On Memory for Everyday Symbols
Notice bibliographique
Résumé
This thesis investigated the memorability of common graphic symbols (e.g., !@#$%) and logos. In an initial set of 4 experiments, participants were presented during study with symbols or words (e.g., $ or ‘dollar’). In Experiment 1, memory performance assessed using free recall demonstrated markedly better memory for symbols relative to their word counterparts, manipulated within-subject. Experiment 2 systematically varied whether symbols and words were presented at encoding and during a subsequent recognition test, manipulated between-subjects. Results conceptually replicated the findings of the first experiment, showing superior memory for symbols even when the retrieval test and study design were changed. Furthermore, by analyzing group data based on which stimuli (words or symbols) were used in the encoding and retrieval phases of the experiment, symbol superiority in memory was determined to be driven by encoding-based mechanisms. An alternative explanation holds that symbols may benefit memory as a result of their smaller overall set size compared to words. Experiment 3 addressed this potential issue by restricting the to-be-remembered set of words to a single category (common kitchen produce) whose set size was like that of the symbols that I used. Once again, symbols were better remembered than the words. This experiment showed not only that set size was not likely to be driving the previously seen memory benefit for symbols, but also that representing abstract concepts with symbols successfully reversed the concreteness effect in memory: Symbols were better remembered even when compared to highly concrete nouns. A fourth experiment directly tested a dual coding account by comparing memory for symbols, pictures, and words. There, symbols and pictures were both better remembered than words, and memory for symbols and pictures did not differ. Symbols not only were remembered just as well as images—as I predicted based on dual coding theory—but they also entirely eliminated the concreteness advantage in memory for pictures as well: Memory for symbols representing abstract concepts was equivalent to that for pictures depicting concrete objects. In Experiment 5A and 5B, I compared memory for professional sports teams presented in three encoding conditions: team names only, team logos without team names, and team logos with integrated team names. Across two experiments, while memory was often best for logos relative to team names, familiarity moderated this relation. When assessing memory for team names, the magnitude of the benefit for the logos-only condition depended on whether participants knew what the logos represented. In the sixth and final experiment, 337 naïve participants rated the set of symbols used in Experiments 1-4 on their meaning-based familiarity with each symbol and on their frequency of encountering it. Machine learning estimations of inherent stimulus memorability were provided by the ResMem residual neural network. These computer-derived memorability estimates correlated with memory for symbols, but familiarity and frequency ratings did not. Hierarchical linear regression revealed that inherent memorability estimates explained significant portions of variance for symbol memory, over and above effects of familiarity and frequency. This dissertation is the first to present evidence that, like pictures, graphic symbols and logos are better remembered than words, in line with dual coding theory and with distinctiveness accounts. Symbols offer a visual referent for abstract concepts that are otherwise unlikely to be spontaneously imaged. Symbols also provide visual stimuli that are often both physically and conceptually unique. It is the visual nature of symbols that confers the impressive memory performance benefits.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».