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.

A cognitive-computational account of mood swings in adolescence.

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology.

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).

The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition.

To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature.

Eye movements during text reading align with the rate of speech production.

Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3 and 5.5 Hz, reflecting the production and processing of linguistic information chunks (syllables and words) every ~200 ms.

An orthographic prediction error as the basis for efficient visual word recognition.

Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations - typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to 'explain away' redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification.