Over the course of several decades, Demetriou and colleagues have constructed and refined a comprehensive theory of cognitive development that resembles, in many ways, earlier constructivist theories (e.g., Piaget, Bruner, Case, Fischer) while also integrating ideas from the psychometric tradition and generating testable hypotheses that have received empirical support. The theory is notable not only for its broad scope, but also because it addresses the importance of reflection, or “cognizance,” taking account of Piaget’s [2001/1977] later work on reflecting abstraction as well as more recent research on metacognition, theory of mind, and the role of reflective reprocessing in executive function, or the conscious cognitive control of behavior.

According to Demetriou, Makris, Spanoudis, Kazi, Shayer, and Kazali [2018, this issue], the core aspects of human cognition are captured by developmental g (psychometric g × age), which is comprised of attentional control, cognitive flexibility, working memory, cognizance, and inference. From infancy through adolescence, these core aspects work together via an iterative process to abstract common properties of representations, align them, and then reflect upon them, bringing them under conscious control, thereby transforming representations from episodic to realistic to rule-based to principle-based representations. Demetriou et al. [2018, this issue] state that “cognizance allows feedback loops where cycles of abstraction and alignment can become the object of further abstraction and alignment that are represented into new mental units.” This results in systematic changes in the contribution of core processes to g.

Insofar as the theory relies on abstractly specified, domain general developmental mechanisms, it is vulnerable to some of the criticisms that have plagued other grand theories, such as neglecting the roles of emotion, motivation, stress, intuition, and creativity in cognitive development; ignoring the way specific cognitive developmental processes are instantiated in the brain; and failing fully to capture the extent to which development is context dependent and heavily influenced by culture and by specific social interactions.

The theory is novel in many ways, but in what follows, I attempt to abstract several key points of convergence with other theories and observations, with the hope that doing so might reveal a degree of consensus in the field concerning essential features that will need to be addressed by any adequate theory of cognitive development. These features, which might be considered “generalizations about cognitive development” [cf. Bjorklund & Causey, 2018], include the following: (a) increases in intellectual performance are made possible by increases in the abstractness and hierar-chical complexity of underlying representations that in turn allow for greater intentional control; (b) the core cognitive processes that produce changes in underlying representations involve executive function (EF) skills (attentional/inhibitory control, cognitive flexibility, and working memory) and reflection, and (c) reflection can be trained, leading to generalizable improvements in intellectual function.

Demetriou et al. [2018, this issue] follow a long line of developmental theorists who identify four distinct levels of intellectual performance corresponding to infancy (e.g., Piaget’s sensorimotor stage), early childhood (e.g., preoperational), middle childhood (e.g., concrete operational), and adolescence (e.g., formal operational), and then suggest that these levels are supported by different kinds of mental representation. Changes in the nature of children’s representations afford increases in the flexibility and adequacy of reasoning across a wide range of content domains. Like Piaget and others, Demetriou et al. [2018, this issue] argue that with development, representations become increasingly abstract, hierarchically complex, and subject to intentional control. These three features are closely related because more abstract representations are created out of less abstract representations through a process of (hierarchical) integration, resulting in more abstract goals and action-oriented plans that support more flexible, top-down (intentional) control of behavior.

As noted by Demetriou et al. [2018, this issue], abstraction, hierarchical complexity, and increasing intentional control are also key features of theoretical accounts rooted in computational and developmental cognitive neuroscience [e.g., Taylor, Hoobs, Burroni, & Siegelmann, 2015]. For example, Munakata, Synder, and Chatham [2012] describe changes in children’s representations from concrete objects to categories to abstract goals, and they propose that “increasingly abstract representations support selection from among options by providing top-down support for a limited pool of competitors.” Representations become more abstract based on exposure to variable experience with a range of tasks [Rougier, Noelle, Braver, Cohen, & O’Reilly, 2005]. Similarly, according to the iterative reprocessing model [e.g., Zelazo, 2015], there are experience-dependent developmental increases in the hierarchical complexity of the rule systems that can be formulated and maintained in working memory. More complex rule representations are inherently more abstract (in that they apply across a wider range of situations) and allow for more flexibility and intentional control, as manifested in specific EF skills that continue to improve into early adulthood. These accounts are supported by research on the development of rule use [e.g., Amso, Haas, McShane & Badre, 2014; Bunge & Zelazo, 2006; Crone, Donohue, Honomichl, Wendelken, & Bunge; 2006; Unger, Ackerman, Chatham, Amso, & Badre, 2016; Zelazo, Muller, Frye, & Marcovitch, 2003], which shows clear age-related increases in abstraction, hierarchical complexity, and intentional control, including a progressive shift from more reactive to more proactive control over behavior [e.g., Doebel, Barker, Chevalier, Michaelson, Fisher, & Munakata, 2017]. In addition, there is evidence that the formulation and use of more complex rules that control the application of simpler rules (e.g., if color game, then if red, then it goes here) involves the recruitment of increasingly anterior regions of lateral prefrontal cortex into an increasingly complex, hierarchically arranged network of prefrontal cortex regions, where higher levels in the hierarchy operate on the products of lower levels [e.g., Badre & D’Esposito, 2007; Botvinick, 2008; Christoff & Gabrieli, 2000; Goldberg & Bilder, 1987; Koechlin, Ody, & Kouneiher, 2003; Ranti, Chatham, & Badre, 2015]. The growing integration of neural regions yields representations that are increasingly abstracted away from immediate experience, allowing for psychological distance on one’s experiences [Carlson & White, 2013; Dewey, 1985/1931; Sigel, 1993; Werner & Kaplan, 1963].

A growing body of research has highlighted the central contributions to cognitive development of executive function skills, as well as the reflective, metacognitive skills that are intertwined with them [e.g., Chevalier & Blaye, 2016; Lyons & Zelazo, 2011; Roebers, 2017; Zelazo, Blair, & Willoughby, 2017]. Reflection refers to the processes of noticing conflict, pausing, considering options, and putting things into context prior to responding – processes that require EF skills. Having reflected on a problem, children are then in a position to exercise their EF skills more effectively, often using self-directed speech as they do so. EF skills are the neurocognitive skills necessary for the top-down, goal-directed modulation of attention (and behavior), typically measured as inhibitory control, cognitive flexibility, and working memory [Miyake et al., 2000]. With experience, reflection and executive function occur more automatically and more quickly, providing more time for thoughtful consideration of options prior to overt action or to decision making, and allowing for more intentional, flexible, and adaptive behavior.

Makris, Tachmatzidis, Demetriou, and Spanoudis [2017] examined performance on a wide array of measures in an effort to determine which cognitive processes constitute g, a general psychometric factor that accounts for intercorrelations among cognitive tasks. Results suggested that g includes attentional control, cognitive flexibility, working memory, cognizance, and inference. According to Demetriou et al., “cognizance is the part of consciousness applied on cognitive processes; it is the process of becoming conscious of mental content (e.g., ‘I know that I am thinking about numbers’) and cognitive processes (e.g., ‘I know that I am looking for the bigger number in a series,’ ‘I knew this information,’ etc.) and reflecting on and evaluating them vis-à-vis a goal.” Elsewhere, they add an important function that is accomplished by cognizance, “meta-representation encoding the products of abstractions and alignments into new representations.” This function has been studied in the context of theory of mind [e.g., Perner, 1991], and bears some resemblance to representational redescription [Karmiloff-Smith, 1991] as well as the ad hoc formulation of rules in self-directed speech [Zelazo, 2015]. Taken together, this work suggests a major shift in cognitive theorizing towards accounts that take seriously subjective experience (awareness) and emphasize the extent to which development involves increasing conscious control over behavior and cognition.

Demetriou et al. [2018, this issue] describe evidence that cognizance mediates between EF skills and inference, or reasoning, and that training cognizance has the potential to yield transfer to other skills and to a range of novel situations. As Demetriou put it, “there is a special class of processes in concern to transfer of learning: mediating processes, such as cognizance, if directly affected by training, would normally spread the effect both ways, top-down and bottom-up.” This suggestion receives support from a growing body of evidence that indicates that EF skills can be promoted by interventions that provide children with opportunities to practice reflection and EF at increasing levels of challenge [e.g., Diamond & Lee, 2011, for a review]. These interventions typically require children to pause and reflect before responding: in other words, to be more deliberate about their cognition and behavior. The repeated engagement and use of reflection and EF skills in problem solving evidently strengthen those skills, increase the efficiency of the corresponding neural circuitry, and increase the likelihood that the skills will be activated in the future [Zelazo, 2015]. Although there are questions about the extent to which the benefits of EF training transfer to new situations [e.g., Karbach & Kray, 2009], it has been proposed that supplementing direct EF skills training with reflection training facilitates transfer by inducing metacognitive awareness of the skills and their range of application [Zelazo, 2015].

Espinet, Anderson, and Zelazo [2013], for example, provided preschool-age children with approximately 20 min of “reflection training” in the context of a challenging EF task, the Dimensional Change Card Sort (DCCS), and found evidence of transfer to flexible perspective taking (a false belief task) as well as training-induced changes in neural activity. Children who perseverated on the DCCS were taught to pause before responding, reflect on the conflict inherent in the task, and formulate higher-order rules for responding flexibly: “In the color game, if it’s a green pig, then it goes here; but in the shape game, that same green pig goes there.” Compared to children who received only minimal yes/no feedback (without practice in reflection) and to children who received mere DCCS practice with no feedback at all, children who received reflection training showed significant improvements in performance on a subsequent administration of the DCCS. These behavioral changes were accompanied by predictable changes in children’s brain activity, specifically attenuated amplitude of the N2 component in the event-related potential, a marker of conflict detection. Similar findings were reported by Moriguchi, Sakata, Ishibashi, and Ishikawa [2015], who also provided 3- to 5-year-old children with practice on the DCCS, but then had children teach the rules to a puppet, which demands consideration and reconsideration of what is being taught. Compared to controls, trained children showed considerable improvement in performance on the DCCS along with increased brain activity (oxygenated hemoglobin) in the left lateral parts of the prefrontal cortex.

Demetriou et al. [2018, this issue] describe a comprehensive evidence-based theory of cognitive development that aligns well with theory and research from earlier grand theories of development as well as with more recent work in developmental cognitive neuroscience. First, Demetriou et al. characterize three primary dimensions along which development proceeds: there are increases in the abstractness and hierarchical complexity of underlying representations that allow for increasing intentional control over thought and action. Second, EF skills and reflection are identified as foundational skills that make it possible for children to adapt more effectively to the challenges they face. Third, not only can reflection be trained, but improvements in reflection support transfer of training, leading to generalizable improvements in intellectual function. These features of Demetriou et al.’s proposal suggest an emerging consensus around key characteristics of cognitive developmental change that should inform and constrain future work in the field.

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