Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading

Characterizing different types of developmental dyslexias in French: The Malabi screener

Reading is a complex process involving multiple stages. An impairment in any of these stages may cause distinct types of reading deficits- distinct types of dyslexia. We describe the Malabi, a screener to identify deficits in various orthographic, lexical, and sublexical components of the reading process in French. The Malabi utilizes stimuli that are sensitive to different forms of dyslexia, including "attentional dyslexia", as it is traditionally refered to, characterized by letter-to-word binding impairments leading to letter migrations between words (e.g., "bar cat" misread as "bat car"), and "letter-position dyslexia", resulting in letter transpositions within words (e.g., "destiny" misread as "density"). After collecting reading error norms from 138 French middle-school students, we analyzed error types of 16 students with developmental dyslexia. We identified three selective cases of attentional dyslexia and one case of letter-position dyslexia. Further tests confirmed our diagnosis and demonstrate, for the first time, how these dyslexias are manifested in French. These results underscore the significance of recognizing and discussing the existence of multiple dyslexias, both in research contexts when selecting participants for dyslexia studies, and in practical settings where educators and practitioners work with students to develop personalized support. The test and supporting materials are available on Open Science Framework (https://osf.io/3pgzb/).

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.

One more trip to Barcetona: on the special status of visual similarity effects in city names

Previous research has shown that, unlike misspelled common words, misspelled brand names are sensitive to visual letter similarity effects (e.g., amazom is often recognized as a legitimate brand name, but not amazot). This pattern poses problems for those models that assume that word identification is exclusively based on abstract codes. Here, we investigated the role of visual letter similarity using another type of word often presented in a more homogenous format than common words: city names. We found a visual letter similarity effect for misspelled city names (e.g., Barcetona was often recognized as a word, but not Barcesona) for relatively short durations of the stimuli (200 ms; Experiment 2), but not when the stimuli were presented until response (Experiment 1). Notably, misspelled common words did not show a visual letter similarity effect for brief 200- and 150-ms durations (e.g., votume was not as often recognized as a word than vosume; Experiments 3-4). These findings provide further evidence that the consistency in the format of presentations may shape the representation of words in the mental lexicon, which may be more salient in scenarios where processing resources are limited (e.g., brief exposure presentations).

The neuroconnectionist research programme

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.

Artificial neural network modelling of the neural population code underlying mathematical operations

Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent neuroimaging studies have compared distributed representations of the visual, auditory and language domains in ANNs and biological neural networks (BNNs). However, such a relationship has not yet been examined in mathematics. Here we hypothesise that ANN-based distributed representations can explain brain activity patterns of symbolic mathematical operations. We used the fMRI data of a series of mathematical problems with nine different combinations of operators to construct voxel-wise encoding/decoding models using both sparse operator and latent ANN features. Representational similarity analysis demonstrated shared representations between ANN and BNN, an effect particularly evident in the intraparietal sulcus. Feature-brain similarity (FBS) analysis served to reconstruct a sparse representation of mathematical operations based on distributed ANN features in each cortical voxel. Such reconstruction was more efficient when using features from deeper ANN layers. Moreover, latent ANN features allowed the decoding of novel operators not used during model training from brain activity. The current study provides novel insights into the neural code underlying mathematical thought.

Does the visual word form area split in bilingual readers? A millimeter-scale 7-T fMRI study

In expert readers, a brain region known as the visual word form area (VWFA) is highly sensitive to written words, exhibiting a posterior-to-anterior gradient of increasing sensitivity to orthographic stimuli whose statistics match those of real words. Using high-resolution 7-tesla functional magnetic resonance imaging (fMRI), we ask whether, in bilingual readers, distinct cortical patches specialize for different languages. In 21 English-French bilinguals, unsmoothed 1.2-millimeters fMRI revealed that the VWFA is actually composed of several small cortical patches highly selective for reading, with a posterior-to-anterior word-similarity gradient, but with near-complete overlap between the two languages. In 10 English-Chinese bilinguals, however, while most word-specific patches exhibited similar reading specificity and word-similarity gradients for reading in Chinese and English, additional patches responded specifically to Chinese writing and, unexpectedly, to faces. Our results show that the acquisition of multiple writing systems can indeed tune the visual cortex differently in bilinguals, sometimes leading to the emergence of cortical patches specialized for a single language.

Using artificial neural networks to ask 'why' questions of minds and brains

Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

Graphemes are used when reading: Evidence from Monte Carlo simulation using word norms from mega-studies

Recently, Chetail (Journal of Memory and Language, 2020) has claimed there is no strong evidence that multi-letter graphemes are used in reading tasks with proficient adult readers, with most studies being statistically weak or having confounds in the stimuli used. Here, I used Monte Carlo simulation with data from reading mega-studies to examine the extent to which the number of multi-letter graphemes matters in words when letter length is held constant. This was done by simulating thousands of experiments using different sets of items for each of a small number of comparisons (e.g., words with only single-letter graphemes versus words with one multi-letter grapheme). The results showed that words with two multi-letter graphemes tended to cause slower reaction times than words with one or no multi-letter graphemes, with effects found in both naming and lexical decision tasks. Interestingly, when words with no multi-letter graphemes were compared with words with one multi-letter grapheme, the differences were much weaker. Simulations of naming results using two computer models, the connectionist dual-process (CDP) model and the dual-route cascaded (DRC) model, showed only CDP predicted this pattern. Since CDP learns simple associations between graphemes and phonemes whereas DRC uses a set of grapheme-phoneme rules, this suggests that the results may have been caused by simple associations between spelling and sound being relatively easy to learn with words with one compared with two multi-letter graphemes. More generally, the results suggest that graphemes are used when reading, but they often produce relatively weak effects and thus differences in some studies may not have been found due to a lack of power.

Graph theoretical analysis reveals the functional role of the left ventral occipito-temporal cortex in speech processing

The left ventral occipito-temporal cortex (left-vOT) plays a key role in reading. Interestingly, the area also responds to speech input, suggesting that it may have other functions beyond written word recognition. Here, we adopt graph theoretical analysis to investigate the left-vOT's functional role in the whole-brain network while participants process spoken sentences in different contexts. Overall, different connectivity measures indicate that the left-vOT acts as an interface enabling the communication between distributed brain regions and sub-networks. During simple speech perception, the left-vOT is systematically part of the visual network and contributes to the communication between neighboring areas, remote areas, and sub-networks, by acting as a local bridge, a global bridge, and a connector, respectively. However, when speech comprehension is explicitly required, the specific functional role of the area and the sub-network to which the left-vOT belongs change and vary with the quality of speech signal and task difficulty. These connectivity patterns provide insightful information on the contribution of the left-vOT in various contexts of language processing beyond its role in reading. They advance our general understanding of the neural mechanisms underlying the flexibility of the language network that adjusts itself according to the processing context.

From decomposition to distributed theories of morphological processing in reading

The morphological structure of complex words impacts how they are processed during visual word recognition. This impact varies over the course of reading acquisition and for different languages and writing systems. Many theories of morphological processing rely on a decomposition mechanism, in which words are decomposed into explicit representations of their constituent morphemes. In distributed accounts, in contrast, morphological sensitivity arises from the tuning of finer-grained representations to useful statistical regularities in the form-to-meaning mapping, without the need for explicit morpheme representations. In this theoretically guided review, we summarize research into the mechanisms of morphological processing, and discuss findings within the context of decomposition and distributed accounts. Although many findings fit within a decomposition model of morphological processing, we suggest that the full range of results is more naturally explained by a distributed approach, and discuss additional benefits of adopting this perspective.

Acquired dyslexias following temporal lesions

The acquisition of reading by children is supported by deep changes in the brain systems devoted to vision and language. The left temporal lobe contributes critically to both systems, and lesions affecting it may therefore cause both peripheral vision-related and central language-related reading impairments. The diversity of peripheral dyslexias reflects the anatomical and functional division of the visual cortex into early visual regions, whose lesions have a limited impact on reading; ventral regions, whose lesions are mostly associated to Pure Alexia; and dorsal regions, whose lesions may yield spatial, neglect-related, and attentional dyslexias. Similarly, central alexias reflect the broad distinction, within language processes, between phonological and lexico-semantic components. Phonological and surface dyslexias roughly result from impairment of the former and the latter processes, respectively, while deep dyslexia may be seen as the association of both. In this chapter, we review such types of acquired dyslexias, their clinical features, pathophysiology, and anatomical correlates.