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.

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.

How central were psychological features deriving from our visual systems to the early evolution of human visual culture? Art making emerged deep in our evolutionary history, with the earliest art appearing over 100,000 years ago as geometric patterns etched on fragments of ochre and shell, and figurative representations of prey animals flourishing in the Upper Palaeolithic (c. 40,000 – 15,000 years ago). The latter reflects a complex visual process; the ability to represent something that exists in the real world as a flat, two-dimensional image. In this presentation, I argue that pareidolia – the psychological phenomenon of seeing meaningful forms in random patterns, such as perceiving faces in clouds – was a fundamental process that facilitated the emergence of figurative representation. The influence of pareidolia has often been anecdotally observed in Upper Palaeolithic art examples, particularly cave art where the topographic features of cave wall were incorporated into animal depictions. Using novel virtual reality (VR) light simulations, I tested three hypotheses relating to pareidolia in the caves of Upper Palaeolithic cave art in the caves of Las Monedas and La Pasiega (Cantabria, Spain). To evaluate this further, I also developed an interdisciplinary VR eye-tracking experiment, where participants were immersed in virtual caves based on the cave of El Castillo (Cantabria, Spain). Together, these case studies suggest that pareidolia was an intrinsic part of artist-cave interactions (‘conversations’) that influenced the form and placement of figurative depictions in the cave. This has broader implications for conceiving of the role of visual psychological phenomena in the emergence and development of figurative art in the Palaeolithic.

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.

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.

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.

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 role of attention in working memory remains controversial, but there is some agreement on the notion that the focus of attention holds mnemonic representations in a privileged state of heightened accessibility in working memory, resulting in better memory performance for items that receive focused attention during retention. Closely related, representations held in the focus of attention are often observed to be robust and protected from degradation caused by either perceptual interference (e.g., Makovski & Jiang, 2007; van Moorselaar et al., 2015) or decay (e.g., Barrouillet et al., 2007). Recent findings indicate, however, that representations held in the focus of attention are particularly vulnerable to degradation, and thus, appear to be particularly fragile rather than robust (e.g., Hitch et al., 2018; Hu et al., 2014). The present set of experiments aims at understanding the apparent paradox of information in the focus of attention having a protected vs. vulnerable status in working memory. To that end, we examined the effect of perceptual interference on memory performance for information that was held within vs. outside the focus of attention, across different ways of bringing items in the focus of attention and across different time scales.

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.

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.

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.

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.

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.

Visual working memory (VWM) allows us to maintain a small amount of task-relevant information in mind so that we can use them to guide our behavior. Although past studies have successfully characterized its capacity limit and representational quality during maintenance, the consequence of its usage for task-relevant behaviors has been largely unknown. In this talk, I will demonstrate that VWM representations get distorted when they are used for perceptual comparisons with new visual inputs, especially when the inputs are subjectively similar to the VWM representations. Furthermore, I will show that this similarity-induced memory bias (SIMB) occurs for both simple (e.g. , color, shape) and complex stimuli (e.g., real world objects, faces) that are perceptually encoded and retrieved from long-term memory. Given the observed versatility of the SIMB, its implication for other memory distortion phenomena (e.g., distractor-induced distortion, misinformation effect) will be discussed.

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.

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.

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.