Periodic Reporting for period 1 - SFM4VOT (Building a computational basis for the brain response in the left ventral occipito-temporal cortex: Understanding the Sparse Familiarity Model of visual word recognition)
Berichtszeitraum: 2016-04-01 bis 2018-03-31
The core cognitive process implemented in our computational model (originally the sparse familiarity model: SFM, now the lexical categorization model: LCM; Gagl, Richlan, Ludersdorfer, Sassenhagen, & Fiebach, 2016) is the categorization of letter strings in meaningful or meaningless. In work package 1 (WP1) the objective was to investigate how the lexical categorization process is influenced by learning and in WP2 the objective was the generalization of the lexical categorization process to other image categories like faces or objects, which are all known to be processed in the ventral part of the temporal cortex.
In the second study of WP1, I (in cooperation with research assistant Klara Gregorova) realized two extensive intervention studies investigating if an LCM based training increases reading speed for second language learners (Gregorova & Gagl, in preparation). Here we implemented one experiment with an assessment of reading speed before training, three sessions of lexical categorization training (45 min), and an assessment of reading speed afterward (25 participants included). In the second experiment, we paired the lexical categorization training with a phonics training, currently the only effective training for dyslexia, in a randomized controlled trial (32 participants included in both interventions). Both studies showed transfer effects of the intervention to reading speed indicating the potential of the training.
In WP2, the objective was to evaluate if the core process of the LCM generalizes to other visual stimuli. Central to the implementation of the LCM for words is the estimation of a similarity index, now realized on the basis of the orthographic Levenshtein distance (Yarkoni, Balota, & Yap, 2008), which cannot be estimated for other visual stimuli. To bypass this issue, we originally planned to study generalization using a noise manipulation for words and other visual stimuli like faces. Instead, we decided to implement an image-based similarity index, with the potential for generalization, generating an explicit mechanistic explanation. For WP2, I (in cooperation with Jona Sassenhagen, Sophia Haan, Fabio Richlan, and Christian Fiebach), therefore, implemented a second computational model, the visual-orthographic prediction model (VOP), which allows realizing an orthographic similarity measure based on the images of words only (Gagl et al., submitted). In the study, we implemented and evaluated the VOP model with four behavioral studies (87 measured participants and open data from English and French), one fMRI (39 measured participants) and one EEG study (31 measured participants) both investigating brain activation. In addition, we conducted a first generalization investigation by showing that the VOP model, typically realized with computerized scripts, can be used to index the readability of handwritten scripts (48 participants).
Beyond this, the processes described in the models (lexical categorization and sensory information optimization) provide a better understanding of the visual word recognition process. Hence, all proposed processes generate new hypotheses for treatment and diagnostics of slow reading. This is especially important as current evidence from treatment approaches (Galuschka, Ise, Krick, & Schulte-Körne, 2014) suggests that only one treatment approach was found effective with only a small effect size. The implementation of a promising intervention on the basis of lexical categorization (Gregorova & Gagl, in preparation) is the first step towards a neurocognitively motivated training approach of orthographic processing. Currently, we focus on second language learners, a large and growing group of individuals, and plan to adopt the approach for young readers at the beginning of literacy acquisition. The wider impact of an efficient diagnostic and treatment program is that higher reading skills increase information processing skills of slow readers. This is critical for daily life decisions at work and elsewhere since these are based only on the available information which is limited when the individual capacities, one being the speed of reading, are low.
References
Eisenhauer, S., Fiebach, C. J., & Gagl, B. (2018). Dissociable prelexical and lexical contributions to visual word recognition and priming: Evidence from MEG and behavior. BioRxiv, 410795.
Gagl, B., Richlan, F., Ludersdorfer, P., Sassenhagen, J., & Fiebach, C. J. (2016). The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition. BioRxiv, 085332.
Gagl, B., Sassenhagen, J., Haan, S., Richlan, F., & Fiebach, C. J. (submitted). Visual word recognition relies on a sensory prediction error signal.
Galuschka, K., Ise, E., Krick, K., & Schulte-Körne, G. (2014). PLOS ONE, 9(2), e89900.
Gregorova, K., & Gagl, B. (in preparation). Lexical categorization training is successful in increasing reading speed of L2-German readers.
Yarkoni, T., Balota, D., & Yap, M. (2008). Psychonomic Bulletin & Review, 15(5), 971–979.