EU
Latest
Digital Traces of Human Behaviour: From Political Mobilisation to Conspiracy Narratives
Digital platforms generate unprecedented traces of human behaviour, offering new methodological approaches to understanding collective action, polarisation, and social dynamics. Through analysis of millions of digital traces across multiple studies, we demonstrate how online behaviours predict offline action: Brexit-related tribal discourse responds to real-world events, machine learning models achieve 80% accuracy in predicting real-world protest attendance from digital signals, and social validation through "likes" emerges as a key driver of mobilization. Extending this approach to conspiracy narratives reveals how digital traces illuminate psychological mechanisms of belief and community formation. Longitudinal analysis of YouTube conspiracy content demonstrates how narratives systematically address existential, epistemic, and social needs, while examination of alt-tech platforms shows how emotions of anger, contempt, and disgust correlate with violence-legitimating discourse, with significant differences between narratives associated with offline violence versus peaceful communities. This work establishes digital traces as both methodological innovation and theoretical lens, demonstrating that computational social science can illuminate fundamental questions about polarisation, mobilisation, and collective behaviour across contexts from electoral politics to conspiracy communities.
An Ecological and Objective Neural Marker of Implicit Unfamiliar Identity Recognition
We developed a novel paradigm measuring implicit identity recognition using Fast Periodic Visual Stimulation (FPVS) with EEG among 16 students and 12 police officers with normal face processing abilities. Participants' neural responses to a 1-Hz tagged oddball identity embedded within a 6-Hz image stream revealed implicit recognition with high-quality mugshots but not CCTV-like images, suggesting optimal resolution requirements. Our findings extend previous research by demonstrating that even unfamiliar identities can elicit robust neural recognition signatures through brief, repeated passive exposure. This approach offers potential for objective validation of face processing abilities in forensic applications, including assessment of facial examiners, Super-Recognisers, and eyewitnesses, potentially overcoming limitations of traditional behavioral assessment methods.
Short and Synthetically Distort: Investor Reactions to Deepfake Financial News
Recent advances in artificial intelligence have led to new forms of misinformation, including highly realistic “deepfake” synthetic media. We conduct three experiments to investigate how and why retail investors react to deepfake financial news. Results from the first two experiments provide evidence that investors use a “realism heuristic,” responding more intensely to audio and video deepfakes as their perceptual realism increases. In the third experiment, we introduce an intervention to prompt analytical thinking, varying whether participants make analytical judgments about credibility or intuitive investment judgments. When making intuitive investment judgments, investors are strongly influenced by both more and less realistic deepfakes. When making analytical credibility judgments, investors are able to discern the non-credibility of less realistic deepfakes but struggle with more realistic deepfakes. Thus, while analytical thinking can reduce the impact of less realistic deepfakes, highly realistic deepfakes are able to overcome this analytical scrutiny. Our results suggest that deepfake financial news poses novel threats to investors.
Using Fast Periodic Visual Stimulation to measure cognitive function in dementia
Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detect subtle deficits in recognition memory, visual processing and attention in dementia patients using EEG in the lab, at home and in clinic. The method is quick, cost-effective, and scalable, utilizing widely available EEG technology. FPVS holds significant potential as a functional biomarker for early diagnosis and monitoring of dementia, paving the way for timely interventions and improved patient outcomes.
Deepfake emotional expressions trigger the uncanny valley brain response, even when they are not recognised as fake
Facial expressions are inherently dynamic, and our visual system is sensitive to subtle changes in their temporal sequence. However, researchers often use dynamic morphs of photographs—simplified, linear representations of motion—to study the neural correlates of dynamic face perception. To explore the brain's sensitivity to natural facial motion, we constructed a novel dynamic face database using generative neural networks, trained on a verified set of video-recorded emotional expressions. The resulting deepfakes, consciously indistinguishable from videos, enabled us to separate biological motion from photorealistic form. Results showed that conventional dynamic morphs elicit distinct responses in the brain compared to videos and photos, suggesting they violate expectations (n400) and have reduced social salience (late positive potential). This suggests that dynamic morphs misrepresent facial dynamism, resulting in misleading insights about the neural and behavioural correlates of face perception. Deepfakes and videos elicited largely similar neural responses, suggesting they could be used as a proxy for real faces in vision research, where video recordings cannot be experimentally manipulated. And yet, despite being consciously undetectable as fake, deepfakes elicited an expectation violation response in the brain. This points to a neural sensitivity to naturalistic facial motion, beyond conscious awareness. Despite some differences in neural responses, the realism and manipulability of deepfakes make them a valuable asset for research where videos are unfeasible. Using these stimuli, we proposed a novel marker for the conscious perception of naturalistic facial motion – Frontal delta activity – which was elevated for videos and deepfakes, but not for photos or dynamic morphs.
A Novel Neurophysiological Approach to Assessing Distractibility within the General Population
Vulnerability to distraction varies across the general population and significantly affects one’s capacity to stay focused on and successfully complete the task at hand, whether at school, on the road, or at work. In this talk, I will begin by discussing how distractibility is typically assessed in the literature and introduce our innovative ERP approach to measuring it. Since distractibility is a cardinal symptom of ADHD, I will introduce its most widely used paper-and-pencil screening tool for the general population as external validation. Following that, I will present the Load Theory of Attention and explain how we used perceptual load to test the reliability of our neural marker of distractibility. Finally, I will highlight potential future applications of this marker in clinical and educational settings.
Neural makers of lapses in attention during sustained ‘real-world’ task performance
Lapses in attention are ubiquitous and, unfortunately, the cause of many tragic accidents. One potential solution may be to develop assistance systems which can use objective, physiological signals to monitor attention levels and predict a lapse in attention before it occurs. As it stands, it is unclear which physiological signals are the most reliable markers of inattention, and even less is known about how reliably they will work in a more naturalistic setting. My project aims to address these questions across two experiments: a lab-based experiment and a more ‘real-world’ experiment. In this talk I will present the findings from my lab experiment, in which we combined EEG and pupillometry to detect markers of inattention during two computerised sustained attention tasks. I will then present the methods for my second, more ‘naturalistic’ experiment in which we use the same methods (EEG and pupillometry) to examine whether these markers can still be extracted from noisier data.
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lag
Recent research has seen many behavioral comparisons between humans and deep neural networks (DNNs) in the domain of image classification. Often, comparison studies focus on the end-result of the learning process by measuring and comparing the similarities in the representations of object categories once they have been formed. However, the process of how these representations emerge—that is, the behavioral changes and intermediate stages observed during the acquisition—is less often directly and empirically compared. In this talk, I'm going to report a detailed investigation of the learning dynamics in human observers and various classic and state-of-the-art DNNs. We develop a constrained supervised learning environment to align learning-relevant conditions such as starting point, input modality, available input data and the feedback provided. Across the whole learning process we evaluate and compare how well learned representations can be generalized to previously unseen test data. Comparisons across the entire learning process indicate that DNNs demonstrate a level of data efficiency comparable to human learners, challenging some prevailing assumptions in the field. However, our results also reveal representational differences: while DNNs' learning is characterized by a pronounced generalisation lag, humans appear to immediately acquire generalizable representations without a preliminary phase of learning training set-specific information that is only later transferred to novel data.
Error Consistency between Humans and Machines as a function of presentation duration
Within the last decade, Deep Artificial Neural Networks (DNNs) have emerged as powerful computer vision systems that match or exceed human performance on many benchmark tasks such as image classification. But whether current DNNs are suitable computational models of the human visual system remains an open question: While DNNs have proven to be capable of predicting neural activations in primate visual cortex, psychophysical experiments have shown behavioral differences between DNNs and human subjects, as quantified by error consistency. Error consistency is typically measured by briefly presenting natural or corrupted images to human subjects and asking them to perform an n-way classification task under time pressure. But for how long should stimuli ideally be presented to guarantee a fair comparison with DNNs? Here we investigate the influence of presentation time on error consistency, to test the hypothesis that higher-level processing drives behavioral differences. We systematically vary presentation times of backward-masked stimuli from 8.3ms to 266ms and measure human performance and reaction times on natural, lowpass-filtered and noisy images. Our experiment constitutes a fine-grained analysis of human image classification under both image corruptions and time pressure, showing that even drastically time-constrained humans who are exposed to the stimuli for only two frames, i.e. 16.6ms, can still solve our 8-way classification task with success rates way above chance. We also find that human-to-human error consistency is already stable at 16.6ms.
How to tell if someone is hiding something from you? An overview of the scientific basis of deception and concealed information detection
I my talk I will give an overview of recent research on deception and concealed information detection. I will start with a short introduction on the problems and shortcomings of traditional deception detection tools and why those still prevail in many recent approaches (e.g., in AI-based deception detection). I want to argue for the importance of more fundamental deception research and give some examples for insights gained therefrom. In the second part of the talk, I will introduce the Concealed Information Test (CIT), a promising paradigm for research and applied contexts to investigate whether someone actually recognizes information that they do not want to reveal. The CIT is based on solid scientific theory and produces large effects sizes in laboratory studies with a number of different measures (e.g., behavioral, psychophysiological, and neural measures). I will highlight some challenges a forensic application of the CIT still faces and how scientific research could assist in overcoming those.
The Role of Cognitive Appraisal in the Relationship between Personality and Emotional Reactivity
Emotion is defined as a rapid psychological process involving experiential, expressive and physiological responses. These emerge following an appraisal process that involves cognitive evaluations of the environment assessing its relevance, implication, coping potential, and normative significance. It has been suggested that changes in appraisal processes lead to changes in the resulting emotional nature. Simultaneously, it was demonstrated that personality can be seen as a predisposition to feel more frequently certain emotions, but the personality-appraisal-emotional response chain is rarely fully investigated. The present project thus sought to investigate the extent to which personality traits influence certain appraisals, which in turn influence the subsequent emotional reactions via a systematic analysis of the link between personality traits of different current models, specific appraisals, and emotional response patterns at the experiential, expressive, and physiological levels. Major results include the coherence of emotion components clustering, and the centrality of the pleasantness, coping potential and consequences appraisals, in context; and the differentiated mediating role of cognitive appraisal in the relation between personality and the intensity and duration of an emotional state, and autonomic arousal, such as Extraversion-pleasantness-experience, and Neuroticism-powerlessness-arousal. Elucidating these relationships deepens our understanding of individual differences in emotional reactivity and spot routes of action on appraisal processes to modify upcoming adverse emotional responses, with a broader societal impact on clinical and non-clinical populations.
Exploring Lifespan Memory Development and Intervention Strategies for Memory Decline through a Unified Model-Based Assessment
Understanding and potentially reversing memory decline necessitates a comprehensive examination of memory's evolution throughout life. Traditional memory assessments, however, suffer from a lack of comparability across different age groups due to the diverse nature of the tests employed. Addressing this gap, our study introduces a novel, ACT-R model-based memory assessment designed to provide a consistent metric for evaluating memory function across a lifespan, from 5 to 85-year-olds. This approach allows for direct comparison across various tasks and materials tailored to specific age groups. Our findings reveal a pronounced U-shaped trajectory of long-term memory function, with performance at age 5 mirroring those observed in elderly individuals with impairments, highlighting critical periods of memory development and decline. Leveraging this unified assessment method, we further investigate the therapeutic potential of rs-fMRI-guided TBS targeting area 8AV in individuals with early-onset Alzheimer’s Disease—a region implicated in memory deterioration and mood disturbances in this population. This research not only advances our understanding of memory's lifespan dynamics but also opens new avenues for targeted interventions in Alzheimer’s Disease, marking a significant step forward in the quest to mitigate memory decay.
Enabling witnesses to actively explore faces and reinstate study-test pose during a lineup increases discrimination accuracy
In 2014, the US National Research Council called for the development of new lineup technologies to increase eyewitness identification accuracy (National Research Council, 2014). In a police lineup, a suspect is presented alongside multiple individuals known to be innocent who resemble the suspect in physical appearance know as fillers. A correct identification decision by an eyewitness can lead to a guilty suspect being convicted or an innocent suspect being exonerated from suspicion. An incorrect decision can result in the perpetrator remaining at large, or even a wrongful conviction of a mistakenly identified person. Incorrect decisions carry considerable human and financial costs, so it is essential to develop and enact lineup procedures that maximise discrimination accuracy, or the witness’ ability to distinguish guilty from innocent suspects. This talk focuses on new technology and innovation in the field of eyewitness identification. We will focus on the interactive lineup, which is a procedure that we developed based on research and theory from the basic science literature on face perception and recognition. The interactive lineup enables witnesses to actively explore and dynamically view the lineup members. The procedure has been shown to maximize discrimination accuracy, which is the witness’ ability to discriminate guilty from innocent suspects. The talk will conclude by reflecting on emerging technological frontiers and research opportunities.
Ganzflicker: Using light-induced hallucinations to predict risk factors of psychosis
Rhythmic flashing light, or “Ganzflicker”, can elicit altered states of consciousness and hallucinations, bringing your mind’s eye out into the real world. What do you experience if you have a super mind’s eye, or none at all? In this talk, I will discuss how Ganzflicker has been used to simulate psychedelic experiences, how it can help us predict symptoms of psychosis, and even tap into the neural basis of hallucinations.
Where Cognitive Neuroscience Meets Industry: Navigating the Intersections of Academia and Industry
In this talk, Mirta will share her journey from her education a mathematically-focused high school to her currently unconventional career in London, emphasizing the evolution from a local education in Croatia to international experiences in the US and UK. We will explore the concept of interdisciplinary careers in the modern world, viewing them through the framework of increasing demand, flexibility, and dynamism in the current workplace. We will underscore the significance of interdisciplinary research for launching careers outside of academia, and bolstering those within. I will challenge the conventional norm of working either in academia or industry, and encourage discussion about the opportunities for combining the two in a myriad of career opportunities. I’ll use examples from my own and others’ research to highlight opportunities for early career researchers to extend their work into practical applications. Such an approach leverages the strengths of both sectors, fostering innovation and practical applications of research findings. I hope these insights can offer valuable perspectives for those looking to navigate the evolving demands of the global job market, illustrating the advantages of a versatile skill set that spans multiple disciplines and allows extensions into exciting career options.
Characterising Representations of Goal Obstructiveness and Uncertainty Across Behavior, Physiology, and Brain Activity Through a Video Game Paradigm
The nature of emotions and their neural underpinnings remain debated. Appraisal theories such as the component process model propose that the perception and evaluation of events (appraisal) is the key to eliciting the range of emotions we experience. Here we study whether the framework of appraisal theories provides a clearer account for the differentiation of emotional episodes and their functional organisation in the brain. We developed a stealth game to manipulate appraisals in a systematic yet immersive way. The interactive nature of video games heightens self-relevance through the experience of goal-directed action or reaction, evoking strong emotions. We show that our manipulations led to changes in behaviour, physiology and brain activations.
Use of Artificial Intelligence by Law Enforcement Authorities in the EU
Recently, artificial intelligence (AI) has become a global priority. Rapid and ongoing technological advancements in AI have prompted European legislative initiatives to regulate its use. In April 2021, the European Commission submitted a proposal for a Regulation that would harmonize artificial intelligence rules across the EU, including the law enforcement sector. Consequently, law enforcement officials await the outcome of the ongoing inter-institutional negotiations (trilogue) with great anticipation, as it will define how to capitalize on the opportunities presented by AI and how to prevent criminals from abusing this emergent technology.
The contribution of mental face representations to individual face processing abilities
People largely differ with respect to how well they can learn, memorize, and perceive faces. In this talk, I address two potential sources of variation. One factor might be people’s ability to adapt their perception to the kind of faces they are currently exposed to. For instance, some studies report that those who show larger adaptation effects are also better at performing face learning and memory tasks. Another factor might be people’s sensitivity to perceive fine differences between similar-looking faces. In fact, one study shows that the brain of good performers in a face memory task shows larger neural differences between similar-looking faces. Capitalizing on this body of evidence, I present a behavioural study where I explore the relationship between people’s perceptual adaptability and sensitivity and their individual face processing performance.
How AI is advancing Clinical Neuropsychology and Cognitive Neuroscience
This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.
A new science of emotion: How brain-mind-body processes form functional neurological disorder
One of the most common medical conditions you’ve (maybe) never heard of – functional neurological disorder – lays at the interface of neurology and psychiatry and offers a window into fundamental brain-mind-body processes. Across ancient and modern times, functional neurological disorder has had a long and tumultuous history, with an evolving debate and understanding of how biopsychosocial factors contribute to the manifestation of the disorder. A central issue in contemporary discussions has revolved around questioning the extent to which emotions play a mechanistic and aetiological role in functional neurological disorder. Critical in this context, however, is that this ongoing debate has largely omitted the question of what emotions are in the first place. This talk first brings together advances in the understanding of working principles of the brain fundamental to introducing a new understanding of what emotions are. Building on recent theoretical frameworks from affective neuroscience, the idea of how the predictive process of emotion construction can be an integral component of the pathophysiology of functional neurological disorder is discussed.
Face and voice perception as a tool for characterizing perceptual decisions and metacognitive abilities across the general population and psychosis spectrum
Humans constantly make perceptual decisions on human faces and voices. These regularly come with the challenge of receiving only uncertain sensory evidence, resulting from noisy input and noisy neural processes. Efficiently adapting one’s internal decision system including prior expectations and subsequent metacognitive assessments to these challenges is crucial in everyday life. However, the exact decision mechanisms and whether these represent modifiable states remain unknown in the general population and clinical patients with psychosis. Using data from a laboratory-based sample of healthy controls and patients with psychosis as well as a complementary, large online sample of healthy controls, I will demonstrate how a combination of perceptual face and voice recognition decision fidelity, metacognitive ratings, and Bayesian computational modelling may be used as indicators to differentiate between non-clinical and clinical states in the future.
Diagnosing dementia using Fastball neurocognitive assessment
Fastball is a novel, fast, passive biomarker of cognitive function, that uses cheap, scalable electroencephalography (EEG) technology. It is sensitive to early dementia; language, education, effort and anxiety independent and can be used in any setting including patients’ homes. It can capture a range of cognitive functions including semantic memory, recognition memory, attention and visual function. We have shown that Fastball is sensitive to cognitive dysfunction in Alzheimer’s disease and Mild Cognitive Impairment, with data collected in patients’ homes using low-cost portable EEG. We are now preparing for significant scale-up and the validation of Fastball in primary and secondary care.
A Better Method to Quantify Perceptual Thresholds : Parameter-free, Model-free, Adaptive procedures
The ‘quantification’ of perception is arguably both one of the most important and most difficult aspects of perception study. This is particularly true in visual perception, in which the evaluation of the perceptual threshold is a pillar of the experimental process. The choice of the correct adaptive psychometric procedure, as well as the selection of the proper parameters, is a difficult but key aspect of the experimental protocol. For instance, Bayesian methods such as QUEST, require the a priori choice of a family of functions (e.g. Gaussian), which is rarely known before the experiment, as well as the specification of multiple parameters. Importantly, the choice of an ill-fitted function or parameters will induce costly mistakes and errors in the experimental process. In this talk we discuss the existing methods and introduce a new adaptive procedure to solve this problem, named, ZOOM (Zooming Optimistic Optimization of Models), based on recent advances in optimization and statistical learning. Compared to existing approaches, ZOOM is completely parameter free and model-free, i.e. can be applied on any arbitrary psychometric problem. Moreover, ZOOM parameters are self-tuned, thus do not need to be manually chosen using heuristics (eg. step size in the Staircase method), preventing further errors. Finally, ZOOM is based on state-of-the-art optimization theory, providing strong mathematical guarantees that are missing from many of its alternatives, while being the most accurate and robust in real life conditions. In our experiments and simulations, ZOOM was found to be significantly better than its alternative, in particular for difficult psychometric functions or when the parameters when not properly chosen. ZOOM is open source, and its implementation is freely available on the web. Given these advantages and its ease of use, we argue that ZOOM can improve the process of many psychophysics experiments.
The speaker identification ability of blind and sighted listeners
Previous studies have shown that blind individuals outperform sighted controls in a variety of auditory tasks; however, only few studies have investigated blind listeners’ speaker identification abilities. In addition, existing studies in the area show conflicting results. The presented empirical investigation with 153 blind (74 of them congenitally blind) and 153 sighted listeners is the first of its kind and scale in which long-term memory effects of blind listeners’ speaker identification abilities are examined. For the empirical investigation, all listeners were evenly assigned to one of nine subgroups (3 x 3 design) in order to investigate the influence of two parameters with three levels, respectively, on blind and sighted listeners’ speaker identification performance. The parameters were a) time interval; i.e. a time interval of 1, 3 or 6 weeks between the first exposure to the voice to be recognised (familiarisation) and the speaker identification task (voice lineup); and b) signal quality; i.e. voice recordings were presented in either studio-quality, mobile phone-quality or as recordings of whispered speech. Half of the presented voice lineups were target-present lineups in which the previously heard target voice was included. The other half consisted of target-absent lineups which contained solely distractor voices. Blind individuals outperformed sighted listeners only under studio quality conditions. Furthermore, for blind and sighted listeners no significant performance differences were found with regard to the three investigated time intervals of 1, 3 and 6 weeks. Blind as well as sighted listeners were significantly better at picking the target voice from target-present lineups than at indicating that the target voice was absent in target-absent lineups. Within the blind group, no significant correlations were found between identification performance and onset or duration of blindness. Implications for the field of forensic phonetics are discussed.
Exploring the Potential of High-Density Data for Neuropsychological Testing with Coregraph
Coregraph is a tool under development that allows us to collect high-density data patterns during the administration of classic neuropsychological tests such as the Trail Making Test and Clock Drawing Test. These tests are widely used to evaluate cognitive function and screen for neurodegenerative disorders, but traditional methods of data collection only yield sparse information, such as test completion time or error types. By contrast, the high-density data collected with Coregraph may contribute to a better understanding of the cognitive processes involved in executing these tests. In addition, Coregraph may potentially revolutionize the field of cognitive evaluation by aiding in the prediction of cognitive deficits and in the identification of early signs of neurodegenerative disorders such as Alzheimer's dementia. By analyzing high-density graphomotor data through techniques like manual feature engineering and machine learning, we can uncover patterns and relationships that would be otherwise hidden with traditional methods of data analysis. We are currently in the process of determining the most effective methods of feature extraction and feature analysis to develop Coregraph to its full potential.
The future of neuropsychology will be open, transdiagnostic, and FAIR - why it matters and how we can get there
Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).
Biological and experience-based trajectories in adolescent brain and cognitive development
Adolescent development is not only shaped by the mere passing of time and accumulating experience, but it also depends on pubertal timing and the cascade of maturational processes orchestrated by gonadal hormones. Although individual variability in puberty onset confounds adolescent studies, it has not been efficiently controlled for. Here we introduce ultrasonic bone age assessment to estimate biological maturity and disentangle the independent effects of chronological and biological age on adolescent cognitive abilities, emotional development, and brain maturation. Comparing cognitive performance of participants with different skeletal maturity we uncover the impact of biological age on both IQ and specific abilities. With respect to emotional development, we find narrow windows of highest vulnerability determined by biological age. In terms of neural development, we focus on the relevance of neural states unrelated to sensory stimulation, such as cortical activity during sleep and resting states, and we uncover a novel anterior-to-posterior pattern of human brain maturation. Based on our findings, bone age is a promising biomarker of adolescent maturity.
Disentangling neural correlates of consciousness and task relevance using EEG and fMRI
How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In this talk, I will present recent EEG and fMRI studies dissociating neural correlates of consciousness and task-related processing in visual and auditory perception. Our results suggest that consciousness emerges during early sensory processing, while late, fronto-parietal activity is associated with post-perceptual processes rather than awareness. These findings challenge predominant theories of consciousness and highlight the importance of considering task relevance as a confound across different neuroscientific methods, experimental paradigms and sensory modalities.
ItsAllAboutMotion: Encoding of speed in the human Middle Temporal cortex
The human middle temporal complex (hMT+) has a crucial biological relevance for the processing and detection of direction and speed of motion in visual stimuli. In both humans and monkeys, it has been extensively investigated in terms of its retinotopic properties and selectivity for direction of moving stimuli; however, only in recent years there has been an increasing interest in how neurons in MT encode the speed of motion. In this talk, I will explore the proposed mechanism of speed encoding questioning whether hMT+ neuronal populations encode the stimulus speed directly, or whether they separate motion into its spatial and temporal components. I will characterize how neuronal populations in hMT+ encode the speed of moving visual stimuli using electrocorticography ECoG and 7T fMRI. I will illustrate that the neuronal populations measured in hMT+ are not directly tuned to stimulus speed, but instead encode speed through separate and independent spatial and temporal frequency tuning. Finally, I will show that this mechanism plays a role in evaluating multisensory responses for visual, tactile and auditory motion stimuli in hMT+.
Consistency of Face Identity Processing: Basic & Translational Research
Previous work looking at individual differences in face identity processing (FIP) has found that most commonly used lab-based performance assessments are unfortunately not sufficiently sensitive on their own for measuring performance in both the upper and lower tails of the general population simultaneously. So more recently, researchers have begun incorporating multiple testing procedures into their assessments. Still, though, the growing consensus seems to be that at the individual level, there is quite a bit of variability between test scores. The overall consequence of this is that extreme scores will still occur simply by chance in large enough samples. To mitigate this issue, our recent work has developed measures of intra-individual FIP consistency to refine selection of those with superior abilities (i.e. from the upper tail). For starters, we assessed consistency of face matching and recognition in neurotypical controls, and compared them to a sample of SRs. In terms of face matching, we demonstrated psychophysically that SRs show significantly greater consistency than controls in exploiting spatial frequency information than controls. Meanwhile, we showed that SRs’ recognition of faces is highly related to memorability for identities, yet effectively unrelated among controls. So overall, at the high end of the FIP spectrum, consistency can be a useful tool for revealing both qualitative and quantitative individual differences. Finally, in conjunction with collaborators from the Rheinland-Pfalz Police, we developed a pair of bespoke work samples to get bias-free measures of intraindividual consistency in current law enforcement personnel. Officers with higher composite scores on a set of 3 challenging FIP tests tended to show higher consistency, and vice versa. Overall, this suggests that not only is consistency a reasonably good marker of superior FIP abilities, but could present important practical benefits for personnel selection in many other domains of expertise.
The diachronic account of attentional selectivity
Many models of attention assume that attentional selection takes place at a specific moment in time which demarcates the critical transition from pre-attentive to attentive processing of sensory input. We argue that this intuitively appealing account is not only inaccurate, but has led to substantial conceptual confusion (to the point where some attention researchers offer to abandon the term ‘attention’ altogether). As an alternative, we offer a “diachronic” framework that describes attentional selectivity as a process that unfolds over time. Key to this view is the concept of attentional episodes, brief periods of intense attentional amplification of sensory representations that regulate access to working memory and response-related processes. We describe how attentional episodes are linked to earlier attentional mechanisms and to recurrent processing at the neural level. We present data showing that multiple sequential events can be involuntarily encoded in working memory when they appear during the same attentional episode, whether they are relevant or not. We also discuss the costs associated with processing multiple events within a single episode. Finally, we argue that breaking down the dichotomy between pre-attentive and attentive (as well as early vs. late selection) offers new solutions to old problems in attention research that have never been resolved. It can provide a unified and conceptually coherent account of the network of cognitive and neural processes that produce the goal-directed selectivity in perceptual processing that is commonly referred to as “attention”.
Age-related dedifferentiation across representational levels and their relation to memory performance
Episodic memory performance decreases with advancing age. According to theoretical models, such memory decline might be a consequence of age-related reductions in the ability to form distinct neural representations of our past. In this talk, I want to present our new age-comparative fMRI study investigating age-related neural dedifferentiation across different representational levels. By combining univariate analyses and searchlight pattern similarity analyses, we found that older adults show reduced category selective processing in higher visual areas, less specific item representations in occipital regions and less stable item representations. Dedifferentiation on all these representational levels was related to memory performance, with item specificity being the strongest contributor. Overall, our results emphasize that age-related dedifferentiation can be observed across the entire cortical hierarchy which may selectively impair memory performance depending on the memory task.
Removing information from working memory
Holding information in working memory is essential for cognition, but removing unwanted thoughts is equally important. There is great flexibility in how we can manipulate information in working memory, but the processes and consequences of these operations are poorly understood. In this talk I will discuss our recent findings using multivariate pattern analyses of fMRI brain data to demonstrate the successful removal of information from working memory using three different strategies: suppressing a specific thought, replacing a thought with a different one, and clearing the mind of all thought. These strategies are supported by distinct brain regions and have differential consequences on the encoding of new information. I will discuss implications of these results on theories of memory and I will highlight some new directions involving the use of real-time neurofeedback to investigate causal links between brain and behavior.
Enhanced perception and cognition in deaf sign language users: EEG and behavioral evidence
In this talk, Dr. Quandt will share results from behavioral and cognitive neuroscience studies from the past few years of her work in the Action & Brain Lab, an EEG lab at Gallaudet University, the world's premiere university for deaf and hard-of-hearing students. These results will center upon the question of how extensive knowledge of signed language changes, and in some cases enhances, people's perception and cognition. Evidence for this effect comes from studies of human biological motion using point light displays, self-report, and studies of action perception. Dr. Quandt will also discuss some of the lab's efforts in designing and testing a virtual reality environment in which users can learn American Sign Language from signing avatars (virtual humans).
Characterising the brain representations behind variations in real-world visual behaviour
Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.
Spatio-temporal large-scale organization of the trimodal connectome derived from concurrent EEG-fMRI and diffusion MRI
While time-averaged dynamics of brain functional connectivity are known to reflect the underlying structural connections, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. Large-scale networks are traditionally observed by correlation of fMRI timecourses, and connectivity of source-reconstructed electrophysiological measures are less prominent. Accessing the brain by using multimodal recordings combining EEG, fMRI and diffusion MRI (dMRI) can help to refine the understanding of the spatio-temporal organization of both static and dynamic brain connectivity. In this talk I will discuss our prior findings that whole-brain connectivity derived from source-reconstructed resting-state (rs) EEG is both linked to the rs-fMRI and dMRI connectome. The EEG connectome provides complimentary information to link function to structure as compared to an fMRI-only perspective. I will present an approach extending the multimodal data integration of concurrent rs-EEG-fMRI to the temporal domain by combining dynamic functional connectivity of both modalities to better understand the neural basis of functional connectivity dynamics. The close relationship between time-varying changes in EEG and fMRI whole-brain connectivity patterns provide evidence for spontaneous reconfigurations of the brain’s functional processing architecture. Finally, I will talk about data quality of connectivity derived from concurrent EEG-fMRI recordings and how the presented multimodal framework could be applied to better understand focal epilepsy. In summary this talk will give an overview of how to integrate large-scale EEG networks with MRI-derived brain structure and function. In conclusion EEG-based connectivity measures not only are closely linked to MRI-based measures of brain structure and function over different time-scales, but also provides complimentary information on the function of underlying brain organization.
Differential working memory functioning
The integrated conflict monitoring theory of Botvinick introduced cognitive demand into conflict monitoring research. We investigated effects of individual differences of cognitive demand and another determinant of conflict monitoring entitled reinforcement sensitivity on conflict monitoring. We showed evidence of differential variability of conflict monitoring intensity using the electroencephalogram (EEG), functional magnet resonance imaging (fMRI) and behavioral data. Our data suggest that individual differences of anxiety and reasoning ability are differentially related to the recruitment of proactive and reactive cognitive control (cf. Braver). Based on previous findings, the team of the Leue-Lab investigated new psychometric data on conflict monitoring and proactive-reactive cognitive control. Moreover, data of the Leue-Lab suggest the relevance of individual differences of conflict monitoring for the context of deception. In this respect, we plan new studies highlighting individual differences of the functioning of the Anterior Cingulate Cortex (ACC). Disentangling the role of individual differences in working memory-related cognitive demand, mental effort, and reinforcement-related processes opens new insights for cognitive-motivational approaches of information processing (Passcode to rewatch: 0R8v&m59).
Impact evaluation for COVID-19 non-pharmaceutical interventions: what is (un)knowable?
COVID-19 non-pharmaceutical intervention (NPI) policies have been one of the most important and contentious decisions of our time. Beyond even the "normal" inherent difficulties in impact evaluation with observational data, COVID-19 NPI policy evaluation is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. Randomized controlled trials also suffer from what is feasible and ethical to randomize as well as the sheer scale, scope, time, and resources required for an NPI trial to be informative (or at least not misinformative). In this talk, Dr. Haber will discuss the challenges in generating useful evidence for COVID-19 NPIs, the landscape of the literature, and highlight key controversies in several high profile studies over the course of the pandemic. Chasing after unknowables poses major problems for the metascience/replicability movement, institutional research science, and decision makers. If the only choices for informing an important topic are "weak study design" vs "do nothing," when is "do nothing" the best choice?
Memory for Latent Representations: An Account of Working Memory that Builds on Visual Knowledge for Efficient and Detailed Visual Representations
Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memory (WM) representations are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items’ attributes to tokenized representations. The simulation results revealed that shape information for stimuli that the model was trained on, can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, the representation of a given stimulus can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results acquired from WM tasks. The model provides a compelling demonstration of visual knowledge yielding the formation of compact visual representation for efficient memory encoding.
Investigating visual recognition and the temporal lobes using electrophysiology and fast periodic visual stimulation
The ventral visual pathway extends from the occipital to the anterior temporal regions, and is specialized in giving meaning to objects and people that are perceived through vision. Numerous studies in functional magnetic resonance imaging have focused on the cerebral basis of visual recognition. However, this technique is susceptible to magnetic artefacts in ventral anterior temporal regions and it has led to an underestimation of the role of these regions within the ventral visual stream, especially with respect to face recognition and semantic representations. Moreover, there is an increasing need for implicit methods assessing these functions as explicit tasks lack specificity. In this talk, I will present three studies using fast periodic visual stimulation (FPVS) in combination with scalp and/or intracerebral EEG to overcome these limitations and provide high SNR in temporal regions. I will show that, beyond face recognition, FPVS can be extended to investigate semantic representations using a face-name association paradigm and a semantic categorisation paradigm with written words. These results shed new light on the role of temporal regions and demonstrate the high potential of the FPVS approach as a powerful electrophysiological tool to assess various cognitive functions in neurotypical and clinical populations.
Flexible codes and loci of visual working memory
Neural correlates of visual working memory have been found in early visual, parietal, and prefrontal regions. These findings have spurred fruitful debate over how and where in the brain memories might be represented. Here, I will present data from multiple experiments to demonstrate how a focus on behavioral requirements can unveil a more comprehensive understanding of the visual working memory system. Specifically, items in working memory must be maintained in a highly robust manner, resilient to interference. At the same time, storage mechanisms must preserve a high degree of flexibility in case of changing behavioral goals. Several examples will be explored in which visual memory representations are shown to undergo transformations, and even shift their cortical locus alongside their coding format based on specifics of the task.
Perception, attention, visual working memory, and decision making: The complete consort dancing together
Our research investigates how processes of attention, visual working memory (VWM), and decision-making combine to translate perception into action. Within this framework, the role of VWM is to form stable representations of transient stimulus events that allow them to be identified by a decision process, which we model as a diffusion process. In psychophysical tasks, we find the capacity of VWM is well defined by a sample-size model, which attributes changes in VWM precision with set-size to differences in the number evidence samples recruited to represent stimuli. In the first part of the talk, I review evidence for the sample-size model and highlight the model's strengths: It provides a parameter-free characterization of the set-size effect; it has plausible neural and cognitive interpretations; an attention-weighted version of the model accounts for the power-law of VWM, and it accounts for the selective updating of VWM in multiple-look experiments. In the second part of the talk, I provide a characterization of the theoretical relationship between two-choice and continuous-outcome decision tasks using the circular diffusion model, highlighting their common features. I describe recent work characterizing the joint distributions of decision outcomes and response times in continuous-outcome tasks using the circular diffusion model and show that the model can clearly distinguish variable-precision and simple mixture models of the evidence entering the decision process. The ability to distinguish these kinds of processes is central to current VWM studies.
Getting to know you: emerging neural representations during face familiarization
The successful recognition of familiar persons is critical for social interactions. Despite extensive research on the neural representations of familiar faces, we know little about how such representations unfold as someone becomes familiar. In three EEG experiments, we elucidated how representations of face familiarity and identity emerge from different qualities of familiarization: brief perceptual exposure (Experiment 1), extensive media familiarization (Experiment 2) and real-life personal familiarization (Experiment 3). Time-resolved representational similarity analysis revealed that familiarization quality has a profound impact on representations of face familiarity: they were strongly visible after personal familiarization, weaker after media familiarization, and absent after perceptual familiarization. Across all experiments, we found no enhancement of face identity representation, suggesting that familiarity and identity representations emerge independently during face familiarization. Our results emphasize the importance of extensive, real-life familiarization for the emergence of robust face familiarity representations, constraining models of face perception and recognition memory.
Neuroimaging reproducibility - pain points and roadmap for solid and reusable results
There is a growing body of evidence that reproducibility or replication is low in neuroscience and in neuroimaging in particular, but the factors affecting studies solidity are still generally poorly understood, and the solutions are not clearly exposed to the neuroimaging scientific community. In this talk, I will review the key factors contributing to irreproducible results in neuroimaging specifically in the context of explanatory or prediction studies and propose a series of practical steps to improve the neuroimaging (and neuroscience) results robustness and re-usability.
The Jena Voice Learning and Memory Test (JVLMT)
The ability to recognize someone’s voice spans a broad spectrum with phonagnosia on the low end and super recognition at the high end. Yet there is no standardized test to measure the individual ability to learn and recognize newly-learnt voices with samples of speech-like phonetic variability. We have developed the Jena Voice Learning and Memory Test (JVLMT), a 20 min-test based on item response theory and applicable across different languages. The JVLMT consists of three phases in which participants are familiarized with eight speakers in two stages and then perform a three-alternative forced choice recognition task, using pseudo sentences devoid of semantic content. Acoustic (dis)similarity analyses were used to create items with different levels of difficulty. Test scores are based on 22 Rasch-conform items. Items were selected and validated in online studies based on 232 and 454 participants, respectively. Mean accuracy is 0.51 with an SD of .18. The JVLMT showed high and moderate correlations with convergent validation tests (Bangor Voice Matching Test; Glasgow Voice Memory Test) and a weak correlation with a discriminant validation test (Digit Span). Empirical (marginal) reliability is 0.66. Four participants with super recognition (at least 2 SDs above the mean) and 7 participants with phonagnosia (at least 2 SDs below the mean) were identified. The JVLMT is a promising screen too for voice recognition abilities in a scientific and neuropsychological context.
Lessons from the credibility revolution – social thermoregulation as a case study
The goal of this talk is to first provide a realization of why the replication crisis is omnipresent and then point to several tools via which the listener can improve their own work. To do so, I will go through our own work on social thermoregulation, point out why I thought changes were necessary, discuss which shortcomings we have in our own work, which measures we have taken to reduce those shortcomings, which tools we have relied on to do so, and which steps I believe we still need to make. Specifically, I will go through the following points: Major replication failures and data fabrication in the field of psychology; Replication failures of social thermoregulation studies; Realization that many of our studies were underpowered; Realization that many of our studies were very narrow in scope (i.e., in undergraduate students and mostly in EU/US); Realization that a lot of our measures were not independently validated. I will show these for our own work (but will also show why, via a meta-analysis, we have enough confidence to proceed with social thermoregulation research). Throughout the talk I will point you to the following tools that facilitate our work: Templates for exploratory and confirmatory research and for meta-analyses (developed for our work, but easily adaptable for other programs). I will also show you how to fork our templates; A lab philosophy; A research milestones sheet for collaborations and overviews; Excel sheet for contributorship; A tutorial for exploratory research; I would recommend listeners to read through this chapter before the talk (I will repeat a lot of that work, but I will go into greater depth). own work. To do so, I will go through our own work on social thermoregulation, point out why I thought changes were necessary, discuss which shortcomings we have in our own work, which measures we have taken to reduce those shortcomings, which tools we have relied on to do so, and which steps I believe we still need to make.
The contribution of the dorsal visual pathway to perception and action
The human visual system enables us to recognize objects (e.g., this is a cup) and act upon them (e.g., grasp the cup) with astonishing ease and accuracy. For decades, it was widely accepted that these different functions rely on two separated cortical pathways. The ventral occipitotemporal pathway subserves object recognition, while the dorsal occipitoparietal pathway promotes visually guided actions. In my talk, I will discuss recent evidence from a series of neuropsychological, developmental and neuroimaging studies that were aimed to explore the nature of object representations in the dorsal pathway. The results from these studies highlight the plausible role of the dorsal pathway in object perception and reveal an interplay between shape representations derived by the two pathways. Together, these findings challenge the binary distinction between the two pathways and are consistent with the view that object recognition is not the sole product of ventral pathway computations, but instead relies on a distributed network of regions.
The problem of power in single-case neuropsychology
Case-control comparisons are a gold standard method for diagnosing and researching neuropsychological deficits and dissociations at the single-case level. These statistical tests, developed by John Crawford and collaborators, provide quantitative criteria for the classical concepts of deficit, dissociation and double-dissociation. Much attention has been given to the control of Type I (false positive) errors for these tests, but far less to the avoidance of Type II (false negative) errors; that is, to statistical power. I will describe the origins and limits of statistical power for case-control comparisons, showing that there are hard upper limits on power, which have important implications for the design and interpretation of single-case studies. My aim is to stimulate discussion of the inferential status of single-case neuropsychological evidence, particularly with respect to contemporary ideals of open science and study preregistration.
Markers of brain connectivity and sleep-dependent restoration: basic research and translation into clinical populations
The human brain is a heavily interconnected structure giving rise to complex functions. While brain functionality is mostly revealed during wakefulness, the sleeping brain might offer another view into physiological and pathological brain connectivity. Furthermore, there is a large body of evidence supporting that sleep mediates plastic changes in brain connectivity. Although brain plasticity depends on environmental input which is provided in the waking state, disconnection during sleep might be necessary for integrating new into existing information and at the same time restoring brain efficiency. In this talk, I will present structural, molecular, and electrophysiological markers of brain connectivity and sleep-dependent restoration that we have evaluated using Magnetic Resonance Imaging and electroencephalography in a healthy population. In a second step, I will show how we translated the gained findings into two clinical populations in which alterations in brain connectivity have been described, the neuropsychiatric disorder attention-deficit/hyperactivity disorder (ADHD) and the neurologic disorder thalamic ischemic stroke.
Algorithmic advances in face matching: Stability of tests in atypical groups
Face matching tests have traditionally been developed to assess human face perception in the neurotypical range, but methods that underlie their development often make it difficult for these measures to be applied in atypical populations (developmental prosopagnosics, super recognizers) due to unadjusted difficulty. We have recently presented the development of the Oxford Face Matching Test, a measure that bases individual item-difficulty on algorithmically derived similarity of presented stimuli. The measure seems useful as it can be given online or in-laboratory, has good discriminability and high test-retest reliability in the neurotypical groups. In addition, it has good validity in separating atypical groups at either of the spectrum ends. In this talk, I examine the stability of the OFMT and other traditionally used measures in atypical groups. On top of the theoretical significance of determining whether reliability of tests is equivalent in atypical population, this is an important question because of the practical concerns of retesting the same participants across different lab groups. Theoretical and practical implications for further test development and data sharing are discussed.
EU coverage
50 items