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36th Annual Meeting of the cognitive science Society, CogSci 2014

作者： Frazer, Alexandra K. O'Séaghdha, Padraig G. Munoz-Avila, Hector Roessler, Nicholas Department of Psychology and Cognitive Science Program Lehigh University United States Computer Science and Engineering and Cognitive Science Program Lehigh University United States

ISBN: (纸本)9780991196708

Incremental learning explanations state that semantic interference is driven by activation levels of competitors. To explore nonsemantic contributions to interference, we examined the combined and separate effects of facilitatory phonological form preparation and semantic relatedness in a blocked cyclic picture naming procedure. Phonological similarity was facilitatory when tested separately, but had little effect when tested with the other conditions. We found about twice as much interference in word sets that shared both meaning and form (e.g., cyclically name puffin, pigeon, and peacock) as in semantic-only sets. Thus, phonological similarity impacted interference when it was combined with a semantic attribute. A computational model that isolated the learning mechanism and eliminated carryover effects simulated this result and additionally showed cumulative interference over naming cycles. Together with other findings from our research group and in the literature, these results suggest that co-activation from a variety of sources can drive interference. © 2014 Proceedings of the 36th Annual Meeting of the cognitive science Society, CogSci 2014. All rights reserved.

关键词： Semantics

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Role of simulation models in understanding the generation of behavior in C. elegans

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Current Opinion in Systems Biology 2019年 13卷 93-101页

作者： Izquierdo, Eduardo J. Cognitive Science Program School of Informatics Computing and Engineering Indiana University United States

There has been a proliferation of studies using simulation models to achieve a system-level understanding of behavior in Caenorhabditis elegans. Here I discuss the different aims of these modeling approaches and review examples of each in the context of locomotion and spatial navigation in the worm. I argue that as the focus shifts from understanding sensorimotor transformations to understanding neuromechanical instantiations for how behaviors are generated, simulation models will become a more integral part of the experiment–theory cycle. I discuss the insights that simulation modeling provides that complement information obtained from experimental discovery methods. © 2018 Elsevier Ltd

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Autolysosomal Dysfunction in Obesity-induced Metabolic Inflammation and Related Disorders

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Current obesity reports 2025年第1期14卷 43页

作者： Lenny Yi Tong Cheong Eka Norfaishanty Saipuljumri Gavin Wen Zhao Loi Jialiu Zeng Chih Hung Lo Lee Kong Chian School of Medicine Nanyang Technological University Singapore 308232 Singapore. Program in Neuroscience & Cognitive Science University of Arizona Tucson AZ 85721 USA. School of Biomedical Sciences The University of Queensland St Lucia QLD 4072 Australia. Department of Biomedical and Chemical Engineering Syracuse University Syracuse NY 13244 USA. jzeng22@syr.edu. Interdisciplinary Neuroscience Program Syracuse University Syracuse NY 13244 USA. jzeng22@syr.edu. Interdisciplinary Neuroscience Program Syracuse University Syracuse NY 13244 USA. clo101@syr.edu. Department of Biology Syracuse University Syracuse NY 13244 USA. clo101@syr.edu.

PURPOSE OF REVIEW:Obesity is a global health crisis affecting individuals across all age groups, significantly increasing the risk of metabolic disorders such as type 2 diabetes (T2D), metabolic dysfunction-associated fatty liver disease (MAFLD), and cardiovascular diseases. The World Health Organization reported in 2022 that 2.5 billion adults were overweight, with 890 million classified as obese, emphasizing the urgent need for effective interventions. A critical aspect of obesity's pathophysiology is meta-inflammation-a chronic, systemic low-grade inflammatory state driven by excess adipose tissue, which disrupts metabolic homeostasis. This review examines the role of autolysosomal dysfunction in obesity-related metabolic disorders, exploring its impact across multiple metabolic organs and evaluating potential therapeutic strategies that target autophagy and lysosomal function. RECENT FINDINGS:Emerging research highlights the importance of autophagy in maintaining cellular homeostasis and metabolic balance. Obesity-induced lysosomal dysfunction impairs the autophagic degradation process, contributing to the accumulation of damaged organelles and toxic aggregates, exacerbating insulin resistance, lipotoxicity, and chronic inflammation. Studies have identified autophagic defects in key metabolic tissues, including adipose tissue, skeletal muscle, liver, pancreas, kidney, heart, and brain, linking autophagy dysregulation to the progression of metabolic diseases. Preclinical investigations suggest that pharmacological and nutritional interventions-such as AMPK activation, caloric restriction mimetics, and lysosomal-targeting compounds-can restore autophagic function and improve metabolic outcomes in obesity models. Autolysosomal dysfunction is a pivotal contributor to obesity-associated metabolic disorders , influencing systemic inflammation and metabolic dysfunction. Restoring autophagy and lysosomal function holds promise as a therapeutic strategy to mitigate obesi

关键词： Autophagy function Lysosomal acidification Metabolic inflammation Neurodegeneration Neuroinflammation Therapeutic strategies

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Does dynamic cursor control gain improve the performance of selection task in wearable computing?

Does dynamic cursor control gain improve the performance of ...

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9th IEEE International Symposium on Wearable Computers, ISWC 2005

作者： Hong, Ji-Young Chae, Haeng-Suk Yoo, Sang Kim, Moon-Ju Han, Kwang-Hee Cognitive Engineering Lab. Grad. Program in Cognitive Science Yonsei Univ. Korea Republic of

ISBN: (纸本)0769524192

Recent research investigating the use of a trackball mouse in a wearable computing context showed that users have difficulty moving the cursor to the correct location [1]. Dynamic gain control may make cursor not to slip out of the target [2]. In this study, we test the use of dynamic gain with a portable trackball mouse in improving users' performance in a pointing task Experimental results reveal that dynamic gain control improves the pointing task performance in terms of speed and index of performance [4] of hitting the targets. © 2005 IEEE.

关键词： Wearable computers

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Multifunctionality in embodied agents: Three levels of neural reuse 40

Multifunctionality in embodied agents: Three levels of neura...

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40th Annual Meeting of the cognitive science Society: Changing Minds, CogSci 2018

作者： Candadai, Madhavun Izquierdo, Eduardo J. Cognitive Science Program Indiana University BloomingtonIN47406 United States Cognitive Science Program School of Informatics Computing and Engineering Indiana University BloomingtonIN47406 United States

ISBN: (纸本)9780991196784

The brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. Although mechanisms that underlie this ability are not well understood, they are largely attributed to neuromodulation. In this work, we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors, in the absence of modulatory mechanisms. Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics. Notably, transient dynamics reuse could only be explained by studying the brain-body-environment system as a whole and not just the brain. The multifunctional agent we present here demonstrates neural reuse at all three levels. © 2018 Proceedings of the 40th Annual Meeting of the cognitive science Society, CogSci 2018. All rights reserved.

关键词： Neural networks

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A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTATION ACROSS ANIMALS AND MACHINES 12

A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Garimella, Manjulata Pak, Denizhan Wood, Justin N. Wood, Samantha M.W. Department of Informatics School of Informatics Computing & Engineering Department of Psychological & Brain Sciences Cognitive Science Program Program in Neuroscience

Newborn brains rapidly learn to solve challenging object perception tasks, including segmenting objects from backgrounds and recognizing objects across new viewing situations. Conversely, modern machine learning (ML) algorithms are "data hungry," requiring more training data than brains to reach similar performance levels. How do we close this learning gap between brains and machines? Here, we introduce a new benchmark-a Newborn Embodied Turing Test (NETT) for object segmentation-in which newborn animals and machines are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning. First, newborn chicks were raised in controlled environments containing a single object rotating on a single background, then their recognition performance was tested across new backgrounds and viewpoints. Second, we performed "digital twin" experiments in which artificial agents were reared and tested in virtual environments that mimicked the rearing and testing conditions of the chicks. We inserted a variety of ML "brains" into the artificial agents and measured whether those algorithms learned common object recognition behavior as chicks. All newborn chicks solved this one-shot object segmentation task, successfully learning background-invariant object representations that generalized across new backgrounds and viewpoints. In contrast, none of the artificial agents solved the task, instead learning background-dependent representations that failed to generalize across new backgrounds and viewpoints. This digital twin design exposes core limitations in current ML algorithms in developing brain-like object perception. Our NETT is publicly available for comparing ML algorithms with newborn chicks. We argue that NETT benchmarks can help researchers build embodied AI systems that learn as efficiently and robustly as newborn brains. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Animals

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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Multi-focus attention network for efficient deep reinforcement learning 31

Multi-focus attention network for efficient deep reinforceme...

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Choi, Jinyoung Lee, Beom-Jin Zhang, Byoung-Tak Interdisciplinary Program in Cognitive Science Seoul National University Korea Republic of School of Computer Science and Engineering Seoul National University Korea Republic of

ISBN: (纸本)9781577357865

Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute the sensory input. Because of this difference, DRL needs vast amount of experience samples to learn. In this paper, we propose a Multi-focus Atten-tion Network (MANet) which mimics human ability to spatially abstract the low-level sensory input into multiple entities and attend to them simultaneously. The proposed method first divides the low-level input into several segments which we refer to as partial states. After this segmentation, parallel attention layers attend to the partial states relevant to solving the task. Our model estimates state-action values using these attended partial states. In our experiments, MANet attains highest scores with significantly less experience samples. Additionally, the model shows higher performance compared to the Deep Q-network and the single attention model as benchmarks. Furthermore, we extend our model to attentive communication model for performing multi-agent cooperative tasks, in multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Benchmarking

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Perpetual Crossers without Sensory Delay: Revisiting the Perceptual Crossing Simulation Studies

Perpetual Crossers without Sensory Delay: Revisiting the Per...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Severino, Gabriel J. Merritt, Haily Cognitive Science Program Indiana University Bloomington Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

We revisit the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition by studying multi-agent real-time interactions. To date, all of these simulation studies have reported that it is practically impossible to evolve artificially a robust behavioral strategy without introducing temporal delays into the simulation. Also, all of the studies report on a single strategy: a perpetually crossing agent pair. Here, we systematically report on the evolutionary success of neural circuits on the perceptual crossing task, with and without sensory delay. We also report on two different strategies in the ensemble of successful solutions, only one of which had been discussed in the literature previously. © Artificial Life Conference Proceedings 2022.

关键词： Delay circuits

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Communicating, interpreting, and executing high-level instructions for human-robot interaction

Communicating, interpreting, and executing high-level instru...

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2011 AAAI Fall Symposium

作者： Trivedi, Nishant Langley, Pat Schermerhorn, Paul Scheutz, Matthias Computer Science and Engineering Arizona State University Tempe AZ 85287 United States Cognitive Science Program Indiana University Bloomington IN 47406 United States

ISBN: (纸本)9781577355458

In this paper, we address the problem of communicating, interpreting, and executing complex yet abstract instructions to a robot team member. This requires specifying the tasks in an unambiguous manner, translating them into operational procedures, and carrying out those procedures in a persistent yet reactive manner. We report our response to these issues, after which we demonstrate their combined use in controlling a mobile robot in a multi-room office setting on tasks similar to those in search-and-rescue operations. We conclude by discussing related research and suggesting directions for future work. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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