How Visual Expertise Changes Representational Geometry: A Behavioral and Neural Perspective

Many people develop expertise in specific domains of interest, such as chess, microbiology, radiology, and, the case in point in our study: ornithology. It is poorly understood to what extent such expertise alters brain function. Previous neuroimaging studies of expertise have typically focused upon the category level, for example, selectivity for birds versus nonbird stimuli. We present a multivariate fMRI study focusing upon the representational similarity among objects of expertise at the subordinate level. We compare the neural representational spaces of experts and novices to behavioral judgments. At the behavioral level, ornithologists (n = 20) have more fine-grained and task-dependent representations of item similarity that are more consistent among experts compared to control participants. At the neural level, the neural patterns of item similarity are more distinct and consistent in experts than in novices, which is in line with the behavioral results. In addition, these neural patterns in experts show stronger correlations with behavior compared to novices. These findings were prominent in frontal regions, and some effects were also found in occipitotemporal regions. This study illustrates the potential of an analysis of representational geometry to understand to what extent expertise changes neural information processing.

Artificial neural network for the recognition of human emotions under a backpropagation algorithm

The era of the technological revolution increasingly encourages the development of technologies that facilitate in one way or another people's daily activities, thus generating a great advance in information processing. The purpose of this work is to implement a neural network that allows classifying the emotional states of a person based on the different human gestures. A database is used with information on students from the PUCE-E School of Computer Science and Engineering. Said information are images that express the gestures of the students and with which the comparative analysis with the input data is carried out. The environment in which this work converges proposes that the implementation of this project be carried out under the programming of a multilayer neuralnetwork. Multilayer feeding neural networks possess a number of properties that make them particularly suitable for complex pattern classification problems [8]. Back-Propagation [4], which is a backpropagation algorithm used in the Feedforward neural network, was taken into consideration to solve the classification of emotions. Keywords: Image processing, neural networks, gestures, back-propagation, feedforward, classification, emotions. References [1]S. Gangwar, S. Shukla, D. Arora. “Human Emotion Recognition by Using Pattern Recognition Network”, Journal of Engineering Research and Applications, Vol. 3, Issue 5, pp.535-539, 2013. [2]K. Rohit. “Back Propagation Neural Network based Emotion Recognition System”. International Journal of Engineering Trends and Technology (IJETT), Vol. 22, Nº 4, 2015. [3]S. Eishu, K. Ranju, S. Malika, “Speech Emotion Recognition using BFO and BPNN”, International Journal of Advances in Science and Technology (IJAST), ISSN2348-5426, Vol. 2 Issue 3, 2014. [4]A. Fiszelew, R. García-Martínez and T. de Buenos Aires. “Generación automática de redes neuronales con ajuste de parámetros basado en algoritmos genéticos”. Revista del Instituto Tecnológico de Buenos Aires, 26, 76-101, 2002. [5]Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. “Handwritten digit recognition with a back-propagation network”. In Advances in neural information processing systems. pp. 396-404, 1990. [6]G. Bebis and M. Georgiopoulos. “Feed-forward neural networks”. IEEE Potentials, 13(4), 27-31, 1994. [7]G. Huang, Q. Zhu and C. Siew. “Extreme learning machine: a new learning scheme of feedforward neural networks”. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference. Vol. 2, pp. 985-990. IEEE, 2004. [8]D. Montana and L. Davis. “Training Feedforward Neural Networks Using Genetic Algorithms”. In IJCAI, Vol. 89, pp. 762-767, 1989. [9]I. Sutskever, O. Vinyals and Q. Le. “Sequence to sequence learning with neural networks”. In Advances in neural information processing systems. pp. 3104-3112, 2014. [10]J. Schmidhuber. “Deep learning in neural networks: An overview”. Neural networks, 61, 85-117, 2015. [11]R. Santos, M. Ruppb, S. Bonzi and A. Filetia, “Comparación entre redes neuronales feedforward de múltiples capas y una red de función radial para detectar y localizar fugas en tuberías que transportan gas”. Chem. Ing.Trans , 32 (1375), e1380, 2013.

High-dimensional distribution generation through deep neural networks

We show that every d-dimensional probability distribution of bounded support can be generated through deep ReLU networks out of a 1-dimensional uniform input distribution. What is more, this is possible without incurring a cost—in terms of approximation error measured in Wasserstein-distance—relative to generating the d-dimensional target distribution from d independent random variables. This is enabled by a vast generalization of the space-filling approach discovered in Bailey and Telgarsky (in: Bengio (eds) Advances in neural information processing systems vol 31, pp 6489–6499. Curran Associates, Inc., Red Hook, 2018). The construction we propose elicits the importance of network depth in driving the Wasserstein distance between the target distribution and its neural network approximation to zero. Finally, we find that, for histogram target distributions, the number of bits needed to encode the corresponding generative network equals the fundamental limit for encoding probability distributions as dictated by quantization theory.

Where We Mentalize: Main Cortical Areas Involved in Mentalization

When discussing “mentalization,” we refer to a very special ability that only humans and few species of great apes possess: the ability to think about themselves and to represent in their mind their own mental state, attitudes, and beliefs and those of others. In this review, a summary of the main cortical areas involved in mentalization is presented. A thorough literature search using PubMed MEDLINE database was performed. The search terms “cognition,” “metacognition,” “mentalization,” “direct electrical stimulation,” “theory of mind,” and their synonyms were combined with “prefrontal cortex,” “temporo-parietal junction,” “parietal cortex,” “inferior frontal gyrus,” “cingulate gyrus,” and the names of other cortical areas to extract relevant published papers. Non-English publications were excluded. Data were extracted and analyzed in a qualitative manner. It is the authors' belief that knowledge of the neural substrate of metacognition is essential not only for the “neuroscientist” but also for the “practical neuroscientist” (i.e., the neurosurgeon), in order to better understand the pathophysiology of mentalizing dysfunctions in brain pathologies, especially those in which integrity of cortical areas or white matter connectivity is compromised. Furthermore, in the context of neuro-oncological surgery, understanding the anatomical structures involved in the theory of mind can help the neurosurgeon obtain a wider and safer resection. Though beyond of the scope of this paper, an important but unresolved issue concerns the long-range white matter connections that unify these cortical areas and that may be themselves involved in neural information processing.

Consciousness, decision making, and volition: freedom beyond chance and necessity

What is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

Timing is everything: Early and late neural measures of auditory habituation and discrimination in autism and their relationship to autistic traits and sensory overresponsivity

BackgroundSensory differences are included in the DSM-5 criteria of autism for the first time, yet it is unclear how sensory behaviors are related to neural indicators of perception. We sought to disentangle this complex relationship by studying early brain signatures of perception using event-related potentials (ERPs) and examining their relationship to sensory overresponsivity and autistic traits.MethodsThirteen autistic children and 13 Typically Developing (TD) children matched on chronological age and nonverbal IQ participated in a passive oddball task, in which P1 habituation and P1 and MMN discrimination were evoked by pure tones. ERPs were compared between groups, and correlations were conducted between ERPs and autistic traits and sensory features.ResultsAutistic children had marginally enhanced neural discrimination and reduced habituation to auditory stimuli compared to the TD group. Better P1 and MMN discrimination and lower P1 habituation corresponded with more autistic traits. Further, the MMN component, but not P1 components, mapped on to sensory overresponsivity.LimitationsStimuli in the oddball paradigm were not counterbalanced in their presentation as standards or deviants, and participants were not directly asked about their reactions to the auditory stimuli, which would be advantageous in determining whether appraisal of stimuli moderates neural response. The sample size is small and warrants replication.ConclusionsSignificant correlations between auditory ERP components and autistic traits, even when group differences were not present, suggests benefits to taking a more dimensional approach to autism than using strictly categorical methods. Our findings highlight the significance of temporal and contextual factors in neural information processing as it relates to autistic traits and sensory behaviors.

Cortical response to naturalistic stimuli is largely predictable with deep neural networks

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

EEG Cortical Activities and Networks Altered by Watching 2D/3D Virtual Reality Videos

Virtual reality (VR), which can represent real-life events and situations, is being increasingly applied to many fields, such as education, entertainment, and medical rehabilitation. Correspondingly, the neural information processing of VR has attracted attention. However, the underlying neural mechanisms of VR environments have not yet been fully revealed. The purpose of this study was to examine the possible differences in brain activities and networks between the less immersive 2D and the fully immersive 3D VR environments. 3D VR videos and the same 2D scenes were presented to the participants and the scalp electroencephalogram (EEG) was recorded, respectively. Power spectral density (PSD) and the functional connectivity of these EEG signals were analyzed. The results showed that 3D VR videos significantly enhanced the PSD of θ rhythm (4–7 Hz) in the frontal lobe; decreased the PSD of α rhythm (8–13 Hz) in the parietal and the occipital lobes; increased the PSD of β rhythm (14–30 Hz) in the frontal, the parietal, the temporal, and the occipital lobes, relative to 2D VR watching. Furthermore, 3D versus 2D VR-induced alterations in the patterns of brain networks were similar to the patterns of PSD. Specifically, for the θ rhythm, 3D VR significantly enhanced the frontal and the temporal brain functional connectivity; for the α rhythm, 3D VR increased the parietal and the occipital networks; for the β rhythm, 3D VR remarkably increased the frontal, the occipital, the frontal-temporal and the frontal-occipital brain functional connectivity, relative to 2D VR. These significant differences between 3D and 2D VR video-watching suggest that the neural information processing of cortical activities and networks is correlated to the degree of immersion. The present results, collected with previous researches, implicate that some visual-related information processes, such as visual attention, visual perception, and visual immersion are more robust in 3D VR environments.

The neural basis of intelligence in fine-grained cortical topographies

Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods could not resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.

Causal discovery in climate research: Overview and recent progress

<p>Discovering causal dependencies from observational time series datasets is a major problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity (PCMCI, Runge et al. Sci. Adv. 2019), as well as instantaneous causal links (PCMCI+, Runge UAI, 2020) and hidden variables (LPCMCI, Gerhardus and Runge, 2020), but many more remain. In this presentation I will give an overview of challenges and methods and present a recent approach, Ensemble-PCMCI, to analyze ensembles of climate time series. An example for this are initialized ensemble forecasts. Since the individual samples can then be created from several time series instead of different time steps from a single time series, such cases allow to relax the assumption of stationarity and hence to analyze whether and how the underlying causal relationships change over time. We compare Ensemble-PCMCI to other methods and discuss preliminary applications.</p><p>Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).</p><p>Runge, J. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, Toronto, Canada, 2019, AUAI Press, 2020</p><p>Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders. Advances in Neural Information Processing Systems, 2020, 33</p>