Biology subjects

Alday, P. M.

Publications and source records attributed to Alday, P. M..

7 recordsLinked to original sources

bioRxiv2022

Effects of neural noise on predictive model updating across the adult lifespan

AbstractIn the perceptual and sensorimotor domains, ageing is accompanied by a stronger reliance on top-down predictive model information and reduced sensory learning, thus promoting simpler, more efficient internal models in older adults. Here, we demonstrate analogous effects in higher-order language processing. One-hundred and twenty adults ranging in age from 18 to 83 years listened to short auditory passages containing manipulations of adjective order, with order probabilities varying between two speakers. As a measure of model adaptation, we examined attunement of the N400 event-related potential, a measure of precision-weighted prediction errors in language, to a trial-by-trial measure of speaker-based adjective order expectedness ("speaker-based surprisal") across the course of the experiment. Adaptation was strongest for young adults, weaker for middle-aged adults, and absent for older adults. Over and above age-related differences, we observed individual differences in model adaptation, with aperiodic (1/f) slope and intercept metrics derived from resting-state EEG showing the most pronounced modulations. We suggest that age-related changes in aperiodic slope, which have been linked to neural noise, may be associated with individual differences in the magnitude of stimulus-related prediction error signals. By contrast, changes in aperiodic intercept, which reflects aggregate population spiking, may relate to an individuals updating of inferences regarding stimulus precision. These two mechanisms jointly contribute to age-related changes in the precision-weighting of prediction errors and the degree of sensory learning.

neuroscience↗

bioRxiv2021

Additive Effects of Cognitive Load, Motor Demand, and Environmental Complexity on Attention: A Real-World EEG-study

The capacity to regulate ones attention in accordance with fluctuating task demands and environmental contexts is an essential feature of adaptive behavior. Although the electrophysiological correlates of attentional processing have been extensively studied in the laboratory, relatively little is known about the way they unfold under more variable, ecologically-valid conditions. Accordingly, this study employed a real-world EEG design to investigate how attentional processing varies under increasing cognitive, motor, and environmental demands. Forty-four participants were exposed to an auditory oddball task while (1) sitting in a quiet room inside the lab, (2) walking around a sports field, and (3) wayfinding across a university campus. In each condition, participants were instructed to either count or ignore oddball stimuli. While behavioral performance was similar across the lab and field conditions, oddball count accuracy was significantly reduced in the campus condition. Moreover, event-related potential components (mismatch negativity and P3) elicited in both real-world settings differed significantly from those obtained under laboratory conditions. These findings demonstrate the impact of environmental factors on attentional processing during simultaneously-performed motor and cognitive tasks, highlighting the value of incorporating dynamic and unpredictable contexts within naturalistic designs.

neuroscience↗

bioRxiv2020

Oscillatory responses to generated and perceived rhythms

Neural oscillations have been proposed as a mechanism for structure building in language and music. In music, this idea is appealing because of the intuitive mapping between perceptual and neural rhythms. The strongest evidence has come from studies in which participants listened to isochronous sequences of identical tones and were asked to imagine hearing them in binary (march) or ternary meter (waltz). The critical finding was that in addition to increased signal at the frequency corresponding to the tone rate there was increased signal at the imagined meter frequencies. While it is striking that meter tracking was observed without any acoustic cues in the input, rhythm perception was confounded with rhythm imagery involving active generation of rhythmic structure. We conducted two electroencephalography experiments with musicians and non-musicians, teasing apart the effects of rhythm perception and rhythm generation. Evidence for meter-related neural oscillations was only observed in situations where rhythmic structure was actively generated, either via rhythm imagery or in the form of overt behavior (tapping). Thus, our data suggest that mere rhythm perception is not sufficient to elicit oscillations at the meter frequency and that they are instead driven by the active generation of rhythm. This undermines the proposal that neural oscillations constitute a basic structure building mechanism in rhythm perception and raises questions about the role of oscillations in language processing.

neuroscience↗

bioRxiv2020

Structure and meaning organize neural oscillations into a content-specific hierarchy

Neural oscillations track linguistic information during speech comprehension (e.g., Ding et al., 2016; Keitel et al., 2018), and are known to be modulated by acoustic landmarks and speech intelligibility (e.g., Zoefel & VanRullen, 2015). But, it is unclear what information (e.g., timing, rhythm, or content) the brain utilizes to generate linguistic structure and meaning beyond the information that is present in the physical stimulus. We used electroencephalography (EEG) to investigate whether oscillations are modulated by linguistic content over and above the speech stimulus rhythmicity and temporal distribution. We manipulated the presence of semantic and syntactic information apart from the timescale of their occurrence, and controlled for the acoustic-prosodic and lexical-semantic information in the signal. EEG was recorded while 29 adult native speakers of all genders listened to naturally-spoken Dutch sentences, jabberwocky controls with a sentence-like prosodic rhythm and morphemes, word lists with lexical content but no phrase structure, and backwards acoustically-matched controls. Mutual information (MI) analysis revealed sensitivity to linguistic content: Phase MI was highest for sentences at the phrasal (0.8-1.1 Hz) and lexical timescale (1.9-2.8 Hz), suggesting that the delta-band is modulated by lexically-driven combinatorial processing beyond prosody, and that linguistic content (i.e., structure and meaning) organizes the phase of neural oscillations beyond the timescale and rhythmicity of the stimulus. This pattern is consistent with neurophysiologically-inspired models of language comprehension (Martin, 2016, 2020; Martin & Doumas, 2017) where oscillations encode endogenously-generated linguistic content over and above exogenous or stimulus-driven timing and rhythm information. Significance StatementBiological systems like the brain encode their environment not only by reacting in a series of stimulus-driven responses, but by combining stimulus-driven information with endogenous, internally-generated, inferential knowledge and meaning. Understanding language from speech is the human benchmark for this. Much research focusses on the purely stimulus-driven response, but here, we focus on the goal of language behavior: conveying structure and meaning. To that end, we use naturalistic stimuli that contrast acoustic-prosodic and lexical-semantic information to show that, during spoken language comprehension, oscillatory modulations reflect computations related to inferring structure and meaning from the acoustic signal. Our experiment provides the first evidence to date that compositional structure and meaning organize the oscillatory response, above and beyond acoustic and lexical controls.

neuroscience↗

bioRxiv2017

Alpha Oscillatory Activity Prior to Encoding Modulates Memory Consolidation during Wake but not Sleep

Sleep promotes memory consolidation through unique neuromodulatory activity. However, little is known about the impact of attention during pre-sleep memory encoding on later memory performance. The current study aimed to address the question of whether attentional state prior to encoding, as indexed by alpha oscillatory activity, modulates the consolidation of images across periods of sleep and wake. 22 participants aged 18 - 41 years (mean age = 27.3) viewed 120 emotionally valenced images (positive, negative, neutral) before a 2hr afternoon sleep opportunity and an equivalent period of wake. Following the sleep and wake conditions, participants were required to distinguish between 120 previously seen (target) images and 120 new (distractor) images. Relative alpha power - adjusted according to participants individual alpha frequency - was computed to index attentional state prior to the learning phase. Generalised linear mixed-effects modelling revealed memory performance was modulated by attention, such that greater pre-encoding alpha power preferentially promoted memory consolidation during wake compared to sleep. There was no difference in memory performance between positive, negative and neutral stimuli. Modulations in alpha oscillatory activity may help to coordinate the flow of information between task-relevant cortical regions and a thalamo-cortical loop that preferentially subserves the formation of memory during times of wake relative to sleep.

neuroscience↗

bioRxiv2017

Towards a reliable, automated method of individual alpha frequency (IAF) quantification

Individual alpha frequency (IAF) is a promising electrophysiological marker of interindividual differences in cognitive function. IAF has been linked with trait-like differences in information processing and general intelligence, and provides an empirical basis for the definition of individualised frequency bands. Despite its widespread application, however, there is little consensus on the optimal method for estimating IAF, and many common approaches are prone to bias and inconsistency. Here, we describe an automated strategy for deriving two of the most prevalent IAF estimators in the literature: peak alpha frequency (PAF) and centre of gravity (CoG). These indices are calculated from resting-state power spectra that have been smoothed using a Savitzky-Golay filter (SGF). We evaluate the performance characteristics of this analysis procedure in both empirical and simulated EEG datasets. Applying the SGF technique to resting-state data from n = 63 healthy adults furnished 61 PAF, and 62 CoG estimates. The statistical properties of these estimates were consistent with previous reports. Simulation analyses revealed that the SGF routine was able to reliably extract target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak detection that did not involve spectral smoothing. The SGF technique is fast, open-source, and available in two popular programming languages (MATLAB and Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip and MNE-Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF-related research.

bioinformatics↗

bioRxiv2017

The Decision Decoding ToolBOX (DDTBOX) - A novel multivariate pattern analysis toolbox for event-related potentials

In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig, 2004; Lopez-Calderon and Luck, 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

neuroscience↗