Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners

Efficient reading is essential for societal participation, so reading proficiency is a central educational goal. Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responders. We (i) motivated a training procedure based on the Lexical Categorization Model (LCM) to introduce the framework. The LCM describes pre-lexical orthographic processing implemented in the left-ventral occipital cortex and is vital to reading. German language learners trained their lexical categorization abilities while we monitored reading speed change. In three studies, most language learners increased their reading skills. Next, we (ii) estimated, for each word, the LCM-based features and assessed each reader's lexical categorization capabilities. Finally, we (iii) explored machine learning procedures to find the optimal feature selection and regression model to predict the benefit of the lexical categorization training for each individual. The best-performing pipeline increased reading speed from 23% in the unselected group to 43% in the machine-selected group. This selection process strongly depended on parameters associated with the LCM. Thus, training in lexical categorization can increase reading skills, and accurate computational descriptions of brain functions that allow the motivation of a training procedure combined with machine learning can be powerful for individualized reading training procedures.

Access to meaning from visual input: Object and word frequency effects in categorization behavior

Object and word recognition are both cognitive processes that transform visual input into meaning. When reading words, the frequency of their occurrence ("word frequency," WF) strongly modulates access to their meaning, as seen in recognition performance. Does the frequency of objects in our world also affect access to their meaning? With object labels available in real-world image datasets, one can now estimate the frequency of occurrence of objects in scenes ("object frequency," OF). We explored frequency effects in word and object recognition behavior by employing a natural versus man-made categorization task (Experiment 1) and a matching-mismatching priming task (Experiments 2-3). In Experiment 1, we found a WF effect for both words and objects but no OF effect. In Experiment 2, we replicated the WF effect for both stimulus types during cross-modal priming but not uni-modal priming. Moreover, in cross-modal priming, we found an OF effect for both objects and words, but with faster responses when objects occur less frequently in image datasets. We replicated this counterintuitive OF effect in Experiment 3 and suggest that better recognition of rare objects might interact with the structure of object categories: while access to the meaning of objects and words is faster when their meaning often occurs in our language, the homogeneity of object categories seems to also impact recognition, mainly when semantic processing happens in the context of previously presented information. These findings have major implications for studies attempting to include frequency measures in investigations of access to meaning from visual inputs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).