Biology subjects

Woolgar, A.

Publications and source records attributed to Woolgar, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Establishing parallel and scalar measures of executive and semantic demand: an online norming study of matched, cross-domain judgement tasks

Task demand has a long-standing history in experimental psychology, with numerous investigations of the capacity of different systems and processes under varying cognitive load. Whilst demand is a broadly used term in the literature, it is limited by a lack of cross-domain operationalisation, and thus can be a confound in many inter-domain investigations. To address this, we generated and validated two large-scale, open-source, widely applicable stimuli sets that: (i) can probe a wide, continuous range of objective behaviourally-measured processing demand, and (ii) can be directly matched across two highly contrastive cognitive domains (semantic and visuospatial processing). We normed these tasks in a large, online cohort of healthy adults, enabling future studies to utilise and benefit from reliable baseline measures of demand. Using a matching algorithm that takes into account both speed and accuracy, we then closely paired each semantic judgement trial with a trial of comparable difficulty from the corresponding visuospatial task to produce a cross domain matched set of over 400 items. Finally, we analysed the psycholinguistic and perceptual properties of these stimuli to explore some of the underlying factors driving demand.

neuroscience↗

bioRxiv2026

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

The brain builds meaning from speech in stages, transforming acoustic input into linguistic comprehension. Yet where comprehension separates from general acoustic processing has been difficult to localize, because the two are tightly entangled in continuous speech. Here we align the activity of ~145,000 individual artificial neurons of an audio large language model with high-resolution magnetoencephalography, using single-neuron interpretability to track, layer by layer, which computations the cortex follows during natural listening. Contrasting native listeners with listeners hearing an unfamiliar language, while holding the acoustics physically identical, we find that comprehension selectively sustains brain-model alignment through the model's deep layers. Without comprehension, alignment in the acoustic encoder is preserved but deep-layer tracking abruptly collapses, and the sparse alignments that survive correspond only to physical acoustic features. This pinpoints the stage at which comprehension departs from perception and yields an interpretable, non-invasive index of whether speech is understood.

neuroscience↗

bioRxiv2026

Aperiodic neural activity links electromagnetic and hemodynamic representations of domain-general cognitive demand across the cortical hierarchy

The human brain demonstrates remarkable flexibility and capacity for domain-general cognitive control, allowing us to perform diverse and complex tasks. Central to this ability is the multiple- demand (MD) network, a domain-general system that is robustly engaged during demanding tasks in fMRI studies. However, the electrophysiological signatures underlying these domain-general responses remain elusive. While recent research has implicated aperiodic neural activity as a promising candidate, the limited spatial resolution of non-invasive electrophysiology has left it unresolved how this aperiodic signal relates to demand-related activity within the MD network and whether this relationship reflects a broader organizational principle across the cortex. To address these questions, we used a multimodal fusion framework to integrate fMRI and magnetoencephalography (MEG) data acquired while participants performed a diverse set of cognitive control tasks. We found that raw MEG- fMRI correspondence was strongest in unimodal sensorimotor cortices and progressively decreased toward transmodal association cortex during cognitive control tasks, revealing a hierarchical decline in correspondence between the electromagnetic and hemodynamic signals measured by these technologies. However, the proportion of this variance that was attributable to cognitive demand and carried by aperiodic signals showed the reverse gradient, systematically increasing along the sensorimotor-association axis. In particular, in the MD network, aperiodic broadband power showed the strongest demand-specific cross-modal commonality, outperforming canonical oscillatory components. These findings reveal two opposing hierarchical gradients: overall MEG-fMRI correspondence across all electrophysiological signals decreased toward association cortex, whereas the proportion attributable to aperiodic signals associated with cognitive demand increased. Our results identify aperiodic neural activity as a key electrophysiological substrate of cognitive control and a bridge linking electromagnetic and hemodynamic representations across the cortical hierarchy.

neuroscience↗

bioRxiv2026

Multiple-Demand Network encoding geometry balances generalization and dimensionality during novel task assembly.

On the basis of verbal instructions, humans can accomplish novel and diverse demands on the very first try. This complex phenomenon recruits structured brain activity across the frontoparietal Multiple Demand Network (MDN), which is thought to encode upcoming task parameters and guide behavior. However, it is uncertain how novel instructions are translated into efficient neural task representations. To address this, we collected functional magnetic resonance imaging (fMRI) data while participants followed a rich set of novel verbal instructions. These varied along three core dimensions: the overarching task demand (to select or to integrate stimuli information), the relevant target category (animate or inanimate items), and the visual feature that participants responded to (color or shape). We used multivariate pattern analysis (MVPA) to examine whether and how each of these dimensions was reflected in MDN activity. We contrasted two alternative representational geometries that may underpin novel task coding: low-dimensional spaces based on abstract and generalizable representations, and high-dimensional architectures hosting context-unique, conjunctive neural codes. Our results showed that anticipatory MDN activity was sensitive to the content of instructions. While selection vs. integration task demands were broadly encoded across the MDN, coding of the relevant categories and features was restricted to lateral MDN regions, namely, the intraparietal sulcus and the inferior frontal junction. Critically, the representational spaces across the MDN displayed a mixture of geometrical motifs, partially supporting our two alternative hypotheses. On the one hand, Cross-Condition Generalization Performance revealed the presence of abstract and transferable neural codes for task demand information. On the other hand, Shattering Dimensionality showed complex, high dimensional coding spaces across the MDN, structured around both task-informative and non-informative axes. Despite this, no evidence of conjunctive neural codes was observed. Overall, these findings highlight that novel instructed behavior may recruit both abstraction and high dimensionality to promote generalization while maximizing the expressivity of MDN coding spaces. More broadly, they emphasize the importance of considering encoding geometry for a computational understanding of cognitive control processes.

neuroscience↗

bioRxiv2026

HeteroRC: Decoding latent information from dynamic neural responses with interpretable heterogeneous reservoir computing

Time-resolved neural decoding is widely used to track information represented in neural activity, but conventional linear decoders primarily capture phase-locked evoked responses and often fail to recover representations embedded in nonlinear or non-phase-locked dynamics, potentially limiting the interpretation of neural coding. Here, we introduce HeteroRC, a biologically inspired and interpretable decoding framework based on heterogeneous reservoir computing. HeteroRC projects neural signals into a high-dimensional recurrent state space with heterogeneous time constants, enabling nonlinear feature expansion and multiscale temporal integration directly from raw neural time series. Simulations demonstrate that HeteroRC significantly outperforms linear decoders and a suite of artificial neural networks (including RNNs, LSTMs, Transformers and EEGNet) on evoked responses while robustly capturing induced oscillatory power, phase synchrony, and aperiodic modulations--dynamics that are largely latent to conventional linear methods. We further validate HeteroRC on two empirical EEG datasets. In a motor imagery task, it substantially improves decoding accuracy and exhibits superior cross-temporal generalisation, revealing dynamic representational transformations. In an attentional priority task, HeteroRC uncovers statistically learned spatial priority information that remains hidden from conventional methods, successfully decoding these latent states previously thought to be activity-silent. Furthermore, we develop a dual-level interpretability framework linking reservoir dynamics to virtual sources and sensor space, revealing the temporal, spectral, and spatial signatures underlying decoding performance at both the individual and group levels. Together, HeteroRC offers an interpretable approach to decode information from dynamic neural responses, broadening the analytical scope of neural decoding while remaining computationally efficient and free from manual feature engineering, making it particularly suitable for small-sample electrophysiological studies.

neuroscience↗

bioRxiv2025

Neural dynamics in ventrolateral prefrontal cortex underlie learning from feedback

Learning often depends on feedback, yet how positive and negative outcomes reorganize target representations to support later memory retrieval remains poorly understood. Accumulating evidence suggests that the ventrolateral prefrontal cortex (vlPFC) acts as a central hub linking learning and retrieval, raising the possibility that it plays a critical role in this process. Here we analysed spiking activity and local field potentials (LFPs) recorded from vlPFC while monkeys performed a multi-cycle object-learning task. During the initial learning cycle, correct and incorrect feedback elicited distinct vlPFC neural responses in both spiking and LFPs. In particular, positive feedback produced elevated theta power and enhanced phase-amplitude coupling (PAC) between theta phase and high-frequency amplitude, associated with sustained suppression of neural spiking. Incorrect feedback induced stronger beta power. Despite comparable levels of object information under both feedback conditions, decoders trained and tested within the same feedback state outperformed those tested across states, revealing feedback-dependent coding formats. State-space and cross-period generalisation analyses further showed that object representations following positive feedback were geometrically closer to and shared a common coding format with those reinstated during later retrieval, indicating that feedback reshapes neural geometry toward retrieval-compatible states. Moreover, these geometric and generalisation effects were selectively expressed on electrodes showing stronger PAC or beta power, suggesting that oscillatory coordination may regulate how feedback signals are transformed into stable target codes. Together, our results reveal how vlPFC serves as a critical bridge between learning and memory retrieval, with feedback-driven dynamics reorganizing population geometry through rhythmic coordination and bringing successful outcome states closer to future retrieval representations.

neuroscience↗

bioRxiv2025

A plastic attractor model of flexible rule-based selective attention

A defining feature of human cognition is the ability to select and respond to stimuli flexibly in different circumstances. Here we show that a recently proposed class of small associative neural network, plastic attractors, can perform such flexible cognitive functions through the rapid formation of task-based attractors. We simulated a rule-based selective attention paradigm, in which agents respond to one task-relevant feature of a visual stimulus, while ignoring another, irrelevant feature, and respond to the attended feature according to a predefined stimulus-response mapping rule. The model consists of a broadly tuned prefrontal population with rapidly changing recurrent connections to sensory neurons that compete via lateral inhibition. In this framework, the rules governing the focus of selective attention are not coded explicitly, but arise as an emergent property of temporary associations between stimulus features and motor responses. The model exhibited activation properties that embody cornerstone concepts in current attentional theory including mixed selectivity, adaptive coding and biased competition, and reproduced a number of classic behavioral and neural findings. A causal test of the model using non-invasive brain stimulation concurrent with functional magnetic resonance imaging (TMS-fMRI) in humans showed that network perturbation reproduced neural decoding and behavioural data. When features were task-relevant, they rapidly formed synaptic connections with frontal cortex binding them into an attracting state, which manifest as prioritized representation of attended information, but this state was readily corrupted by brain stimulation. The model shows mechanistically that rapid synaptic changes could explain flexible rule-based control of attention.

animal behavior and cognition↗

bioRxiv2025

Using EEG to Detect Lapses in Sustained Attention to Moving Stimuli

Sustaining attention is effortful but crucial for daily life. Despite this, attentional lapses are common and can have fatal consequences (e.g., when driving). The spontaneous nature of these lapses make studying their underlying phenomena elusive. As such, methods capable of determining when lapses have occurred may be fruitful research tools, with the potential to save lives if implemented within real world settings. Here, we capitalised on a recent hierarchical classification method, which uses multivariate decoding to index how well human observers sustain their attention within a dynamic visual environment. We asked whether this method could be used to anticipate behavioural errors based on neural activity measured with electroencephalography (EEG). We first decoded patterns of EEG activity that systematically correlated with critical aspects of a Multiple Object Monitoring (MOM) task. The extent to which we could decode this information depended on whether a stimulus was relevant for behaviour, which was lower before participants failed to detect (or missed) target stimuli, presumably due to attentional lapses. Here, we exploited this drop in neural decodability to predict whether errors were about to occur on each trial. The results form a foundation for sensitive and specific methods to objectively detect lapses in sustained attention based on patterns of brain activity.

neuroscience↗

bioRxiv2025

The Taco Setup: A Novel TMS-fMRI Setup for High Resolution Whole Brain Imaging

Simultaneous TMS-fMRI holds significant opportunities for advancing basic and translational neuroscience. However, current configurations face technical limitations, particularly the need to accommodate TMS hardware within the MRI environment. Previous solutions have used low numbers of radio-frequency (RF) channels, limiting fMRI data quality and whole-brain coverage. Here, we introduce a novel 22-channel "Taco" TMS-fMRI configuration that re-purposes flexible RF coils, wrapping them around both the participants head and the TMS coil to preserve whole brain signal reception. Guided by precision fMRI principles, we optimized acquisition protocols to achieve to achieve high temporal signal-to-noise ratio (tSNR) across the cortex. Data from three pilot participants demonstrate robust signal quality, including in regions proximal to the TMS coil. This setup offers a relatively simple and cost-effective approach to integrating precision fMRI into TMS-fMRI research.

neuroscience↗

bioRxiv2025

Distinct and complementary mechanisms of oscillatory and aperiodic alpha activity in visuospatial attention

Alpha oscillations are thought to play a key role in visuospatial attention, particularly through lateralisation mechanisms. However, whether this function is driven purely by oscillatory activity or also involves aperiodic neural components remains unclear, making it difficult to develop precise theoretical models of alpha function and attention. Using EEG and concurrent TMS-EEG, this study aimed to (1) disentangle the contributions of oscillatory and aperiodic alpha activity to visuospatial attention and (2) examine their causal roles by differentially modulating aperiodic and oscillatory components. First, across three independent EEG datasets, we found that both oscillatory and aperiodic responses in the alpha band contribute to spatial attention encoding and univariate lateralisation effects. The two signals were uncorrelated across electrodes and their combination yielded stronger effects than either signal separately, suggesting that they may play complementary roles. Then, we used concurrent TMS-EEG to modulate the two signals. Compared to arrhythmic TMS (ar-TMS), rhythmic TMS (rh-TMS), enhanced oscillatory alpha power, especially at the stimulated area, while decreasing aperiodic alpha power across the scalp. Despite these opposing effects, rh-TMS improved visuospatial attention representation carried by both oscillatory and aperiodic alpha signals, suggesting that both signals may support attentional processing through different mechanisms. Moreover, TMS-induced changes in oscillatory and aperiodic alpha decoding differentially predicted behavioural performance, with TMS-induced changes in oscillatory alpha decoding correlating with response errors and changes in aperiodic alpha decoding correlating with response speed. Together these findings reveal a functional dissociation between oscillatory and aperiodic activity in the alpha band. We suggest a dual mechanism for alpha band activity in supporting visuospatial attention, where the two components have distinct but complementary roles. Oscillatory components may primarily support attentional filtering and target prioritization, while aperiodic components may reflect overall neural excitability and cognitive efficiency. Both of these mechanisms contribute to successful visuospatial attention.

neuroscience↗

bioRxiv2024

Spatial and feature-selective attention interact to drive selective coding in frontoparietal cortex

Attention enables the selective processing of relevant information. Two types of selective attention, spatial and feature attention, have separable neural effects but in real life are often used together. Here, we asked how these types of attention interact to affect information coding in a frontoparietal multiple-demand (MD) network, essential for attentional control. Using functional magnetic resonance imaging (fMRI) with multivariate pattern analysis, we examined how covert attention to object features (colour or shape) and spatial locations (left or right) influences coding of task-related stimulus information. We found that spatial and feature attention interacted multiplicatively on information coding in MD and visual regions, such that there was above-chance decoding of the attended feature of the attended object and no detectable coding of visually equivalent but behaviourally irrelevant aspects of the visual display. The attended information had a multidimensional neural representation, with stimulus information (e.g., colour) and discrimination difficulty (distance from the categorical decision boundary) reflected in separate dimensions. Rather than boosting processing of whole objects or relevant features across space, our results suggest neural activity reflects precise tuning to relevant information, indicating a highly selective control process that codes behaviourally relevant information across multiple dimensions.

neuroscience↗

bioRxiv2024

Spatiotemporal characterisation of information coding and exchange in the multiple demand network

The multiple-demand network (MDN), a brain-wide system with nodes near sensory and higher-order cognitive regions, has been suggested to integrate and exchange task-related information across the brain, supporting cognitive task performance. However, the profile of information coding and the role of each node within this network in information exchange remain unclear. To address this, we combined fMRI and MEG data in a challenging stimulus-response mapping task. Using multivariate pattern analysis (MVPA), we decoded various forms of task information, including coarse and fine stimulus details, motor responses, and stimulus-response mapping rules, across the MDN and visual regions. Early in the task, visual regions responded to large physical differences in stimuli, while later on, fine stimulus information and rules were encoded across the MDN. To assess information exchange between regions, we developed Fusion-RCA, a novel connectivity analysis method based on fMRI-MEG fusion profiles. Our findings revealed significant transfer of fine stimulus information, rules, and responses, but little evidence for the transfer of coarse stimulus information. These results highlight distinct information encoding patterns within MDN nodes and suggest that the anterior cingulate cortex (ACC) plays a key role in distributing task-relevant information. This study offers new insights into the dynamic function of the MDN and introduces Fusion-RCA as a powerful tool for exploring brain-wide information transfer.

neuroscience↗

bioRxiv2024

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link the frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, with aperiodic and oscillatory components separated, to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

neuroscience↗

bioRxiv2024

Comparison of MR coil options for concurrent TMS-fMRI

Concurrent transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging (fMRI) is increasingly being used to study how TMS affects neural processing in local and remote connected brain regions. However, there are many technical challenges that arise when operating this unique combination of techniques. One such challenge is MR head coil compatibility and the image resolution that can be achieved. Here we compare the temporal-signal-to noise-ratio (tSNR) between 3 different MR head coils that can be used with TMS. We show that the 7-channel TMS-dedicated surface coils result in a very high tSNR directly under the TMS and the supplementary coil in comparison to the TxRx and the Magnetica. However, there is low tSNR elsewhere. This field inhomogeneity may not be suitable for all research questions, for example where the aim is to look at distributed neural responses. In these cases, an MR coil with a more homogeneous tSNR such as the Magnetica, may be more appropriate.

neuroscience↗

bioRxiv2024

Domain general frontoparietal regions show modality-dependent coding of auditory and visual rules

A defining feature of human cognition is our ability to respond flexibly to what we see and hear, changing how we respond depending on our current goals. In fact, we can rapidly associate almost any input stimulus with any arbitrary behavioural response. This remarkable ability is thought to depend on a frontoparietal "multiple demand" circuit which is engaged by many types of cognitive demand and widely referred to as domain general. However, it is not clear how responses to multiple input modalities are structured within this system. Domain generality could be achieved by holding information in an abstract form that generalises over input modality, or in a modality-tagged form, which uses similar resources but produces unique codes to represent the information in each modality. We used a stimulus-response task, with conceptually identical rules in two sensory modalities (visual and auditory), to distinguish between these possibilities. Multivariate decoding of functional magnetic resonance imaging data showed that representations of visual and auditory rules recruited overlapping neural resources but were expressed in modality-tagged non-generalisable neural codes. Our data suggest that this frontoparietal system may draw on the same or similar resources to solve multiple tasks, but does not create modality-general representations of task rules, even when those rules are conceptually identical between domains.

neuroscience↗

bioRxiv2023

The causal roles of parietal alpha oscillations and evoked potentials in coding task-relevant information during selective attention

Selective attention is a fundamental cognitive mechanism that allows people to prioritise task-relevant information while ignoring irrelevant information. Previous research has suggested key roles of parietal evoked potentials and alpha oscillatory responses in spatial attention tasks. However, the informational content of these signals is less clear, and their causal effects on the coding of multiple task elements are yet unresolved. Here, we used concurrent TMS-EEG to causally manipulate parietal alpha power and evoked potentials and investigate their roles in coding multiple task features (where to attend, what to attend to, and visual stimulus) in a selective attention task. First, using EEG-only data, we found that evoked potentials coded all three types of task-relevant information with distinct temporal dynamics, and alpha oscillations carried information regarding both where to attend and what to attend to. Then, we applied rhythmic-TMS (rTMS) at individual alpha frequency over the right intraparietal sulcus (IPS), while concurrently measuring EEG. Compared with control arrhythmic-TMS, alpha rTMS increased alpha power and inter-trial phase coherence and yielded more negative posterior-contralateral evoked potentials. Moreover, alpha rTMS causally and specifically improved multivariate decoding of the information about where to attend (but not what to attend to or feature information) during task performance, with decoding improvements predicting changes in behavioural performance. These findings illuminate the dynamics with which the complementary aspects of a selective attention task are encoded in evoked and oscillatory brain activity. Moreover, they reveal a specific and causal role of IPS-controlled evoked and oscillatory activity in carrying behaviour-driving information about where to focus attention.

neuroscience↗

bioRxiv2023

Optimising analysis choices for multivariate decoding: creating pseudotrials using trial averaging and resampling

Multivariate pattern analysis (MVPA) is a popular technique that can distinguish between condition-specific patterns of activation. Applied to neuroimaging data, MVPA decoding for inference uses above chance decoding to identify statistically reliable condition-specific information in neuroimaging data which may be missed by univariate methods. However, several analysis choices influence decoding results, and the combined effects of these choices have not been fully evaluated. In particular, an increasingly popular approach is to average data from several trials together before training an MVPA classifier, but the decision about how much averaging to do is arbitrary and the effect of varying this parameter has not been documented. Here we systematically assessed the influence of trial averaging and resampling on decoding accuracy and subsequent statistical outcome on simulated data. Although the optimal parameters varied with the classifier and cross-validation approach used, we found that modest trial averaging using up to 5-10% of the total number of trials per condition improved decoding accuracy and associated t-statistics. In addition, a small amount of resampling could improve t-statistics and classification performance, but was not always necessary. We provide code to allow researchers to optimise these analysis choices for the parameters of their data.

neuroscience↗

bioRxiv2022

Fractionating distraction: How past- and future-relevant distractors influence integrated decisions

Many everyday tasks require us to integrate information from multiple steps to make a decision. Dominant accounts of flexible cognition suggest that we are able to navigate such complex tasks by attending to each step in turn, yet few studies measure how we direct our attention to immediate and future task steps. Here, we used a two-step task to test whether participants are sensitive to information that is currently irrelevant, but will be relevant in a future task step. Participants viewed two displays in sequence, each containing two superimposed moving dot clouds of different colours. Participants attended to one cued target colour in each display and reported the average direction of the two target dot clouds. In a subset of trials, we presented a "decoy" distractor: the second target colour appeared as the distractor in the first display. We regressed behavioural responses on the dot clouds motion directions to track how this future-relevant "decoy" distractor influenced participants reporting of the average target direction. We compared the influence of decoy distractors to never-relevant, recently relevant, and globally relevant distractor baselines. Across four experiments, we found that responses reflected what was immediately relevant, as well as the broader historical relevance of the distractors. However, relevance for a future task step did not reliably influence attention. We propose that attention in multi-step tasks is shaped by what has been relevant in the current setting, and by the immediate demands of each task step. Public SignificanceOur everyday functioning depends on our ability to piece together information to make coherent decisions. Understanding how we efficiently select and integrate goal-relevant information is critical if we wish to anticipate how decision-making can go wrong, whether because of fatigue, mental load, or illness. This study shows that decisions in multi-step tasks reflect two distinct processes: narrow focus on what is relevant in each step, alongside broader awareness of what has been relevant in this setting.

neuroscience↗