Cognition In The Wild
01 Jan 2016-
TL;DR: The cognition in the wild is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for reading cognition in the wild. Maybe you have knowledge that, people have look hundreds times for their favorite books like this cognition in the wild, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. cognition in the wild is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the cognition in the wild is universally compatible with any devices to read.
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TL;DR: The authors argue that the affordances an environment offers to an animal are dependent on the skills the animal possesses and that the landscape of affordances we inhabit as humans is very rich and resourceful.
Abstract: How broad is the class of affordances we can perceive? Affordances (Gibson, 1979/1986) are possibilities for action provided to an animal by the environment—by the substances, surfaces, objects, and other living creatures that surround it. A widespread assumption has been that affordances primarily relate to motor action—to locomotion and manual behaviors such as reaching and grasping. We propose an account of affordances according to which the concept of affordances has a much broader application than has hitherto been supposed. We argue that the affordances an environment offers to an animal are dependent on the skills the animal possesses. By virtue of our many abilities, the landscape of affordances we inhabit as humans is very rich and resourceful.
628 citations
Cites methods from "Cognition In The Wild"
...Researchers concerned with the distributed nature of cognitive processes have made this observation as well, using a very different conceptual framework to describe it (Hutchins, 1995; Perry, 2010)....
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TL;DR: Situative analyses include hypotheses about principles of coordination that support communication and reasoning in activity systems, including construction of meaning and understanding as discussed by the authors, which is a program of research in the learning sciences that I call "situative".
Abstract: This chapter discusses a program of research in the learning sciences that I call “situative.” The defining characteristic of a situative approach is that instead of focusing on individual learners, the main focus of analysis is on activity systems : complex social organizations containing learners, teachers, curriculum materials, software tools, and the physical environment. Over the decades, many psychologists have advocated a study of these larger systems (Dewey, 1896, 1929/1958; Lewin, 1935, 1946/1997; Mead, 1934; Vygotsky, 1987), although they remained outside the mainstream of psychology, which instead focused on individuals. Situative analyses include hypotheses about principles of coordination that support communication and reasoning in activity systems, including construction of meaning and understanding. Other terms for the perspective I refer to as situative include sociocultural psychology (Cole, 1996; Rogoff, 1995), activity theory (Engestrom, 1993; 1999), distributed cognition (Hutchins, 1995a), and ecological psychology (Gibson, 1979; Reed, 1996). I use the term “situative” because I was introduced to the perspective by scholars who referred to their perspective as situated action (Suchman, 1985), situated cognition (Lave, 1988), or situated learning (Lave & Wenger, 1991). I prefer the term “situative,” a modifier of “perspective,” “analysis,” or “theory,” to “situated,” used to modify “action,” “cognition,” or “learning,” because the latter adjective invites a misconception: that some instances of action, cognition, or learning are situated and others are not. During the 1980s and 1990s these scholars and others provided analyses in which concepts of cognition and learning are relocated at the level of activity systems.
545 citations
Cites background or methods from "Cognition In The Wild"
...Other terms for the perspective I refer to as situative include sociocultural psychology (Cole, 1996; Rogoff, 1995), activity theory (Engeström, 1993 ; 1999), distributed cognition (Hutchins, 1995a), and ecological psychology (Gibson, 1979; Reed, 1996)....
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...Material and other informational resources also contribute to the construction of information, in ways investigated in research on distributed cognition (e.g., Hutchins, 1995a) and in social studies of science (e.g., Pickering, 1995)....
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...The problemsolving processes of the lab were distributed throughout the cognitive system, which comprised both the researchers and the cognitive artifacts that they use (cf. Hutchins, 1995a)....
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...For example, Hutchins (1995b) studied remembering in the activity of flying commercial airplanes and gave an analysis of remembering to change the settings of flaps and slats during a descent as an accomplishment of the activity system of the cockpit, including the two pilots along with instruments…...
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...Other terms for the perspective I refer to as situative include sociocultural psychology (Cole, 1996; Rogoff, 1995), activity theory (Engeström, 1993 ; 1999), distributed cognition (Hutchins, 1995a), and ecological psychology (Gibson, 1979; Reed, 1996)....
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TL;DR: This work argues that advances in digital technologies increase innovation network connectivity by reducing communication costs and increasing its reach and scope and increase the speed and scope of digital convergence, which increases network knowledge heterogeneity and need for integration.
Abstract: The increased digitization of organizational processes and products poses new challenges for understanding product innovation. It also opens new horizons for information systems research. We analyse how ongoing pervasive digitization of product innovation reshapes knowledge creation and sharing in innovation networks. We argue that advances in digital technologies 1 increase innovation network connectivity by reducing communication costs and increasing its reach and scope and 2 increase the speed and scope of digital convergence, which increases network knowledge heterogeneity and need for integration. These developments, in turn, stretch existing innovation networks by redistributing control and increasing the demand for knowledge coordination across time and space presenting novel challenges for knowledge creation, assimilation and integration. Based on this foundation, we distinguish four types of emerging innovation networks supported by digitalization: 1 project innovation networks; 2 clan innovation networks; 3 federated innovation networks; and 4 anarchic innovation networks. Each network involves different cognitive and social translations - or ways of identifying, sharing and assimilating knowledge. We describe the role of five novel properties of digital infrastructures in supporting each type of innovation network: representational flexibility, semantic coherence, temporal and spatial traceability, knowledge brokering and linguistic calibration. We identify several implications for future innovation research. In particular, we focus on the emergence of anarchic network forms that follow full-fledged digital convergence founded on richer innovation ontologies and epistemologies calling to critically re-examine the nature and impact of modularization for innovation.
418 citations
Cites background from "Cognition In The Wild"
...Social translations are also involved in the interactions between people and artefacts during innovation (Hutchins, 1995)....
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TL;DR: A blind IQA model is proposed, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison and is not only much more natural than the regression-based models, but also robust to the small sample size problem.
Abstract: This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model’s effectiveness, efficiency, and robustness.
360 citations
TL;DR: The current state of the descriptive information-processing model, and its relation to the major topics in empirical aesthetics today, including the nature of aesthetic emotions, the role of context, and the neural and evolutionary foundations of art and aesthetics are reviewed.
Abstract: About a decade ago, psychology of the arts started to gain momentum owing to a number of drives: technological progress improved the conditions under which art could be studied in the laboratory, neuroscience discovered the arts as an area of interest, and new theories offered a more comprehensive look at aesthetic experiences. Ten years ago, Leder, Belke, Oeberst, and Augustin (2004) proposed a descriptive information-processing model of the components that integrate an aesthetic episode. This theory offered explanations for modern art's large number of individualized styles, innovativeness, and for the diverse aesthetic experiences it can stimulate. In addition, it described how information is processed over the time course of an aesthetic episode, within and over perceptual, cognitive and emotional components. Here, we review the current state of the model, and its relation to the major topics in empirical aesthetics today, including the nature of aesthetic emotions, the role of context, and the neural and evolutionary foundations of art and aesthetics.
329 citations
Cites background from "Cognition In The Wild"
...By highlighting the role of contextual factors on aesthetic experience, themodelwas alignedwith the growing realization that cognition is contextually situated (Clark, 1997; Hutchins, 1995), and with evidence showing that presentation format influences interest and liking ratings of artworks, even though it has little effect on formal features, such as complexity or composition (Locher et al....
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...…of contextual factors on aesthetic experience, themodelwas alignedwith the growing realization that cognition is contextually situated (Clark, 1997; Hutchins, 1995), and with evidence showing that presentation format influences interest and liking ratings of artworks, even though it has little…...
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References
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TL;DR: The authors discuss the development of the Beat Machine and how creative constraints and opportunities were introduced by the particularities of low-cost microprocessors and associated electronics.
Abstract: The Beat Machine is a handheld music synthesizer and sequencer. The authors discuss the development of the Beat Machine and how creative constraints and opportunities were introduced by the particularities of low-cost microprocessors and associated electronics. The discussion is framed as an exemplar of Kare Poulsgaard's concept of enactive individuation, a framework for relating material engagement to digital design and fabrication. In reflecting on the design and making of the Beat Machine the authors connect this framework with more established notions of creative interaction and the affordances of digital media.
3 citations
01 Jan 2019
TL;DR: In this article, Helms describes how imagination breaks down in complex social situations where multiple individual perspectives must be imagined simultaneously, and argues that characters rely on the empathic connection they have with others, and overconfidence in that empathy leads to misreading.
Abstract: Using Shakespeare’s Much Ado About Nothing, Helms describes how imagination breaks down in complex social situations where multiple individual perspectives must be imagined simultaneously. To circumvent this difficulty, characters rely on the empathic connection they have with others, and overconfidence in that empathy leads to misreading. Recent criticism has focused on the inferential errors that Claudio makes when he misinterprets Hero’s blush in act four. Drawing on theories of cognitive ecology and the embodied, embedded, and extended mind, Helms shifts the focus of the discussion from misinterpretation of facts to misreading of emotions, arguing that Claudio’s trouble is not external—social forces or erroneous impressions—but internal; Claudio follows the tide of imagination, failing to combine inferential distance with his own readings of others.
3 citations
27 Aug 2020
TL;DR: This paper uses novel qualitative research methods (phenomenology, ethnography and enactivism) to understand the cognitive processes through which radiologists interpret medical images to arrive at a diagnosis and a novel framework for understanding how diagnostic errors arise in this process.
Abstract: This paper uses novel qualitative research methods (phenomenology, ethnography and enactivism) to understand the cognitive processes through which radiologists interpret medical images to arrive at a diagnosis. From this perspective, diagnosis is not simply a matching of findings to retrieved mental images, but more properly an act of embodied or situated cognition, one that involves perception along with the actualization of professional memory and imagination and an expert-level understanding of the involved technology. Image interpretation involves a diverse set of factors, each of which is critical to arriving at the correct diagnostic interpretations, and conversely, may be the source of mis-interpretations and diagnostic error. Interpretation depends on the radiologist's understanding of the imaging modality that was used, a deep appreciation of anatomy and comprehensive knowledge of relevant diseases and how they manifest in medical imaging. A range of personal and inter-personal factors may also come into play, including understanding the actions, values and goals of the patient, the imaging technicians and the clinicians and other medical professionals involved in the patient's care. This multi-dimensional perspective provides novel insights regarding the cognitive aspects of diagnostic radiology and a novel framework for understanding how diagnostic errors arise in this process. Some of the findings of this research may have applications for diagnostic praxis in general, that is, beyond radiology diagnostics.
3 citations
TL;DR: Across 3 experiments, it is shown that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speed-accuracy emphasis, and training, and is a promising model for furthering the understanding of applied decision-making with naturally varying visual stimuli.
Abstract: Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving 2-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if 2 different prints belong to the same finger or different fingers. Here, we apply a dynamic decision-making model-the linear ballistic accumulator (LBA)-to fingerprint discrimination decisions to gain insight into the cognitive processes underlying these complex perceptual judgments. Across 3 experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speed-accuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli.
3 citations
Posted Content•
TL;DR: The core problem of generalization is explored and it is shown that long-accepted Occam's razor and parsimony principles are insufficient to ground learning, and a set of relativistic principles are derived that yield clearer insight into the nature and dynamics of learning.
Abstract: Lately there has been a lot of discussion about why deep learning algorithms perform better than we would theoretically suspect. To get insight into this question, it helps to improve our understanding of how learning works. We explore the core problem of generalization and show that long-accepted Occam's razor and parsimony principles are insufficient to ground learning. Instead, we derive and demonstrate a set of relativistic principles that yield clearer insight into the nature and dynamics of learning. We show that concepts of simplicity are fundamentally contingent, that all learning operates relative to an initial guess, and that generalization cannot be measured or strongly inferred, but that it can be expected given enough observation. Using these principles, we reconstruct our understanding in terms of distributed learning systems whose components inherit beliefs and update them. We then apply this perspective to elucidate the nature of some real world inductive processes including deep learning.
3 citations