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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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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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Dogs or Not Dogs? Examining ACI Authors' Reporting on Animal Participants' Willingness to Engage in Research: A Spotlight on Mediated and Contingent Consent 23

Dogs or Not Dogs? Examining ACI Authors' Reporting on Animal...

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Proceedings of the Tenth International Conference on Animal-Computer Interaction

作者： Eli B. McGraw Cristina Bosco Lisa Brey Christena Nippert-Eng Department of Informatics Luddy School of Informatics Computing and Engineering and the Cognitive Science Program Indiana University USA Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University USA

ISBN: (纸本)9798400716560

How do Animal-Computer Interaction (ACI) researchers working with live animal participants assess the animals’ willingness to participate in their research? In this paper we present the results of a systematic literature review designed to answer this question by examining the Proceedings of the ACM International Animal Computer Interaction Conference. From 2016-2022, these proceedings included 38 full papers that reported results from research with live animal participants. We found 1) only 74% or 28/38 of the papers reported how they assessed animal participants’ willingness to engage during their research, 2) the authors of papers focused on species other than dogs had a much higher rate of providing this information than did the authors of dog-based studies (100% or 12/12 non-dog papers v 62% or 16/26 of dog-based papers), 3) most researchers who addressed the issue of an animal participant’s willingness to engage in the research relied on some form of mediated consent, informed by behavioral observation methods, to do so. However, the researchers focused on non-dog species were much more likely than researchers focused on dogs to include elements of contingent consent in their protocols (75% (9/12) of the non-dog studies v 12% (3/26) of the dog-related studies). We argue that providing each other with more details about our research methods and possibly more fully embracing the principles of contingent consent would further ACI researchers’ existing ethical commitment to our animal participants, increase our adherence to standard scientific research practice, and accelerate the continued development of the field of Animal-Computer Interaction.

关键词： Animal-Computer Interaction animal behavior animal well-being animal-centered design animal-centered ethics contingent consent dogs mediated consent multi-species interaction design reporting practices research methods scientific transparency voluntary engagement

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Getting Situated: Comparative Analysis of Language Models With Experimental Categorization Tasks 44

Getting Situated: Comparative Analysis of Language Models Wi...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Edinger, Andy Goldstone, Robert L. Program in Cognitive Science & Luddy School of Informatics Computing and Engineering Indiana University 1101 E. 10th Street BloomingtonIN47405 United States Program in Cognitive Science Department of Psychological and Brain Sciences Indiana University 1101 E. 10th Street BloomingtonIN47405 United States

Common critiques of natural language processing (NLP) methods cite their lack of multimodal sensory information, claiming an inability to learn situated, action-oriented relations through language alone. Barsalou's (1983) theory of ad hoc categories, which are formed from to achieve goals in real-world scenarios, correspond theoretically to those types of relations with which language models ought to have great difficulty. Recent NLP models have developed dynamic approaches to word representations, where the same word can have different encodings depending on the context in which it appears. Testing these models using categorization tasks with human response data demonstrates that situated properties may be partially captured through semantic analysis. We discuss possible ways in which different notions of situatedness may be distinguished for future development and testing of NLP models. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Semantics

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Cultivating the Garden of Eden

arXiv

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arXiv 2022年

作者： Beer, Randall D. Cognitive Science Program Informatics Dept Luddy School of Informatics Computing and Engineering Indiana University United States Cognitive Science Program Indiana University 3052 Geology Building BloomingtonIN47405 United States

Garden of Eden (GOE) states in cellular automata are grid configurations which have no precursors, that is, they can only occur as initial conditions. Finding individual configurations that minimize or maximize some criterion of interest (e.g., grid size, density, etc.) has been a popular sport in recreational mathematics, but systematic studies of the set of GOEs for a cellular automaton have been rare. This paper presents the current results of an ongoing computational study of GOE configurations in Conway’s Game of Life (GoL) cellular automaton. Specifically, we describe the current status of a map of the layout of GOEs and nonGOEs in density/size space, characterize how the density-dependent structure of the number of precursors varies with increasing grid size as we approach the point where GOEs begin to occur, provide a catalog of all known GOE configurations up to a grid size of 11 × 11, and initiate a study of the structure of the network of constraints that characterize GOE vs. nonGOE configurations. © 2022, CC BY.

关键词：

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Generating reward structures on a parameterized distribution of dynamics tasks

Generating reward structures on a parameterized distribution...

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2021 Conference on Artificial Life, ALIFE 2021

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

In order to make lifelike, versatile learning adaptive in the artificial domain, one needs a very diverse set of behaviors to learn. We propose a parameterized distribution of classic control-style tasks with minimal information shared between tasks. We discuss what makes a task trivial and offer a basic metric, time in convergence, that measures triviality. We then investigate analytic and empirical approaches to generating reward structures for tasks based on their dynamics in order to minimize triviality. Contrary to our expectations, populations evolved on reward structures that incentivized the most stable locations in state space spend the least time in convergence as we have defined it, because of the outsized importance our metric assigns to behavior fine-tuning in these contexts. This work paves the way towards an understanding of which task distributions enable the development of learning. © 2021 ALIFE. All Rights Reserved.

关键词： Multi-task learning

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Human Learners Integrate Visual and Linguistic Information in Cross-Situational Verb Learning 43

Human Learners Integrate Visual and Linguistic Information i...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Zhang, Yayun Amatuni, Andrei Cain, Ellis Wang, Xizi Crandall, David Yu, Chen Department of Psychology University of Texas Austin United States Cognitive Science Program Indiana University Bloomington United States Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Learning verbs is challenging because it is difficult to infer the precise meaning of a verb when there are a multitude of relations that one can derive from a single event. To study this verb learning challenge, we used children’s egocentric view collected from naturalistic toy-play interaction as learning materials and investigated how visual and linguistic information provided in individual naming moments as well as cross-situational information provided from multiple learning moments can help learners resolve this mapping problem using the Human Simulation Paradigm. Our results show that learners benefit from seeing children’s egocentric views compared to third-person observations. In addition, linguistic information can help learners identify the correct verb meaning by eliminating possible meanings that do not belong to the linguistic category. Learners are also able to integrate visual and linguistic information both within and across learning situations to reduce the ambiguity in the space of possible verb meanings. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Toys

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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The global structure of codimension-2 local bifurcations in continuous-time recurrent neural networks

arXiv

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arXiv 2021年

作者： Beer, Randall D. Cognitive Science Program Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

If we are ever to move beyond the study of isolated special cases in theoretical neuroscience, we need to develop more general theories of neural circuits over a given neural model. The present paper considers this challenge in the context of continuous-time recurrent neural networks (CTRNNs), a simple but dynamically-universal model that has been widely utilized in both computational neuroscience and neural networks. Here we extend previous work on the parameter space structure of codimension-1 local bifurcations in CTRNNs to include codimension-2 local bifurcation manifolds. Specifically, we derive the necessary conditions for all generic local codimension-2 bifurcations for general CTRNNs, specialize these conditions to circuits containing from one to four neurons, illustrate in full detail the application of these conditions to example circuits, derive closed-form expressions for these bifurcation manifolds where possible, and demonstrate how this analysis allows us to find and trace several global codimension-1 bifurcation manifolds that originate from the codimension-2 bifurcations. Copyright © 2021, The Authors. All rights reserved.

关键词： Computation theory

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