Computational models of cognitive control

Understanding dual process cognition via the minimum description length principle

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions

Attentional cognitive control regulates the perception to enhance human behaviour. The current study examines the atltentional mechanisms in terms of time and frequency of EEG signals. The cognitive load is higher for processing local attentional stimulus, thereby demanding higher response time (RT) with low response accuracy (RA). On the other hand, the global attentional mechanisms broadly promote the perception while demanding a low cognitive load with faster RT and high RA. Attentional mechanisms refer to perceptual systems that afford and allocate the adaptive behaviours for prioritizing the processing of relevant stimuli based on the local and global features. The early sensory component of C1, which was associated with the local attentional mechanism, showed higher amplitudes than the global attentional mechanisms in parieto-occipital regions. Further, the local attentional mechanisms were also sustained in N2 and P3 components increasing higher amplitude in the left and right hemispheric sides of temporal regions (T7 and T8). Theta band frequency had shown higher power spectrum density (PSD) values while processing local attentional mechanisms. However, the significance of other frequency bands was noticeably minute. Hence, integrating the attentional mechanisms in terms of ERP and frequency signatures, a hybrid custom weight allocation model (CWAM) was built to assess and predict the contribution of insignificant channels to significant ones. The CWAM model was formulated based on the computational linear regression derivatives. All the derivatives are computationally derived the significant score while channelizing the hierarchical performance of each channel with respect to the frequent and deviant occurrences of global-local stimulus. This model enables us to configure the neural dynamicity of cognitive allocation of resources within the different locations of the human brain while processing the attentional stimulus. CWAM is reported to be the first model to evaluate the performance of the non-significant channels for enhancing the response of significant channels. The findings of the CWAM model suggest that the brain's performance may be determined by the underlying contribution of the non-significant channels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09888-x.

Conflict in a word-based approach-avoidance task is stronger with positive words

BACKGROUND

Valence and motivational direction are linked. We approach good things and avoid bad things, and experience overriding these links as conflicting. Positive valence is more consistently linked with approach than negative valence is linked with avoidance. Therefore, avoiding positive stimuli should produce greater behavioral and neural signs of conflict than approaching negative stimuli.

METHODS

In the present event-related potential study, we tested this assumption by contrasting positive and negative conflict. We used the manikin task, in which we read positive and negative words that they needed to approach and avoid.

RESULTS

Consistent with our prediction, positive conflict prolonged reaction times more than negative conflict did. A late (500-1000 ms following word onset) event-related potential that we identified as the Conflict slow potential, was only sensitive to positive conflict.

CONCLUSION

The results of this study support the notion that avoiding positive stimuli is more conflicting than approaching negative stimuli. The fact that the conflict slow potential is typically sensitive to response conflict rather than stimulus conflict suggests that the manikin task primarily requires people to override prepotent responses rather than to identify conflicting stimuli. Thus, the present findings also shed light on the psychological processes subserving conflict resolution in the manikin task.

Resisting Visual, Phonological, and Semantic Interference - Same or Different Processes? A Focused Mini-Review

The unitary nature of resistance to interference (RI) processes remains a strongly debated question: are they central cognitive processes or are they specific to the stimulus domains on which they operate? This focused mini-review examines behavioral, neuropsychological and neuroimaging evidence for and against domain-general RI processes, by distinguishing visual, verbal phonological and verbal semantic domains. Behavioral studies highlighted overall low associations between RI capacity across domains. Neuropsychological studies mainly report dissociations for RI abilities between the three domains. Neuroimaging studies highlight a left vs. right hemisphere distinction for verbal vs. visual RI, with furthermore distinct neural processes supporting phonological versus semantic RI in the left inferior frontal gyrus. While overall results appear to support the hypothesis of domain-specific RI processes, we discuss a number of methodological caveats that ask for caution in the interpretation of existing studies.

Common and unique neural mechanisms of social and nonsocial conflict resolving and adaptation

Humans often need to deal with various forms of information conflicts that arise when they receive inconsistent information. However, it remains unclear how we resolve them and whether the brain may recruit similar or distinct brain mechanisms to process different domains (e.g. social vs. nonsocial) of conflicts. To address this, we used functional magnetic resonance imaging and scanned 50 healthy participants when they were asked to perform 2 Stroop tasks with different forms of conflicts: social (i.e. face-gender incongruency) and nonsocial (i.e. color-word incongruency) conflicts. Neuroimaging results revealed that the ventral lateral prefrontal cortex was generally activated in processing incongruent versus congruent stimuli regardless of the task type, serving as a common mechanism for conflict resolving across domains. Notably, trial-based and model-based results jointly demonstrated that the dorsal and rostral medial prefrontal cortices were uniquely engaged in processing social incongruent stimuli, suggesting distinct neural substrates of social conflict resolving and adaptation. The findings uncover that the common but unique brain mechanisms are recruited when humans resolve and adapt to social conflicts.

A global framework for a systemic view of brain modeling

The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

How Sequentially Changing Reward Prospect Modulates Meta-control: Increasing Reward Prospect Promotes Cognitive Flexibility

Meta-control is necessary to regulate the balance between cognitive stability and flexibility. Evidence from (voluntary) task switching studies suggests performance-contingent reward as one modulating factor. Depending on the immediate reward history, reward prospect seems to promote either cognitive stability or flexibility: Increasing reward prospect reduced switch costs and increased the voluntary switch rate, suggesting increased cognitive flexibility. In contrast, remaining high reward prospect increased switch costs and reduced the voluntary switch rate, suggesting increased cognitive stability. Recently we suggested that increasing reward prospect serves as a meta-control signal toward cognitive flexibility by lowering the updating threshold in working memory. However, in task switching paradigms with two tasks only, this could alternatively be explained by facilitated switching to the other of two tasks. To address this issue, a series of task switching experiments with uncued task switching between three univalent tasks was conducted. Results showed a reduction in reaction time (RT) switch costs to a nonsignificant difference and a high voluntary switch rate when reward prospect increased, whereas repetition RTs were faster, switch RTs slower, and voluntary switch rate was reduced when reward prospect remained high. That is, increasing reward prospect put participants in a state of equal readiness to respond to any target stimulus-be it a task repetition or a switch to one of the other two tasks. The study thus provides further evidence for the assumption that increasing reward prospect serves as a meta-control signal to increase cognitive flexibility, presumably by lowering the updating threshold in working memory.

Reward-Sensitive Basal Ganglia Stabilize the Maintenance of Goal-Relevant Neural Patterns in Adolescents

Maturation of basal ganglia (BG) and frontoparietal circuitry parallels developmental gains in working memory (WM). Neurobiological models posit that adult WM performance is enhanced by communication between reward-sensitive BG and frontoparietal regions, via increased stability in the maintenance of goal-relevant neural patterns. It is not known whether this reward-driven pattern stability mechanism may have a role in WM development. In 34 young adolescents (12.16-14.72 years old) undergoing fMRI, reward-sensitive BG regions were localized using an incentive processing task. WM-sensitive regions were localized using a delayed-response WM task. Functional connectivity analyses were used to examine the stability of goal-relevant functional connectivity patterns during WM delay periods between and within reward-sensitive BG and WM-sensitive frontoparietal regions. Analyses revealed that more stable goal-relevant connectivity patterns between reward-sensitive BG and WM-sensitive frontoparietal regions were associated with both greater adolescent age and WM ability. Computational lesion models also revealed that functional connections to WM-sensitive frontoparietal regions from reward-sensitive BG uniquely increased the stability of goal-relevant functional connectivity patterns within frontoparietal regions. Findings suggested (1) the extent to which goal-relevant communication patterns within reward-frontoparietal circuitry are maintained increases with adolescent development and WM ability and (2) communication from reward-sensitive BG to frontoparietal regions enhances the maintenance of goal-relevant neural patterns in adolescents' WM. The maturation of reward-driven stability of goal-relevant neural patterns may provide a putative mechanism for understanding the developmental enhancement of WM.

Failure of activation of striatum during the performance of executive function tasks in adult patients with bipolar disorder

BACKGROUND

Although numerous studies have used functional neuroimaging to identify executive dysfunction in patients with bipolar disorder (BD), the findings are not consistent. The aim of this meta-analysis is to identify the most reliable functional anomalies in BD patients during performance of Executive Function (EF) tasks.

METHODS

A web-based search was performed on publication databases to identify functional magnetic resonance imaging studies of BD patients performing EF tasks and a voxel-based meta-analytic method known as anisotropic Effect Size Signed Differential Mapping (ES-SDM) was used to identify brain regions which showed anomalous activity in BD patients compared with healthy controls (HC).

RESULTS

Twenty datasets consisting of 463 BD patients and 484 HC were included. Compared with HC, BD patients showed significant hypo-activation or failure of activation in the left striatum (p = 0.00007), supplementary motor area (BA 6, p = 0.00037), precentral gyrus (BA 6, p = 0.0014) and cerebellum (BA 37, p = 0.0019), and hyper-activation in the left gyrus rectus (BA 11, p ≈ 0) and right middle temporal gyrus (BA 22, p = 0.00031) during performance of EF tasks. Sensitivity and subgroup analyses showed that the anomaly of left striatum is consistent across studies and present in both euthymic and BD I patients.

CONCLUSIONS

Patients with BD consistently showed abnormal activation in the cortico-striatal system during performance of EF tasks compared with HC. Failure of activation of the striatum may be a reliable marker for impairment in performance of especially inhibition tasks by patients with BD.

Domain-Specific Inhibitory Control Training to Improve Children's Learning of Counterintuitive Concepts in Mathematics and Science

Evidence from cognitive neuroscience suggests that learning counterintuitive concepts in mathematics and science requires inhibitory control (IC). This prevents interference from misleading perceptual cues and naïve theories children have built from their experiences of the world. Here, we (1) investigate associations between IC, counterintuitive reasoning, and academic achievement and (2) evaluate a classroom-based computerised intervention, called Stop & Think, designed to embed IC training within the learning domain (i.e. mathematics and science content from the school curricula). Cross-sectional analyses of data from 627 children in Years 3 and 5 (7- to 10-year-olds) demonstrated that IC, measured on a Stroop-like task, was associated with counterintuitive reasoning and mathematics and science achievement. A subsample (n = 456) participated either in Stop & Think as a whole-class activity (teacher-led, STT) or using individual computers (pupil-led, STP), or had teaching as usual (TAU). For Year 3 children (but not Year 5), Stop & Think led to better counterintuitive reasoning (i.e. near transfer) in STT (p < .001, ηp 2 = .067) and STP (p < .01, ηp 2 = .041) compared to TAU. Achievement data was not available for Year 3 STP or Year 5 STT. For Year 3, STT led to better science achievement (i.e. far transfer) compared to TAU (p < .05, ηp 2 = .077). There was no transfer to the Stroop-like measure of IC. Overall, these findings support the idea that IC may contribute to counterintuitive reasoning and mathematics and science achievement. Further, we provide preliminary evidence of a domain-specific IC intervention with transferable benefits to academic achievement for Year 3 children.

Hemispheric Asymmetry of Globus Pallidus Relates to Alpha Modulation in Reward-Related Attentional Tasks

Whereas subcortical structures such as the basal ganglia have been widely explored in relation to motor control, recent evidence suggests that their mechanisms extend to the domain of attentional switching. We here investigated the subcortical involvement in reward related top-down control of visual alpha-band oscillations (8-13 Hz), which have been consistently linked to mechanisms supporting the allocation of visuospatial attention. Given that items associated with contextual saliency (e.g., monetary reward or loss) attract attention, it is not surprising that the acquired salience of visual items further modulates. The executive networks controlling such reward-dependent modulations of oscillatory brain activity have yet to be fully elucidated. Although such networks have been explored in terms of corticocortical interactions, subcortical regions are likely to be involved. To uncover this, we combined MRI and MEG data from 17 male and 11 female participants, investigating whether derived measures of subcortical structural asymmetries predict interhemispheric modulation of alpha power during a spatial attention task. We show that volumetric hemispheric lateralization of globus pallidus (GP) and thalamus (Th) explains individual hemispheric biases in the ability to modulate posterior alpha power. Importantly, for the GP, this effect became stronger when the value saliency parings in the task increased. Our findings suggest that the GP and Th in humans are part of a subcortical executive control network, differentially involved in modulating posterior alpha activity in the presence of saliency. Further investigation aimed at uncovering the interaction between subcortical and neocortical attentional networks would provide useful insight in future studies.SIGNIFICANCE STATEMENT Whereas the involvement of subcortical regions into higher level cognitive processing, such as attention and reward attribution, has been already indicated in previous studies, little is known about its relationship with the functional oscillatory underpinnings of said processes. In particular, interhemispheric modulation of alpha band (8-13 Hz) oscillations, as recorded with magnetoencephalography, has been previously shown to vary as a function of salience (i.e., monetary reward/loss) in a spatial attention task. We here provide novel insights into the link between subcortical and cortical control of visual attention. Using the same reward-related spatial attention paradigm, we show that the volumetric lateralization of subcortical structures (specifically globus pallidus and thalamus) explains individual biases in the modulation of visual alpha activity.

Clarifying cognitive control and the controllable connectome

Cognitive control researchers aim to describe the processes that support adaptive cognition to achieve specific goals. Control theorists consider how to influence the state of systems to reach certain user-defined goals. In brain networks, some conceptual and lexical similarities between cognitive control and control theory offer appealing avenues for scientific discovery. However, these opportunities also come with the risk of conceptual confusion. Here, I suggest that each field of inquiry continues to produce novel and distinct insights. Then, I describe opportunities for synergistic research at the intersection of these subdisciplines with a critical stance that reduces the risk of conceptual confusion. Through this exercise, we can observe that both cognitive neuroscience and systems engineering have much to contribute to cognitive control research in human brain networks. This article is categorized under: Neuroscience > Cognition Computer Science > Neural Networks Neuroscience > Clinical Neuroscience.

The neural basis of motivational influences on cognitive control

Cognitive control mechanisms support the deliberate regulation of thought and behavior based on current goals. Recent work suggests that motivational incentives improve cognitive control and has begun to elucidate critical neural substrates. We conducted a quantitative meta-analysis of neuroimaging studies of motivated cognitive control using activation likelihood estimation (ALE) and Neurosynth to delineate the brain regions that are consistently activated across studies. The analysis included studies that investigated changes in brain activation during cognitive control tasks when reward incentives were present versus absent. The ALE analysis revealed consistent recruitment in regions associated with the frontoparietal control network including the inferior frontal sulcus and intraparietal sulcus, as well as regions associated with the salience network including the anterior insula and anterior mid-cingulate cortex. As a complementary analysis, we performed a large-scale exploratory meta-analysis using Neurosynth to identify regions that are recruited in studies using of the terms cognitive control and incentive. This analysis replicated the ALE results and also identified the rostrolateral prefrontal cortex, caudate nucleus, nucleus accumbens, medial thalamus, inferior frontal junction, premotor cortex, and hippocampus. Finally, we separately compared recruitment during cue and target periods, which tap into proactive engagement of rule-outcome associations, and the mobilization of appropriate viscero-motor states to execute a response, respectively. We found that largely distinct sets of brain regions are recruited during cue and target periods. Altogether, these findings suggest that flexible interactions between frontoparietal, salience, and dopaminergic midbrain-striatal networks may allow control demands to be precisely tailored based on expected value.

Hierarchical Active Inference: A Theory of Motivated Control

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.

A Nimble Working Memory

In this issue of Neuron, Sprague et al. (2016) report fMRI evidence that a degraded working memory representation can be restored by a later cue. The findings raise new questions about the neural mechanisms that underlie such dynamic representational shifts.

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden deviations from the monotonicity of its evolution trigger a reset after which another expert subnetwork can be activated-if it has already been established before-or new categories can be recruited and associated with novel behavioral patterns.

Principles of Network Architecture Emerging from Comparisons of the Cerebral Cortex in Large and Small Brains

The cerebral cortex retains its fundamental organization, layering, and input-output relations as it scales in volume over many orders of magnitude in mammals. How is its network architecture affected by size scaling? By comparing network organization of the mouse and rhesus macaque cortical connectome derived from complete neuroanatomical tracing studies, a recent study in PLOS Biology shows that an exponential distance rule emerges that reveals the falloff in connection probability with distance in the two brains that in turn determines common organizational features.

Prefrontal Goal Codes Emerge as Latent States in Probabilistic Value Learning

The prefrontal cortex (PFC) supports goal-directed actions and exerts cognitive control over behavior, but the underlying coding and mechanism are heavily debated. We present evidence for the role of goal coding in PFC from two converging perspectives: computational modeling and neuronal-level analysis of monkey data. We show that neural representations of prospective goals emerge by combining a categorization process that extracts relevant behavioral abstractions from the input data and a reward-driven process that selects candidate categories depending on their adaptive value; both forms of learning have a plausible neural implementation in PFC. Our analyses demonstrate a fundamental principle: goal coding represents an efficient solution to cognitive control problems, analogous to efficient coding principles in other (e.g., visual) brain areas. The novel analytical-computational approach is of general interest because it applies to a variety of neurophysiological studies.

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects' behavior and found that attention-like features in the behavioral model are essential for explaining subjects' responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects.

Multisensory Competition Is Modulated by Sensory Pathway Interactions with Fronto-Sensorimotor and Default-Mode Network Regions

Multisensory information competes for preferential access to consciousness. It remains unknown what neural processes cause one particular modality to win multisensory competition and eventually dominate behavior. Thus, in a paradigm in which human participants sought to make simultaneous auditory and visual detection responses, we sought to identify prestimulus and poststimulus neural signals that were associated with auditory and visual dominance on each trial. Behaviorally, visual detection responses preceded auditory responses more frequently than vice versa. Even when visual responses were preceded by auditory responses, they recovered more quickly from previous responses, indicating the dominance of vision over audition. Neurally, visual precedence was associated with increased prestimulus activity in the prefrontal cortex and reduced prestimulus activity in the default-mode network, and increased poststimulus connectivity between the prefrontal cortex and the visual system. Moreover, the dorsal visual stream showed not only increased activity in post-perceptual phases but also enhanced connectivity with the sensorimotor cortex, indicating the functional role of the dorsal visual stream in prioritizing the flow of visual information into the motor system. In contrast, auditory precedence was associated with increased prestimulus activity in the auditory cortex and increased poststimulus neural coupling between the auditory and the sensorimotor cortex. Finally, whenever one modality lost multisensory competition, the corresponding sensory cortex showed enhanced connectivity with the default-mode network. Overall, the outcome of audiovisual competition depended on dynamic interactions between sensory systems and both the fronto-sensorimotor and the default-mode network. Together, these results revealed both the neural causes and the neural consequences of visual and auditory dominance during multisensory competition.

A Programmer–Interpreter Neural Network Architecture for Prefrontal Cognitive Control

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input–output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter–programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer–interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.

Causal Interactions Within a Frontal-Cingulate-Parietal Network During Cognitive Control: Convergent Evidence from a Multisite-Multitask Investigation

Cognitive control plays an important role in goal-directed behavior, but dynamic brain mechanisms underlying it are poorly understood. Here, using multisite fMRI data from over 100 participants, we investigate causal interactions in three cognitive control tasks within a core Frontal-Cingulate-Parietal network. We found significant causal influences from anterior insula (AI) to dorsal anterior cingulate cortex (dACC) in all three tasks. The AI exhibited greater net causal outflow than any other node in the network. Importantly, a similar pattern of causal interactions was uncovered by two different computational methods for causal analysis. Furthermore, the strength of causal interaction from AI to dACC was greater on high, compared with low, cognitive control trials and was significantly correlated with individual differences in cognitive control abilities. These results emphasize the importance of the AI in cognitive control and highlight its role as a causal hub in the Frontal-Cingulate-Parietal network. Our results further suggest that causal signaling between the AI and dACC plays a fundamental role in implementing cognitive control and are consistent with a two-stage cognitive control model in which the AI first detects events requiring greater access to cognitive control resources and then signals the dACC to execute load-specific cognitive control processes.

A perspective on neural and cognitive mechanisms of error commission

Behavioral adaptation and cognitive control are crucial for goal-reaching behaviors. Every creature is ubiquitously faced with choices between behavioral alternatives. Common sense suggests that errors are an important source of information in the regulation of such processes. Several theories exist regarding cognitive control and the processing of undesired outcomes. However, most of these models focus on the consequences of an error, and less attention has been paid to the mechanisms that underlie the commissioning of an error. In this article, we present an integrative review of neuro-cognitive models that detail the determinants of the occurrence of response errors. The factors that may determine the likelihood of committing errors are likely related to the stability of task-representations in prefrontal networks, attentional selection mechanisms and mechanisms of action selection in basal ganglia circuits. An important conclusion is that the likelihood of committing an error is not stable over time but rather changes depending on the interplay of different functional neuro-anatomical and neuro-biological systems. We describe factors that might determine the time-course of cognitive control and the need to adapt behavior following response errors. Finally, we outline the mechanisms that may proof useful for predicting the outcomes of cognitive control and the emergence of response errors in future research.

Dopamine bioavailability in the mPFC modulates operant learning performance in rats: an experimental study with a computational interpretation

Dopamine encodes reward and its prediction in reinforcement learning. Catechol-O-methyltransferase (COMT) activity in the medial prefrontal cortex (mPFC) has been shown to influence cognitive abilities by modifying dopamine clearance. Nevertheless, it is unknown how COMT in the mPFC influences operant learning. Systemic entacapone (50mg/kg), as well as local entacapone (3 pg) and recombinant COMT (17 μg) in the mPFC were administered to male Long Evans rats prior to training in an operant conditioning task. We found that systemic and local administration of the COMT inhibitor entacapone significantly improves learning performance. Conversely, recombinant COMT administration totally impaired learning. These data have been interpreted through a computational model where the phasic firing of dopaminergic neurons was computed by means of a temporal difference algorithm and dopamine bioavailability in the mPFC was simulated with a gating window. The duration of this window was selected to simulate the effects of inhibited or enhanced COMT activity (by entacapone or recombinant COMT respectively). The model accounts for an improved performance reproducing the entacapone effects, and a detrimental impact on learning when the clearance is increased reproducing the recombinant COMT effects. The experimental and computational results show that learning performance can be deeply influenced by COMT manipulations in the mPFC.

Understanding human original actions directed at real-world goals: the role of the lateral prefrontal cortex

Adaptive, original actions, which can succeed in multiple contextual situations, require understanding of what is relevant to a goal. Recognizing what is relevant may also help in predicting kinematics of observed, original actions. During action observation, comparisons between sensory input and expected action kinematics have been argued critical to accurate goal inference. Experimental studies with laboratory tasks, both in humans and nonhuman primates, demonstrated that the lateral prefrontal cortex (LPFC) can learn, hierarchically organize, and use goal-relevant information. To determine whether this LPFC capacity is generalizable to real-world cognition, we recorded functional magnetic resonance imaging (fMRI) data in the human brain during comprehension of original and usual object-directed actions embedded in video-depictions of real-life behaviors. We hypothesized that LPFC will contribute to forming goal-relevant representations necessary for kinematic predictions of original actions. Additionally, resting-state fMRI was employed to examine functional connectivity between the brain regions delineated in the video fMRI experiment. According to behavioral data, original videos could be understood by identifying elements relevant to real-life goals at different levels of abstraction. Patterns of enhanced activity in four regions in the left LPFC, evoked by original, relative to usual, video scenes, were consistent with previous neuroimaging findings on representing abstract and concrete stimuli dimensions relevant to laboratory goals. In the anterior left LPFC, the activity increased selectively when representations of broad classes of objects and actions, which could achieve the perceived overall behavioral goal, were likely to bias kinematic predictions of original actions. In contrast, in the more posterior regions, the activity increased even when concrete properties of the target object were more likely to bias the kinematic prediction. Functional connectivity was observed between contiguous regions along the rostro-caudal LPFC axis, but not between the regions that were not immediately adjacent. These findings generalize the representational hierarchy account of LPFC function to diverse core principles that can govern both production and comprehension of flexible real-life behavior.

Motivation and cognitive control: from behavior to neural mechanism

Research on cognitive control and executive function has long recognized the relevance of motivational factors. Recently, however, the topic has come increasingly to center stage, with a surge of new studies examining the interface of motivation and cognitive control. In the present article we survey research situated at this interface, considering work from cognitive and social psychology and behavioral economics, but with a particular focus on neuroscience research. We organize existing findings into three core areas, considering them in the light of currently vying theoretical perspectives. Based on the accumulated evidence, we advocate for a view of control function that treats it as a domain of reward-based decision making. More broadly, we argue that neuroscientific evidence plays a critical role in understanding the mechanisms by which motivation and cognitive control interact. Opportunities for further cross-fertilization between behavioral and neuroscientific research are highlighted.

The computational and neural basis of cognitive control: charted territory and new frontiers

Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive control, with a focus on the influence that the PDP approach has brought to bear in this area. Rather than providing a comprehensive review, we offer a framework for thinking about past and future modeling efforts in this domain. We define control in terms of the optimal parameterization of task processing. From this vantage point, the development of control systems in the brain can be seen as responding to the structure of naturalistic tasks, through the filter of the brain systems with which control directly interfaces. This perspective lays open a set of fascinating but difficult research questions, which together define an important frontier for future computational research.

A physiological perspective on the neuroscience of eating

I present the thesis that 'being physiological,' i.e., analyzing eating under conditions that do not perturb, or minimally perturb, the organism's endogenous processes, should be a central goal of the neuroscience of eating. I describe my understanding of 'being physiological' based on [i] the central neural-network heuristic of CNS function that traces back to Cajal and Sherrington, [ii] research on one of the simpler problems in the neuroscience of eating, identification of endocrine signals that control eating. In this context I consider natural meals, physiological doses and ranges, and antagonist studies. Several examples involve CCK. Next I describe my view of the cutting edge in the molecular neuroscience of eating as it has evolved from the discovery of leptin signaling through the application of optogenetic and pharmacogenetic methods. Finally I describe some novel approaches that may advance the neuroscience of eating in the foreseeable future. I conclude that [i] the neuroscience of eating may soon be able to discern 'physiological' function in the operation of CNS networks mediating eating, [ii] the neuroscience of eating should capitalize on methods developed in other areas of neuroscience, e.g., improved methods to record and manipulate CNS function in behaving animals, identification of canonical regional circuits, use of population electrophysiology, etc., and [iii] subjective aspects of eating are crucial aspects of eating science, but remain beyond mechanistic understanding.

Dysfunctions of decision-making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research

Disadvantageous decision-making and impaired volitional control over actions, thoughts, and emotions are characteristics of a wide range of mental disorders such as addiction, eating disorders, depression, and anxiety disorders and may reflect transdiagnostic core mechanisms and possibly vulnerability factors. Elucidating the underlying neurocognitive mechanisms is a precondition for moving from symptom-based to mechanism-based disorder classifications and ultimately mechanism-targeted interventions. However, despite substantial advances in basic research on decision-making and cognitive control, there are still profound gaps in our current understanding of dysfunctions of these processes in mental disorders. Central unresolved questions are: (i) to which degree such dysfunctions reflect transdiagnostic mechanisms or disorder-specific patterns of impairment; (ii) how phenotypical features of mental disorders relate to dysfunctional control parameter settings and aberrant interactions between large-scale brain systems involved in habit and reward-based learning, performance monitoring, emotion regulation, and cognitive control; (iii) whether cognitive control impairments are consequences or antecedent vulnerability factors of mental disorders; (iv) whether they reflect generalized competence impairments or context-specific performance failures; (v) whether not only impaired but also chronic over-control contributes to mental disorders. In the light of these gaps, needs for future research are: (i) an increased focus on basic cognitive-affective mechanisms underlying decision and control dysfunctions across disorders; (ii) longitudinal-prospective studies systematically incorporating theory-driven behavioural tasks and neuroimaging protocols to assess decision-making and control dysfunctions and aberrant interactions between underlying large-scale brain systems; (iii) use of latent-variable models of cognitive control rather than single tasks; (iv) increased focus on the interplay of implicit and explicit cognitive-affective processes; (v) stronger focus on computational models specifying neurocognitive mechanisms underlying phenotypical expressions of mental disorders.

Cortical high-density counterstream architectures

Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.

A cortical network for the encoding of object change

Understanding events often requires recognizing unique stimuli as alternative, mutually exclusive states of the same persisting object. Using fMRI, we examined the neural mechanisms underlying the representation of object states and object-state changes. We found that subjective ratings of visual dissimilarity between a depicted object and an unseen alternative state of that object predicted the corresponding multivoxel pattern dissimilarity in early visual cortex during an imagery task, while late visual cortex patterns tracked dissimilarity among distinct objects. Early visual cortex pattern dissimilarity for object states in turn predicted the level of activation in an area of left posterior ventrolateral prefrontal cortex (pVLPFC) most responsive to conflict in a separate Stroop color-word interference task, and an area of left ventral posterior parietal cortex (vPPC) implicated in the relational binding of semantic features. We suggest that when visualizing object states, representational content instantiated across early and late visual cortex is modulated by processes in left pVLPFC and left vPPC that support selection and binding, and ultimately event comprehension.

Too good to be true: rhesus monkeys react negatively to better-than-expected offers

To succeed in a dynamically changing world, animals need to predict their environments. Humans, in fact, exhibit such a strong desire for consistency that one of the most well-established findings in social psychology is the effort people make to maintain consistency among their beliefs, attitudes, and behavior. However, displeasure with unpredictability leads to a potential paradox, because a positive outcome that exceeds one's expectations often leads to increased subjective value and positive affect, not the opposite. We tested the hypothesis that two evolutionarily-conserved evaluation processes underlie goal-directed behavior: (1) consistency, concerned with prediction errors, and (2) valuation, concerned with outcome utility. Rhesus monkeys (Macaca mulatta) viewed a food item and then were offered an identical, better, or worse food, which they could accept or reject. The monkeys ultimately accepted all offers, attesting to the influence of the valuation process. However, they were slower to accept the unexpected offers, and they exhibited aversive reactions, especially to the better-than-expected offers, repeatedly turning their heads and looking away before accepting the food item. Our findings (a) provide evidence for two separable evaluation processes in primates, consistency and value assessment, (b) reveal a direct relationship between consistency assessment and emotional processes, and (c) show that our wariness with events that are much better than expected is shared with other social primates.

Relationship between frontostriatal morphology and executive function deficits in bipolar I disorder following a first manic episode: data from the Systematic Treatment Optimization Program for Early Mania (STOP-EM)

OBJECTIVES

Executive function impairments are a core feature of bipolar I disorder (BD-I), not only present during acute episodes but also persisting following remission of mood symptoms. Despite advances in knowledge regarding the neural basis of executive functions in healthy subjects, research into morphological abnormalities underlying the deficits in BD-I is lacking.

METHODS

Patients with BD-I within three months of sustained remission from their first manic episode (n = 41) underwent neuropsychological testing and a 3T magnetic resonance imaging scan and were compared to healthy subjects matched for age, sex, and premorbid IQ (n = 30). Group dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46) and caudate volumes were examined and analyzed for relationships with the average score from three computerized tests of executive function: Spatial Working Memory, Stockings of Cambridge, and Intradimensional/Extradimensional Shift.

RESULTS

Right caudate volumes were enlarged in patients (z = 3.57, p < 0.05 corrected). No differences in DLPFC volumes were found. Patients showed large deficits in executive function relative to healthy subjects (d = -0.92, p < 0.001). While in healthy subjects, a larger right (r = +0.39, p < 0.05) and left (r = +0.44, p < 0.05) caudate was associated with better executive function score, in patients, larger right (r = -0.36, p < 0.05) and left (r = -0.34, p < 0.05) volumes correlated with poorer performance.

CONCLUSIONS

Although the etiology of gray matter changes is unknown, volume increases in the right caudate may be an important factor underlying executive function impairments during remission in patients with BD-I.

Strategic cognitive sequencing: a computational cognitive neuroscience approach

We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or "self-instruction"). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a "bridging" state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

Neural dynamics of attentional cross-modality control

Attentional networks that integrate many cortical and subcortical elements dynamically control mental processes to focus on specific events and make a decision. The resources of attentional processing are finite. Nevertheless, we often face situations in which it is necessary to simultaneously process several modalities, for example, to switch attention between players in a soccer field. Here we use a global brain mode description to build a model of attentional control dynamics. This model is based on sequential information processing stability conditions that are realized through nonsymmetric inhibition in cortical circuits. In particular, we analyze the dynamics of attentional switching and focus in the case of parallel processing of three interacting mental modalities. Using an excitatory-inhibitory network, we investigate how the bifurcations between different attentional control strategies depend on the stimuli and analyze the relationship between the time of attention focus and the strength of the stimuli. We discuss the interplay between attention and decision-making: in this context, a decision-making process is a controllable bifurcation of the attention strategy. We also suggest the dynamical evaluation of attentional resources in neural sequence processing.

Habits, action sequences and reinforcement learning

It is now widely accepted that instrumental actions can be either goal-directed or habitual; whereas the former are rapidly acquired and regulated by their outcome, the latter are reflexive, elicited by antecedent stimuli rather than their consequences. Model-based reinforcement learning (RL) provides an elegant description of goal-directed action. Through exposure to states, actions and rewards, the agent rapidly constructs a model of the world and can choose an appropriate action based on quite abstract changes in environmental and evaluative demands. This model is powerful but has a problem explaining the development of habitual actions. To account for habits, theorists have argued that another action controller is required, called model-free RL, that does not form a model of the world but rather caches action values within states allowing a state to select an action based on its reward history rather than its consequences. Nevertheless, there are persistent problems with important predictions from the model; most notably the failure of model-free RL correctly to predict the insensitivity of habitual actions to changes in the action-reward contingency. Here, we suggest that introducing model-free RL in instrumental conditioning is unnecessary, and demonstrate that reconceptualizing habits as action sequences allows model-based RL to be applied to both goal-directed and habitual actions in a manner consistent with what real animals do. This approach has significant implications for the way habits are currently investigated and generates new experimental predictions.

The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition

How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain’s binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding – it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

A critical review of habit learning and the Basal Ganglia

The current paper briefly outlines the historical development of the concept of habit learning and discusses its relationship to the basal ganglia. Habit learning has been studied in many different fields of neuroscience using different species, tasks, and methodologies, and as a result it has taken on a wide range of definitions from these various perspectives. We identify five common but not universal, definitional features of habit learning: that it is inflexible, slow or incremental, unconscious, automatic, and insensitive to reinforcer devaluation. We critically evaluate for each of these how it has been defined, its utility for research in both humans and non-human animals, and the evidence that it serves as an accurate description of basal ganglia function. In conclusion, we propose a multi-faceted approach to habit learning and its relationship to the basal ganglia, emphasizing the need for formal definitions that will provide directions for future research.

Prefrontal dopamine and the dynamic control of human long-term memory

Dopaminergic projections to the prefrontal cortex support higher-order cognitive functions, and are critically involved in many psychiatric disorders that involve memory deficits, including schizophrenia. The role of prefrontal dopamine in long-term memory, however, is still unclear. We used an imaging genetics approach to examine the hypothesis that dopamine availability in the prefrontal cortex selectively affects the ability to suppress interfering memories. Human participants were scanned via functional magnetic resonance imaging while practicing retrieval of previously studied target information in the face of interference from previously studied non-target information. This retrieval practice (RP) rendered the non-target information less retrievable on a later final test-a phenomenon known as retrieval-induced forgetting (RIF). In total, 54 participants were genotyped for the catechol-O-methyltransferase (COMT) Val(108/158)Met polymorphism. The COMT Val(108/158)Met genotype showed a selective and linear gene-dose effect on RIF, with the Met allele, which leads to higher prefrontal dopamine availability, being associated with greater RIF. Mirroring the behavioral pattern, the functional magnetic resonance imaging data revealed that Met allele carriers, compared with Val allele carriers, showed a greater response reduction in inhibitory control areas of the right inferior frontal cortex during RP, suggesting that they more efficiently reduced interference. These data support the hypothesis that the cortical dopaminergic system is centrally involved in the dynamic control of human long-term memory, supporting efficient remembering via the adaptive suppression of interfering memories.

The role of diffusion tensor imaging in the study of cognitive aging

This chapter gives an overview of the role that diffusion tensor MRI (DTI) can play in the study of cognitive decline that is associated with advancing age. A brief overview of biological injury processes that impinge on the aging brain is provided, and their overall effect on the integrity of neural architecture is described. Cognitive decline associated with aging, and white matter connectivity degradation as a biological substrate for that decline, is then described. We then briefly describe the technology of DTI as a means for in vivo, non-invasive interrogation of white matter connectivity, and relate it to FLAIR, a more traditional MRI method for assessing white matter injury. We then survey the existing findings on relationships between aging-associated neuropathological processes and DTI measurements on one hand; and relationships between DTI measurements and late-life cognitive function on the other. We conclude with a summary of current research directions in relation to DTI studies of cognitive aging.