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Enregistrement W2574368019

Preparing and Presenting Complex Images for Perceptual Cognitive Studies

2011· article· en· W2574368019 sur OpenAlexaboutno aff
Javid Sadr

Notice bibliographique

RevueeScholarship (California Digital Library) · 2011
Typearticle
Langueen
DomaineNeuroscience
ThématiqueFace Recognition and Perception
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPerceptionCognitionPsychologyCategorizationCognitive neuroscienceCognitive scienceCognitive psychologyPerceptual learningComputer scienceArtificial intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Preparing and Presenting Complex Images for Perceptual Cognitive Studies Javid Sadr (sadr@uleth.ca) Departments of Psychology and Neuroscience, University of Lethbridge 4401 University Drive, Lethbridge, AB T1K 3M4 CANADA tel.: 403.332.4530, fax: 403.329.2555 Keywords: perception; methods; image processing; priming; perceptual learning; masking; imaging; neural correlates; detection; categorization; recognition; objects; faces; scenes Pollack & Sinha, 2002); neural correlates of perception, perceptual learning, and new measures of priming (Eger, Henson, Driver & Dolan, 2007; Liu, Harris & Kanwisher, 2002; Sadr & Sinha, 2003, 2004); dissociating sequential stages of object and face processing (Liu, Harris & Kanwisher, 2002; Mack, Gauthier, Sadr & Palmeri, 2008); mechanisms of scene perception and explorations of different masking techniques/stimuli (Loschky et al, 2010). Introduction: Objectives and Scope The goal of this tutorial is to reduce the barriers of entry for cognitive scientists interested in studying perceptual/ cognitive processes with complex, real-world stimuli - and in doing so with confidence in their underlying techniques and conceptual approach. The content of this session will span basic topics in the selection/creation and (crucial) pre- processing of complex images; powerful stimulus manipu- lation techniques, including image degradation and filtering methods; and important considerations in experimental presentation (e.g., display choice and calibration, web-based studies) and design of perceptual-cognitive tasks/paradigms. Motivations, Applications, and Audience From even a quick survey of publications in the field, it's clear that interest in perceptual (particularly visual) research in the cognitive sciences is not merely enormous but ever- growing. This is not surprising in a sense: the role of perceptual processes in cognition can hardly be overstated, and in some ways it's hard to imagine one without the other. However, many studies limit themselves, for good reason, to very basic visual stimuli (e.g., dots, lines, simple shapes), while in other studies the move to complex stimuli and high-level perceptual/cognitive phenomena (e.g., object, face, and scene perception) has at times led to unfortunate missteps or misinterpretations relating to stimulus control, manipulation, and experimental presentation or task design - including potential confounds in behavioural and neural measures resulting from low-level image properties. A very simple example (Fig. 1) illustrates how attempts to study spatial-frequency effects in a perception task could coincide with large shifts in image contrast, a critical stimulus variable; such confounds may plague a variety of stimuli and image manipulations, greatly undermining a study's findings and interpretations (e.g., Rainer et al, 2001). We have previously reviewed in detail a wide range of these methodological concerns, consequences, and corrective measures (Sadr & Sinha, 2001a, 2004), and the fundamental concepts and techniques covered in this tutorial (informed in part by our technical and experimental work [e.g., Sadr & Sinha, 2001a, 2001b, 2003, 2004; Mack, Gauthier, Sadr & Palmeri, 2008; Willenbockel et al, 2010]) are now being employed in a wide range of cognitive and neuro- science research, including: developmental and clinical studies (e.g., Bernstein, Loftus & Meltzoff, 2005; Figure 1: Original image versus typical low-pass ( blur ) and high-pass ( edge ) images: potential confound in image contrast, seen in luminance histogram's standard deviation. With sharply growing interest and activity in such research areas, and a enduring concern for implementing these techniques soundly, this tutorial is tailored for scientists interested in, but new to, higher-level perceptual/ cognitive processes and complex images, as well as those currently exploring such research but perhaps seeking greater comfort with and intuition for underlying techniques and concepts. Given the diversity of the audience, our session is intended to be flexible in its scope, depth, and progression and is primarily conceived at a level well-suited to a range of participants, from those with little or no back- ground to those with an intermediate level of experience. Tutorial Approach and Participation Our tutorial's overall structure will follow a progression of core topics and techniques, from basic concepts and handling of images all the way to stimulus manipulation and experimental presentation. Along the way, we will try to address questions and requests regarding subtopics or special applications as fitting the participants' interests. At each step, the topics and techniques will be illustrated by the tutorial organizer or optionally performed as activities by participants who might wish to bring a computer. Tutorial content will be provided partly in print (e.g., content from presentations) and partly via electronic resources online.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,184
Score d'incertitude au seuil0,615

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0020,003
Science ouverte0,0010,002
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,1840,082

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,139
Tête enseignante GPT0,306
Écart entre enseignants0,167 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2011
Routes d'admission1
Résumé présentoui

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