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  1. Jos J. Adam & Ron F. Keulen (2004). FMRI Evidence for and Behavioral Evidence Against the Planning–Control Model. Behavioral and Brain Sciences 27 (1):24-24.
    Consistent with the planning–control model, recent fMRI data reveal that the inferior parietal lobe, the frontal lobes, and the basal ganglia are involved in motor planning. Inconsistent with the planning–control model, however, recent behavioral data reveal a spatial repulsion effect, indicating that the visual context surrounding the target can sometimes influence the on-line control of goal-directed action.
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  2. Bryan Adams, Rodney A. Brooks & Brian Scassellati, Humanoid Robots: A New Kind of Tool.
    In his 1923 play R.U.R.: Rossum s Universal Robots, Karel Capek coined robot as a derivative of the Czech robota (forced labor). Limited to work too tedious or dangerous for humans, today s robots weld parts on assembly lines, inspect nuclear plants, and explore other planets. Generally, robots are still far from achieving their fictional counterparts intelligence and flexibility. Humanoid robotics labs worldwide are working on creating robots that are one step closer to science fiction s androids. Building a humanlike (...)
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  3. Nicholas Agar (2012). On the Irrationality of Mind-Uploading: A Rely to Neil Levy. [REVIEW] AI and Society 27 (4):431-436.
    In a paper in this journal, Neil Levy challenges Nicholas Agar’s argument for the irrationality of mind-uploading. Mind-uploading is a futuristic process that involves scanning brains and recording relevant information which is then transferred into a computer. Its advocates suppose that mind-uploading transfers both human minds and identities from biological brains into computers. According to Agar’s original argument, mind-uploading is prudentially irrational. Success relies on the soundness of the program of Strong AI—the view that it may someday be possible to (...)
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  4. Michael Anderson, Circuit Sharing and the Implementation of Intelligent Systems.
    One of the most foundational and continually contested questions in the cognitive sciences is the degree to which the functional organization of the brain can be understood as modular. In its classic formulation, a module was defined as a cognitive sub-system with (all or most of) nine specific properties; the classic module is, among other things, domain specific, encapsulated (i.e. maintains proprietary representations to which other modules have no access), and implemented in dedicated neural substrates. Most of the examinations—and especially (...)
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  5. Michael Anderson, Projects.
    Description: The massive redeployment hypothesis (MRH) is a theory about the functional organization of the human cortex, offering a middle course between strict localization on the one hand, and holism on the other. Central to MRH is the claim that cognitive evolution proceeded in a way analogous to component reuse in software engineering, whereby existing components—originally developed to serve some specific purpose—were used for new purposes and combined to support new capacities, without disrupting their participation in existing programs.
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  6. Ian O. Angell (2010). Science's First Mistake: Delusions in Pursuit of Theory. Bloomsbury Academic.
    because whenever an observer observes, he creates a contingent distinction between what is observed and what is by necessity left unobserved. ...
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  7. Colin M. Angle & Rodney A. Brooks, Small Planetary Rovers.
    We have previously built a small IKg ([Angle 89] and [Brooks 89]) six legged walking robot named Genghis. It was remarkably successful as a testbed to develop walking and learning algorithms. It encouraged us to build a more fully engineered robot with higher performance. We are building two copies of the robot, both 1.6Kg in mass. Their generic name is Attila. Attila has 24 actuators and over 150 sensors, all connected via a local network (the I2C bus) to 11 onboard (...)
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  8. Stuart Armstrong, Anders Sandberg & Nick Bostrom (2012). Thinking Inside the Box: Controlling and Using an Oracle AI. [REVIEW] Minds and Machines 22 (4):299-324.
    There is no strong reason to believe that human-level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed motivation system. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act in (...)
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  9. Peter M. Asaro (2001). Hans Moravec, Robot. Mere Machine to Transcendent Mind, New York, NY: Oxford University Press, Inc., 1999, IX + 227 Pp., $25.00 (Cloth), ISBN 0-19-511630-. [REVIEW] Minds and Machines 11 (1):143-147.
  10. Harald Atmanspacher, Correlates of Perceptive Instabilities in Event-Related Potentials.
    The study of instabilities in perception has attracted much interest in recent decades. The presented investigations focus on electrophysiological correlates of orientation reversals of both ambiguous visual stimuli and alternating non-ambiguous stimuli, representing the two options of the ambiguous version. Based on a refined experimental setup, significant features in the event-related potentials associated with the perception of orientation reversal were found in both cases. Their occipital location, their early occurence (200–250 ms), and their latency difference (50 ms) offer interesting perspectives (...)
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  11. Harald Atmanspacher (2005). Acategoriality as Mental Instability. Journal of Mind and Behavior 26 (3):181.
    Mental representations are based upon categories in which the state of a mental system is stable. Acategorial states, on the other hand, are distinguished by unstable behavior. A refined and compact terminology for the description of categorial and acategorial mental states and their stability properties is introduced within the framework of the theory of dynamical systems. The relevant concepts are illustrated by selected empirical observations in cognitive neuroscience. Alterations of the category of the first person singular and features of creative (...)
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  12. Andrew beedle (1998). Sixteen Years of Artificial Intelligence: Mind Design and Mind Design II. Philosophical Psychology 11 (2):243 – 250.
    John Haugeland's Mind design and Mind design II are organized around the idea that the fundamental idea of cognitive science is that, “intelligent beings are semantic engines — in other words, automatic formal systems with interpretations under which they consistently make sense”. The goal of artificial intelligence research, or the problem of “mind design” as Haugeland calls it, is to develop computers that are in fact semantic engines. This paper canvasses the changes in artificial intelligence research reflected in the different (...)
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  13. Sander Begeer (2005). Book Reviews - Roberto Cordeschi, the Discovery of the Artificial: Behaviour, Mind and Machines Before and Beyond Cybernetics, Dordrecht, the Netherlands: Kluwer Academic Publishers, 2002, XX + 312, ISBN 1-4020-0606-. [REVIEW] Minds and Machines 15 (2):264-268.
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  14. Maria Bittner (2006). Ontology for Human Talk and Thought (Not Robotics). Theoretical Linguistics 32 (1):47-56.
    Hamm, Kamp, and van Lambalgen 2006 (hereafter HLK) propose to relate NL discourse to cognitive representations that also deal with world knowledge, planning, belief revision, etc. Surprisingly, to represent human cognition they use an event calculus "which has found applications in robotics". This comment argues that the robotics-based theory of HLK attributes too much to world knowledge and not enough to the ontology, centering, and other universals of NL semantics. It is also too Anglo-centric to generalize to languages of other (...)
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  15. Margaret Boden (2006). Of Islands and Interactions. Journal of Consciousness Studies 13 (5):53-63.
    John Ziman-- the much-missed-- reminds us that 'no man is an island', and takes us to task for working from an individualistic theoretical base. That 'us' includes nearly all social scientists, and most Anglo-American philosophers too. For sure, it includes cognitive scientists, who theorize people in terms of concepts drawn from cybernetics and/or artificial intelligence. (I'll use the term 'computational concepts' broadly, to cover both types.) Indeed, it's a common complaint that cognitive science is overly individualistic.
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  16. Margaret A. Boden (ed.) (1990). The Philosophy of Artificial Intelligence. Oxford University Press.
    This interdisciplinary collection of classical and contemporary readings provides a clear and comprehensive guide to the many hotly-debated philosophical issues at the heart of artificial intelligence.
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  17. Matthew Botvinick (2012). Commentary: Why I Am Not a Dynamicist. Topics in Cognitive Science 4 (1):78-83.
    The dynamical systems approach in cognitive science offers a potentially useful perspective on both brain and behavior. Indeed, the importation of formal tools from dynamical systems research has already paid off for our field in many ways. However, like some other theoretical perspectives in cognitive science, the dynamical systems approach comes in both moderate or pragmatic and “fundamentalist” varieties (Jones & Love, 2011). In the latter form, dynamical systems theory can rise to some stirring rhetorical heights. However, as argued here, (...)
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  18. William F. Brewer (1999). Perceptual Symbols: The Power and Limitations of a Theory of Dynamic Imagery and Structured Frames. Behavioral and Brain Sciences 22 (4):611-612.
    The perceptual symbol approach to knowledge representation combines structured frames and dynamic imagery. The perceptual symbol approach provides a good account of the representation of scientific models, of some types of naive theories held by children and adults, and of certain reconstructive memory phenomena. The ontological status of perceptual symbols is unclear and this form of representation does not succeed in accounting for all forms of human knowledge.
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  19. Rodney A. Brooks, A Robot That Walks; Emergent Behaviors From a Carefully Evolved Network.
    Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous (...)
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  20. Rodney A. Brooks, How to Build Complete Creatures Rather Than Isolated Cognitive Simulators.
    Artificial Intelligence as a discipline has gotten bogged down in subproblems of intelligence. These subproblems are the result of applying reductionist methods to the goal of creating a complete artificial thinking mind. In Brooks (1987) 1 have argued that these methods will lead us to solving irrelevant problems; interesting as intellectual puzzles, but useless in the long run for creating an artificial being.
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  21. Rodney A. Brooks, Learning to Coordinate Behaviors.
    We describe an algorithm which allows a behavior-based robot to learn on the basis of positive and negative feedback when to activate its behaviors. In accordance with the philosophy of behavior-based robots, the algorithm is completely distributed: each of the behaviors independently tries to find out (i) whether it is relevant (ie. whether it is at all correlated to positive feedback) and (ii) what the conditions are under which it becomes reliable (i.e. the conditions under which i t maximizes the (...)
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  22. Rodney A. Brooks, Prospects for Human Level Intelligence for Humanoid Robots.
    Both direct, and evolved, behavior-based approaches to mobile robots have yielded a number of interesting demonstrations of robots that navigate, map, plan and operate in the real world. The work can best be described as attempts to emulate insect level locomotion and navigation, with very little work on behavior-based non-trivial manipulation of the world. There have been some behavior-based attempts at exploring social interactions, but these too have been modeled after the sorts of social interactions we see in insects. But (...)
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  23. Rodney A. Brooks, The Intelligent Room Project.
    At the MIT Arti cial Intelligence Laboratory we have been working on technologies for an Intelligent Room. Rather than pull people into the virtual world of the computer we are trying to pull the computer out into the real world of people. To do this we are combining robotics and vision technology with speech understanding systems, and agent based architectures to provide ready at hand computation and information services for people engaged in day to day activities, both on their own (...)
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  24. Rodney A. Brooks, The Role of Learning in Autonomous Robots.
    Applications of learning to autonomous agents (simulated or real) have often been restricted to learning a mapping from perceived state of the world to the next action to take. Often this is couched in terms of learning from no previous knowledge. This general case for real autonomous robots is very difficult. In any case, when building a real robot there is usually a lot of a priori knowledge (e.g., from the engineering that went into its design) which doesn’t need to (...)
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  25. Rodney A. Brooks & Liana M. Lorigo, Visually-Guided Obstacle Avoidance in Unstructured Environments.
    This paper presents an autonomous vision-based obstacle avoidance system. The system consists of three independent vision modules for obstacle detection, each of which is computationally simple and uses a di erent criterion for detection purposes. These criteria are based on brightness gradients, RGB Red, Green, Blue color, and HSV Hue, Saturation, Value color, respectively. Selection of which modules are used to command the robot proceeds exclusively from the outputs of the modules themselves. The system is implemented on a small monocular (...)
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  26. Joanna J. Bryson (2010). Why Robot Nannies Probably Won't Do Much Psychological Damage. Interaction Studies 11 (2):196-200.
  27. Joanna J. Bryson (2006). The Attentional Spotlight. Minds and Machines 16 (1):21-28.
    One of the interesting and occasionally controversial aspects of Dennett’s career is his direct involvement in the scientific process. This article describes some of Dennett’s participation on one particular project conducted at MIT, the building of the humanoid robot named Cog. One of the intentions of this project, not to date fully realized, was to test Dennett’s multiple drafts theory of consciousness. I describe Dennett’s involvement and impact on Cog from the perspective of a graduate student. I also describe the (...)
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  28. William C. Charron (1972). "Brain, Mind and Computers," by Stanley L. Jaki. Modern Schoolman 49 (3):270-273.
  29. Yoonsuck Choe (2006). How Neural is the Neural Blackboard Architecture? Behavioral and Brain Sciences 29 (1):72-73.
    The target article does not provide insight into how the proposed neural blackboard architecture can be mapped to known neural structures in the brain. There are theories suggesting that the thalamus may be a good candidate. However, the experimental evidence suggests that the cortex may be involved (if in fact the blackboard is implemented in the brain). Issues arising from such a mapping will be discussed.
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  30. Ronald L. Chrisley (1998). What Might Dynamical Intentionality Be, If Not Computation? Behavioral and Brain Sciences 21 (5):634-635.
    (1) Van Gelder's concession that the dynamical hypothesis is not in opposition to computation in general does not agree well with his anticomputational stance. (2) There are problems with the claim that dynamic systems allow for nonrepresentational aspects of computation in a way in which digital computation cannot. (3) There are two senses of the “cognition is computation” claim and van Gelder argues against only one of them. (4) Dynamical systems as characterized in the target article share problems of universal (...)
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  31. Brian Christian (2011). The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive. Doubleday.
  32. Andy Clark (2008). The Frozen Cyborg: A Reply to Selinger and Engström. [REVIEW] Phenomenology and the Cognitive Sciences 7 (3):343-346.
    Selinger and Engstrom, A moratorium on cyborgs: Computation, cognition and commerce, 2008 (this issue) urge upon us a moratorium on ‘cyborg discourse’. But the argument underestimates the richness and complexity of our ongoing communal explorations. It leans on a somewhat outdated version of the machine metaphor (exemplified perhaps by a frozen 1970’s Cyborg). The modern cyborg, informed by an evolving computational model of mind, can play a positive role in the critical discussions that Selinger and Engstrom seek.
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  33. Kenneth M. Colby, Peter M. Colby & Robert J. Stoller (1990). Dialogues in Natural Language with Guru, a Psychologic Inference Engine. Philosophical Psychology 3 (2 & 3):171 – 186.
    The aim of this project was to explore the possibility of constructing a psychologic inference engine that might enhance introspective self-awareness by delivering inferences about a user based on what he said in interactive dialogues about his closest opposite-sex relation. To implement this aim, we developed a computer program (guru) with the capacity to simulate human conversation in colloquial natural language. The psychologic inferences offered represent the authors' simulations of their commonsense psychology responses to expected user-input expressions. The heuristics of (...)
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  34. Rodney M. J. Cotterill (2000). Movement, Acquisition of Novel Context-Specific Reflexes and Amyotrophic Lateral Sclerosis: Reply to Jesse Prinz. [REVIEW] Brain and Mind 1 (2):257-263.
  35. Hans F. M. Crombag (1993). On the Artificiality of Artificial Intelligence. Artificial Intelligence and Law 2 (1):39-49.
    In this article the question is raised whether artificial intelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificial intelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificial intelligence of the connectionist kind may have psychological relevance, not because the brain is a neural network, but because connectionist networks (...)
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  36. Lisa Damm (2011). Moral Machines: Teaching Robots Right From Wrong. Philosophical Psychology 25 (1):149 - 153.
  37. Pierre De Loor, Kristen Manac’H. & Jacques Tisseau (2009). Enaction-Based Artificial Intelligence: Toward Co-Evolution with Humans in the Loop. [REVIEW] Minds and Machines 19 (3):319-343.
    This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate (...)
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  38. Aurea Anguera de Sojo, Juan Ares, Juan A. Lara, David Lizcano, María A. Martínez & Juan Pazos (2013). Turing and the Serendipitous Discovery of the Modern Computer. Foundations of Science 18 (3):545-557.
    In the centenary year of Turing’s birth, a lot of good things are sure to be written about him. But it is hard to find something new to write about Turing. This is the biggest merit of this article: it shows how von Neumann’s architecture of the modern computer is a serendipitous consequence of the universal Turing machine, built to solve a logical problem.
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  39. Manuel de Vega, Arthur M. Glenberg & Arthur C. Graesser (eds.) (2008). Symbols and Embodiment: Debates on Meaning and Cognition. Oxford University Press.
    Cognitive scientists have a variety of approaches to studying cognition: experimental psychology, computer science, robotics, neuroscience, educational psychology, philosophy of mind, and psycholinguistics, to name but a few. In addition, they also differ in their approaches to cognition - some of them consider that the mind works basically like a computer, involving programs composed of abstract, amodal, and arbitrary symbols. Others claim that cognition is embodied - that is, symbols must be grounded on perceptual, motoric, and emotional experience. The existence (...)
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  40. Daniel Dennett, Cog as a Thought Experiment.
    In her presentation at the Monte Verità workshop, Maja Mataric showed us a videotape of her robots cruising together through the lab, and remarked, aptly: "They're flocking, but that's not what they think they're doing." This is a vivid instance of a phenomenon that lies at the heart of all the research I learned about at Monte Verità: the execution of surprisingly successful "cognitive" behaviors by systems that did not explicitly represent, and did not need to explicitly represent, what they (...)
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  41. Eric Dietrich (2001). It Does So. [REVIEW] AI Magazine 22 (4):141-144.
    Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. But all of them come down to one simple observation: humans seem a lot smarter that computers -- not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. (Actually, I think there are deeper (...)
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  42. Eric Dietrich (2001). It Does So: Review of Jerry Fodor, The Mind Doesn't Work That Way. [REVIEW] AI Magazine 22 (4):121-24.
    Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. But all of them come down to one simple observation: humans seem a lot smarter that computers -- not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. (Actually, I think there are deeper (...)
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  43. Gordana Dodig Crnkovic (2010). The Cybersemiotics and Info-Computationalist Research Programmes. Entropy 12 (4):878-901.
    Both Cybersemiotics and Info-computationalist research programmes represent attempts to unify understanding of information, knowledge and communication. The first one takes into account phenomenological aspects of signification which are insisting on the human experience "from within". The second adopts solely the view "from the outside" based on scientific practice, with an observing agent generating inter-subjective knowledge in a research community. The process of knowledge production, embodied into networks of cognizing agents interacting with the environment and developing through evolution is studied on (...)
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  44. Eli Dresner (2012). Turing, Matthews and Millikan: Effective Memory, Dispositionalism and Pushmepullyou Mental States. International Journal of Philosophical Studies 20 (4):461-472.
    Abstract In the first section of the paper I present Alan Turing?s notion of effective memory, as it appears in his 1936 paper ?On Computable Numbers, With an Application to The Entscheidungsproblem?. This notion stands in surprising contrast with the way memory is usually thought of in the context of contemporary computer science. Turing?s view (in 1936) is that for a computing machine to remember a previously scanned string of symbols is not to store an internal symbolic image of this (...)
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  45. Jitesh Dundas & Maurice Ling, Higher Level Intelligence in Machines.
    here has been a large number of studies in neurological sciences on how human brain works, especially in reading and parallel information processing. So I think this statement is really sweeping. Perhaps it is better to knowledge the abilities of human brains and to comment on the limitations of the human brain. The book “Adapt” by Tim Hartford advocates micro-step changes. An important aspect in this area is to understand the processes involved behind the scenes so that it gives us (...)
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  46. Dominika Dzwonkowska (2013). The Machine Question: Critical Perspectives on AI, Robots, and Ethics. By David J. Gunkel. International Philosophical Quarterly 53 (1):91-93.
  47. Nicholas Everitt (2008). Minds and Computers: An Introduction to AI, by Matt Carter. Philosophy Now 68:41-42.
  48. Kerstin Fischer, Kilian Foth, Katharina J. Rohlfing & Britta Wrede (2011). Mindful Tutors: Linguistic Choice and Action Demonstration in Speech to Infants and a Simulated Robot. Interaction Studies 12 (1):134-161.
    It has been proposed that the design of robots might benefit from interactions that are similar to caregiver-child interactions, which is tailored to children's respective capacities to a high degree. However, so far little is known about how people adapt their tutoring behaviour to robots and whether robots can evoke input that is similar to child-directed interaction. The paper presents detailed analyses of speakers' linguistic behaviour and non-linguistic behaviour, such as action demonstration, in two comparable situations: In one experiment, parents (...)
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  49. Brian Fisher, Tera Marie Green & Richard Arias-Hernández (2011). Visual Analytics as a Translational Cognitive Science. Topics in Cognitive Science 3 (3):609-625.
    Visual analytics is a new interdisciplinary field of study that calls for a more structured scientific approach to understanding the effects of interaction with complex graphical displays on human cognitive processes. Its primary goal is to support the design and evaluation of graphical information systems that better support cognitive processes in areas as diverse as scientific research and emergency management. The methodologies that make up this new field are as yet ill defined. This paper proposes a pathway for development of (...)
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  50. Keith Frankish (2009). Systems and Levels: Dual-System Theories and the Personal-Subpersonal Distinction. In Jonathan Evans & Keith Frankish (eds.), In Two Minds: Dual Processes and Beyond. Oup Oxford.
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