This study examined the effectiveness of embedded picture mnemonic alphabet cards on the acquisition of letter name and letter sound knowledge with at-risk kindergarten students in a rural Texas public school. The study compared student achievement against a zero baseline when the student(s) are trained using a dissassociated picture mnemonic to an embedded picture mnemonic. A secondary area of investigation was the “Degree of Difficulty in Learning Letter Names” theory proposed by Treiman, Tincoff, Rodriguez, Mouzaki, & Francis. The theory states that consonant letter names can be divided into three categories based on phoneme characteristics: Easy to learn letters have a consonant-vowel pattern (the letter name for “D” is /d/ /e/); hard letters have a converse pattern of vowel-consonant (the letter name for “M” is /e/ /m/); and the other category has no phoneme pattern reflective to the letter name (the letter name “W” is “double” “you”). Students were randomly selected to either the treatment or the control group and after a ten-day (two week) training period, the students were given one week with no intervention then administered a posttest, followed by another week with no intervention followed by a post-posttest. The purpose for this assessment design was to determine if the training had an effect on long-term memory. Results revealed that children taught with the embedded picture mnemonics learned more letter name associations than did the control group. The embedded picture mnemonic had a positive effect on long term memory reflecting an increase from a moderate effect sizes for letter naming (d = .69) on the first week post test to a large effect size for letter naming (d =1.12) on the second week post test. The results also revealed inconclusive support for Treiman’s et al. (1998) degree of difficulty in learning letter names theory.

Yes / Purpose.: Crowding describes the increased difficulty in identifying a target object when it is surrounded by nearby objects (flankers). A recent study investigated the effect of age on visual crowding and found equivocal results: Although crowded visual acuity was worse in older participants, crowding expressed as a ratio did not change with age. However, the spatial extent of crowding is a better index of crowding effects and remains unknown. In the present study, we used established psychophysical methods to characterize the effect of age on visual crowding (magnitude and extent) in a letter recognition task. Methods.: Letter recognition thresholds were determined for three different flanker separations in 54 adults (aged 18–76 years) with normal vision. Additionally, the spatial extent of crowding was established by measuring spacing thresholds: the flanker-to-target separation required to produce a given reduction in performance. Uncrowded visual acuity, crowded visual acuity, and spacing thresholds were expressed as a function of age, avoiding arbitrary categorization of young and old participants. Results.: Our results showed that uncrowded and crowded visual acuities do not change significantly as a function of age. Furthermore, spacing thresholds did not change with age and approximated Bouma's law (half eccentricity). Conclusions.: These data show that crowding in adults is unaffected by senescence and provide additional evidence for distinct neural mechanisms mediating surround suppression and visual crowding, since the former shows a significant age effect. Finally, our data suggest that the well-documented age-related decline in peripheral reading ability is not due to age-related changes in visual crowding. / Supported by an Age UK Scholarship (AJB); a National Institute of Health Research (NIHR) Postdoctoral Fellowship (ATA); and a Wellcome Trust Career Development Fellowship (BSW).

Visual processing is the earliest core process required to support a normal reading system. However, little attention has been given to its role in any of the existing cognitive/computational models of reading. The ultimate goal of this thesis is to create a large-scale model of reading, which can generate phonology and semantics from print. Building such a model will allow for the exploration of a number of theoretically important cognitive phenomena in both normal and impaired reading including: font and size invariance; letter confusability; length effects; and pure alexic reading patterns. To achieve this goal, there are a number of important sub-goals that need to be achieved: (1) to develop a visual processing component which is capable of recognising letters in different fonts and sizes; (2) to produce a model that can develop useful intermediate (orthographic) representations as a consequence of learning; (3) to develop a set of semantic representations compact enough to allow efficient learning but that can still capture realistic semantic similarity relationships; (4) to integrate all the components together into a large-scale recurrent reading model; and (5) to extend the model to support picture naming, and to explore whether damage to the visual system can produce symptoms similar to those found in PA patients. Chapter 2 started by developing a simple feedforward network for letter recognition. The model was trained with letters in various transformations, which allowed the model to learn to deal with size and shape invariance problems as well as accounting for letter confusability effects and generalising to previously unseen letters. The model achieved this by extracting key features from visual input which could be used to support accurate letter recognition. Chapter 3 incorporated the letter recognition component developed in Chapter 2 into a word reading model. The reading model was trained on the mappings between print and phonology, with the orthographic representations which learn to emerge over training. The model could support accurate nonword naming and simulated the length by lexicality interaction observed in normal reading. A system of semantic representations was developed in Chapter 4 by using co-occurrence statistics to generate semantic codes that preserved realistic similarity relationships. Chapter 5 integrated all the components developed in the previous chapters together into a large-scale recurrent reading model. Finally, Chapter 6 extended the reading model to perform object recognition along with the reading task. When the model's visual system was damaged it was able to simulate the abnormal length effect typically seen in PA patients. The damaged model also showed impaired reaction times in object naming and preserved sensitivity to lexical/semantic variables in reading. The picture naming performance was modulated by visual complexity. In summary, the results highlight the importance of incorporating visual information into computational models of single word reading, and suggest that doing so will enable the exploration of a wide range of effects that were previously inaccessible to these types of connectionist models.

Le but du présent travail de thèse était d’examiner dans quelle mesure les programmes moteurs graphiques acquis au cours de l’apprentissage par écriture manuscrite contribuent au processus de reconnaissance. Précédemment, l’avantage de l’écriture manuscrite sur la dactylographie a été attribué à la contribution des programmes moteurs graphiques (Longcamp et al. 2010). Toutefois, les données recueillies dans le présent travail suggèrent que l’analyse visuelle détaillée requise par l’écriture manuscrite explique une grande partie de cet avantage. En effet, lorsque le symbole à reconnaître est présenté dans des conditions visuelles optimales, les apprentissages par écriture manuscrite et par composition – nécessitant tous les deux une analyse visuelle détaillée – aboutissent à un niveau de reconnaissance similaire et supérieur à celui faisant suite à la dactylographie. Ces résultats sont en accord avec la conception visuelle de la reconnaissance des lettres. Dans cette perspective, l’association entre la perception des lettres et l’activation motrice peut être interprétée comme la conséquence de l’apprentissage conjoint de la lecture et de l’écriture et non comme une condition nécessaire à l’encodage et à la reconnaissance.Toutefois, lorsque les conditions visuelles de présentation sont altérées, l’apprentissage par écriture manuscrite tend à aboutir à une meilleure reconnaissance que les apprentissages par composition et par dactylographie. Ces données suggèrent que les programmes moteurs graphiques pourraient contribuer à la reconnaissance lorsque les informations visuelles sont limitées et que la mémoire visuelle ne peut être utilisée de façon optimale. Notons que cette hypothèse mériterait d’être examinée de façon plus directe.Même si les résultats montrent une contribution importante de l’analyse visuelle détaillée dans l’avantage de l’écriture manuscrite sur la dactylographie, il convient de souligner que dans des conditions d’apprentissage naturelles, l’écriture manuscrite constitue le moyen le plus évident et spontané de promouvoir une telle analyse détaillée. Par ailleurs, pour que l’écriture manuscrite puisse aboutir à un encodage optimal, il est important que l’automaticité des mouvements d’écriture soit préservée. En effet, rompre l’automaticité de l’écriture par une perturbation de l’activité graphomotrice durant l’apprentissage affecte les performances ultérieures en reconnaissance. / Doctorat en Sciences psychologiques et de l'éducation / info:eu-repo/semantics/nonPublished

Il a été démontré que les mots sont plus faciles à reconnaître lorsque leur moitié inférieure est effacée, laissant leur moitié supérieure intacte, que lorsque leur moitié supérieure est effacée. Si la reconnaissance de lettres sous-tend la reconnaissance de mots tel qu'il est généralement pris pour acquis, alors un tel effet devrait aussi être présent au niveau des lettres, mais ce n'est pas le cas. Le but de ce mémoire a d'abord été d'investiguer cette différence entre lettres et mots et, ensuite, de démontrer que la préférence pour le haut des mots ne peut pas s'expliquer par les lettres. Finalement, nous nous questionnons sur l'existence d'un construit intermédiaire entre lettres et mots et proposons les chaînes lexicales. / It has been demonstrated that words are more readily recognized when their lower half has been erased, leaving the upper half intact, than when the upper half has been erased. If letter recognition subtends word recognition as it is so often assumed, then we would expect to find a similar effect with letters, but it is not the case. The goal of this master's thesis has first been to investigate this difference between words and letters and then to demonstrate that the preference for the upper half of words cannot be accounted for by letters. In the end, we look into the existence of a construct between features and words and propose that lexical strings could be that construct.

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Il a été démontré que les mots sont plus faciles à reconnaître lorsque leur moitié inférieure est effacée, laissant leur moitié supérieure intacte, que lorsque leur moitié supérieure est effacée. Si la reconnaissance de lettres sous-tend la reconnaissance de mots tel qu'il est généralement pris pour acquis, alors un tel effet devrait aussi être présent au niveau des lettres, mais ce n'est pas le cas. Le but de ce mémoire a d'abord été d'investiguer cette différence entre lettres et mots et, ensuite, de démontrer que la préférence pour le haut des mots ne peut pas s'expliquer par les lettres. Finalement, nous nous questionnons sur l'existence d'un construit intermédiaire entre lettres et mots et proposons les chaînes lexicales. / It has been demonstrated that words are more readily recognized when their lower half has been erased, leaving the upper half intact, than when the upper half has been erased. If letter recognition subtends word recognition as it is so often assumed, then we would expect to find a similar effect with letters, but it is not the case. The goal of this master's thesis has first been to investigate this difference between words and letters and then to demonstrate that the preference for the upper half of words cannot be accounted for by letters. In the end, we look into the existence of a construct between features and words and propose that lexical strings could be that construct.