Search results for 'Categorization' (try it on Scholar)

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  1. B. A. C. Saunders & J. van Brakel (1997). Are There Nontrivial Constraints on Colour Categorization? Behavioral and Brain Sciences 20 (2):167-179.score: 24.0
    In this target article the following hypotheses are discussed: (1) Colour is autonomous: a perceptuolinguistic and behavioural universal. (2) It is completely described by three independent attributes: hue, brightness, and saturation: (3) Phenomenologically and psychophysically there are four unique hues: red, green, blue, and yellow; (4) The unique hues are underpinned by two opponent psychophysical and/or neuronal channels: red/green, blue/yellow. The relevant literature is reviewed. We conclude: (i) Psychophysics and neurophysiology fail to set nontrivial constraints on colour categorization. (ii) (...)
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  2. Soonja Choi & Kate Hattrup (2012). Relative Contribution of Perception/Cognition and Language on Spatial Categorization. Cognitive Science 36 (1):102-129.score: 24.0
    This study investigated the relative contribution of perception/cognition and language-specific semantics in nonverbal categorization of spatial relations. English and Korean speakers completed a video-based similarity judgment task involving containment, support, tight fit, and loose fit. Both perception/cognition and language served as resources for categorization, and allocation between the two depended on the target relation and the features contrasted in the choices. Whereas perceptual/cognitive salience for containment and tight-fit features guided categorization in many contexts, language-specific semantics influenced (...) where the two features competed for similarity judgment and when the target relation was tight support, a domain where spatial relations are perceptually diverse. In the latter contexts, each group categorized more in line with semantics of their language, that is, containment/support for English and tight/loose fit for Korean. We conclude that language guides spatial categorization when perception/cognition alone is not sufficient. In this way, language is an integral part of our cognitive domain of space. (shrink)
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  3. Eric Hauser (2011). Generalization: A Practice of Situated Categorization in Talk. [REVIEW] Human Studies 34 (2):183-198.score: 24.0
    This paper analyzes four instances in talk of generalization about people, that is, of using statements about one or more people as the basis of stating something about a category. Generalization can be seen as a categorization practice which involves a reflexive relationship between the generalized-from person or people and the generalized-to category. One thing that is accomplished through generalization is instruction in how to understand the identity of the generalized-from person or people, so in addition to being understood (...)
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  4. Sung-Joo Lim & Lori L. Holt (2011). Learning Foreign Sounds in an Alien World: Videogame Training Improves Non-Native Speech Categorization. Cognitive Science 35 (7):1390-1405.score: 24.0
    Although speech categories are defined by multiple acoustic dimensions, some are perceptually weighted more than others and there are residual effects of native-language weightings in non-native speech perception. Recent research on nonlinguistic sound category learning suggests that the distribution characteristics of experienced sounds influence perceptual cue weights: Increasing variability across a dimension leads listeners to rely upon it less in subsequent category learning (Holt & Lotto, 2006). The present experiment investigated the implications of this among native Japanese learning English /r/-/l/ (...)
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  5. Yasmina Jraissati (2013). Proving Universalism Wrong Does Not Prove Relativism Right: Considerations on the Ongoing Color Categorization Debate. Philosophical Psychology (3):1-24.score: 24.0
    For over a century, the question of the relation of language to thought has been extensively discussed in the case of color categorization, where two main views prevail. The relativist view claims that color categories are relative while the universalistic view argues that color categories are universal. Relativists also argue that color categories are linguistically determined, and universalists that they are perceptually determined. Recently, the argument for the perceptual determination of color categorization has been undermined, and the relativist (...)
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  6. Cornelia Zelinsky-Wibbelt (2000). Discourse and the Continuity of Reference: Representing Mental Categorization. Mouton De Gruyter.score: 24.0
    Chapter Introduction This work deals with two contrasting, but mutually interrelated capabilities of the human mind: reference and categorization. ...
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  7. Brett K. Hayes & Bob Rehder (2012). The Development of Causal Categorization. Cognitive Science 36 (6):1102-1128.score: 24.0
    Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were (...)
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  8. Jennifer Summerville & Barbara Adkins (2007). Enrolling the Citizen in Sustainability: Membership Categorization, Morality and Civic Participation. [REVIEW] Human Studies 30 (4):429 - 446.score: 24.0
    This article examines the common-sense and methodical ways in which “the citizen” is produced and enrolled as an active participant in “sustainable” regional planning. Using Membership Categorization Analysis, we explicate how the categorization procedures in the Foreword of a draft regional planning policy interactionally produce the identity of “the citizen” and “civic values and obligations” in relation to geographic place and institutional categories. Furthermore, we show how positioning practices establish a relationship between authors (government) and readers (citizens) where (...)
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  9. J. DeGutis & M. D'Esposito (2008). Network Changes in the Transition From Initial Learning to Well-Practiced Visual Categorization. Frontiers in Human Neuroscience 3:44-44.score: 24.0
    Visual categorization is a remarkable ability that allows us to effortlessly identify objects and efficiently respond to our environment. The neural mechanisms of how visual categories become well-established are largely unknown. Studies of initial category learning implicate a network of regions that include inferior temporal cortex (ITC), medial temporal lobe (MTL), basal ganglia (BG), premotor cortex (PMC) and prefrontal cortex (PFC). However, how these regions change with extended learning is poorly characterized. To understand the neural changes in the transition (...)
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  10. Peter Eglin & Stephen Hester (1999). “You're All a Bunch of Feminists:” Categorization and the Politics of Terror in the Montreal Massacre. [REVIEW] Human Studies 22 (2-4):253-272.score: 24.0
    Following Sacks's model membership categorization analysis (MCA) of a suicidal person's conclusion 'I have no one to turn to,' the paper examines in MCA terms a political actor's twin conclusions that murder-suicide is a rational course of action. The case in question is the killer's reasoning in the Montreal Massacre as revealed in his reported announcement at the scene (notably 'You're all a bunch of feminists. I hate feminists') and recovered suicide letter (for example, 'For why persevere to exist (...)
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  11. Thomas J. Palmeri Michael L. Mack (2011). The Timing of Visual Object Categorization. Frontiers in Psychology 2.score: 24.0
    An object can be categorized at different levels of abstraction: as natural or man-made, animal or plant, bird or dog, or as a Northern Cardinal or Pyrrhuloxia. There has been growing interest in understanding how quickly categorizations at different levels are made and how the timing of those perceptual decisions changes with experience. We specifically contrast two perspectives on the timing of object categorization at different levels of abstraction. By one account, the relative timing implies a relative timing of (...)
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  12. Benoit Montalan, Thierry Lelard, Olivier Godefroy & Harold Mouras (2012). Behavioral Investigation of the Influence of Social Categorization on Empathy for Pain: A Minimal Group Paradigm Study. Frontiers in Psychology 3.score: 24.0
    Research on empathy for pain has provided evidence of an empathic bias toward racial ingroup members. In this study, we used for the first time the “minimal group paradigm” in which participants were assigned to artificial groups and required to perform pain judgments of pictures of hands and feet in painful or non-painful situations from self, ingroup and outgroup-perspectives. Findings showed that the mere categorization of people into two distinct arbitrary social groups appears to be sufficient to elicit an (...)
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  13. Lisandro Nicolas Kaunitz, Juan Esteban Kamienkowski, Emanuele Olivetti, Paolo Avesani, Brian Murphy & David Paul Melcher (2011). Intercepting the First Pass: Rapid Categorization is Suppressed for Unseen Stimuli. Frontiers in Psychology 2.score: 24.0
    The operations and processes that the human brain employs to achieve fast visual categorization remain a matter of debate. A first issue concerns the timing and place of rapid visual categorization and to what extent it can be performed with an early feed-forward pass of information through the visual system. A second issue involves the categorization of stimuli that do not reach visual awareness. There is disagreement over the degree to which these stimuli activate the same early (...)
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  14. Guillaume A. Rousselet, Cyril R. Pernet, Roberto Caldara & Philippe G. Schyns (2011). Visual Object Categorization in the Brain: What Can We Really Learn From ERP Peaks? Frontiers in Human Neuroscience 5.score: 24.0
    Visual Object Categorization in the Brain: What Can We Really Learn from ERP Peaks?
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  15. A. Delorme, G. Richard & M. Fabre-Thorpe (2009). Key Visual Features for Rapid Categorization of Animals in Natural Scenes. Frontiers in Psychology 1:21-21.score: 24.0
    In speeded categorization tasks, decisions could be based on diagnostic target features or they may need the activation of complete representations of the object. Depending on task requirements, the priming of feature detectors through top-down expectation might lower the threshold of selective units or speed up the rate of information accumulation. In the present paper, 40 subjects performed a rapid go/no-go animal/non-animal categorization task with 400 briefly flashed natural scenes to study how performance depends on physical scene characteristics, (...)
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  16. Michèle Fabre-Thorpe (2011). The Characteristics and Limits of Rapid Visual Categorization. Frontiers in Psychology 2:243.score: 24.0
    Visual categorization appears both effortless and virtually instantaneous, the study by Thorpe et al. (1996) was the first to estimate the processing time necessary to perform fast visual categorization of animals in briefly flashed (20ms) natural photographs. They observed a large differential EEG activity between target and distrater correct trials that developed from 150 ms after stimulus onset. A value that was later shown to be even shorter in monkeys! With such strong processing time constraints, it was difficult (...)
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  17. Lena Jayyusi (1984). Categorization and the Moral Order. Routledge & K. Paul.score: 21.0
    INTRODUCTION My underlying concern in this work is with the sociological analysis and description of members' practical activities and their practical ...
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  18. Evan Heit & Stephen P. Nicholson (2010). The Opposite of Republican: Polarization and Political Categorization. Cognitive Science 34 (8):1503-1516.score: 21.0
    Two experiments examined the typicality structure of contrasting political categories. In Experiment 1, two separate groups of participants rated the typicality of 15 individuals, including political figures and media personalities, with respect to the categories Democrat or Republican. The relation between the two sets of ratings was negative, linear, and extremely strong, r = −.9957. Essentially, one category was treated as a mirror image of the other. Experiment 2 replicated this result, showing some boundary conditions, and extending the result to (...)
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  19. Paul Egré, Vincent de Gardelle & David Ripley (2013). Vagueness and Order Effects in Color Categorization. Journal of Logic, Language and Information 22 (4):391-420.score: 21.0
    This paper proposes an experimental investigation of the use of vague predicates in dynamic sorites. We present the results of two studies in which subjects had to categorize colored squares at the borderline between two color categories (Green vs. Blue, Yellow vs. Orange). Our main aim was to probe for hysteresis in the ordered transitions between the respective colors, namely for the longer persistence of the initial category. Our main finding is a reverse phenomenon of enhanced contrast (i.e. negative hysteresis), (...)
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  20. Benjamin M. Rottman, Dedre Gentner & Micah B. Goldwater (2012). Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena. Cognitive Science 36 (5):919-932.score: 21.0
    We investigated the understanding of causal systems categories—categories defined by common causal structure rather than by common domain content—among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the (...)
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  21. Elizabeth F. Loftus & Ronald W. Scheff (1971). Categorization Norms for Fifty Representative Instances. Journal of Experimental Psychology 91 (2):355.score: 21.0
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  22. Bob Rehder (2003). Categorization as Causal Reasoning⋆. Cognitive Science 27 (5):709-748.score: 21.0
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  23. Shimon Edelman & Sharon Duvdevani-Bar (1997). Similarity-Based Viewspace Interpolation and the Categorization of 3D Objects. In Proc. Edinburgh Workshop on Similarity and Categorization.score: 21.0
    Visual objects can be represented by their similarities to a small number of reference shapes or prototypes. This method yields low-dimensional (and therefore computationally tractable) representations, which support both the recognition of familiar shapes and the categorization of novel ones. In this note, we show how such representations can be used in a variety of tasks involving novel objects: viewpoint-invariant recognition, recovery of a canonical view, estimation of pose, and prediction of an arbitrary view. The unifying principle in all (...)
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  24. Valentina Gliozzi, Julien Mayor, Jon‐Fan Hu & Kim Plunkett (2009). Labels as Features (Not Names) for Infant Categorization: A Neurocomputational Approach. Cognitive Science 33 (4):709-738.score: 21.0
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  25. Caroline Michel, Olivier Corneille & Bruno Rossion (2007). Race Categorization Modulates Holistic Face Encoding. Cognitive Science 31 (5):911-924.score: 21.0
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  26. Elizabeth Goldenberg & Catherine M. Sandhofer (2013). Who is She? Changes in the Person Context Affect Categorization. Frontiers in Psychology 4.score: 21.0
    Changes between the learning and testing contexts affect learning, memory, and generalization. We examined whether a change (between learning and testing) in the person children were interacting with affects generalization. Three, 4-, and 5-year-old children were trained on eight novel noun categories by one experimenter. Children were tested for their ability to generalize the label to a new category member by either the same experimenter who trained them or by a novel experimenter. Three-year-old children’s performance was not affected by who (...)
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  27. Nancy W. Ingling (1972). Categorization: A Mechanism for Rapid Information Processing. Journal of Experimental Psychology 94 (3):239.score: 21.0
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  28. Elizabeth F. Loftus (1973). Category Dominance, Instance Dominance, and Categorization Time. Journal of Experimental Psychology 97 (1):70.score: 21.0
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  29. Bob McMurray, Joel L. Dennhardt & Andrew Struck‐Marcell (2008). Context Effects on Musical Chord Categorization: Different Forms of Top‐Down Feedback in Speech and Music? Cognitive Science 32 (5):893-920.score: 21.0
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  30. Raymond S. Nickerson (1967). Categorization Time with Categories Defined by Disjunctions and Conjunctions of Stimulus Attributes. Journal of Experimental Psychology 73 (2):211.score: 21.0
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  31. Michelle C. St Clair, Padraic Monaghan & Michael Ramscar (2009). Relationships Between Language Structure and Language Learning: The Suffixing Preference and Grammatical Categorization. Cognitive Science 33 (7):1317-1329.score: 21.0
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  32. Janet H. Walker (1973). Pronounceability Effects on Word-Nonword Encoding in Categorization and Recognition Tasks. Journal of Experimental Psychology 99 (3):318-322.score: 21.0
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  33. Eric Dietrich (2010). Analogical Insight: Toward Unifying Categorization and Analogy. Cognitive Processing 11 (4):331-.score: 18.0
    The purpose of this paper is to present two kinds of analogical representational change, both occurring early in the analogy-making process, and then, using these two kinds of change, to present a model unifying one sort of analogy-making and categorization. The proposed unification rests on three key claims: (1) a certain type of rapid representational abstraction is crucial to making the relevant analogies (this is the first kind of representational change; a computer model is presented that demonstrates this kind (...)
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  34. Stevan Harnad (2005). To Cognize is to Categorize: Cognition is Categorization. In C. Lefebvre & H. Cohen (eds.), Handbook of Categorization. Elsevier.score: 18.0
    2. Invariant Sensorimotor Features ("Affordances"). To say this is not to declare oneself a Gibsonian, whatever that means. It is merely to point out that what a sensorimotor system can do is determined by what can be extracted from its motor interactions with its sensory input. If you lack sonar sensors, then your sensorimotor system cannot do what a bat's can do, at least not without the help of instruments. Light stimulation affords color vision for those of us with the (...)
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  35. George Psathas (1999). Studying the Organization in Action: Membership Categorization and Interaction Analysis. [REVIEW] Human Studies 22 (2-4):139-162.score: 18.0
    A current set of concerns in ethnomethodology and conversation analysis includes the question of how conversation analysis (CA) can deal with studies of social structure or studies of talk in institutional settings.In this paper a focus is placed on how the accomplishment of "work" and "categorization" are interrelated. Two particular instances are examined: a ski school and a package delivery service. Membership categorization is shown to be a complex, on-going, interactive accomplishment. The parties act in ways that are (...)
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  36. William Bechtel (1988). Studies of Categorization: A Review Essay of Neisser's 'Concepts and Conceptual Development' and Hamad's 'Categorical Perception'. Philosophical Psychology 1 (3):381-389.score: 18.0
    Concepts and Conceptual Development: Ecological and Intellectual Factors in Categorization ULRIC NEISSER, 1987 Cambridge, Cambridge University Press x+384 pp., $39.50 Categorical Perception STEVAN HARNAD, 1987 Cambridge, Cambridge University Press x+599 pp., $59.50.
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  37. Reese M. Heitner (2004). The Cyclical Ontogeny of Ontology: An Integrated Developmental Account of Object and Speech Categorization. Philosophical Psychology 17 (1):45 – 57.score: 18.0
    More than a decade of experimental research confirms that external linguistic information provided in the form of word labels can induce a "mutually exclusive" bias against double naming and lead children to infer the name of novel objects and parts (Markman, 1989). Linguistic labels have also been shown to encourage more sophisticated reasoning, particularly with respect to superordinate and atypical object categorization (Gelman & Coley, 1990; Waxman & Markow, 1995). By contrast, however, the inverse possibility that the linguistic (...)
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  38. Nick Braisby & Bradley Franks (1997). Semantics Versus Pragmatics in Colour Categorization. Behavioral and Brain Sciences 20 (2):181-182.score: 18.0
    We argue that the confusing pattern of evidence concerning colour categorization reported by Saunders & van Brakel is unsurprising. On a perspectival view, categorization may follow semantic or pragmatic attributes. Colour lacks clear semantic attributes; as a result categorization is necessarily pragmatic and context-sensitive. This view of colour categorization helps explain the developmental delay in colour naming.
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  39. Sara Dellantonio, Claudio Mulatti & Remo Job (2013). Artifact and Tool Categorization. Review of Philosophy and Psychology 4 (3):407-418.score: 18.0
    This study addresses the issue of artifact kinds from a psychological and cognitive perspective. The primary interest of the investigation lies in understanding how artifacts are categorized and what are the properties people rely on for their identification. According to a classical philosophical definition artifacts form an autonomous class of instances including all and only those objects that do not exist in nature, but are artificial, in the sense that they are made by an artĭfex. This definition suggests that artifacts (...)
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  40. Gregory Ashby & Michael B. Casale (2005). Empirical Dissociations Between Rule-Based and Similarity-Based Categorization. Behavioral and Brain Sciences 28 (1):15-16.score: 18.0
    The target article postulates that rule-based and similarity-based categorization are best described by a unitary process. A number of recent empirical dissociations between rule-based and similarity-based categorization severely challenge this view. Collectively, these new results provide strong evidence that these two types of category learning are mediated by separate systems.
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  41. Yasmina Jraissati (2014). On Color Categorization: Why Do We Name Seven Colors in the Rainbow? Philosophy Compass 9 (6):382-391.score: 18.0
    What makes it the case that we draw the boundary between “blue” and “green” where we draw it? Do we draw this boundary where we draw it because our perceptual system is biologically determined in this way? Or is it culture and language that guide the way we categorize colors? These two possible answers have shaped the historical discussion opposing so-called universalists to relativists. Yet, the most recent theoretical developments on color categorization reveal the limits of such a polarization.
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  42. Robert E. MacLaury (1998). Domain-Specificity in Folk Biology and Color Categorization: Modularity Versus Global Process. Behavioral and Brain Sciences 21 (4):582-583.score: 18.0
    Universal ranks in folk biological taxonomy probably apply to taxonomies of cultural artifacts. We cannot call folk biological cognition domain-specific and modular. Color categorization may manifest unique organization, which would result from known neurology and the nature of color as an attribute. But folk biology does not adduce equivalent evidence. A global process of increasing differentiation similarly affects folk taxonomy, color categorization, and other practices germane to Atran's anthropology of science; this is beclouded by claims of specificity and (...)
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  43. Stefano Borgo, Noemi Spagnoletti, Laure Vieu & Elisabetta Visalberghi (2013). Artifact and Artifact Categorization: Comparing Humans and Capuchin Monkeys. Review of Philosophy and Psychology 4 (3):375-389.score: 18.0
    We aim to show that far-related primates like humans and the capuchin monkeys show interesting correspondences in terms of artifact characterization and categorization. We investigate this issue by using a philosophically-inspired definition of physical artifact which, developed for human artifacts, turns out to be applicable for cross-species comparison. In this approach an artifact is created when an entity is intentionally selected and some capacities attributed to it (often characterizing a purpose). Behavioral studies suggest that this notion of artifact is (...)
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  44. Martin Jüttner (1998). Representation of Similarities – a Psychometric but Not an Explanatory Concept for Categorization. Behavioral and Brain Sciences 21 (4):475-476.score: 18.0
    The representation of similarities is a viable concept for a cognitive extension of visual psychophysics to the recognition of shapes, bringing issues such as similarity and categorization back into that field. However, as a framework it appears too general to place constraints on a particular process model for categorization. In particular, a preference for Chorus-like schemes with respect to structure-oriented approaches is unwarranted.
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  45. Bence Nanay (2013). Artifact Categorization and the Modal Theory of Artifact Function. Review of Philosophy and Psychology 4 (3):515-526.score: 18.0
    Philosophers and psychologists widely hold that artifact categories – just like biological categories – are individuated by their function. But recent empirical findings in psychology question this assumption. My proposal is to suggest a way of squaring these findings with the central role function should play in individuating artifact categories. But in order to do so, we need to give up on the standard account of artifact function, according to which function is fixed by design, and replace it with a (...)
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  46. Peter F. Dominey (1998). Flexible Categorization Requires the Creation of Relational Features. Behavioral and Brain Sciences 21 (1):23-24.score: 18.0
    Flexible categorization clearly requires an adaptive component, but at what level of representation? We have investigated categorization in sequence learning that requires the extraction of abstract rules, but no modification of sensory primitives. This motivates the need to make explicit the distinction between sensory-level “atomic” features as opposed to concept-level “abstract” features, and the proposal that flexible categorization probably relies on learning at the abstract feature level.
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  47. Robert M. French & Elizabeth Thomas (2000). Why Localist Connectionist Models Are Inadequate for Categorization. Behavioral and Brain Sciences 23 (4):477-477.score: 18.0
    Two categorization arguments pose particular problems for localist connectionist models. The internal representations of localist networks do not reflect the variability within categories in the environment, whereas networks with distributed internal representations do reflect this essential feature of categories. We provide a real biological example of perceptual categorization in the monkey that seems to require population coding (i.e., distributed internal representations).
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  48. Scott Atran, Douglas I. Medin & Norbert Ross (2002). Thinking About Biology. Modular Constraints on Categorization and Reasoning in the Everyday Life of Americans, Maya, and Scientists. Mind and Society 3 (2):31-63.score: 18.0
    This essay explores the universal cognitive bases of biological taxonomy and taxonomic inference using cross-cultural experimental work with urbanized Americans and forest-dwelling Maya Indians. A universal, essentialist appreciation of generic species appears as the causal foundation for the taxonomic arrangement of biodiversity, and for inference about the distribution of causally-related properties that underlie biodiversity. Universal folkbiological taxonomy is domain-specific: its structure does not spontaneously or invariably arise in other cognitive domains, like substances, artifacts or persons. It is plausibly an innately-determined (...)
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  49. Professor Ronaldo Vigo & Colin Allen, How to Reason Without Words: Inference as Categorization.score: 18.0
    The idea that reasoning is a singular accomplishment of the human species has an ancient pedigree.Yet this idea remains as controversial as it is ancient. Those who would deny reasoning to nonhuman animals typically hold a language-based conception of inference which places it beyond the reach of languageless creatures. Others reject such an anthropocentric conception of reasoning on the basis of similar performance by humans and animals in some reasoning tasks, such as transitive inference. Here, building on the modal similarity (...)
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  50. Danièle Dubois (1997). Cultural Beliefs as Nontrivial Constraints on Categorization: Evidence From Colors and Odors. Behavioral and Brain Sciences 20 (2):188-188.score: 18.0
    The following provides further arguments for the nonuniversality of color as an autonomous dimension. Research on odors suggests that there are cultural constraints on the abstraction of dimensions for objects. Color vision analysis leads to an overemphasis on the role of perceptual processes in categorization. The study of odors points to human activities as a more important principle of categorization that drives the perceptual processing and suggests a reconsideration of vision itself.
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