The other side of cognitive control: can a lack of cognitive control benefit language and cognition?

Children's automatic evaluation of self-generated actions is different from adults

Performance monitoring (PM) is central to learning and decision making. It allows individuals to swiftly detect deviations between actions and intentions, such as response errors, and adapt behavior accordingly. Previous research showed that in adult participants, error monitoring is associated with two distinct and robust behavioral effects. First, a systematic slowing down of reaction time speed is typically observed following error commission, which is known as post-error slowing (PES). Second, response errors have been reported to be automatically evaluated as negative events in adults. However, it remains unclear whether (1) children process response errors as adults do (PES), (2) they also evaluate them as negative events, and (3) their responses vary according to the pedagogy experienced. To address these questions, we adapted a simple decision-making task previously validated in adults to measure PES as well as the affective processing of response errors. We recruited 8- to 12-year-old children enrolled in traditional (N = 56) or Montessori (N = 45) schools, and compared them to adults (N = 46) on the exact same task. Results showed that children processed correct actions as positive events, and that adults processed errors as negative events. By contrast, PES was similarly observed in all groups. Moreover, the former effect was observed in traditional schoolchildren, but not in Montessori schoolchildren. These findings suggest that unlike PES, which likely reflects an age-invariant attention orienting toward response errors, their affective processing depends on both age and pedagogy.

Uncovering the association between fatigue and fatigability in multiple sclerosis using cognitive control

BACKGROUND

Fatigue and cognitive dysfunction are two common symptoms experienced by patients with multiple sclerosis (MS). The relationship between subjective and objective fatigue (fatigability) in MS is poorly understood. Cognitive control tasks might be more conducive to fatigability and more likely to show associations between subjective and objective cognitive fatigue in MS.

OBJECTIVE

To study the association between objective fatigability, as induced by a cognitive control task called the Blocked Cyclic Naming Task (BCNT), subjective fatigue and baseline cognitive functioning in patients with MS.

METHODS

Twenty-one patients with MS completed baseline questions about their disease, the Montreal Cognitive Assessment (MoCA) battery and self-reported questionnaires on trait fatigue, sleep and depression. Disability was captured using the expanded disability status scale (EDSS). Participants then performed the BCNT and were asked about their level of state momentary fatigue before and after the BCNT. The BCNT consists of several blocks of either related or unrelated pictures that participants name as quickly as possible. The pictures cycled 4 times in each block and the difference in the response times (RTs) between related and unrelated blocks was captured. Data were analyzed using repeated measures analysis of variance and Pearson correlations.

RESULTS

MS participants' performance declined for the related, but not unrelated blocks. The difference in RTs between related and unrelated conditions increased with repetition across cycles (p < 0.001). Participants also showed objective fatigability with less repetition priming (p = 0.02) in the 4th quarter and with greater differences between related and unrelated conditions in the later part of the task. Objective fatigability was strongly associated with participants' assessment of their level of momentary state fatigue (r = 0.612, p = 0.007).

CONCLUSION

Using the appropriate tools, this study showed an association between subjective and objective cognitive fatigue in people with MS. The BCNT and cognitive control are useful tools in assessing patients with MS and should be explored in future, larger studies in this population.

Developmental changes in automatic rule-learning mechanisms across early childhood

Infants' ability to learn complex linguistic regularities from early on has been revealed by electrophysiological studies indicating that 3-month-olds, but not adults, can automatically detect non-adjacent dependencies between syllables. While different ERP responses in adults and infants suggest that both linguistic rule learning and its link to basic auditory processing undergo developmental changes, systematic investigations of the developmental trajectories are scarce. In the present study, we assessed 2- and 4-year-olds' ERP indicators of pitch discrimination and linguistic rule learning in a syllable-based oddball design. To test for the relation between auditory discrimination and rule learning, ERP responses to pitch changes were used as predictor for potential linguistic rule-learning effects. Results revealed that 2-year-olds, but not 4-year-olds, showed ERP markers of rule learning. Although, 2-year-olds' rule learning was not dependent on differences in pitch perception, 4-year-old children demonstrated a dependency, such that those children who showed more pronounced responses to pitch changes still showed an effect of rule learning. These results narrow down the developmental decline of the ability for automatic linguistic rule learning to the age between 2 and 4 years, and, moreover, point towards a strong modification of this change by auditory processes. At an age when the ability of automatic linguistic rule learning phases out, rule learning can still be observed in children with enhanced auditory responses. The observed interrelations are plausible causes for age-of-acquisition effects and inter-individual differences in language learning.

Predicting future learning from baseline network architecture

Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration

The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germane to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called non-negative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine learning approach automatically groups temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role that subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.

Developmental increases in white matter network controllability support a growing diversity of brain dynamics

As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. Here we use a network representation of diffusion imaging data from 882 youth ages 8-22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development. Notably, stable controllers in subcortical areas are negatively related to cognitive performance. Investigating structural mechanisms supporting these changes, we simulate network evolution with a set of growth rules. We find that all brain networks are structured in a manner highly optimized for network control, with distinct control mechanisms predicted in child vs. older youth. We demonstrate that our results cannot be explained by changes in network modularity. This work reveals a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture.

A novel ecological account of prefrontal cortex functional development

In this paper, we argue that prefrontal cortex ontogenetic functional development is best understood through an ecological lens. We first begin by reviewing evidence supporting the existing consensus that PFC structural and functional development is protracted based on maturational constraints. We then examine recent findings from neuroimaging studies in infants, early life stress research, and connectomics that support the novel hypothesis that PFC functional development is driven by reciprocal processes of neural adaptation and niche construction. We discuss implications and predictions of this model for redefining the construct of executive functions and for informing typical and atypical child development. This ecological account of PFC functional development moves beyond descriptions of development that are characteristic of existing frameworks, and provides novel insights into the mechanisms of developmental change, including its catalysts and influences. (PsycINFO Database Record

Evolution of brain network dynamics in neurodevelopment

Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8-11 and 19-22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to young adulthood. Collectively, these results suggest that healthy development is accompanied by an increasing precedence of executive networks and a greater switching of the regions and interactions subserving these networks.

Feminist theory and contemporary developmental psychology: The case of children’s executive function

Burman’s landmark book, Deconstructing Developmental Psychology, criticized the field from three perspectives: decontextualized measurements and depictions of children’s behavior; androcentric biases; and covert political frameworks. In this article, Burman’s analysis is applied to the current state of cognitive developmental research in general, and then specifically with a focus on a hot topic, children’s executive function (cognitive self-control). Suggestions are made for how adopting Burman’s framework to deconstruct executive function research and theorizing can be used to construct an enriched, more complete, account of the development of executive function.

The development and malleability of executive control abilities

Executive control (EC) generally refers to the regulation of mental activity. It plays a crucial role in complex cognition, and EC skills predict high-level abilities including language processing, memory, and problem solving, as well as practically relevant outcomes such as scholastic achievement. EC develops relatively late in ontogeny, and many sub-groups of developmental populations demonstrate an exaggeratedly poor ability to control cognition even alongside the normal protracted growth of EC skills. Given the value of EC to human performance, researchers have sought means to improve it through targeted training; indeed, accumulating evidence suggests that regulatory processes are malleable through experience and practice. Nonetheless, there is a need to understand both whether specific populations might particularly benefit from training, and what cortical mechanisms engage during performance of the tasks used in the training protocols. This contribution has two parts: in Part I, we review EC development and intervention work in select populations. Although promising, the mixed results in this early field make it difficult to draw strong conclusions. To guide future studies, in Part II, we discuss training studies that have included a neuroimaging component – a relatively new enterprise that also has not yet yielded a consistent pattern of results post-training, preventing broad conclusions. We therefore suggest that recent developments in neuroimaging (e.g., multivariate and connectivity approaches) may be useful to advance our understanding of the neural mechanisms underlying the malleability of EC and brain plasticity. In conjunction with behavioral data, these methods may further inform our understanding of the brain–behavior relationship and the extent to which EC is dynamic and malleable, guiding the development of future, targeted interventions to promote executive functioning in both healthy and atypical populations.

Convergent and Divergent fMRI Responses in Children and Adults to Increasing Language Production Demands

In adults, patterns of neural activation associated with perhaps the most basic language skill—overt object naming—are extensively modulated by the psycholinguistic and visual complexity of the stimuli. Do children's brains react similarly when confronted with increasing processing demands, or they solve this problem in a different way? Here we scanned 37 children aged 7–13 and 19 young adults who performed a well-normed picture-naming task with 3 levels of difficulty. While neural organization for naming was largely similar in childhood and adulthood, adults had greater activation in all naming conditions over inferior temporal gyri and superior temporal gyri/supramarginal gyri. Manipulating naming complexity affected adults and children quite differently: neural activation, especially over the dorsolateral prefrontal cortex, showed complexity-dependent increases in adults, but complexity-dependent decreases in children. These represent fundamentally different responses to the linguistic and conceptual challenges of a simple naming task that makes no demands on literacy or metalinguistics. We discuss how these neural differences might result from different cognitive strategies used by adults and children during lexical retrieval/production as well as developmental changes in brain structure and functional connectivity.

The role of domain-general cognitive control in language comprehension

What role does domain-general cognitive control play in understanding linguistic input? Although much evidence has suggested that domain-general cognitive control and working memory resources are sometimes recruited during language comprehension, many aspects of this relationship remain elusive. For example, how frequently do cognitive control mechanisms get engaged when we understand language? And is this engagement necessary for successful comprehension? I here (a) review recent brain imaging evidence for the neural separability of the brain regions that support high-level linguistic processing vs. those that support domain-general cognitive control abilities; (b) define the space of possibilities for the relationship between these sets of brain regions; and (c) review the available evidence that constrains these possibilities to some extent. I argue that we should stop asking whether domain-general cognitive control mechanisms play a role in language comprehension, and instead focus on characterizing the division of labor between the cognitive control brain regions and the more functionally specialized language regions.

A matched filter hypothesis for cognitive control

The prefrontal cortex exerts top-down influences on several aspects of higher-order cognition by functioning as a filtering mechanism that biases bottom-up sensory information toward a response that is optimal in context. However, research also indicates that not all aspects of complex cognition benefit from prefrontal regulation. Here we review and synthesize this research with an emphasis on the domains of learning and creative cognition, and outline how the appropriate level of cognitive control in a given situation can vary depending on the organism's goals and the characteristics of the given task. We offer a matched filter hypothesis for cognitive control, which proposes that the optimal level of cognitive control is task-dependent, with high levels of cognitive control best suited to tasks that are explicit, rule-based, verbal or abstract, and can be accomplished given the capacity limits of working memory and with low levels of cognitive control best suited to tasks that are implicit, reward-based, non-verbal or intuitive, and which can be accomplished irrespective of working memory limitations. Our approach promotes a view of cognitive control as a tool adapted to a subset of common challenges, rather than an all-purpose optimization system suited to every problem the organism might encounter.