evaluates selective attention, were predictors of math, language, and basic science scores in children. In adolescents, on the other hand, they predicted scores in math, language, English, and biology, corroborating previous studies in the American population. Similar results were found by Abreu et al. (2017) on selective attention tasks, which found correlations with a test of performance in reading, writing, and arithmetic, although these correlations were weak. In none of these studies, from Argentina and Brazil, did the authors control for socioeconomic variables. What is important for teachers and professionals who work with children and adolescents? The literature generally points out that sustained attention is especially relevant in the school context because complex problem solving is an important prerequisite for school performance; there is a causal relationship between visual attention and reading acquisition; attention and visual perception predict math scores (Anobile et al., 2013; Steinmayr et al., 2010), and that the literature in non-WEIRD populations is especially sparse to conclude how cultural factors, and here we emphasize socioeconomic level, influence attention development and academic performance.

According to Henrich et al. (2017, p. 79),

perhaps there are some domains in which researchers could expect phenomena to be more universal than they are in other domains. We believe that the degree of universality does likely vary across domains, although this has yet to be demonstrated.

According to this author, some cognitive functions, such as attention, have a low interpopulation variability and this may be due to physiological or even genetic factors. In fact, in some studies conducted by our research group, we compared the performance of Brazilian children and adolescents in the CCPT, which assesses sustained attention, with American performance norms. Both young children (4 and 5 years old) and older children (6–11 years old) had better scores in the Brazilian sample on some measures than in the American sample. We attribute these differences to sample selection, as the Brazilian sample excluded participants with attention deficit disorder, screened by the Conners’ scale, unlike the American sample (Miranda et al., 2008, 2009).

Conclusion

Attention is a prerequisite for good performance in other cognitive functions (such as memory and executive functions) and is involved in the performance of motor or mental activities. Thus, interest in the functioning of attention (in its healthy or pathological state) permeates the study of several fields, such as cognitive, learning, and developmental models. However, despite a large number of studies on attention, most of them have samples from the WEIRD population, raising the question whether the findings of these studies can be extended to the non-WEIRD population. Thus, this chapter sought to present the main attentional models, different taxonomies proposed, and the explanation of their non-unitary functioning. We also presented studies with a WEIRD population, which were contrasted with those with non-WEIRD populations. Finally, it is noted that there is still a gap to be filled in this field, requiring more studies, and especially, with methods that allow more robust evidence regarding possible divergences between the development and functioning of attention in WEIRD and non-WEIRD populations.