Humans have the ability to rapidly extract summary statistics from object groupings through a specific capability known as ensemble coding. Previous literature has reported that this ability can become biased by prior...
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Humans have the ability to rapidly extract summary statistics from object groupings through a specific capability known as ensemble coding. Previous literature has reported that this ability can become biased by prior perceptual experiences at the individual level. However, it remains unknown whether interpersonal prior information could also bias ensemble perception through a co-representation process. Experiment 1 found that participants' summary estimations were biased toward their co-actor's stimuli. Experiment 2 confirmed a causal relationship between the bias effect and the co-representation process by showing a reduction in biased estimation after pairing participants with an out-group partner. These findings extend the sources of prior information exploited by humans during perceptual average from individual-level information (i.e., self-tasks) to interpersonal-level information (i.e., co-actor's tasks). More specifically, interpersonal prior information is shown to act in a top-down and implicit manner, biasing ensemble perception.
Social cognitive processes, particularly Theory of Mind (ToM) reasoning, appear to differ between autistic and non-autistic individuals. This has been proposed to reflect the autistic core symptomatology of communicat...
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Social cognitive processes, particularly Theory of Mind (ToM) reasoning, appear to differ between autistic and non-autistic individuals. This has been proposed to reflect the autistic core symptomatology of communication and social interaction difficulties. According to the predictive coding theory, autistic individuals' ToM reasoning difficulties arise from an attenuated use of prior information about others' mental states to explain and predict their behavior. This reduced use of prior assumptions makes the social world less predictable for autistic people, causing interactive mismatch and stress. Despite strong theoretical claims, robust and replicable neural differences in ToM brain regions remain elusive. Here, we investigated whether brain regions supporting ToM reasoning anticipate a narrative during repeated exposure (i.e., the narrative anticipation effect) in non-autistic adults (Experiment 1) and tested whether this effect was attenuated in autistic adults (Experiment 2). We presented a short movie with a plot including mental states with associated actions, twice, to 61 non-autistic adults who underwent functional magnetic resonance imaging [Experiment 1: M(SD)age = 25.9(4.4) years]. In Experiment 2, we used the same protocol with 30 autistic [M(SD)age = 32.4(10.7) years] and 30 non-autistic adults [M(SD)age = 33.2(10.1) years]. Analyses revealed no narrative anticipation effect in the ToM network in either group. Exploratory reverse correlation analyses identified a ToM scene that evoked a smaller difference in response between movie viewings (i.e., less repetition suppression) in autistic adults, compared to non-autistic adults. In sum, our study shows that predictive processing in the ToM network during a naturalistic movie-viewing experiment was absent in adults. Subtle differences in a key scene provide preliminary neural evidence for the predictive coding theory and open a promising avenue for future research to bet
AI-based compression is gaining popularity for traditional photos and videos. However, such techniques do not typically scale well to the task of compressing hyperspectral images, and may have computational requiremen...
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ISBN:
(纸本)9798350360332;9798350360325
AI-based compression is gaining popularity for traditional photos and videos. However, such techniques do not typically scale well to the task of compressing hyperspectral images, and may have computational requirements in terms of memory usage and total floating point operations that are prohibitive for usage onboard of satellites. In this paper, we explore the design of a predictive compression method based on a novel neural network design, called LineRWKV. Our neural network predictor works in a line-by-line fashion limiting memory and computational requirements thanks to a recurrent inference mechanism. However, in contrast to classic recurrent networks, it relies on an attention operation that can be parallelized for training, akin to Transformers, unlocking efficient training on large datasets, which is critical to learn complex predictors. In our preliminary results, we show that LineRWKV significantly outperforms the state-of-the-art CCSDS-123 standard and has competitive throughput.
Although research including biological concepts and variables has gained more prominence in sociology, progress assimilating the organ of experience, the brain, has been theoretically and technically challenging. Form...
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Although research including biological concepts and variables has gained more prominence in sociology, progress assimilating the organ of experience, the brain, has been theoretically and technically challenging. Formal uptake and assimilation have thus been slow. Within psychology and neuroscience, the traditional brain, which has made brief appearances in sociological research, is a "bottom-up" processor in which sensory signals are passed up the neural hierarchy where they are eventually cognitively and emotionally processed, after which actions and responses are generated. In this paper, we introduce the Active Inference Framework (AIF), which casts the brain as a Bayesian "inference engine" that tests its "top-down" predictive models against "bottom-up" sensory error streams in its attempts to resolve uncertainty and make the world more predictable. After assembling and presenting key concepts in the AIF, we describe an integrated neuro-bio-social model that prioritizes the microsociological assertion that the scene of action is the situation, wherein brains enculturate. Through such social dynamics, enculturated brains share models of the world with one another, enabling collective realities that disclose the actions afforded in those times and places. We conclude by discussing this neuro-bio-social model within the context of exemplar sociological research areas, including the sociology of stress and health, the sociology of emotions, and cognitive cultural sociology, all areas where the brain has received some degree of recognition and incorporation. In each case, sociological insights that do not fit naturally with the traditional brain model emerge intuitively from the predictive AIF model, further underscoring the interconnections and interdependencies between these areas, while also providing a foundation for a probabilistic sociology. Trotz der zunehmenden Prominenz einer biologische Konzepte und Variablen einbeziehenden Forschung in der Soziologie steh
This article aims to delve into the theoretical perspective on imagination in education, focusing specifically on Imaginative Education theory. The approach involves a dual objective: critically analyzing the limitati...
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This article aims to delve into the theoretical perspective on imagination in education, focusing specifically on Imaginative Education theory. The approach involves a dual objective: critically analyzing the limitations and specific potentials of Imaginative Education to stimulate contemporary discourse on imagination in education and using it as a foundation to frame current research in philosophy and psychology within an educational context. Key elements of this theoretical operation include a critical examination of philosophical taxonomies on the concept of imagination and its reinterpretation in relation to predictive coding and Embodied Cognition theories. The fundamental goal of this conceptual clarification goes beyond the theoretical realm. The article aspires to contribute to the growing interest in imagination, fantasy, and creativity as essential elements of curriculum design and teacher education, serving both as educational objectives and resources to create meaningful learning environments and experiences. Hence, the focus on the IE theory aims to bridge the gap between theoretical reflections on imagination and learning and their practical implications in curriculum design and teacher education or professional development.
One of the biggest challenges in robotics is interacting under uncertainty. Unlike robots, humans learn, adapt, and perceive their body as a unity when interacting with the world. Here, we investigate the suitability ...
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One of the biggest challenges in robotics is interacting under uncertainty. Unlike robots, humans learn, adapt, and perceive their body as a unity when interacting with the world. Here, we investigate the suitability of active inference, a computational model proposed for the brain and governed by the free-energy principle, for robotic body perception and action in a nonsimulated environment. We designed and deployed the algorithm on the humanoid iCub showing how our proposed model enabled the robot to have adaptive body perception and to perform robust upper body reaching and head object tracking behaviors even under high levels of sensor noise and discrepancies between the model and the real robot. Estimation and control are formalized as an inference problem where the body posterior state distribution is approximated by means of the variational free-energy bound, yielding to a minimization of the prediction error. Besides, our study forecasts reactive actions in the presence of sensorimotor conflicts, a mechanism that may be relevant in human body adaptation to uncertain situations.
Background: Although noted in the earliest descriptions of autism, differences in sensory processing, including hyper-responsivity, hypo-responsivity and sensory seeking, have only been included as a diagnostic criter...
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Background: Although noted in the earliest descriptions of autism, differences in sensory processing, including hyper-responsivity, hypo-responsivity and sensory seeking, have only been included as a diagnostic criterion more recently. Understanding how these unique features of sensory processing are related to the emotional and social aspects of autism is an ongoing question. Here we ask whether differences in sensory processing are associated with alexithymia, a trait characterised by an inability to identify and describe one's feelings, which has a high incidence in autism. Method: Parents of autistic children (n = 38) and parents of non-autistic children (n = 35) completed four standardized scales, the Short Sensory Profile-2, Social Responsiveness Scale-2, Social Communication Questionnaire, and the Children's Alexithymia Measure. Results: Across all four scales the autistic children showed higher scores than the non-autistic children, with large effect sizes. Using the subscales of the SSP-2 we show that, for the autistic children but not for the non-autistic children, sensory hyper-responsivity is predictive of alexithymia, of core social features of autism and of restricted interests and repetitive behaviour (RIRB), after controlling for hypo-responsivity and sensory seeking. Conclusions: These results add to a small but growing literature on the relationship between sensory processing and social and emotional behaviours in autistic children, and are discussed with reference to predictive coding and 'sensory first' accounts of autism. As sensory differences in autism impact children's daily functioning and educational opportunities, understanding how these differences relate to social and emotional behaviour is important.
Experimental and theoretical studies have tried to gain insights into the involvement of the Temporal Parietal Junction (TPJ) in a broad range of cognitive functions like memory, attention, language, self-agency and t...
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Experimental and theoretical studies have tried to gain insights into the involvement of the Temporal Parietal Junction (TPJ) in a broad range of cognitive functions like memory, attention, language, self-agency and theory of mind. Recent investigations have demonstrated the partition of the TPJ in discrete subsectors. Nonetheless, whether these subsectors play different roles or implement an overarching function remains debated. Here, based on a review of available evidence, we propose that the left TPJ codes both matches and mismatches between expected and actual sensory, motor, or cognitive events while the right TPJ codes mismatches. These operations help keeping track of statistical contingencies in personal, environmental, and conceptual space. We show that this hypothesis can account for the participation of the TPJ in disparate cognitive functions, including "humour", and explain: a) the higher incidence of spatial neglect in right brain damage;b) the different emotional reactions that follow left and right brain damage;c) the hemispheric lateralisation of optimistic bias mechanisms;d) the lateralisation of mechanisms that regulate routine and novelty behaviours. We propose that match and mismatch operations are aimed at approximating "free energy", in terms of the free energy principle of decision-making. By approximating "free energy", the match/mismatch TPJ system supports both information seeking to update one's own beliefs and the pleasure of being right in one's own' current choices. This renewed view of the TPJ has relevant clinical implications because the misfunctioning of TPJ-related "match" and "mismatch" circuits in unilateral brain damage can produce low-dimensional deficits of active-inference and predictive coding that can be associated with different neuropsychological disorders.(c) 2022 Elsevier B.V. All rights reserved.
An improved lossless compression method with adaptive band reordering and minimum mean square error prediction was proposed to address the problems of huge data volume of remote sensing images, great pressure on trans...
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An improved lossless compression method with adaptive band reordering and minimum mean square error prediction was proposed to address the problems of huge data volume of remote sensing images, great pressure on transmission and storage and a low compression ratio. This method may determine the optimal band ordering adaptively, and make full use of the ordering correlation to eliminate the image redundancy according to the minimum mean square error criterion. First, it adaptively grouped hyperspectral image bands, and used the minimum spanning tree algorithm for band ordering within each group to enhance the inter-spectral correlation of adjacent bands. Later, it selected the contexts for inter- and intra-spectral prediction adaptively for the bands within the group to remove the redundancy of hyperspectral images. Finally, it conducted binary arithmetic coding of the predicted residuals to remove the statistical redundancy, and complete the lossless compression of hyperspectral images. The test results based on the hyperspectral images of ZY1-02D show that the method in this paper effectively utilizes the intra- and inter-spectral correlations, improves the prediction performance, and outperforms the commonly used compression methods.
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement and the circu...
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Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement and the circumstances in which it takes place remain unclear. This paper presents the first attempt to study perceptual attunement using ecological child -centered audio data. We show that a simple prediction algorithm exhibits perceptual attunement when applied on unrealistic clean audio -book data, but fails to do so when applied on ecologically -valid child -centered data. In the latter scenario, perceptual attunement only emerges when the prediction mechanism is supplemented with inductive biases that force the algorithm to focus exclusively on speech segments while learning speaker-, pitch-, and room -invariant representations. We argue these biases are plausible given previous research on infants and non -human animals. More generally, we show that what our model learns and how it develops through exposure to speech depends exquisitely on the details of the input signal. By doing so, we illustrate the importance of considering ecologically valid input data when modeling language acquisition.
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