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.

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. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

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).

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. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.

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. Interestingly, ~200 ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at ~5 Hz. A subsequent meta-analysis of 142 studies from 14 languages replicates this result and shows that sampling frequencies vary across languages between 3.9 Hz and 5.2 Hz. This variation systematically depends on the complexity of the writing systems (character-based versus alphabetic systems and orthographic transparency). Finally, we empirically demonstrate a positive correlation between speech spectrum and eye movement sampling in low-skilled non-native readers, with tentative evidence from post hoc analysis suggesting the same relationship in low-skilled native readers. On the basis of this convergent evidence, we propose that during reading, our brain's linguistic processing systems imprint a preferred processing rate-that is, the rate of spoken language production and perception-onto the oculomotor system.

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. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 â€‹ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.