13 found
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  1.  53
    On Computational Explanations.Anna-Mari Rusanen & Otto Lappi - 2016 - Synthese 193 (12):3931-3949.
    Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: What type of explanations they are, in what sense computational explanations are explanatory and to what extent they involve a special, “independent” (...)
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  2.  29
    Systematic Observation of an Expert Driver's Gaze Strategy—An On-Road Case Study.Otto Lappi, Paavo Rinkkala & Jami Pekkanen - 2017 - Frontiers in Psychology 8.
  3.  4
    The Racer’s Brain – How Domain Expertise is Reflected in the Neural Substrates of Driving.Otto Lappi - 2015 - Frontiers in Human Neuroscience 9.
  4.  4
    Flow Experiences During Visuomotor Skill Acquisition Reflect Deviation From a Power-Law Learning Curve, but Not Overall Level of Skill.Benjamin Ultan Cowley, Jussi Palomäki, Tuisku Tammi, Roosa Frantsi, Ville-Pekka Inkilä, Noora Lehtonen, Pasi Pölönen, Juha Vepsäläinen & Otto Lappi - 2019 - Frontiers in Psychology 10.
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  5.  5
    An Information Semantic Account of Scientific Models.Anna-Mari Rusanen & Otto Lappi - 2012 - In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer. pp. 315--327.
  6.  6
    Editorial: High Performance Cognition: Information-Processing in Complex Skills, Expert Performance, and Flow.Benjamin Ultan Cowley, Frederic Dehais, Stephen Fairclough, Alexander John Karran, Jussi Palomäki & Otto Lappi - 2020 - Frontiers in Psychology 11.
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  7.  52
    Models as Information Carrying Artifacts.Otto Lappi & Anna-Mari Rusanen - unknown
    In science, models are used in many different ways: to test empirical hypotheses, to help in theory formation, to visualize data, and so on. Scientists construct and study the behavior of models, and compare this to observed behavior of a target system. We propose that for this to be possible models must carry information about their targets. When models are viewed as information carrying entities, this property can be used as a foundation for a representational theory of models. This account (...)
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  8.  10
    Qualitative Quantitative and Experimental Concept Possession, Criteria for Identifying Conceptual Change in Science Education.Otto Lappi - 2013 - Science & Education 22 (6):1347-1359.
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  9. Turing Machines and Causal Mechanisms in Cognitive Science.Otto Lappi & Anna-Mari Rusanen - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press. pp. 224--239.
     
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  10.  17
    The Racer's Mind—How Core Perceptual-Cognitive Expertise Is Reflected in Deliberate Practice Procedures in Professional Motorsport.Otto Lappi - 2018 - Frontiers in Psychology 9.
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  11.  5
    Effect of Meditative Movement on Affect and Flow in Qigong Practitioners.Pasi Pölönen, Otto Lappi & Mari Tervaniemi - 2019 - Frontiers in Psychology 10.
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  12.  57
    Scientific Models as Information Carrying Artifacts.Anna-Mari Rusanen & Otto Lappi - unknown
    We present an information theoretic account of models as scientific representations, where scientific models are understood as information carrying artifacts. We propose that the semantics of models should be based on this information coupling of the model to the world. The information theoretic account presents a way of avoiding the need to refer to agents' intentions as constitutive of the semantics of scientific representations, and it provides a naturalistic account of model semantics, which can deal with the problems of asymmetry, (...)
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  13.  1
    Looking at the Road When Driving Around Bends: Influence of Vehicle Automation and Speed.Damien Schnebelen, Otto Lappi, Callum Mole, Jami Pekkanen & Franck Mars - 2019 - Frontiers in Psychology 10.
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