Our lab is interested in human visual cognition; particularly face processing. Our goal is to better understand the relationship between brain function and behavior. To this end, we use multiple neuroscientific methods to study inter-individual differences at various levels of face processing - from detection to identification. We seek to bridge the gap between empirical research and its application outside the lab in order to address outstanding questions and solve real-world problems.

 and led by Meike Ramon, who was awarded a 

PRIMA (Promoting Women in Academia) grant

 involves a collaborative network between researchers and practitioners from international police agencies to study Super-Recognizers - individuals with exceptional face processing capacities.

Meike Ramon

Andrea Fröhlich

Maren Mayer

Lavazza

Kim Uittenhove

Lionel Boudry

Jeff Nador

Jobila Eigenmann

Neil Docherty

Representational Connectivity Analysis (RCA) has gained mounting interest in the past few years. This is because, rather than conventional tracking of signal, RCA allows for the tracking of information across the brain. It can also provide insights into the content and potential transformations of the transferred information. This presentation explains several variations of the method in terms of implementation and how it can be adopted for different modalities (E/MEG and fMRI). I will also present caveats and nuances of the method which should be considered when using the RCA.

Representational Connectivity Analysis (RCA): a Method for Investigating Flow of Content-Specific Information in the Brain

Frequency tagging based on fast periodic visual stimulation (FPVS) provides a window into ongoing visual and cognitive processing and can be leveraged to measure rule learning and high-level categorization. In this talk, I will present data demonstrating highly proficient categorization as living and non-living in preschool children, and characterize the development of this ability during infancy. In addition to associating cognitive functions with development, an intriguing question is whether frequency tagging also captures enduring individual differences, e.g. in general cognitive abilities. First studies indicate high psychometric quality of FPVS categorization responses (XU et al., Dzhelyova), providing a basis for research on individual differences. I will present results from a pilot study demonstrating high correlations between FPVS categorization responses and behavioral measures of processing speed and fluid intelligences. Drawing upon this first evidence, I will discuss the potential of frequency tagging for diagnosing cognitive functions across development.  

Brain and Behavior: Employing Frequency Tagging as a Tool for Measuring Cognitive Abilities

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.

How AI is advancing Clinical Neuropsychology and Cognitive Neuroscience

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. 

A new science of emotion: How brain-mind-body processes form functional neurological disorder

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.

Face and voice perception as a tool for characterizing perceptual decisions and metacognitive abilities across the general population and psychosis spectrum

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. 


Diagnosing dementia using Fastball neurocognitive assessment

With the increasing use of automated face recognition (AFR) technologies, it is important to consider whether these systems not only perform accurately, but also equitability or without “bias”.  Despite rising public, media, and scientific attention to this issue, the sources of bias in AFR are not fully understood.  This talk will explore how human cognitive biases may impact our assessments of performance differentials in AFR systems and our subsequent use of those systems to make decisions.  We’ll also show how, if we adjust our definition of what a “biased” AFR algorithm looks like, we may be able to create algorithms that optimize the performance of a human+algorithm team, not simply the algorithm itself. 

Understanding and Mitigating Bias in Human & Machine Face Recognition

Visual working memory (VWM) is limited in capacity, but memorizing meaningful
objects may refine this limitation. However, meaningless and meaningful stimuli usually
differ perceptually and an object’s association with meaning is typically already established
before the actual experiment. We applied a strict control over these potential confounds
by asking observers (N=45) to actively learn associations of (initially) meaningless objects.
To this end, a change detection task presented Chinese characters, which were meaningless to
our observers. Subsequently, half of the characters were consistently paired with pictures of
animals. Then, the initial change detection task was repeated. The results revealed enhanced
VWM performance after learning, in particular for meaning-associated characters (though not
quite reaching the accuracy level attained by N=20 native Chinese observers). These results
thus provide direct experimental evidence that the short-term retention of objects benefits
from active learning of an object’s association with meaning in long-term memory.

Dissociating learning-induced effects of meaning and familiarity in visual working memory for Chinese characters.

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.


A Better Method to Quantify Perceptual Thresholds : Parameter-free, Model-free, Adaptive procedures

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.

The speaker identification ability of blind and sighted listeners

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.

Exploring the Potential of High-Density Data for Neuropsychological Testing with Coregraph

I describe a well-tested Python module that does automated alignment and warping of faces images, and some advantages over existing solutions. An additional tool I’ve developed does automated extraction of facial features, which can be used in a number of interesting ways. I illustrate the value of wavelet-based features with a brief description of 2 recent studies: perceptual in-painting, and the robustness of the whole-part advantage across a large stimulus set.  Finally, I discuss the suitability of various deep learning models for generating stimuli to study perceptual face spaces. I believe those interested in the forensic aspects of face perception may find this talk useful.

Automated generation of face stimuli: Alignment, features and face spaces

Developmental prosopagnosia is characterised by severe, lifelong difficulties when recognising facial identity. Most researchers require prosopagnosia cases exhibit ultra-conservative levels of impairment on the Cambridge Face Memory Test before they include them in their experiments. This results in the majority of people who believe that they have this condition being excluded from the scientific literature. In this talk I outline the many issues that will afflict prosopagnosia research if this continues, and show that these excluded cases do exhibit impairments on all commonly used diagnostic tests when a group-based method of assessment is utilised. I propose a paradigm shift away from cognitive task-based approaches to diagnosing prosopagnosia, and outline a new way that researchers can investigate this condition.

What's wrong with the prosopagnosia literature? A new approach to diagnosing and researching the condition

In vision, there is, surprisingly, very little evidence of common factors. Most studies have found only weak correlations between performance in different visual tests; meaning that, a participant performing better in one test is not more likely to perform also better in another test. Likewise in ageing, cross-sectional studies have repeatedly shown that older adults show deteriorated performance in most visual tests compared to young adults. However, within the older population, there is no evidence for a common factor underlying visual abilities. To investigate further the decline of visual abilities, we performed a longitudinal study with a battery of nine visual tasks three times, with two re-tests after about 4 and 7 years. Most visual abilities are rather stable across 7 years, but not visual acuity. I will discuss possible causes of these paradoxical outcomes.

How do visual abilities relate to each other?

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).

The future of  neuropsychology will be open, transdiagnostic, and FAIR - why it matters and how we can get there

Face detection is an initial step of many social interactions involving a comparison between a visual input and a mental representation of faces, built from previous experience. Whilst emotional state was found to affect the way humans attend to faces, little research has explored the effects of emotions on the mental representation of faces. Here, we examined the specific perceptual modulation of geometric properties of the mental representations associated with state anxiety and state depression on face detection, and to compare their emotional expression. To this end, we used an adaptation of the reverse correlation technique inspired by Gosselin and Schyns’, (2003) ‘Superstitious Approach’, to construct visual representations of observers’ mental representations of faces and to relate these to their mental states. In two sessions, on separate days, participants were presented with ‘colourful’ noise stimuli and asked to detect faces, which they were told were present. Based on the noise fragments that were identified as faces, we reconstructed the pictorial mental representation utilised by each participant in each session. We found a significant correlation between the size of the mental representation of faces and participants’ level of depression. Our findings provide a preliminary insight about the way emotions affect appearance expectation of faces. To further understand whether the facial expressions of participants’ mental representations reflect their emotional state, we are conducting a validation study with a group of naïve observers who are asked to classify the reconstructed face images by emotion. Thus, we assess whether the faces communicate participants’ emotional states to others.

The Effects of Negative Emotions on Mental Representation of Faces

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.

Disentangling neural correlates of consciousness and task relevance using EEG and fMRI

Tests used in the empirical sciences are often (implicitly) assumed to be representative of a target mechanism in the sense that similar tests should lead to similar results. In this talk, using resting-state electroencephalogram (EEG) as an example, I will argue that this assumption does not necessarily hold true. Typically EEG studies are conducted selecting one analysis method thought to be representative of the research question asked. Using multiple methods, we extracted a variety of features from a single resting-state EEG dataset and conducted correlational and case-control analyses. We found that many EEG features revealed a significant effect in the case-control analyses. Similarly, EEG features correlated significantly with cognitive tasks. However, when we compared these features pairwise, we did not find strong correlations. A number of explanations to these results will be discussed.

Do we measure what we think we are measuring?

Humans, as a social species, have an increased ability to detect and perceive visual elements involved in social exchanges, such as faces and eyes. The gaze, in particular, conveys information crucial for social interactions and social cognition. Researchers have hypothesized that in order to engage in dynamic face-to-face communication in real time, our brains must quickly and automatically process the direction of another person's gaze. There is evidence that direct gaze improves face encoding and attention capture and that direct gaze is perceived and processed more quickly than averted gaze. These results are summarized as the "direct gaze effect". However, in the recent literature, there is evidence to suggest that the mode of visual information processing modulates the direct gaze effect. In this presentation, I argue that top-down processing, and specifically the relevance of eye features to the task, promotes the early preferential processing of direct versus indirect gaze. On the basis of several recent evidences, I propose that low task relevance of eye features will prevent differences in eye direction processing between gaze directions because its encoding will be superficial. Differential processing of direct and indirect gaze will only occur when the eyes are relevant to the task. To assess the implication of task relevance on the temporality of cognitive processing, we will measure event-related potentials (ERPs) in response to facial stimuli. In this project, instead of typical ERP markers such as P1, N170 or P300, we will measure lateralized ERPs (lERPS) such as lateralized N170 and N2pc, which are markers of early face encoding and attentional deployment respectively. I hypothesize that the relevance of the eye feature task is crucial in the direct gaze effect and propose to revisit previous studies, which had questioned the existence of the direct gaze effect. This claim will be illustrate with different past studies and recent preliminary data of my lab. Overall, I propose a systematic evaluation of the role of top-down processing in early direct gaze perception in order to understand the impact of context on gaze perception and, at a larger scope, on social cognition.

The role of top-down mechanisms in gaze perception

Self-motion through a visual world creates a pattern of expanding visual motion called optic flow. Heading estimation from the optic flow is accurate in rigid environments. But it becomes challenging when other humans introduce an independent motion to the scene. The biological motion of human walkers consists of translation through space and associated limb articulation. The characteristic motion pattern is regular, though complex. A world full of humans moving around is nonrigid, causing heading errors. But limb articulation alone does not perturb the global structure of the flow field, matching the rigidity assumption. For heading perception from optic flow analysis, limb articulation alone should not impair heading estimates. But we observed heading biases when participants encountered a group of point-light walkers. Our research investigates the interactions between optic flow perception and biological motion perception. We further analyze the impact of environmental information.

Heading perception in crowded environments

Visual perception is perceived as continuous despite frequent disruptions in our visual environment. For example, internal events, such as saccadic eye-movements, and external events, such as object occlusion temporarily prevent visual information from reaching the brain. Combining evidence from these two models of visual disruption (occlusion and saccades), we will describe what information is maintained and how it is updated across the sensory interruption. Lina Teichmann will focus on dynamic occlusion and demonstrate how object motion is processed through perceptual gaps. Grace Edwards will then describe what pre-saccadic information is maintained across a saccade and how it interacts with post-saccadic processing in retinotopically relevant areas of the early visual cortex. Both occlusion and saccades provide a window into how the brain bridges perceptual disruptions. Our evidence thus far suggests a role for extrapolation, integration, and potentially suppression in both models. Combining evidence from these typically separate fields enables us to determine if there is a set of mechanisms which support visual processing during visual disruptions in general.

Perception during visual disruptions

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+. 

ItsAllAboutMotion: Encoding of speed in the human Middle Temporal cortex

Interactions between romantic partners may be disturbed by problematic mobile phone use, i.e., phubbing. Research shows that phubbing reduces the ability to be responsive, but emotional aspects of phubbing, such as experiences of anger and loneliness, have not been explored. Anger has been linked to partner blame in negative social interactions, whereas loneliness has been associated with low social acceptance. Moreover, two aspects of partner responsiveness, understanding and validation, refer to the ability to recognize partner’s perspective and convey acceptance of their point of view, respectively. High understanding and validation by partner have been found to prevent from negative affect during social interaction. The impact of understanding and validation on emotions has not been investigated in the context of phubbing, therefore we posit the following exploratory hypotheses. (1) Participants will report higher levels of anger and loneliness on days with phubbing by partner, compared to days without; (2) understanding and validation will moderate the relationship between phubbing intensity and levels of anger and loneliness. We conducted a daily diary study over seven days. Based on a sample of 133 participants in intimate relationships and living with their partners, we analyzed the nested within and between-person data using multilevel models. Participants reported higher levels of anger and loneliness on days they experienced phubbing. Both, understanding and validation, buffer the relationship between phubbing intensity and negative experiences, and the interaction effects indicate certain nuances between the two constructs. Our research provides a unique insight into how specific mechanisms related to couple interactions may explain experiences of anger and loneliness.

Emotions and Partner Phubbing: The Role of Understanding and Validation in Predicting Anger and Loneliness

Perception and action are biased by our recent experiences. Even when a sequence of stimuli are randomly presented, responses are sometimes attracted toward the past. The mechanism of such  bias, recently termed serial dependence, is still under investigation. Currently, there is mixed evidence indicating that such bias could be either from a sensory and perceptual origin or occurring only at decisional stages. In this talk, I will present recent findings from our group showing that biases are decreased when disrupting the memory trace in a premotor region in a simple visuomotor task. In addition, we have shown that this bias is stable over periods of up to 8 s. At the end, I will show ongoing analysis of a recent experiment and argue that serial dependence may rely on distributed memory representations of stimuli and task relevant features.

Distributed and stable memory representations may lead to serial dependence

With the increasing development of automatic systems and artificial intelligence, face recognition is becoming increasingly important in forensic and civil contexts. However, face recognition has yet to be thoroughly empirically studied to provide an adequate scientific and legal framework for investigative and court purposes. This observation sets the foundation for the research. We focus on issues related to face images and the use of automatic systems. Our objective is to validate a likelihood ratio computation methodology for interpreting comparison scores from automatic face recognition systems (score-based likelihood ratio, SLR). We collected three types of traces: portraits (ID), video surveillance footage recorded by ATM and by a wide-angle camera (CCTV). The performance of two automatic face recognition systems is compared: the commercial IDEMIA Morphoface (MFE) system and the open source FaceNet algorithm.

Forensic use of face recognition systems for investigation 

The nature of facial information that is stored by humans to recognise large amounts of faces is unclear despite decades of research in the field. To complicate matters further, little is known about how representations may evolve as novel faces become familiar, and there are large individual differences in the ability to recognise faces. I will present a theory I am developing and that assumes that facial representations are cost-efficient. In that framework, individual facial representations would incorporate different diagnostic features in different faces, regardless of familiarity, and would evolve depending on the relative stability in appearance over time. Further, coarse information would be prioritised over fine details in order to decrease storage demands. This would create low-cost facial representations that refine over time if appearance changes. Individual differences could partly rest on that ability to refine representation if needed. I will present data collected in the general population and in participants with developmental prosopagnosia. In support of the proposed view, typical observers and those with developmental prosopagnosia seem to rely on coarse peripheral features when they have no reason to expect someone’s appearance will change in the future.

Cost-efficient face learning in typical populations and in developmental prosopagnosia

3D Morphable Models are my favorite class of generative models and are commonly used to model faces. They are typically applied to ill-posed problems such as 3D reconstruction from 2D data. I'll start my presentation with an introduction into 3D Morphable Models and show what they are capable of doing. I'll then focus on our recent finding, the Identity-Expression Ambiguity: We demonstrate that non-orthogonality of the variation in identity and expression can cause identity-expression ambiguity in 3D Morphable Models, and that in practice expression and identity are far from orthogonal and can explain each other surprisingly well. Whilst previously reported ambiguities only arise in an inverse rendering setting, identity-expression ambiguity emerges in the 3D shape generation process itself.
The goal of this presentation is to demonstrate the ambiguity and discuss its potential consequences in a computer vision setting as well as for understanding face perception mechanisms in the human brain.

Identity-Expression Ambiguity in 3D Morphable Face Models

The goal of this pilot study is to develop a test through which people with extraordinary voice recognition and discrimination skills can be found (for forensic purposes). Since interest in this field has emerged, three studies have been published with the goal of finding people with potential super-recognition skills in voice processing. One of them is a discrimination test and two are recognition tests, but neither combines the two test scenarios and their test designs cannot be directly compared to a casework scenario in forensics phonetics. The pilot study at hand attempts to bridge this gap and analyses if the skills of voice discrimination and recognition correlate. The study is guided by a practical, forensic application, which further complicates the process of creating a viable test. The participants for the pilot consist of different classes of police cadets, which means the test can be redone and adjusted over time.

Developing a test to assess the ability of Zurich’s police cadets to discriminate, learn and recognize voices

Unfamiliar face processing (face cognition) ability varies considerably in the general population. However, the means of its assessment are not standardised, and selected laboratory tests vary between studies. It is also unclear whether 1) the most commonly employed tests are reliable, 2) participants show a degree of consistency in their performance, 3) and the face cognition tests broadly measure one underlying ability, akin to general intelligence.  In this study, we asked participants to perform eight tests frequently employed in the individual differences literature. We examined the reliability of these tests, relationships between them, consistency in participants’ performance, and used data driven approaches to determine factors underpinning performance.  Overall, our findings suggest that the reliability of these tests is poor to moderate, the correlations between them are weak, the consistency in participant performance across tasks is low and that performance can be broadly split into two factors: telling faces together, and telling faces apart. We recommend that future studies adjust analyses to account for stimuli (face images) and participants as random factors, routinely assess reliability, and that newly developed tests of face cognition are examined in the context of convergent validity with other commonly used measures of face cognition ability.

Commonly used face cognition tests yield low reliability and inconsistent performance: Implications for test design, analysis, and interpretation of individual differences data

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.

Consistency of Face Identity Processing: Basic & Translational Research

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”.

The diachronic account of attentional selectivity

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.

Age-related dedifferentiation across representational levels and their relation to memory performance

The literature suggests that ensemble coding, the ability to represent the gist of sets, may be an underlying mechanism for becoming familiar with newly encountered faces. This phenomenon was investigated by introducing a new training paradigm that involves incidental learning of target identities interspersed among distractors. The effectiveness of this training paradigm was explored in Study 1, which revealed that unfamiliar observers who learned the faces incidentally performed just as well as the observers who were instructed to learn the faces, and the intervening distractors did not disrupt familiarization. Using the same training paradigm, ensemble coding was investigated as an underlying mechanism for face familiarization in Study 2 by measuring familiarity with the targets at different time points using average images created either by seen or unseen encounters of the target. The results revealed that observers whose familiarity was tested using seen averages outperformed the observers who were tested using unseen averages, however, this discrepancy diminished over time. In other words, successful recognition of the target faces became less reliant on the previously encountered exemplars over time, suggesting an exemplar-independent representation that is likely achieved through ensemble coding. Taken together, the results from the current experiment provide direct evidence for ensemble coding as a viable underlying mechanism for face familiarization, that faces that are interspersed among distractors can be learned incidentally.

Statistical Summary Representations in Identity Learning: Exemplar-Independent Incidental Recognition

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).

Enhanced perception and cognition in deaf sign language users: EEG and behavioral evidence

One challenge in exploring the internal representation of faces is the lack of controlled stimuli transformations. Researchers are often limited to verbalizable transformations in the creation of a dataset. An alternative approach to verbalization for interpretability is finding image-based measures that allow us to quantify image transformations. In this study, we explore whether PCA could be used to create controlled transformations to a face by testing the effect of these transformations on human perceptual similarity and on computational differences in Gabor, Pixel and DNN spaces. We found that perceptual similarity and the three image-based spaces are linearly related, almost perfectly in the case of the DNN, with a correlation of 0.94. This provides a controlled way to alter the appearance of a face. In experiment 2, the effect of familiarity on the perception of multidimensional transformations was explored. Our findings show that there is a positive relationship between the number of components transformed and both the perceptual similarity and the same three image-based spaces used in experiment 1. Furthermore, we found that familiar faces are rated more similar overall than unfamiliar faces. That is, a change to a familiar face is perceived as making less difference than the exact same change to an unfamiliar face. The ability to quantify, and thus control, these transformations is a powerful tool in exploring the factors that mediate a change in perceived identity.

Link to the pre-print: https://psyarxiv.com/reh2k/

Exploring perceptual similarity and its relation to image-based spaces: an effect of familiarity

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.

Characterising the brain representations behind variations in real-world visual  behaviour

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. 

Spatio-temporal large-scale organization of the trimodal connectome derived from concurrent EEG-fMRI and diffusion MRI 

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?

Impact evaluation for COVID-19 non-pharmaceutical interventions: what is (un)knowable?

If we want good work to get done, we should credit those who do it. In science, researchers are credited predominantly via authorship on publications. But many contributions to modern research are not recognized with authorship, due in part to the high bar imposed by the authorship criteria of many journals. “Contributorship” is a more inclusive framework for indicating who did what in the work described by a paper, and many scientific journals have recently implemented versions of it. I will consider the motivation for and specifics of this change, describe the tenzing tool we created to facilitate it, and how we might want to support and shape the shift toward contributorship.

Redressing imbalances in the kind of research that gets done and who gets credit for it

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.

Investigating visual recognition and the temporal lobes using electrophysiology and fast periodic visual stimulation

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.

Getting to know you: emerging neural representations during face familiarization

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.  

Neuroimaging reproducibility - pain points and roadmap for solid and reusable results

The last two decades have seen a rise in online projects such as Wikipedia or OpenStreetMap in which people collaborate to create a common product. Contributors in such projects often work together sequentially. Essentially, the first contributor generates an entry (e.g., a Wikipedia article) independently which is then adjusted in the following by other contributors by adding or correcting information. We refer to this way of working together as sequential collaboration. This process has not yet been studied in the context of judgment and decision making even though research has demonstrated that Wikipedia and OpenStreetMap yield very accurate information. In this talk, I give first insights into the structure of sequential collaboration, how adjusting each other’s judgments can yield more accurate final estimates, which boundary conditions need to be met, and which underlying mechanisms may be responsible for successful collaboration. A preprint is available at https://psyarxiv.com/w4xdk/ 

Why does online collaboration work? Insights into sequential collaboration

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.

The Jena Voice Learning and Memory Test (JVLMT)

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:

(1) Major replication failures and data fabrication in the field of psychology
(2) Replication failures of social thermoregulation studies
(3) Realization that many of our studies were underpowered
(4) Realization that many of our studies were very narrow in scope (i.e., in undergraduate students and mostly in EU/US)
(5) 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:

(a) 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.
(b) A lab philosophy
(c) A research milestones sheet for collaborations and overviews
(d) Excel sheet for contributorship
(e) 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).

Lessons from the credibility revolution – social thermoregulation as a case study

A striking range of individual differences has been reported in a variety of visual search tasks, which naturally leads to the idea that some people are better at finding things than others. However, this conclusion appears to be premature. We carried out a replication of three recent visual search experiments and found that each task showed a wide range of individual differences as predicted, and observed good test-retest reliability in all three. However, performance on any one task was not correlated with the performance in the others: participants who naturally adopt efficient search strategies in one paradigm may perform at chance in another! Furthermore, we also show that behaviour in different versions of the same paradigm can be radically different: When simple line segments are used for search items, a large range of search strategies are found. If we instead use more complex search items, all our participants effortlessly adopt an optimal strategy. These results suggest search strategies are stable over time, but context-specific. To understand visual search we, therefore, need to account not only for differences between individuals but also how individuals interact with the search task and context.

Searching for the Super-Searchers

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 contribution of the dorsal visual pathway to perception and action

Visual attention refers to a set of processes allowing selection of relevant and filtering out of irrelevant information in the visual environment. A large amount of research on visual attention has involved visual search paradigms, where observers are asked to report whether a single target is present or absent. However, recent studies have revealed that these classic single-target visual search tasks only provide a snapshot of how attention is allocated in the visual environment, and that multitarget visual foraging tasks may capture the dynamics visual attention more accurately. In visual foraging, observers are asked to select multiple instances of multiple target types, as fast as they can. A critical question in foraging research concerns the factors driving the next target selection. Most likely, this would require two steps: (1) identifying a set of candidates for the next selection, and (2) selecting the best option among these candidates. After having briefly described the advantage of visual foraging over visual search, I will review recent visual foraging studies testing the influence of several manipulations (e.g., target crypticity, number of items, selection modality) on foraging behaviour. Overall, these studies revealed that the next target selection during visual foraging is determined by the competition between three factors: target value, target proximity, and priming of features. I will explain how the analysis of individual differences in foraging behaviour can provide important information, with the idea that individuals show by-default internal biases toward value, proximity and priming that determine their search strategy and behaviour.

Beyond visual search: studying visual attention with multitarget visual foraging tasks

In recent years, more and more evidence has accumulated showing that many studies in psychological research cannot be replicated, effects are often overestimated, and little is publicly known about unsuccessful studies. What are the mechanisms behind this crisis? In this talk, I will explain how we got there and why it is still difficult to break free from the current system. I will further explain which role Open Science plays within the replication crisis and how it can help to improve science. This might sound like a pessimistic, negative talk, but I will end it on a positive note, I promise!

The recent history of the replication crisis in psychology & how Open Science can be part of the solution

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.



McIntosh, R. D., & Rittmo, J. Ö. (2021). Power calculations in single-case neuropsychology: a practical primer. Cortex, 135, 146-158. https://doi.org/10.1016/j.cortex.2020.11.005 [preprint version: https://psyarxiv.com/fxz49]

McIntosh, R. D. (2018). Simple dissociations for a higher-powered neuropsychology. Cortex, 103, 256-265. https://doi.org/10.1016/j.cortex.2018.03.015 [preprint version: https://psyarxiv.com/mnhct]

The problem of power in single-case neuropsychology

State-of-the-art machine vision models can predict human recognition memory for complex scenes with astonishing accuracy. In this talk I present work that investigated how memorable scenes are actually remembered and experienced by human observers. We found that memorable scenes were recognized largely based on recollection of specific episodic details but also based on familiarity for an entire scene. I thus highlight current limitations in machine vision models emulating human recognition memory, with promising opportunities for future research. Moreover, we were interested in what observers specifically remember about complex scenes. We thus considered the functional role of eye-movements as a window into the content of memories, particularly when observers recollected specific information about a scene. We found that when observers formed a memory representation that they later recollected (compared to scenes that only felt familiar), the overall extent of exploration was broader, with a specific subset of fixations clustered around later to-be-recollected scene content, irrespective of the memorability of a scene. I discuss the critical role that our viewing behavior plays in visual memory formation and retrieval and point to potential implications for machine vision models predicting the content of human memories. 

Exploring Memories of Scenes

Deciding whether two different face photographs or voice samples are from the same person represent fundamental challenges within applied settings. To date, most research has focussed on average performance in these tests, failing to consider individual differences and within-person consistency in responses. In the current studies, participants completed the same face or voice matching test on two separate occasions, allowing comparison of overall accuracy across the two timepoints as well as consistency in trial-level responses. In both experiments, participants were highly consistent in their performances. In addition, we demonstrated a large association between consistency and accuracy, with the most accurate participants also tending to be the most consistent. This is an important result for applied settings in which organisational groups of super-matchers are deployed in real-world contexts. Being able to reliably identify these high performers based upon only a single test informs regarding recruitment for law enforcement agencies worldwide.

Accuracy versus consistency: Investigating face and voice matching abilities

Progress in psychology has been frustrated by challenges concerning replicability, generalizability, strategy selection, inferential reproducibility, and computational reproducibility. Although often discussed separately, I argue that these five challenges share a common cause: insufficient investment of resources into the typical psychology study. I further suggest that big team science can help address these challenges by allowing researchers to pool their resources to efficiently and drastically increase the amount of resources available for a single study. However, the current incentives, infrastructure, and institutions in academic science have all developed under the assumption that science is conducted by solo Principal Investigators and their dependent trainees. These barriers must be overcome if big team science is to be sustainable. Big team science likely also carries unique risks, such as the potential for big team science institutions to monopolize power, become overly conservative, make mistakes at a grand scale, or fail entirely due to mismanagement and a lack of financial sustainability. I illustrate the promise, barriers, and risks of big team science with the experiences of the Psychological Science Accelerator, a global research network of over 1400 members from 70+ countries.

A Manifesto for Big Team Science

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.

Markers of brain connectivity and sleep-dependent restoration: basic research and translation into clinical populations

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.

Algorithmic advances in face matching: Stability of tests in atypical groups

Experimental psychology research typically employs paradigms that greatly simplify the real- world conditions within which cognition occurs. This approach has been successful for isolating cognitive processes, but cannot adequately capture how perception operates in complex environments. In turn, real world environments rarely afford the access and control required for rigorous scientific experimentation. In recent years, technology has advanced to provide a solution to these problems, through the development of affordable high-capability virtual reality (VR) equipment. The application of VR is now increasing rapidly in Psychology, but a stimulus that has been used widely in this field – the human face – is captured poorly in current VR experiments. To overcome this issue, we have recently, we have developed a user-friendly method for creating photo-realistic avatars of real people, that can be imported into VR. Across a series of experiments, we demonstrate that avatar faces of familiar people are recognised with high accuracy (Study 1), replicate the familiarity advantage typically observed in real-world face matching (Study 2), and show that these avatars produce a similarity-space that corresponds closely with real photographs of the same faces (Study 3). These studies open up the way to conduct psychological experiments on visual perception and social cognition under increased realism in VR. 

Identifying Avatars of Real People

Regional specialization is a hallmark of neural speech processing. Two of the most important specializations concern hemispheric lateralization and the intra-hemispheric differentiation between motor and sensory speech areas. One important question concerns how information processed in different hemispheres or between sensory and motor speech areas is integrated. One increasingly dominant hypothesis is that the brain overcomes this “binding problem” by the synchronization of oscillatory activity across the relevant regions. Two temporal scales seem to be of particular relevance for speech processing: the theta frequency band corresponding roughly to the length of syllables (~100–300 ms), and in the gamma frequency band corresponding to the duration of phonemes (~20–50 ms). In my talk, I will introduce different experiments where we probed oscillatory synchronization as a mechanism for inter- and intrahemispheric communication in the speech network by manipulating oscillatory synchrony from the outside through the application of electric brain stimulation.

Selective modulation of connectivity in the speech network by electric brain stimulation

Vision creates an individually unique window to our world. Past research has shown that individuals show highly consistent differences in their gaze behavior towards certain semantic features of a scene, but it is unclear whether further such features of individual divergence exist. Here, I will present ongoing work zooming in on person perception. A first line of work expanded the annotations of an existing stimulus set with pixel masks for body and face parts. Preliminary results of eye-tracking experiments using these stimuli suggest consistent individual differences, especially in the tendency to fixate mouth and eye regions. A second line of work focusses on intraindividual differences in face processing across time and across the visual field. Previous work has shown that the recognition of inner face features is best at their expected / usual location in the visual field. To date, it is still unclear if this enhanced recognition performance is a learned adaptation to input statistics or following an innate, face-specific template. The ongoing global pandemic with a surge of people wearing face masks gives us an exceptional chance to investigate the processing of artificial face features. I will present preliminary fMRI results suggesting that over time the ventral processing of face masks becomes increasingly similar to that of faces. This result is currently being followed-up by behavioral experiments testing whether the feature-location contingency observed for natural facial features can be found for artificial features as well. Taken together, these results suggest that individual gaze behavior and perception is the result of an interplay between our unique visual brain and environment.

Intra- and interindividual differences in person perception

Perception can be strongly serially-dependent (i.e. biased toward previously seen stimuli). Recently, serial dependencies in perception were proposed as a mechanism for perceptual stability, increasing the apparent continuity of the complex environments we experience in everyday life. For example, stable scene perception can be actively achieved by the visual system through global serial dependencies, a special kind of serial dependence between summary statistical representations. Serial dependence occurs also between emotional expressions, but it is highly selective for the same identity. Overall, these results further support the notion of serial dependence as a global, highly specialized, and purposeful mechanism. However, serial dependence could also be a deleterious phenomenon in unnatural or unpredictable situations, such as visual search in radiological scans, biasing current judgments toward previous ones even when accurate and unbiased perception is needed. For example, observers make consistent perceptual errors when classifying a tumor- like shape on the current trial, seeing it as more similar to the shape presented on the previous trial. In a separate localization test, observers make consistent errors when reporting the perceived position of an objects on the current trial, mislocalizing it toward the position in the preceding trial. Taken together, these results show two opposite sides of serial dependence; it can be a beneficial mechanism which promotes perceptual stability, but at the same time a deleterious mechanism which impairs our percept when fine recognition is needed.

What is serially-dependent perception good for?

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.

* Disclosure: I am the EEG-BIDS  and Electrophysiology-BIDS derivatives co-lead, lead developer of the LIMO-MEG toolbox and collaborate with EEGLAB and as such my views are totally biased.*

Reproducible EEG from raw data to publication figures

Current computational models of visual salience accurately predict the distribution of fixations on isolated visual stimuli. It is not known, however, whether the global salience of a stimulus, that is its effectiveness in the competition for attention with other stimuli, is a function of the local salience or an independent measure. Further, do task and familiarity with the competing images influence eye movements? In this talk, I will present the analysis of a computational model of the global salience of natural images. We trained a machine learning algorithm to learn the direction of the first saccade of participants who freely observed pairs of images. The pairs balanced the combinations of new and already seen images, as well as task and task-free trials. The coefficients of the model provided a reliable measure of the likelihood of each image to attract the first fixation when seen next to another image, that is their global salience. For example, images of close-up faces and images containing humans were consistently looked first and were assigned higher global salience. Interestingly, we found that global salience cannot be explained by the feature-driven local salience of images, the influence of task and familiarity was rather small and we reproduced the previously reported left-sided bias. This computational model of global salience allows to analyse multiple other aspects of human visual perception of competing stimuli. In the talk, I will also present our latest results from analysing the saccadic reaction time as a function of the global salience of the pair of images.

Global visual salience of competing stimuli

Emotion processing is thought to be impaired in autism and linked to atypical visual exploration and arousal modulation to others faces and gaze, yet evidence is equivocal. We propose that, where observed, atypical socioemotional processing is due to alexithymia, a distinct but frequently co-occurring condition which affects emotional self-awareness and Interoception. In study 1 (N = 80),  we tested this hypothesis by studying the spatio-temporal dynamics and entropy of eye-gaze during emotion processing tasks. Evidence from traditional and novel methods revealed that atypical eye-gaze and emotion recognition is best predicted by alexithymia in both autistic and non-autistic individuals. In Study 2 (N = 70), we assessed interoceptive and autonomic signals implicated in socioemotional processing, and found evidence for alexithymia (not autism) driven effects on gaze and arousal modulation to emotions. We also conducted two large-scale studies (N = 1300), using confirmatory factor-analytic and network modelling and found evidence that Alexithymia and Autism are distinct at both a latent level and their intercorrelations. We argue that: 1) models of socioemotional processing in autism should conceptualise difficulties as intrinsic to alexithymia, and 2) assessment of alexithymia is crucial for diagnosis and personalised interventions in autism.

Is it Autism or Alexithymia? explaining atypical socioemotional processing

Human visual perception results from a complex chain of processes, starting with the orientation-selective encoding of luminance in the primary visual cortex (V1). As the visual signals proceed in the visual system, increasingly more complex shape properties are coded, with several high-level regions specializing for selective categories such as faces, words, natural scenes. We know very little about how such high-level specialization builds upon primary encoding stages in V1. Our work combines multiple investigation techniques: psychophysics, electrophysiology (scalp EEG, ERP and steady-state), as well as neuroimaging (of V1 and high-level visual regions) and suggests that the specialization of face processing, though emerging at high-level stages of visual processing, roots into the processing of selective orientation ranges. These findings encourage the adoption of more integrative approaches to face perception, and vision in general, taking into account both low- and high-level visual mechanisms in order to address whether and how these interact when processing complex stimuli such as faces of our peers.

Faces are made of this… Sensitivity to Orientation when processing faces

Have you ever wondered what type fonts, multisensory decision-making, and consumer psychology all have in common? Join me in exploring how perceptual effects present in everyday situations affect individuals with and without dyslexia differently. I will outline my recent research on whether dyslexia affects only language-based situations and interactions of everyday life, or if there is a hidden side to it that the traditional definitions of dyslexia only hint at? For instance, can small changes of perceptual elements affect students with dyslexia attending higher education? In an attempt to answer these questions, I will draw back on a variety of my studies investigating differential eye movements, EEG components, computational modelling, and behavioural performance in adult dyslexia. I will show how a multi-method approach can be used to provide new insights into the way we approach dyslexia.

The intersection of perceptual decision-making, reading, consumer psychology and dyslexia – a multi-method approach

Visual saliency plays an important role in natural vision as it directs our gaze to relevant features in our environment. Recent evidence shows that where we look in a natural scene is most strongly influenced by the types of embedded objects. Here, I will present data (1) replicating strong and consistent individual differences in eye movements towards objects along semantic dimensions; (2) showing systematic salience differences between Super Recognizers (SRs) and controls; and (3) showing differences in SR gaze behavior towards face and inner face regions embedded in natural scenes. Finally, I will discuss the potential relevance of these differences in gaze behaviour for understanding enhanced face recognition abilities in SRs. 

Evidence for atypical semantic visual salience in Super Recognizers

Eye movements allow for visual exploration which serves to gather information from the environment in order to create, update, and reconstruct memories. There is consistent agreement regarding the positive relationship between cumulative eye movement sampling and subsequent recognition, but the role of aging in this eye movement behavior is currently unknown. Also, it is unclear whether aging influences the eye movement repetition effect (i.e., fewer fixations to repeated, compared to novel, stimuli). This study investigated the influence of aging on cumulative sampling, the repetition effect, and subsequent memory. Eye movements were monitored in two phases: a study phase during which healthy younger and healthy older adults viewed single faces from the same or different viewpoints across multiple blocks, and a test phase, in which each face was displayed from the same or a different viewpoint as seen during the study phase. Previous work by Olsen et al. (2015) indicated that the hippocampus, a region of the brain that could be affected by aging, critically contributed to recognizing faces viewed from different viewpoints. Here, examination of eye movements during encoding indicated that greater cumulative sampling was associated with subsequent recognition among younger adults. Increased sampling, however, was not associated with subsequent recognition among older adults, which suggests that in aging, the accumulation of visual information into a memory representation that supports recognition may be disrupted. Furthermore, the eye movement repetition effect was similar among younger and older adults but was not associated with subsequent recognition for either group. This suggests that, regardless of participants’ age and the nature of the recognition task, the memory representation that is reflected by the repetition effect may not contribute to the conscious recognition of the face. Finally, our findings demonstrated that healthy aging did not disrupt the ability to recognize faces that are presented across small changes in viewpoint. Altogether, this work provides key insights into the role of aging in eye movement expressions of memory and may pave the way for the development of an eye-tracking test that identifies cognitive decline before subjective symptoms emerge.

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