Endogenous control and task representation: an fMRI study in algebraic problem-solving

Cognitive processing stages in mental rotation - How can cognitive modelling inform HsMM-EEG models?

The aspiration for insight into human cognitive processing has traditionally driven research in cognitive science. With methods such as the Hidden semi-Markov Model-Electroencephalography (HsMM-EEG) method, new approaches have been developed that help to understand the temporal structure of cognition by identifying temporally discrete processing stages. However, it remains challenging to assign concrete functional contributions by specific processing stages to the overall cognitive process. In this paper, we address this challenge by linking HsMM-EEG3 with cognitive modelling, with the aim of further validating the HsMM-EEG3 method and demonstrating the potential of cognitive models to facilitate functional interpretation of processing stages. For this purpose, we applied HsMM-EEG3 to data from a mental rotation task and developed an ACT-R cognitive model that is able to closely replicate human performance in this task. Applying HsMM-EEG3 to the mental rotation experiment data revealed a strong likelihood for 6 distinct stages of cognitive processing during trials, with an additional stage for non-rotated conditions. The cognitive model predicted intra-trial mental activity patterns that project well onto the processing stages, while explaining the additional stage as a marker of non-spatial shortcut use. Thereby, this combined methodology provided substantially more information than either method by itself and suggests conclusions for cognitive processing in general.

Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task

Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant’s sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences.

Sleep Enhances Consolidation of Memory Traces for Complex Problem-Solving Skills

Sleep consolidates memory for procedural motor skills, reflected by sleep-dependent changes in the hippocampal-striatal-cortical network. Other forms of procedural skills require the acquisition of a novel strategy to solve a problem, which recruit overlapping brain regions and specialized areas including the caudate and prefrontal cortex. Sleep preferentially benefits strategy and problem-solving skills over the accompanying motor execution movements. However, it is unclear how acquiring new strategies benefit from sleep. Here, participants performed a task requiring the execution of a sequence of movements to learn a novel cognitive strategy. Participants performed this task while undergoing fMRI before and after an interval of either a full night sleep, a daytime nap, or wakefulness. Participants also performed a motor control task, which precluded the opportunity to learn the strategy. In this way, we subtracted motor execution-related brain activations from activations specific to the strategy. The sleep and nap groups experienced greater behavioral performance improvements compared to the wake group on the strategy-based task. Following sleep, we observed enhanced activation of the caudate in addition to other regions in the hippocampal-striatal-cortical network, compared to wakefulness. This study demonstrates that sleep is a privileged time to enhance newly acquired cognitive strategies needed to solve problems.

Individual Differences in Reward-Based Learning Predict Fluid Reasoning Abilities

The ability to reason and problem-solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real-world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self-generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities.

Memory-related cognitive load effects in an interrupted learning task: A model-based explanation

BACKGROUND

The Cognitive Load Theory provides a well-established framework for investigating aspects of learning situations that demand learners' working memory resources. However, the interplay of these aspects at the cognitive and neural level is still not fully understood.

METHOD

We developed four computational models in the cognitive architecture ACT-R to clarify underlying memory-related strategies and mechanisms. Our models account for human data of an experiment that required participants to perform a symbol sequence learning task with embedded interruptions. We explored the inclusion of subsymbolic mechanisms to explain these data and used our final model to generate fMRI predictions.

RESULTS

The final model indicates a reasonable fit for reaction times and accuracy and links the fMRI predictions to the Cognitive Load Theory.

CONCLUSIONS

Our work emphasizes the influence of task characteristics and supports a process-related view on cognitive load in instructional scenarios. It further contributes to the discussion of underlying mechanisms at a neural level.

Spatial contribution of hippocampal BOLD activation in high-resolution fMRI

While the vascular origin of the BOLD-fMRI signal is established, the exact neurovascular coupling events contributing to this signal are still incompletely understood. Furthermore, the hippocampal spatial properties of the BOLD activation are not elucidated, although electrophysiology approaches have already revealed the precise spatial patterns of neural activity. High magnetic field fMRI offers improved contrast and allows for a better correlation with the underlying neuronal activity because of the increased contribution to the BOLD signal of small blood vessels. Here, we take advantage of these two benefits to investigate the spatial characteristics of the hippocampal activation in a rat model before and after changing the hippocampal plasticity by long-term potentiation (LTP). We found that the hippocampal BOLD signals evoked by electrical stimulation at the perforant pathway increased more at the radiatum layer of the hippocampal CA1 region than at the pyramidal cell layer. The return to the baseline of the hippocampal BOLD activation was prolonged after LTP induction compared with that before most likely due vascular or neurovascular coupling changes. Based on these results, we conclude that high resolution BOLD-fMRI allows the segregation of hippocampal subfields probably based on their underlying vascular or neurovascular coupling features.

Cross-decoding supramodal information in the human brain

Perceptual decision making is the cognitive process wherein the brain classifies stimuli into abstract categories for more efficient downstream processing. A system that, during categorization, can process information regardless of the information's original sensory modality (i.e., a supramodal system) would have a substantial advantage over a system with dedicated processes for specific sensory modalities. While many studies have probed decision processes through the lens of one sensory modality, it remains unclear whether there are such supramodal brain areas that can flexibly process task-relevant information regardless of the original "format" of the information. To investigate supramodality, one must ensure that supramodal information exists somewhere within the functional architecture by rendering information from multiple sensory systems necessary but insufficient for categorization. To this aim, we tasked participants with categorizing auditory and tactile frequency-modulated sweeps according to learned, supramodal categories in a delayed match-to-category paradigm while we measured their blood-oxygen-level dependent signal with functional MRI. To detect supramodal information, we implemented a set of cross-modality pattern classification analyses, which demonstrated that the left caudate nucleus encodes category-level information but not stimulus-specific information (such as spatial directions and stimulus modalities), while the right inferior frontal gyrus, showing the opposite pattern, encodes stimulus-specific information but not category-level information. Given our paradigm, these results reveal abstract representations in the brain that are independent of motor, semantic, and sensory-specific processing, instead reflecting supramodal, categorical information, which points to the caudate nucleus as a locus of cognitive processes involved in complex behavior.

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision-Making Models

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia-the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision-making tasks, which are brought to light by Parkinson's disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture (ACT-R) cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed.

Frontal Cortex Supports the Early Structuring of Multiple Solution Steps in Symbolic Problem-solving

problem-solving relies on a sequence of cognitive steps involving phases of task encoding, the structuring of solution steps, and their execution. On the neural level, metabolic neuroimaging studies have associated a frontal-parietal network with various aspects of executive control during numerical and nonnumerical problem-solving. We used EEG-MEG to assess whether frontal cortex contributes specifically to the early structuring of multiple solution steps. Basic multiplication ("3 × 4" vs. "3 × 24") was compared with an arithmetic sequence rule ("first add the two digits, then multiply the sum with the smaller digit") on two complexity levels. This allowed dissociating demands of early solution step structuring from early task encoding demands. Structuring demands were high for conditions that required multiple steps, that is, complex multiplication and the two arithmetic sequence conditions, but low for easy multiplication that mostly relied on direct memory retrieval. Increased right frontal activation in time windows between 300 and 450 msec was observed only for conditions that required multiple solution steps. General task encoding demands, operationalized by problem size (one-digit vs. two-digit numbers), did not predict these early frontal effects. In contrast, parietal effects occurred as a function of problem size irrespectively of structuring demands in early phases of task encoding between 100 and 300 msec. We here propose that frontal cortex subserves domain-general processes of problem-solving, such as the structuring of multiple solution steps, whereas parietal cortex supports number-specific early encoding processes that vary as a function of problem size.

Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model

Numerical inductive reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical inductive reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical inductive reasoning. The current question of interest is whether numerical inductive reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical inductive reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical inductive reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network.

Using data-driven model-brain mappings to constrain formal models of cognition

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings.

Bilingualism trains specific brain circuits involved in flexible rule selection and application

Bilingual individuals have been shown to outperform monolinguals on a variety of tasks that measure non-linguistic executive functioning, suggesting that some facets of the bilingual experience give rise to generalized improvements in cognitive performance. The current study investigated the hypothesis that such advantage in executive functioning arises from the need to flexibly select and apply rules when speaking multiple languages. Such flexible behavior may strengthen the functioning of the fronto-striatal loops that direct signals to the prefrontal cortex. To test this hypothesis, we compared behavioral and brain data from proficient bilinguals and monolinguals who performed a Rapid Instructed Task Learning paradigm, which requires behaving according to ever-changing rules. Consistent with our hypothesis, bilinguals were faster than monolinguals when executing novel rules, and this improvement was associated with greater modulation of activity in the basal ganglia. The implications of these findings for language and executive function research are discussed herein.

Brain activity associated with translation from a visual to a symbolic representation in algebra and geometry

This paper presents a small part of a larger interdisciplinary study that investigates brain activity (using event related potential methodology) of male adolescents when solving mathematical problems of different types. The study design links mathematics education research with neurocognitive studies. In this paper we performed a comparative analysis of brain activity associated with the translation from visual to symbolic representations of mathematical objects in algebra and geometry. Algebraic tasks require translation from graphical to symbolic representation of a function, whereas tasks in geometry require translation from a drawing of a geometric figure to a symbolic representation of its property. The findings demonstrate that electrical activity associated with the performance of geometrical tasks is stronger than that associated with solving algebraic tasks. Additionally, we found different scalp topography of the brain activity associated with algebraic and geometric tasks. Based on these results, we argue that problem solving in algebra and geometry is associated with different patterns of brain activity.

Inhibitory synapses between striatal projection neurons support efficient enhancement of cortical signals: a computational model

The function of lateral inhibitory synapses between striatal projection neurons is currently poorly understood. This paper puts forward a model suggesting that inhibitory collaterals can be used to enhance the incoming cortical signals. In particular, we propose that lateral inhibition between projection neurons performs a signal-enhancing process that resembles the image processing technique of "unsharp masking", where a blurred copy is used to enhance and sharpen an input image. The paper also presents the results of computer simulations deomsntrating that the proposed mechanisms is compatible with known properties of striatal projection neurons, and outperforms alternative models of lateral inhibition. Finally, this paper illustrates the advantages of the proposed model and discusses the relevance of these conclusions for existing computational models of the basal ganglia and their role in cognition.

Cognitive architectures as a tool for investigating the role of oscillatory power and coherence in cognition

In contrast to our increasing knowledge of the role that oscillations in single brain regions play in cognition, very little is known about how coherence between oscillations in distant brain regions is related to information transmission. Here I present a cognitive modeling approach that can address that question. Specifically, I show how a model of the attentional blink implemented in the ACT-R cognitive architecture is related to the amplitude and coherence of EEG oscillations. The dynamics of the model's working memory resource is primarily associated with parietal 4-9 Hz theta oscillations, while the dynamics of the model's declarative memory, visual perception and procedural resources together are correlated with posterior theta oscillations. I further show that model predictions about inter-module communication during the processes of stimulus identification and target consolidation are associated with selective increases in coherence at the predicted points in time.

Distinct contributions of the caudate nucleus, rostral prefrontal cortex, and parietal cortex to the execution of instructed tasks

When we behave according to rules and instructions, our brains interpret abstract representations of what to do and transform them into actual behavior. In order to investigate the neural mechanisms behind this process, we devised an fMRI experiment that explicitly isolated rule interpretation from rule encoding and execution. Our results showed that a specific network of regions (including the left rostral prefrontal cortex, the caudate nucleus, and the bilateral posterior parietal cortices) is responsible for translating rules into executable form. An analysis of activation patterns across conditions revealed that the posterior parietal cortices represent a mental template for the task to perform, that the inferior parietal gyrus and the caudate nucleus are responsible for instantiating the template in the proper context, and that the left rostral prefrontal cortex integrates information across complex relationships.

Contributions of the striatum to learning, motivation, and performance: an associative account

It has long been recognized that the striatum is composed of distinct functional sub-units that are part of multiple cortico-striatal-thalamic circuits. Contemporary research has focused on the contribution of striatal sub-regions to three main phenomena: learning of associations between stimuli, actions and rewards; selection between competing response alternatives; and motivational modulation of motor behavior. Recent proposals have argued for a functional division of the striatum along these lines, attributing, for example, learning to one region and performance to another. Here, we consider empirical data from human and animal studies, as well as theoretical notions from both the psychological and computational literatures, and conclude that striatal sub-regions instead differ most clearly in terms of the associations being encoded in each region.

Acetylcholine-based entropy in response selection: a model of how striatal interneurons modulate exploration, exploitation, and response variability in decision-making

The basal ganglia play a fundamental role in decision-making. Their contribution is typically modeled within a reinforcement learning framework, with the basal ganglia learning to select the options associated with highest value and their dopamine inputs conveying performance feedback. This basic framework, however, does not account for the role of cholinergic interneurons in the striatum, and does not easily explain certain dynamic aspects of decision-making and skill acquisition like the generation of exploratory actions. This paper describes basal ganglia acetylcholine-based entropy (BABE), a model of the acetylcholine system in the striatum that provides a unified explanation for these phenomena. According to this model, cholinergic interneurons in the striatum control the level of variability in behavior by modulating the number of possible responses that are considered by the basal ganglia, as well as the level of competition between them. This mechanism provides a natural way to account for the role of basal ganglia in generating behavioral variability during the acquisition of certain cognitive skills, as well as for modulating exploration and exploitation in decision-making. Compared to a typical reinforcement learning model, BABE showed a greater modulation of response variability in the face of changes in the reward contingences, allowing for faster learning (and re-learning) of option values. Finally, the paper discusses the possible applications of the model to other domains.

What difference does a year of schooling make? Maturation of brain response and connectivity between 2nd and 3rd grades during arithmetic problem solving

Early elementary schooling in 2nd and 3rd grades (ages 7-9) is an important period for the acquisition and mastery of basic mathematical skills. Yet, we know very little about neurodevelopmental changes that might occur over a year of schooling. Here we examine behavioral and neurodevelopmental changes underlying arithmetic problem solving in a well-matched group of 2nd (n = 45) and 3rd (n = 45) grade children. Although 2nd and 3rd graders did not differ on IQ or grade- and age-normed measures of math, reading and working memory, 3rd graders had higher raw math scores (effect sizes = 1.46-1.49) and were more accurate than 2nd graders in an fMRI task involving verification of simple and complex two-operand addition problems (effect size = 0.43). In both 2nd and 3rd graders, arithmetic complexity was associated with increased responses in right inferior frontal sulcus and anterior insula, regions implicated in domain-general cognitive control, and in left intraparietal sulcus (IPS) and superior parietal lobule (SPL) regions important for numerical and arithmetic processing. Compared to 2nd graders, 3rd graders showed greater activity in dorsal stream parietal areas right SPL, IPS and angular gyrus (AG) as well as ventral visual stream areas bilateral lingual gyrus (LG), right lateral occipital cortex (LOC) and right parahippocampal gyrus (PHG). Significant differences were also observed in the prefrontal cortex (PFC), with 3rd graders showing greater activation in left dorsal lateral PFC (dlPFC) and greater deactivation in the ventral medial PFC (vmPFC). Third graders also showed greater functional connectivity between the left dlPFC and multiple posterior brain areas, with larger differences in dorsal stream parietal areas SPL and AG, compared to ventral stream visual areas LG, LOC and PHG. No such between-grade differences were observed in functional connectivity between the vmPFC and posterior brain regions. These results suggest that even the narrow one-year interval spanning grades 2 and 3 is characterized by significant arithmetic task-related changes in brain response and connectivity, and argue that pooling data across wide age ranges and grades can miss important neurodevelopmental changes. Our findings have important implications for understanding brain mechanisms mediating early maturation of mathematical skills and, more generally, for educational neuroscience.

The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking

Background

It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state bottleneck.

Methodology

In the current study we use the complimentary methodologies of computational cognitive modeling and neuroimaging to investigate the neural correlates of this problem state bottleneck. In particular, an existing computational cognitive model was used to generate a priori fMRI predictions for a multitasking experiment in which the problem state bottleneck plays a major role. Hemodynamic responses were predicted for five brain regions, corresponding to five cognitive resources in the model. Most importantly, we predicted the intraparietal sulcus to show a strong effect of the problem state manipulations.

Conclusions

Some of the predictions were confirmed by a subsequent fMRI experiment, while others were not matched by the data. The experiment supported the hypothesis that the problem state bottleneck is a plausible cause of the interference in the experiment and that it could be located in the intraparietal sulcus.

Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination

The basal ganglia play a central role in cognition and are involved in such general functions as action selection and reinforcement learning. Here, we present a model exploring the hypothesis that the basal ganglia implement a conditional information-routing system. The system directs the transmission of cortical signals between pairs of regions by manipulating separately the selection of sources and destinations of information transfers. We suggest that such a mechanism provides an account for several cognitive functions of the basal ganglia. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus-response associations by internalizing transferred cortical representations in the striatum. We discuss how the model is related to production systems and cognitive architectures. A series of simulations is presented to illustrate how the model can perform simple stimulus-response tasks, develop automatic behaviors, and provide an account of impairments in Parkinson's and Huntington's diseases.

Semantic processing of mathematical gestures

OBJECTIVE

To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect.

RESULTS

The N400 effect elicited by words primed with mathematical gestures (e.g. "converging" and "decreasing") was the same in amplitude, latency and topography as that elicited by words primed with action gestures (e.g. drive and lift), and that for terminal words of sentences.

SIGNIFICANCE AND CONCLUSION

Findings provide a within-subject demonstration that the topographies of the gesture N400 effect for both action and mathematical words are indistinguishable from that of the standard language N400 effect. This suggests that mathematical function words are processed by the general language semantic system and do not appear to involve areas involved in other mathematical concepts (e.g. numerosity).

A central circuit of the mind

The methodologies of cognitive architectures and functional magnetic resonance imaging can mutually inform each other. For example, four modules of the ACT-R (adaptive control of thought - rational) cognitive architecture have been associated with four brain regions that are active in complex tasks. Activity in a lateral inferior prefrontal region reflects retrieval of information in a declarative module; activity in a posterior parietal region reflects changes to problem representations in an imaginal module; activity in the anterior cingulate cortex reflects the updates of control information in a goal module; and activity in the caudate nucleus reflects execution of productions in a procedural module. Differential patterns of activation in such central regions can reveal the time course of different components of complex cognition.