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  1. An alternative to working on machine consciousness.Aaron Sloman - 2010 - International Journal of Machine Consciousness 2 (1):1-18.
    This paper extends three decades of work arguing that researchers who discuss consciousness should not restrict themselves only to (adult) human minds, but should study (and attempt to model) many kinds of minds, natural and artificial, thereby contributing to our understanding of the space containing all of them. We need to study what they do or can do, how they can do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets of requirements (...)
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  • P3 and (de)activation.Walton T. Roth & Judith M. Ford - 1988 - Behavioral and Brain Sciences 11 (3):393.
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  • Is the P300 component a manifestation of context updating?Emanuel Donchin & Michael G. H. Coles - 1988 - Behavioral and Brain Sciences 11 (3):357.
    To understand the endogenous components of the event-related brain potential (ERP), we must use data about the components' antecedent conditions to form hypotheses about the information-processing function of the underlying brain activity. These hypotheses, in turn, generate testable predictions about the consequences of the component. We review the application of this approach to the analysis of the P300 component. The amplitude of the P300 is controlled multiplicatively by the subjective probability and the task relevance of the eliciting events, whereas its (...)
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  • Is the P300 component a manifestation of context updating?Emanuel Donchin & Michael G. H. Coles - 1988 - Behavioral and Brain Sciences 11 (3):357.
    To understand the endogenous components of the event-related brain potential (ERP), we must use data about the components' antecedent conditions to form hypotheses about the information-processing function of the underlying brain activity. These hypotheses, in turn, generate testable predictions about the consequences of the component. We review the application of this approach to the analysis of the P300 component. The amplitude of the P300 is controlled multiplicatively by the subjective probability and the task relevance of the eliciting events, whereas its (...)
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  • Event-related potentials and cognition: A critique of the context updating hypothesis and an alternative interpretation of P3.Rolf Verleger - 1988 - Behavioral and Brain Sciences 11 (3):343.
    P3 is the most prominent of the electrical potentials of the human electroencephalogram that are sensitive to psychological variables. According to the most influential current hypothesis about its psychological significance [E. Donchin's], the “context updating” hypothesis, P3 reflects the updating of working memory. This hypothesis cannot account for relevant portions of the available evidence and it entails some basic contradictions. A more general formulation of this hypothesis is that P3 reflects the updating of expectancies. This version implies that P3-evoking stimuli (...)
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  • Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up.Matej Hoffmann & Rolf Pfeifer - 2018 - In Albert Newen, Leon De Bruin & Shaun Gallagher (eds.), The Oxford Handbook of 4E Cognition. Oxford: Oxford University Press.
    A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...)
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  • Cognitive gadgets and cognitive priors.Gian Domenico Iannetti & Giorgio Vallortigara - 2019 - Behavioral and Brain Sciences 42.
    Some of the foundations of Heyes’ radical reasoning seem to be based on a fractional selection of available evidence. Using an ethological perspective, we argue against Heyes’ rapid dismissal of innate cognitive instincts. Heyes’ use of fMRI studies of literacy to claim that culture assembles pieces of mental technology seems an example of incorrect reverse inferences and overlap theories pervasive in cognitive neuroscience.
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  • Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • Autonomous Machine Agency.Don Berkich - 2002 - Dissertation, University of Massachusetts Amherst
    Is it possible to construct a machine that can act of its own accord? There are a number of skeptical arguments which conclude that autonomous machine agency is impossible. Yet if autonomous machine agency is impossible, then serious doubt is cast on the possibility of autonomous human action, at least on the widely held assumption that some form of materialism is true. The purpose of this dissertation is to show that autonomous machine agency is possible, thereby showing that the autonomy (...)
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  • The construction of 'reality' in the robot: Constructivist perspectives on situated artificial intelligence and adaptive robotics. [REVIEW]Tom Ziemke - 2001 - Foundations of Science 6 (1-3):163-233.
    This paper discusses different approaches incognitive science and artificial intelligenceresearch from the perspective of radicalconstructivism, addressing especially theirrelation to the biologically based theories ofvon Uexküll, Piaget as well as Maturana andVarela. In particular recent work in New AI and adaptive robotics on situated and embodiedintelligence is examined, and we discuss indetail the role of constructive processes asthe basis of situatedness in both robots andliving organisms.
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  • A stroll through the worlds of robots and animals: Applying Jakob von Uexkülls theory of meaning to adaptive robots and artificial life.Tom Ziemke & Noel E. Sharkey - 2001 - Semiotica 2001 (134).
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  • Socially robotic: making useless machines.Ceyda Yolgormez & Joseph Thibodeau - 2022 - AI and Society 37 (2):565-578.
    As robots increasingly become part of our everyday lives, questions arise with regards to how to approach them and how to understand them in social contexts. The Western history of human–robot relations revolves around competition and control, which restricts our ability to relate to machines in other ways. In this study, we take a relational approach to explore different manners of socializing with robots, especially those that exceed an instrumental approach. The nonhuman subjects of this study are built to explore (...)
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  • How do local reverberations achieve global integration?J. J. Wright - 1995 - Behavioral and Brain Sciences 18 (4):644-645.
    Amit's Hebbian model risks being overexplanatory, since it does not depend on specific physiological modelling of cortical ANNs, but concentrates on those phenomena which are modelled by a large class of ANNs. While offering a strong demonstration of the presence of Hebb's “cell assemblies,” it does not offer an equal account of Hebb's “phase sequence” concept.
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  • Grasping schemas is (are) difficult.H. T. A. Whiting - 1987 - Behavioral and Brain Sciences 10 (3):450-451.
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  • Connectionist learning and the challenge of real environments.Mark Weaver & Stephen Kaplan - 1990 - Behavioral and Brain Sciences 13 (3):510-511.
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  • Schemas and bridging gaps in the behavioral and brain sciences.Johan P. Wagemans - 1987 - Behavioral and Brain Sciences 10 (3):449-450.
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  • Schema theory: A new approach?W. von Seelen - 1987 - Behavioral and Brain Sciences 10 (3):448-449.
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  • Novelty and the P3.Marinus N. Verbaten - 1988 - Behavioral and Brain Sciences 11 (3):398.
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  • From epistemology to P3-ology.Rolf Verleger - 1988 - Behavioral and Brain Sciences 11 (3):399.
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  • Schemata and representational constraints.Cees van Leeuwen - 1987 - Behavioral and Brain Sciences 10 (3):448-448.
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  • Connectionist models learn what?Timothy van Gelder - 1990 - Behavioral and Brain Sciences 13 (3):509-510.
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  • Robot with slime brains.Soichiro Tsuda - 2009 - Technoetic Arts 7 (2):133-140.
    Despite the exponential progress in computing power of digital computers, the development of lifelike cognitive systems appears has not yet reached the complexity of the simplest kinds of organisms. This may be explained by the lack of robustness of digital computers due to the requirements of structural programmability in the conventional computing architectures. In contrast, biological systems appear to operate in a different mode of information processing. In order to approach more lifelike artificial cognitive systems, the integration of natural and (...)
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  • Schemas: Not yet an interlingua for the brain sciences.John K. Tsotsos - 1987 - Behavioral and Brain Sciences 10 (3):447-448.
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  • The Cambrian Explosion and the Origins of Embodied Cognition.Michael Trestman - 2013 - Biological Theory 8 (1):80-92.
    Around 540 million years ago there was a sudden, dramatic adaptive radiation known as the Cambrian Explosion. This event marked the origin of almost all of the phyla (major lineages characterized by fundamental body plans) of animals that would ever live on earth, as well the appearance of many notable features such as rigid skeletons and other hard parts, complex jointed appendages, eyes, and brains. This radical evolutionary event has been a major puzzle for evolutionary biologists since Darwin, and while (...)
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  • Advances in neural network theory.Gérard Toulouse - 1990 - Behavioral and Brain Sciences 13 (3):509-509.
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  • Simple inference heuristics versus complex decision machines.Peter M. Todd - 1999 - Minds and Machines 9 (4):461-477.
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  • Connectionist models: Too little too soon?William Timberlake - 1990 - Behavioral and Brain Sciences 13 (3):508-509.
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  • Representation Recovers Information.Chris Thornton - 2009 - Cognitive Science 33 (8):1383-1412.
    Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use (...)
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  • Brave mobots use representation: Emergence of representation in fight-or-flight learning. [REVIEW]Chris Thornton - 1997 - Minds and Machines 7 (4):475-494.
    The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provide a model of the development of a fight-or-flight response in a simulated agent. The modelled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structures which can be given a representational interpretation. It also shows how these may form the infrastructure for (...)
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  • The computing frog.G. Székely - 1987 - Behavioral and Brain Sciences 10 (3):446-446.
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  • The Evolutionary Origins of Consciousness.Takayuki Suzuki - 2022 - Annals of the Japan Association for Philosophy of Science 31:55-73.
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  • Problems of extension, representation, and computational irreducibility.Patrick Suppes - 1990 - Behavioral and Brain Sciences 13 (3):507-508.
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  • Where the Smart Things Are: Social Machines and the Internet of Things.Paul Smart, Aastha Madaan & Wendy Hall - 2019 - Phenomenology and the Cognitive Sciences 18 (3):551-575.
    The emergence of large-scale social media systems, such as Wikipedia, Facebook, and Twitter, has given rise to a new multi-disciplinary effort based around the concept of social machines. For the most part, this research effort has limited its attention to the study of Web-based systems. It has also, perhaps unsurprisingly, tended to highlight the social scientific relevance of such systems. The present paper seeks to expand the scope of the social machine research effort to encompass the Internet of Things. One (...)
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  • There is more to learning then meeth the eye.Noel E. Sharkey - 1990 - Behavioral and Brain Sciences 13 (3):506-507.
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  • Variants of expectancy and subjective probability in P300 research.Roland W. Scholz - 1988 - Behavioral and Brain Sciences 11 (3):396.
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  • Neither context updating nor context closure corresponds closely to human performance concepts.Andries F. Sanders & Wilfried Collet - 1988 - Behavioral and Brain Sciences 11 (3):395.
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  • El método de la cognición incorporada / The method embodied cognition.Rómulo Sanmartin - 2013 - Sophia. Colección de Filosofía de la Educación 14:80-125.
    La cognición incorporada entra en la visión epistemológica del realismo mínimo, es decir de cómo es el mundo independientemente de nosotros. Aquel enfoque implica la participación activa del sujeto, evitando el regreso al aristotelismo, con la adecuación del sujeto al objeto. El sujeto antes que inferir, que es un acto de impresión cognitiva, se informa, por lo que las entradas, a través de los sentidos, preforman el conocer. La metodología que sostiene y describe este proceso de entrada e inferencia, se (...)
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  • Event-related potentials and psychological explanation.Michael D. Rugg - 1988 - Behavioral and Brain Sciences 11 (3):394.
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  • The P300 event-related potentials: A one-humped dromedary's saddle on a two-humped camel.Frank Rösler - 1988 - Behavioral and Brain Sciences 11 (3):392.
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  • Reverberations of Hebbian thinking.Josef P. Rauschecker - 1995 - Behavioral and Brain Sciences 18 (4):642-643.
    Cortical reverberations may induce synaptic changes that underlie developmental plasticity as well as long-term memory. They may be especially important for the consolidation of synaptic changes. Reverberations in cortical networks should have particular significance during development, when large numbers of new representations are formed. This includes the formation of representations across different sensory modalities.
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  • How to decide whether a neural representation is a cognitive concept?Maartje E. J. Raijmakers & Peter C. M. Molenaar - 1995 - Behavioral and Brain Sciences 18 (4):641-642.
    A distinction should be made between the formation of stimulus-driven associations and cognitive concepts. To test the learning mode of a neural network, we propose a simple and classic input-output test: the discrimination shift task. Feed-forward PDP models appear to form stimulus-driven associations. A Hopfield network should be extended to apply the test.
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  • The analysis of the learning needs to be deeper.John E. Rager - 1990 - Behavioral and Brain Sciences 13 (3):505-506.
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  • Has the P300 been cost effective?Patrick Rabbitt - 1988 - Behavioral and Brain Sciences 11 (3):390.
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  • Schema theory: A broadening viewpoint.Tang Yi Qun - 1987 - Behavioral and Brain Sciences 10 (3):446-447.
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  • Local or transcortical assemblies? Some evidence from cognitive neuroscience.Friedemann Pulvermüller & Hubert Preissl - 1995 - Behavioral and Brain Sciences 18 (4):640-641.
    Amit defines cell assemblies aslocal cortical neuron populationswith strong internal connections. However, Hebb himself proposed that cell assemblies are distributed over different cortical areas (nonlocal ortranscortical assemblies). We review evidence from cognitive neuroscience and neuropsychology supporting the assumption that cell assemblies are transcortical.
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  • The implications of an externalist theory of rule-following behavior for robot cognition.Diane Proudfoot - 2004 - Minds and Machines 14 (3):283-308.
    Given (1) Wittgensteins externalist analysis of the distinction between following a rule and behaving in accordance with a rule, (2) prima facie connections between rule-following and psychological capacities, and (3) pragmatic issues about training, it follows that most, even all, future artificially intelligent computers and robots will not use language, possess concepts, or reason. This argument suggests that AIs traditional aim of building machines with minds, exemplified in current work on cognitive robotics, is in need of substantial revision.
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  • Beyond persons: extending the personal/subpersonal distinction to non-rational animals and artificial agents.Manuel de Pinedo-Garcia & Jason Noble - 2008 - Biology and Philosophy 23 (1):87-100.
    The distinction between personal level explanations and subpersonal ones has been subject to much debate in philosophy. We understand it as one between explanations that focus on an agent’s interaction with its environment, and explanations that focus on the physical or computational enabling conditions of such an interaction. The distinction, understood this way, is necessary for a complete account of any agent, rational or not, biological or artificial. In particular, we review some recent research in Artificial Life that pretends to (...)
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  • Interdependence as the key for an ethical artificial autonomy.Filippo Pianca & Vieri Giuliano Santucci - forthcoming - AI and Society:1-15.
    Currently, the autonomy of artificial systems, robotic systems in particular, is certainly one of the most debated issues, both from the perspective of technological development and its social impact and ethical repercussions. While theoretical considerations often focus on scenarios far beyond what can be concretely hypothesized from the current state of the art, the term autonomy is still used in a vague or too general way. This reduces the possibilities of a punctual analysis of such an important issue, thus leading (...)
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  • Realistic neural nets need to learn iconic representations.W. A. Phillips, P. J. B. Hancock & L. S. Smith - 1990 - Behavioral and Brain Sciences 13 (3):505-505.
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  • The problems of cognitive dynamical models.Jean Petitot - 1995 - Behavioral and Brain Sciences 18 (4):640-640.
    Amit's “Attractor Neural Network” perspective on cognition raises difficult technical problems already met by prior dynamical models. This commentary sketches briefly some of them concerning the internal topological structure of attractors, the constituency problem, the possibility of activating simultaneously several attractors, and the different kinds of dynamical structures one can use to model brain activity: point attractors, strange attractors, synchronized arrays of oscillators, synfire chains, and so forth.
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