Biology subjects

Feuerriegel, D.

Publications and source records attributed to Feuerriegel, D..

15 recordsLinked to original sources

bioRxiv2026

The Centroparietal Positivity Traces the Path of Evidence Accumulation During Dietary Decisions

Each day we make dozens of decisions about food. These choices are informed not only by information that we receive from our senses, but also by our memories, values, cultural norms, and goals. Evidence accumulation models have been proposed to explain how we transform these disparate sources of information into dietary choices and their corresponding motor actions. These models describe decisions arising from a noisy integration of decisional evidence until a criterion is reached. We investigated whether the centroparietal positivity (CPP), an event-related potential component proposed as a neural correlate of evidence accumulation during perceptual judgements, also traces accumulation trajectories during dietary decisions. Participants (N = 110) made speeded Yes/No judgements about food images relating to three attributes (healthiness, tastiness, and willingness to eat) while electroencephalography was recorded. They subsequently provided continuous ratings for each attribute. After applying signal deconvolution techniques, CPP waveforms exhibited a close correspondence to hypothesised evidence accumulation trajectories, showing steeper amplitude build-up rates for both faster decisions and continuous ratings more strongly in favour of ones categorical choice. Our findings provide clear support for a neurally instantiated evidence accumulation process that integrates multiple sources of information to produce dietary decisions.

neuroscience↗

bioRxiv2025

FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

Investigating the neurocognitive mechanisms underlying food choices has the potential to advance our understanding of eating behaviour and inform health-targeted interventions and policy. Large, publicly available neural and behavioural datasets can enable new discoveries and targeted hypothesis tests, yet no such datasets are currently available. We present the FOODEEG dataset containing electroencephalographic (EEG) responses to a diverse array of food images, as well as behavioural measures of food cognition (food categorisation task, food go/no-go task, and food choice task responses), collected from 117 participants. We also provide normative ratings for the food image stimuli with respect to 22 food attributes, including nutritive, hedonic and taste properties, familiarity, and elicited emotions. Our dataset also includes questionnaire-based measures of participants food motivations, dietary styles, and general motivational tendencies. In the validation analyses, we demonstrate that early food-evoked EEG responses in our dataset are consistent with observations in previous work. The FOODEEG dataset will be valuable for accelerating research into the neural substrates of visual food processing, dietary decisions, and individual differences.

neuroscience↗

bioRxiv2025

Tracing the neural trajectories of evidence accumulation and motor preparation processes during voluntary decisions

Voluntary decisions have previously been described by where they arise in the brain and how actions corresponding to ones choice are prepared. However, the processes by which these internally guided decisions are formed and translated into action remain unclear. We tested whether neural signatures of evidence accumulation and motor preparation - established in perceptual decision-making - are also observed during voluntary decisions. Forty-nine adults made voluntary (free choice between two options) and forced (single specified choice) decisions while electroencephalography was recorded. To isolate decision formation from action selection, all responses were made with a right-handed keypress. After applying signal deconvolution and current source density transformations, we examined three signals: the centro-parietal positivity (CPP), Mu/Beta (8 - 30 Hz) amplitudes, and a left-hemisphere readiness potential contralateral to the response hand. For both voluntary and forced decisions, CPP and Mu/Beta amplitudes showed hallmark accumulation-to-bound dynamics - faster responses had steeper ramping signals that converged to stereotyped pre-response amplitude levels. The left-hemisphere readiness potential likewise showed convergence to a pre-response level but with less reliable evidence for RT-dependent slope scaling, resembling a late, effector-specific motor gate rather than an evolving decision variable. Waveform amplitudes across the time course of these signals also did not differ reliably between voluntary and forced decisions. Our results indicate that, like perceptual decisions, voluntary choices are also formed by a graded accumulation process that culminates in a motor action, with the CPP indexing an evolving decision variable and Mu/Beta indexing effector-specific motor readiness.

neuroscience↗

bioRxiv2025

A Parietal Memory Strength Signal Linked to Evidence Accumulation in Recognition Decisions

Recognising objects from memory requires an integration of sensory and mnemonic information. This process has been theorised to occur via a stochastic evidence accumulation process implemented within the parietal cortex. Recent electroencephalographic (EEG) evidence indicates that the widely studied parietal Late Positive Component (LPC) shows characteristics of such a mnemonic accumulation signal. Here, we formally investigated this hypothesis using generative computational modelling that links a trial-level memory strength variable in the Diffusion Decision Model (DDM) with LPC amplitudes prior to the time of the decision. We recorded EEG from 24 participants making recognition judgements based on either studied or novel words. Each participant completed up to three testing sessions. We replicated recent findings that the LPC ramps up and peaks around the time of the decision and corresponding motor response. LPC amplitudes also covaried with accuracy and response time, as expected of a neural correlate of memory strength. By fitting DDMs to LPC amplitudes and behavioural data using specialised neural network tools, we demonstrate that LPC amplitudes are selectively associated with the rate of evidence accumulation, signifying memory strength. This association was stronger for previously studied words compared to novel words, and strongest at the time window immediately prior to the recognition decision. Our findings therefore recast the LPC as a neural signature of mnemonic strength linked to the rate of evidence accumulation during recognition memory decisions.

neuroscience↗

bioRxiv2025

Characterising the neural time-courses of food attribute representations

Dietary decisions involve the consideration of multiple, often conflicting, food attributes that precede the computation of an overall value for a food. The differences in the speed at which attributes are processed play an important role; however, it is unknown whether different attributes are processed over distinct time windows. We mapped the neural time-courses of 12 choice-relevant food attributes. Participants (N = 110) viewed food images while we recorded brain activity using electroencephalography (EEG). A separate group of participants (N = 421) rated the same images on nutritive properties (healthiness, calorie content, edibility, and level of transformation), hedonic properties (tastiness, willingness to eat, negative and positive valence, and arousal), and familiarity (previous exposure, recognisability, and typicality). Using representational similarity analysis, we quantified differences in patterns of multivariate EEG signals across foods and assessed whether the structure of these differences was correlated with differences in attribute ratings. We observed similar correlation time-courses for many attributes. There was an early window of correlations ([~]200 ms from image onset), followed by sustained windows of correlation from [~]400-650 ms. Using principal components analysis, we identified a set of broader constructs that accounted for variance in ratings across multiple attributes, and were also correlated with the EEG data. Our results indicate that food attributes important for choice are represented rapidly and in parallel, over similar time windows. Furthermore, we reveal that broad dimensions underlying individual attributes are also represented in the neural activity with distinct time-courses, indicating a multilevel structure of food attribute representations.

neuroscience↗

bioRxiv2024

Regressing Away Common Neural Choice Signals does not make them Artifacts. Comment on Frömer et al (2024, Nature Human Behaviour)

Fromer et al (2024, Nature Human Behaviour) apply a deconvolution method to correct for component overlap in the event-related potential. They report that this method eliminates signatures of sensory evidence accumulation from response-aligned measurements of the centro-parietal positivity (CPP), suggesting that these signatures arise artifactually.. Here, we argue that the analysis and interpretation of their perceptual choice data are critically flawed. We demonstrate with simulations that the deconvolution analyses used by the authors are not designed to reliably test for the presence or absence of bounded accumulation signals.

neuroscience↗

bioRxiv2022

Position representations of moving objects align with real-time position in the early visual response

AO_SCPLOWBSTRACTC_SCPLOWWhen localising a moving object, the brain receives outdated sensory information about its position, due to the time required for neural transmission and processing. The brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.

neuroscience↗

bioRxiv2021

Decoding continuous variables from EEG data using linear support vector regression (SVR) analysis with the Decision Decoding Toolbox (DDTBOX)

BackgroundMultivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is Support Vector Regression (SVR), which uses single-trial data to predict continuous variables of interest. New MethodIn this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. ResultsAcross all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 ms - 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Comparison with existing MethodsOur result show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. ConclusionsIn sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.

neuroscience↗

bioRxiv2021

Neural correlates of metacognition across the adult lifespan

Metacognitive accuracy describes the degree of overlap between the subjective perception of ones decision accuracy (i.e., confidence) and objectively observed performance. With older age, the need for accurate metacognitive evaluation increases; however, error detection rates typically decrease. We investigated the effect of ageing on metacognitive accuracy using event-related potentials (ERPs) reflecting error detection and confidence: the error/correct negativity (Ne/c) and the error/correct positivity (Pe/c). Sixty-five healthy adults (20 to 76 years) completed a complex perceptual task and provided confidence ratings. We found that metacognitive accuracy declined with age beyond the expected decline in task performance, while the adaptive adjustment of behaviour was well preserved. Pe/c amplitudes varied by confidence rating, but they did not mirror the reduction in metacognitive accuracy. Ne/c amplitudes decreased with age except for high confidence correct responses. The results suggest that age-related difficulties in metacognitive evaluation could be related to an impaired integration of decision accuracy and confidence information processing. Ultimately, training the metacognitive evaluation of fundamental decisions in older adults might constitute a promising endeavour.

neuroscience↗

bioRxiv2021

Response time modelling reveals evidence for multiple, distinct sources of moral decision caution

People are often cautious in delivering moral judgments of others behaviours, as falsely accusing others of wrongdoing can be costly for social relationships. Caution might further be present when making judgements in information-dynamic environments, as contextual updates can change our minds. This study investigated the processes with which moral valence and context expectancy drive caution in moral judgements. Across two experiments, participants (N = 122) made moral judgements of others sharing actions. Prior to judging, participants were informed whether contextual information regarding the deservingness of the recipient would follow. We found that participants slowed their moral judgements when judging negatively valenced actions and when expecting contextual updates. Using a diffusion decision model framework, these changes were explained by shifts in drift rate and decision bias (valence) and boundary setting (context), respectively. These findings demonstrate how moral decision caution can be decomposed into distinct aspects of the unfolding decision process.

neuroscience↗

bioRxiv2020

An initial 'snapshot' of sensory information biases the likelihood and speed of subsequent changes of mind

We often need to rapidly change our mind about perceptual decisions in order to account for new information and correct mistakes. One fundamental, unresolved question is whether information processed prior to a decision being made ( pre-decisional information) has any influence on the likelihood and speed with which that decision is reversed. We investigated this using a luminance discrimination task in which participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli briefly remained on screen, and participants could change their response. Using psychophysical reverse correlation, we examined how moment-to-moment fluctuations in stimulus luminance affected participants decisions. This revealed that the strength of even the very earliest (pre-decisional) evidence was associated with the likelihood and speed of later changes of mind. To account for this effect, we propose an extended diffusion model in which an initial snapshot of sensory information biases ongoing evidence accumulation. Author SummaryTo avoid harm in an ever-changing world we need to be able to rapidly change our minds about our decisions. For example, imagine being unable to overrule a decision to run across a street when you realise a speeding car is approaching. In this study, we examined the information processing dynamics which underlie perceptual judgements and changes of mind. By reverse correlating participants decisions with the moment-to-moment sensory evidence they received, we show that the very earliest information, processed prior to an initial decision being made, can have a lasting influence over the speed and likelihood of subsequent changes of mind. To account for this, we develop a model of perceptual decisions in which initial sensory evidence exerts a lasting bias over later evidence processing. When fit to participants behavioural responses alone, this model predicted their observed information usage patterns. This suggests that an initial snapshot of sensory information may influence the ongoing dynamics of the perceptual decision process, thus influencing the speed and likelihood of decision reversals.

animal behavior and cognition↗

bioRxiv2018

Temporal expectations modulate face image repetition suppression as indexed by event-related potentials

Repeated exposure to a stimulus leads to reduced responses of stimulus-selective sensory neurons, an effect known as repetition suppression or stimulus-specific adaptation. Several influential models have been proposed to explain repetition suppression within hierarchically-organised sensory systems, with each specifying different mechanisms underlying repetition effects. We manipulated temporal expectations within a face repetition experiment to test a critical prediction of the predictive coding model of repetition suppression: that repetition effects will be larger following stimuli that appear at expected times compared to stimuli that appear at unexpected times. We recorded event-related potentials from 18 participants and mapped the spatiotemporal progression of repetition effects using mass univariate analyses. We then assessed whether the magnitudes of observed face image repetition effects were influenced by temporal expectations. In each trial participants saw an adapter face, followed by a 500ms or 1000ms interstimulus interval (ISI), and then a test face, which was the same or a different face identity to the adapter. Participants expectations for whether the test face would appear after a 500ms ISI were cued by the sex of the adapter face. Our analyses revealed multiple repetition effects with distinct scalp topographies, extending until at least 800ms from stimulus onset. An early (158-203ms) repetition effect was larger for stimuli following surprising, rather than expected, 500ms ISI durations, contrary to the model predictions of the predictive coding model of repetition suppression. Later (230-609ms) repetition effects tended to be larger following expected stimulus onset times, in line with predictive coding models. Our results indicate that the relationship between repetition suppression and temporal expectation differs across the time course of the stimulus-evoked response, suggesting multiple distinct mechanisms driving repetition suppression that operate at different latencies within the visual hierarchy.\n\nHighlights- Multiple face image repetition effects identified from 162-800ms post stimulus onset\n\n- Temporal expectations influenced the magnitudes of repetition effects\n\n- Temporal expectation effects differed for early and late stimulus-evoked responses

neuroscience↗

bioRxiv2018

Immediate stimulus repetition abolishes stimulus expectation and surprise effects in fast periodic visual oddball designs

Oddball designs are widely used to investigate the sensitivity of the visual system to statistical regularities in sensory environments. However, the underlying mechanisms that give rise to visual mismatch responses remain unknown. Much research has focused on identifying separable, additive effects of stimulus repetition and stimulus appearance probability (expectation/surprise) but findings from non-oddball designs indicate that these effects also interact. We adapted the fast periodic visual stimulation (FPVS) unfamiliar face identity oddball design (Liu-Shuang et al., 2014) to test for both additive and interactive effects of stimulus repetition and stimulus expectation. In two experiments, a given face identity was presented at a 6 Hz periodic rate; a different identity face (the oddball) appeared as every 7th image in the sequence (i.e., at 0.857 Hz). Electroencephalographic (EEG) activity was recorded during these stimulation sequences. In Experiment 1, we tested for surprise responses evoked by unexpected face image repetitions by replacing 10% of the commonly-presented oddball faces with exact repetitions of the base rate face identity image. In Experiment 2, immediately repeated or unrepeated face identity oddballs were presented in high and low presentation probability contexts (i.e., expected or surprising), allowing assessment of expectation effects on responses to both repeated and unrepeated stimuli. Across both experiments objective (i.e., frequency-locked) visual mismatch responses driven by stimulus expectation were only found for oddball faces of a different identity to base rate faces (i.e., unrepeated identity oddballs). Our results show that immediate stimulus repetition (i.e., repetition suppression) can reduce or abolish expectation effects as indexed by EEG responses in visual oddball designs.\n\nHighlights- We studied visual mismatch responses with a fast periodic oddball design\n- Our design cleanly separates immediate stimulus repetition and expectation effects\n- Stimulus expectation effects were only present for unrepeated stimuli\n- Immediate stimulus repetition reduced EEG expectation effects

neuroscience↗

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↗

bioRxiv2017

Perceptual Expectations Do Not Modulate Image Repetition Effects As Measured By Event-Related Potentials

Repeated stimulus presentation leads to complex changes in cortical neuron response properties, commonly known as repetition suppression or stimulus-specific adaptation. Circuit-based models of repetition suppression provide a framework for investigating patterns of repetition effects that propagate through cortical hierarchies. To further develop such models it is critical to determine whether (and if so, when) repetition effects are modulated by top-down influences, such as those related to perceptual expectation. We investigated this by presenting pairs of repeated and alternating face images, and orthogonally manipulating expectations regarding the likelihood of stimulus repetition. Event-related potentials (ERPs) were recorded from n=39 healthy adults, to map the spatiotemporal progression of stimulus repetition and expectation effects, and interactions between these factors, using mass univariate analyses. We also tested whether the ability to predict unrepeated (compared to repeated) face identities could influence the magnitude of observed repetition effects, by presenting separate blocks with predictable and unpredictable alternating faces. Multiple repetition and expectation effects were identified between 99-800ms from stimulus onset, which did not statistically interact at any point. Repetition effects in blocks with predictable alternating faces were smaller than in unpredictable alternating face blocks between 117-179ms and 506-652ms, and larger between 246-428ms. ERP repetition effects appear not to be modulated by perceptual expectations, supporting separate mechanisms for repetition and expectation suppression. However, previous studies that aimed to test for repetition effects, in which the repeated (but not unrepeated) stimulus was predictable, are likely to have conflated repetition and stimulus predictability effects.\n\nHighlights- ERP face image repetition effects were apparent between 99-800ms from stimulus onset\n- Expectations of stimulus image properties did not modulate face repetition effects\n- The predictability of unrepeated stimuli influenced repetition effect magnitudes

neuroscience↗