Preparing and Presenting Complex Images for Perceptual Cognitive Studies
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,184 | 0,082 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».