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Deepfake emotional expressions trigger the uncanny valley brain response, even when they are not recognised as fake
Facial expressions are inherently dynamic, and our visual system is sensitive to subtle changes in their temporal sequence. However, researchers often use dynamic morphs of photographs—simplified, linear representations of motion—to study the neural correlates of dynamic face perception. To explore the brain's sensitivity to natural facial motion, we constructed a novel dynamic face database using generative neural networks, trained on a verified set of video-recorded emotional expressions. The resulting deepfakes, consciously indistinguishable from videos, enabled us to separate biological motion from photorealistic form. Results showed that conventional dynamic morphs elicit distinct responses in the brain compared to videos and photos, suggesting they violate expectations (n400) and have reduced social salience (late positive potential). This suggests that dynamic morphs misrepresent facial dynamism, resulting in misleading insights about the neural and behavioural correlates of face perception. Deepfakes and videos elicited largely similar neural responses, suggesting they could be used as a proxy for real faces in vision research, where video recordings cannot be experimentally manipulated. And yet, despite being consciously undetectable as fake, deepfakes elicited an expectation violation response in the brain. This points to a neural sensitivity to naturalistic facial motion, beyond conscious awareness. Despite some differences in neural responses, the realism and manipulability of deepfakes make them a valuable asset for research where videos are unfeasible. Using these stimuli, we proposed a novel marker for the conscious perception of naturalistic facial motion – Frontal delta activity – which was elevated for videos and deepfakes, but not for photos or dynamic morphs.
Current and future trends in neuroimaging
With the advent of several different fMRI analysis tools and packages outside of the established ones (i.e., SPM, AFNI, and FSL), today's researcher may wonder what the best practices are for fMRI analysis. This talk will discuss some of the recent trends in neuroimaging, including design optimization and power analysis, standardized analysis pipelines such as fMRIPrep, and an overview of current recommendations for how to present neuroimaging results. Along the way we will discuss the balance between Type I and Type II errors with different correction mechanisms (e.g., Threshold-Free Cluster Enhancement and Equitable Thresholding and Clustering), as well as considerations for working with large open-access databases.
Do large language models solve verbal analogies like children do?
Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s home). Interestingly, despite state-of-the-art AI-language models having superhuman encyclopedic knowledge and superior memory and computational power, our pilot studies show that these large language models often make mistakes providing associative rather than relational responses to verbal analogies. For example, when we asked four- to eight-year-olds to solve the analogy “body is to feet as tree is to …?” they responded “roots” without hesitation, but large language models tend to provide more associative responses such as “leaves”. In this study we examine the similarities and differences between children's and six large language models' (Dutch/multilingual models: RobBERT, BERT-je, M-BERT, GPT-2, M-GPT, Word2Vec and Fasttext) responses to verbal analogies extracted from an online adaptive learning environment, where >14,000 7-12 year-olds from the Netherlands solved 20 or more items from a database of 900 Dutch language verbal analogies.
Emergence of homochirality in large molecular systems
The question of the origin of homochirality of living matter, or the dominance of one handedness for all molecules of life across the entire biosphere, is a long-standing puzzle in the research on the Origin of Life. In the fifties, Frank proposed a mechanism to explain homochirality based on the properties of a simple autocatalytic network containing only a few chemical species. Following this work, chemists struggled to find experimental realizations of this model, possibly due to a lack of proper methods to identify autocatalysis [1]. In any case, a model based on a few chemical species seems rather limited, because prebiotic earth is likely to have consisted of complex ‘soups’ of chemicals. To include this aspect of the problem, we recently proposed a mechanism based on certain features of large out-of-equilibrium chemical networks [2]. We showed that a phase transition towards an homochiral state is likely to occur as the number of chiral species in the system becomes large or as the amount of free energy injected into the system increases. Through an analysis of large chemical databases, we showed that there is no need for very large molecules for chiral species to dominate over achiral ones; it already happens when molecules contain about 10 heavy atoms. We also analyzed the various conventions used to measure chirality and discussed the relative chiral signs adopted by different groups of molecules [3]. We then proposed a generalization of Frank’s model for large chemical networks, which we characterized using random matrix theory. This analysis includes sparse networks, suggesting that the emergence of homochirality is a robust and generic transition. References: [1] A. Blokhuis, D. Lacoste, and P. Nghe, PNAS (2020), 117, 25230. [2] G. Laurent, D. Lacoste, and P. Gaspard, PNAS (2021) 118 (3) e2012741118. [3] G. Laurent, D. Lacoste, and P. Gaspard, Proc. R. Soc. A 478:20210590 (2022).
Addgene AAV data hub
The Addgene AAV Data Hub was launched to help scientists share data and protocols obtained from AAV experiments. Our longterm goal is to provide scientists with a resource to help guide AAV selection and use by providing data from individual labs on AAV performance.
Student´s Oral Presentation III: Emotional State Classification Using Low-Cost Single-Channel Electroencephalography
Although electroencephalography (EEG) has been used in clinical and research studies for almost a century, recent technological advances have made the equipment and processing tools more accessible outside laboratory settings. These low-cost alternatives can achieve satisfactory results in experiments such as detecting event-related potentials and classifying cognitive states. In our research, we use low-cost single-channel EEG to classify brain activity during the presentation of images of opposite emotional valence from the OASIS database. Emotional classification has already been achieved using research-grade and commercial-grade equipment, but our approach pioneers the use of educational-grade equipment for said task. EEG data is collected with a Backyard Brains SpikerBox, a low-cost and open-source bioamplifier that can record a single-channel electric signal from a pair of electrodes placed on the scalp, and used to train machine learning classifiers.
Using evolutionary algorithms to explore single-cell heterogeneity and microcircuit operation in the hippocampus
The hippocampus-entorhinal system is critical for learning and memory. Recent cutting-edge single-cell technologies from RNAseq to electrophysiology are disclosing a so far unrecognized heterogeneity within the major cell types (1). Surprisingly, massive high-throughput recordings of these very same cells identify low dimensional microcircuit dynamics (2,3). Reconciling both views is critical to understand how the brain operates. " "The CA1 region is considered high in the hierarchy of the entorhinal-hippocampal system. Traditionally viewed as a single layered structure, recent evidence has disclosed an exquisite laminar organization across deep and superficial pyramidal sublayers at the transcriptional, morphological and functional levels (1,4,5). Such a low-dimensional segregation may be driven by a combination of intrinsic, biophysical and microcircuit factors but mechanisms are unknown." "Here, we exploit evolutionary algorithms to address the effect of single-cell heterogeneity on CA1 pyramidal cell activity (6). First, we developed a biophysically realistic model of CA1 pyramidal cells using the Hodgkin-Huxley multi-compartment formalism in the Neuron+Python platform and the morphological database Neuromorpho.org. We adopted genetic algorithms (GA) to identify passive, active and synaptic conductances resulting in realistic electrophysiological behavior. We then used the generated models to explore the functional effect of intrinsic, synaptic and morphological heterogeneity during oscillatory activities. By combining results from all simulations in a logistic regression model we evaluated the effect of up/down-regulation of different factors. We found that muyltidimensional excitatory and inhibitory inputs interact with morphological and intrinsic factors to determine a low dimensional subset of output features (e.g. phase-locking preference) that matches non-fitted experimental data.