Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

The challenge of learning adaptive mental behavior

Many psychotherapies aim to help people replace maladaptive mental behaviors (such as those leading to unproductive worry) with more adaptive ones (such as those leading to active problem solving). Yet, little is known empirically about how challenging it is to learn adaptive mental behaviors. Mental behaviors entail taking mental operations and thus may be more challenging to perform than motor actions; this challenge may enhance or impair learning. In particular, challenge when learning is often desirable because it improves retention. Yet, it is also plausible that the necessity of carrying out mental operations interferes with learning the expected values of mental actions by impeding credit assignment: the process of updating an action's value after reinforcement. Then, it may be more challenging not only to perform-but also to learn the consequences of-mental (vs. motor) behaviors. We designed a task to assess learning to take adaptive mental versus motor actions via matched probabilistic feedback. In two experiments (N = 300), most participants found it more difficult to learn to select optimal mental (vs. motor) actions, as evident in worse accuracy not only in a learning but also test (retention) phase. Computational modeling traced this impairment to an indicator of worse credit assignment (impaired construction and maintenance of expected values) when learning mental actions, accounting for worse accuracy in the learning and retention phases. The results suggest that people have particular difficulty learning adaptive mental behavior and pave the way for novel interventions to scaffold credit assignment and promote adaptive thinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Thalamocortical contributions to hierarchical cognitive control

Cognitive flexibility relies on hierarchically structured task representations that organize task contexts, relevant environmental features, and subordinate decisions. Despite ongoing interest in the human thalamus, its role in cognitive control has been understudied. This study explored thalamic representation and thalamocortical interactions that contribute to hierarchical cognitive control in humans. We found that several thalamic nuclei, including the anterior, mediodorsal, ventrolateral, and pulvinar nuclei, exhibited stronger evoked responses when subjects switch between task contexts. Decoding analysis revealed that thalamic activity preferentially encodes task contexts within the hierarchical task representations. To determine how thalamocortical interactions contribute to task representations, we developed a thalamocortical functional interaction model to predict task-related cortical representation. This data-driven model outperformed comparison models, particularly in predicting activity patterns in cortical regions that encode context representations. Collectively, our findings highlight the significant contribution of thalamic activity and thalamocortical interactions for contextually guided hierarchical cognitive control.

From Tripping and Falling to Ruminating and Worrying: A Meta-Control Account of Repetitive Negative Thinking

Repetitive negative thinking (RNT) is a transdiagnostic construct that encompasses rumination and worry, yet what precisely is shared between rumination and worry is unclear. To clarify this, we develop a meta-control account of RNT. Meta-control refers to the reinforcement and control of mental behavior via similar computations as reinforce and control motor behavior. We propose rumination and worry are coarse terms for failure in meta-control, just as tripping and falling are coarse terms for failure in motor control. We delineate four meta-control stages and risk factors increasing the chance of failure at each, including open-ended thoughts (stage 1), individual differences influencing subgoal execution (stage 2) and switching (stage 3), and challenges inherent to learning adaptive mental behavior (stage 4). Distinguishing these stages therefore elucidates diverse processes that lead to the same behavior of excessive RNT. Our account also subsumes prominent clinical accounts of RNT into a computational cognitive neuroscience framework.

Self-prioritization in working memory gating

Working memory (WM) involves a dynamic interplay between temporary maintenance and updating of goal-relevant information. The balance between maintenance and updating is regulated by an input-gating mechanism that determines which information should enter WM (gate opening) and which should be kept out (gate closing). We investigated whether updating and gate opening/closing are differentially sensitive to the kind of information to be encoded and maintained in WM. Specifically, since the social salience of a stimulus is known to affect cognitive performance, we investigated if self-relevant information differentially impacts maintenance, updating, or gate opening/closing. Participants first learned to associate two neutral shapes with two social labels (i.e., "you" vs. "stranger"), respectively. Subsequently they performed the reference-back paradigm, a well-established WM task that disentangles WM updating, gate opening, and gate closing. Crucially, the shapes previously associated with the self or a stranger served as target stimuli in the reference-back task. We replicated the typical finding of a repetition benefit when consecutive trials require opening the gate to WM. In Study 1 (N = 45) this advantage disappeared when self-associated stimuli were recently gated into WM and immediately needed to be replaced by stranger-associated stimuli. However, this was not replicated in a larger sample (Study 2; N = 90), where a repetition benefit always occurred on consecutive gate-opening trials. Overall, our results do not provide evidence that the self-relevance of stimuli modulates component processes of WM. We discuss possible reasons for this null finding, including the importance of continuous reinstatement and task-relevance of the shape-label associations.

Exploring the role of working memory gate opening process in creativity: An ERP study using the reference-back paradigm

We investigated the relationship between the gate opening process of working memory and an individual's proficiency in divergent (DT) and convergent thinking (CT) using the reference-back paradigm. Event-related potentials and reaction times were measured across groups with varying DT (N = 40, 27.35 ± 5.05 years) and CT levels (N = 40, 27.88 ± 4.95 years). Based on the role of striatal dopamine in supporting cognitive flexibility, which facilitates DT, and considering the significance of phasic dopamine activity as the gate opening signal originating from the basal ganglia, we assumed that the gate opening process may contribute differently to DT and CT. Despite the absence of behavioural differences in gate opening costs, distinct neural patterns emerged. In the early time windows (P1, N1), gate opening effects were detected in both DT and CT groups, with a notable interaction influenced by the level of DT, resulting in significant effects within the lower DT group. The P2 component showed a gate opening effect only in the higher DT group. In the P3 time window, the process unfolded comparably in all groups. Our results suggest that groups with different levels of convergent thinking (based on Matrix reasoning) and those with lower DT (based on Creativity Index) tend to select and activate the prefrontal cortex representation containing the required task information at an earlier stage, compared to those with better DT. This could be beneficial especially in the early phase of idea generation, as more elements become available to create associations and original ideas.

Neural correlates of adaptive cognitive control in working memory

Conflicts in working memory (WM) can occur when retrieval cues activate competing items, which impairs the efficiency of retrieval. It has recently been shown that WM retrieval adapts similarly to these conflicts as predicted by conflict monitoring theory for selective attention tasks. Here, we utilized event-related potentials (ERPs) to investigate whether conflict and adaptive control in WM are reflected by the same neural markers that have previously been described for selective attention tasks. In our task, participants encoded two differently colored memory lists that contained four digits each (i.e., 2 5 7 1 and 4 5 9 1), and had to recognize whether a probe item from a specific list and position was correct or incorrect. Conflict during retrieval emerged when digits at corresponding positions (e.g., 2 and 5 at the first position) were different (incongruent), but not when these digits were the same (congruent). In behavioral data, we found a congruency sequence effect, that is, responses to incongruent probe items were slower, and this effect was reduced following trials with incongruent probe items. In ERPs, this behavioral marker of adaptive control was accompanied by two effects. First, congruency affected the amplitude of an N450, and this conflict effect was reduced after incongruent trials. Second, the posterior P3 amplitude varied with the congruency of the current and the previous trial. Both results resemble those found for the Stroop task and thus highlight the similarity between conflict and adaptive control in WM and selective attention tasks.

Transcutaneous auricular vagus nerve stimulation improves working memory in temporal lobe epilepsy: A randomized double-blind study

AIMS

This study investigated the impact of transcutaneous auricular vagus nerve stimulation (taVNS) on working memory (WM) in refractory temporal lobe epilepsy (rTLE) and the underlying mechanisms.

METHODS

In this randomized double-blind study, 28 rTLE patients were subjected to an active or sham taVNS (a/s-taVNS) protocol for 20 weeks (a-taVNS group, n = 19; s-ta VNS group, n = 9). Patients performed visual WM tasks during stimulation and neural oscillations were simultaneously recorded by 19-channel electroencephalography.

RESULTS

Compared with the baseline state, reaction time was significantly shorter after 20 weeks of taVNS in the a-taVNS group (p = 0.010), whereas no difference was observed in the s-taVNS group (p > 0.05). The power spectral density (PSD) of the theta frequency band in the Fz channel decreased significantly after a-taVNS during WM-encoding (p = 0.020), maintenance (p = 0.038), and retrieval (p = 0.039) phases, but not in the s-taVNS group (all p > 0.05).

CONCLUSION

Neural oscillations during WM were altered by taVNS and WM performance was improved. Alterations in frontal midline theta oscillations may be a marker for the effect of taVNS on cognitive regulation.

Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

Policy abstraction as a predictor of cognitive effort avoidance

Consistent evidence has established that people avoid cognitively effortful tasks. However, the features that make a task cognitively effortful are still not well understood. Multiple hypotheses have been proposed regarding which task demands underlie cognitive effort costs, such as time-on-task, error likelihood, and the general engagement of cognitive control. In this study, we test the novel hypothesis that tasks requiring behavior according to higher degrees of policy abstraction are experienced as more effortful. Accordingly, policy abstraction, operationalized as the levels of contextual contingency required by task rules, drives task avoidance over and above the effects of task performance, such as time-on-task or error likelihood. To test this hypothesis, we combined two previously established cognitive control tasks that parametrically manipulated policy abstraction with the demand selection task procedure. The design of these tasks allowed us to test whether people avoided tasks with higher order policy abstraction while controlling for the contribution of factors such as time-on-task and expected error rate (ER). Consistent with our hypothesis, we observed that policy abstraction was the strongest predictor of cognitive effort choices, followed by ER. This was evident across both studies and in a within-subject cross-study analysis. These results establish at least one task feature independent of performance, which is predictive of task avoidance behavior. We interpret these results within an opportunity cost framework for understanding aversive experiences of cognitive effort while performing a task. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Temporally Dissociable Mechanisms of Spatial, Feature, and Motor Selection during Working Memory-guided Behavior

Working memory (WM) is a capacity- and duration-limited system that forms a temporal bridge between fleeting sensory phenomena and possible actions. But how are the contents of WM used to guide behavior? A recent high-profile study reported evidence for simultaneous access to WM content and linked motor plans during WM-guided behavior, challenging serial models where task-relevant WM content is first selected and then mapped on to a task-relevant motor response. However, the task used in that study was not optimized to distinguish the selection of spatial versus nonspatial visual information stored in memory, nor to distinguish whether or how the chronometry of selecting nonspatial visual information stored in memory might differ from the selection of linked motor plans. Here, we revisited the chronometry of spatial, feature, and motor selection during WM-guided behavior using a task optimized to disentangle these processes. Concurrent EEG and eye position recordings revealed clear evidence for temporally dissociable spatial, feature, and motor selection during this task. Thus, our data reveal the existence of multiple WM selection mechanisms that belie conceptualizations of WM-guided behavior based on purely serial or parallel visuomotor processing.

Internal selective attention is delayed by competition between endogenous and exogenous factors

External attention is mediated by competition between endogenous (goal-driven) and exogenous (stimulus-driven) factors, with the balance of competition determining which stimuli are selected. Occasionally, exogenous factors "win" this competition and drive the selection of task-irrelevant stimuli. Endogenous and exogenous selection mechanisms may also compete to control the selection of internal representations (e.g., those stored in working memory), but whether this competition is resolved in the same way as external attention is unknown. Here, we leveraged the high temporal resolution of human EEG to determine how competition between endogenous and exogenous factors influences the selection of internal representations. Unlike external attention, competition did not prompt the selection of task-irrelevant working memory content. Instead, it delayed the endogenous selection of task-relevant working memory content by several hundred milliseconds. Thus, competition between endogenous and exogenous factors influences internal selective attention, but in a different way than external selective attention.

Neural Index of Reinforcement Learning Predicts Improved Stimulus-Response Retention under High Working Memory Load

Human learning and decision-making are supported by multiple systems operating in parallel. Recent studies isolating the contributions of reinforcement learning (RL) and working memory (WM) have revealed a trade-off between the two. An interactive WM/RL computational model predicts that although high WM load slows behavioral acquisition, it also induces larger prediction errors in the RL system that enhance robustness and retention of learned behaviors. Here, we tested this account by parametrically manipulating WM load during RL in conjunction with EEG in both male and female participants and administered two surprise memory tests. We further leveraged single-trial decoding of EEG signatures of RL and WM to determine whether their interaction predicted robust retention. Consistent with the model, behavioral learning was slower for associations acquired under higher load but showed parametrically improved future retention. This paradoxical result was mirrored by EEG indices of RL, which were strengthened under higher WM loads and predictive of more robust future behavioral retention of learned stimulus-response contingencies. We further tested whether stress alters the ability to shift between the two systems strategically to maximize immediate learning versus retention of information and found that induced stress had only a limited effect on this trade-off. The present results offer a deeper understanding of the cooperative interaction between WM and RL and show that relying on WM can benefit the rapid acquisition of choice behavior during learning but impairs retention.SIGNIFICANCE STATEMENT Successful learning is achieved by the joint contribution of the dopaminergic RL system and WM. The cooperative WM/RL model was productive in improving our understanding of the interplay between the two systems during learning, demonstrating that reliance on RL computations is modulated by WM load. However, the role of WM/RL systems in the retention of learned stimulus-response associations remained unestablished. Our results show that increased neural signatures of learning, indicative of greater RL computation, under high WM load also predicted better stimulus-response retention. This result supports a trade-off between the two systems, where degraded WM increases RL processing, which improves retention. Notably, we show that this cooperative interplay remains largely unaffected by acute stress.

Computational Modeling of the n-Back Task in the ABCD Study: Associations of Drift Diffusion Model Parameters to Polygenic Scores of Mental Disorders and Cardiometabolic Diseases

BACKGROUND

Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with polygenic risk scores (PRSs) for mental disorders and comorbid cardiometabolic diseases.

METHODS

We used the drift diffusion model to infer latent computational processes underlying decision making and working memory during the n-back task in 3707 children ages 9 to 10 years from the Adolescent Brain Cognitive Development (ABCD) Study. Single nucleotide polymorphism-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated n-back task performance, and neurocognitive assessments. PRSs were calculated for Alzheimer's disease, bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia, and type 2 diabetes.

RESULTS

Heritability estimates of cognitive phenotypes ranged from 12% to 38%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRSs for CAD and schizophrenia. Longer nondecision time was associated with higher PRSs for Alzheimer's disease and lower PRSs for CAD. Narrower decision threshold was associated with higher PRSs for CAD. Load-dependent effects on nondecision time and decision threshold were associated with PRSs for Alzheimer's disease and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRSs for any of the mental or cardiometabolic phenotypes.

CONCLUSIONS

We identified distinct associations between computational cognitive processes and genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

Changes in behavioral priority influence the accessibility of working memory content

Evolving behavioral goals require the existence of selection mechanisms that prioritize task-relevant working memory (WM) content for action. Selecting an item stored in WM is known to blunt and/or reverse information loss in stimulus-specific representations of that item reconstructed from human brain activity, but extant studies have focused on all-or-none circumstances that allow or disallow an agent to select one of several items stored in WM. Conversely, behavioral studies suggest that humans can flexibly assign different levels of priority to different items stored in WM, but how doing so influences neural representations of WM content is unclear. One possibility is that assigning different levels of priority to items in WM influences the quality of those representations, resulting in more robust neural representations of high- vs. low-priority WM content. A second - and non-exclusive - possibility is that asymmetries in behavioral priority influence how rapidly neural representations of high- vs. low-priority WM content can be selected and reported. We tested these possibilities in two experiments by decoding high- and low-priority WM content from EEG recordings obtained while human volunteers performed a retrospectively cued WM task. Probabilistic changes in the behavioral relevance of a remembered item had no effect on our ability to decode it from EEG signals; instead, these changes influenced the latency at which above-chance decoding performance was reached. Thus, our results indicate that probabilistic changes in the behavioral relevance of WM content influence the ease with which memories can be selected independently of their strength.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

The role of conjunctive representations in prioritizing and selecting planned actions

For flexible goal-directed behavior, prioritizing and selecting a specific action among multiple candidates are often important. Working memory has long been assumed to play a role in prioritization and planning, while bridging cross-temporal contingencies during action selection. However, studies of working memory have mostly focused on memory for single components of an action plan, such as a rule or a stimulus, rather than management of all of these elements during planning. Therefore, it is not known how post-encoding prioritization and selection operate on the entire profile of representations for prospective actions. Here, we assessed how such control processes unfold over action representations, highlighting the role of conjunctive representations that nonlinearly integrate task-relevant features during maintenance and prioritization of action plans. For each trial, participants prepared two independent rule-based actions simultaneously, then they were retro-cued to select one as their response. Prior to the start of the trial, one rule-based action was randomly assigned to be high priority by cueing that it was more likely to be tested. We found that both full action plans were maintained as conjunctive representations during action preparation, regardless of priority. However, during output selection, the conjunctive representation of the high-priority action plan was more enhanced and readily selected as an output. Furthermore, the strength of the high-priority conjunctive representation was associated with behavioral interference when the low-priority action was tested. Thus, multiple alternate upcoming actions were maintained as integrated representations and served as the target of post-encoding attentional selection mechanisms to prioritize and select an action from within working memory.

A blueprint for examining striatal control of cognition

In a recent study, Bolkan, Stone, and colleagues demonstrated that direct and indirect striatal pathways in mice exert opponent control over choice behavior in a task- and state-dependent manner. This work highlights the need for rigorously controlled behavioral experiments and novel behavioral modeling in investigations of the neural mechanisms of decision making.

How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective

Reinforcement learning and working memory are two core processes of human cognition and are often considered cognitively, neuroscientifically, and algorithmically distinct. Here, we show that the brain networks that support them actually overlap significantly and that they are less distinct cognitive processes than often assumed. We review literature demonstrating the benefits of considering each process to explain properties of the other and highlight recent work investigating their more complex interactions. We discuss how future research in both computational and cognitive sciences can benefit from one another, suggesting that a key missing piece for artificial agents to learn to behave with more human-like efficiency is taking working memory's role in learning seriously. This review highlights the risks of neglecting the interplay between different processes when studying human behavior (in particular when considering individual differences). We emphasize the importance of investigating these dynamics to build a comprehensive understanding of human cognition.

Does the combination of physical activity and attention training affect the motor skills and cognitive activities of individuals with mild intellectual disability?

Individuals with mild intellectual disability (MID) were worse than their peers who typically develop in motor skills and attention-demanding assignments. In this study, effect of a 14-week physical activity and attention training practise on the motor skills, visual retention, perception and attention levels of students with MID were analysed. Twenty-two individuals between 7 and 14 ages participated voluntarily. Activities based on developing attention skills and physical activities enhancing fine-gross motor skills (40 + 60 min./2 days/14 weeks) were given to the experimental group. d2 Test of Attention, Benton Visual Retention and Bruininks-Oseretsky Test of Motor Proficiency-2nd version tests were used as data collection tools. There was significant difference in terms of total number of items processed, commissions, raw score of errors, total number of items minus error scores, concentration performance, Benton visual retention test and perception, fine motor skill precision, fine motor skill integration, manual dexterity and upper-limb coordination values regarding group and time dependant (p< .05). However, there was no significant difference in omissions and fluctuation rate values (p> .05). As a result; it is determined that the combination of physical activity and attention training practises features a positive effect on visual retention, perception, attention and motor skill levels of students with MID.

Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

Adaptive cognitive-control involves a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a task that independently manipulates selective gating operations into working-memory (input gating), from working-memory (output gating), and of responses (motor gating) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. Further, categorical representations of specific maintained items and of motor responses could be decoded from EEG when the corresponding gate was switching, thereby linking gating operations to prioritization. Finally, gate switching at all levels was related to increases in the motor decision threshold as quantified by the drift diffusion model. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.