Biology subjects

Gagl, B.

Publications and source records attributed to Gagl, B..

11 recordsLinked to original sources

bioRxiv2026

Hierarchical Predictive Processing during Natural Reading

Key neural mechanisms underlying reading, especially in the context of sentences and texts, remain elusive. An influential theory states that the brain rapidly generates a hierarchy of probabilistic predictions about upcoming words at multiple levels of linguistic representation. When encountering a word, a reader integrates sensory input with the linguistic predictions, and rely on the resulting prediction error signals to achieve efficient comprehension. Here, we tested this hierarchical predictive account with a natural reading task while EEG and eye movements were simultaneously recorded. We find distinct prediction error signals attributable to word contextual, lexical, and orthographic levels of predictions. These prediction error signals exhibit a temporal order. Our results also indicate that context-based predictions rapidly constrain orthographic-level predictions. We thus provide evidence for a hierarchical predictive process central to efficient natural reading.

neuroscience↗

bioRxiv2026

Eye-Tracking-BIDS: the Brain Imaging Data Structure extended to gaze position and pupil data

The Brain Imaging Data Structure (BIDS) is a widely adopted, community-driven standard to organize neuroimaging data and metadata. Although numerous extensions have been developed to incrementally extend coverage to new modalities and data types, an unambiguous, granular specification for eye-tracking recordings is lacking. Here, we present how BIDS will structure data and metadata produced by eye-tracking devices, including gaze position and pupil data. In addition to prescribing the organization of the unprocessed (raw) recordings and associated metadata as produced by the device, BEP20 also resolves gaps in current BIDS specifications beyond the scope of eye tracking. In particular, it adds a mechanism for including asynchronous model parameters and messages, such as contextual information, statuses, and events, such as triggers, generated by the device. BEP20 includes examples that illustrate its applicability in various experimental settings. This BIDS extension provides a robust standard that supports the development of self-adaptive, open, and automated eye-tracking data structures, thereby bolstering transparency and reliability of results in this field.

neuroscience↗

bioRxiv2025

Understanding the Neuro-Cognitive Mechanisms of Orthographic Learning in Humans and Baboons: A Comparative Study Using Domain-Specific Mechanistic and Domain-General Connectionist Models

Script is a key technology for humans, as mastering reading is essential for successful social participation. Hence, understanding the neuro-cognitive mechanisms underpinning the processes of learning to read is highly relevant. Here, we use two orthographic learning datasets from baboons and humans to investigate how they implement visual orthographic representations in a learning task of known and novel letter strings. We use two connectionist models (i.e., CORnet-Z and ResNet-18) and a mechanistic model (i.e., the Speechless Reader model, SLR) to investigate orthographic learning and infer the underlying neuro-cognitive processes. The connectionist models employ neuronally plausible architectures. The SLR versions are transparent neuro-cognitive models of orthographic decision behavior. Central to the SLR implementations are three types of prediction error representations that we use for computational phenotyping (i.e., pixel, letter, and letter-sequence level prediction errors). This approach allows us to infer the underlying representations in orthographic decisions. First, we fit the models and simulate the datasets to compare their performance (i.e., all models see the same sequence of stimuli as humans and baboons). Second, after comparing the model performance, we evaluate how the orthographic decisions have been implemented based on the representations used in the SLR models. We find that the SLR, especially on the trial-wise metrics, outperforms the CNNs in both datasets, with both connectionist models generating behavioral responses without a considerable overlap with individual human or baboon responses. Inspecting the SLR representations, we found that both species implemented the most informative representations that developed from visual to more complex orthographic representations with increased learning. Thus, we show that domain-specific neuro-cognitive mechanistic models are highly valuable in understanding complex behavior and how it is learned across species.

neuroscience↗

bioRxiv2024

Non-Human Recognition of Orthography: How is it implemented and how does it differ from Human orthographic processing

The ability to robustly recognize strings of letters, a cornerstone of reading, was observed in baboons and pigeons despite their lack of phonological and semantic knowledge. Here, we apply a comparative modeling approach to investigate the neuro-cognitive basis of orthographic decision behavior in humans, baboons, and pigeons, addressing whether phylogenetic relatedness entails similar underlying neuro-cognitive phenotypes. We use the highly transparent SpeechLess Reader (SLR) model, which assumes letter-string recognition based on a computational implementation of predictive coding, whereby orthographic decisions rely on prediction-error signals emerging from multiple representational levels: visual-pixel, letter, and letter-sequence representations. We investigate which representations species use during successful orthographic decision-making. We introduce multiple SLR variants, each including one or more prediction-error representations, and compare the simulations of each variant with orthographic decisions from individuals of three species after learning letter strings without meaning. Humans predominantly relied on letter-sequence-level representations, resulting in the highest task performance in behavior and model simulations. Baboons also relied on sequence-based representations, but in combination with pixel- and letter-level representations. In contrast, pigeons relied more on pixel and letter-level representations. These findings suggest that the orthographic representations used in orthographic decisions reflect phylogenetic distance: Humans and baboons use more similar representations than pigeons. Overall, the description of orthographic decisions based on a small set of representations and computations was highly successful in describing behavior, even for humans who mastered reading in its entirety.

animal behavior and cognition↗

bioRxiv2024

Specifying the orthographic prediction error for a better understanding of efficient visual word recognition in humans and machines

Efficient visual word recognition presumably relies on orthographic prediction error (oPE) representations. Based on a transparent neurocognitive computational model rooted in the principles of the predictive coding framework, we postulated that readers optimize their percept by removing redundant visual signals, allowing them to focus on the informative aspects of the sensory input (i.e., the oPE). Here, we explore alternative oPE implementations, testing whether increased precision by assuming all-or-nothing signaling and more realistic word lexicons results in adequate representations underlying efficient word recognition. We used behavioral and electrophysiological data (i.e., EEG) for model evaluation. More precise oPE representations (i.e., implementing a binary signaling and a frequency-sorted lexicon with the 500 most common five-letter words) explained variance in behavioral responses and electrophysiological data 300 ms after stimulus onset best. The original less-precise oPE representation still best explains early brain activation. This pattern suggests a dynamic adaption of represented visual-orthographic information, where initial graded prediction errors convert into binary representations, allowing accurate retrieval of word meaning. These results offer a neuro-cognitive plausible account of efficient word recognition, emphasizing visual-orthographic information in the form of prediction error representations central to the transition from perceptual processing to the access of word meaning.

neuroscience↗

bioRxiv2021

Rapid neural encoding of the contrast between native and nonnative speech in the alpha band

More than half of the worlds population is multilingual, yet it is not known how the human brain encodes the perception of native vs. nonnative speech. To find out, we asked German native speakers to detect the onset of native and nonnative (English and Turkish) vowels in a roving standard stimulation. Using EEG, we show that nonnativeness is robustly registered by an increase in phase coherence in the alpha band (8-12 Hz), beginning as early as [~]100 ms after stimulus onset and lasting more than 200 ms. The alpha band effect is speech-specific, successfully predicts the response speed advantage of nonnative speech, and grants [~]90% decoding accuracy in distinguishing native vs. nonnative speech irrespective of language familiarity. We propose alpha phase coherence as a candidate neural channel for the online resolution of the native-nonnative contrast in the adult brain. Significance StatementWe show that the human brain takes only [~] 100 ms to distinguish native vs. nonnative speech. This difference is neurally encoded in the alpha band in a reliable and specific way, supporting a processing model in which the mixture of top-down and bottom-up information represented in the speech signal is concurrently partitioned into different frequency channels.

neuroscience↗

bioRxiv2020

Predictive pre-activation of orthographic and lexical-semantic representations facilitates visual word recognition

To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading - visual, orthographic, phonological, and/or lexical-semantic - contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in explaining away predictable stimulus features, but rather in a sharpening of brain responses involved in word processing.

neuroscience↗

bioRxiv2018

Visual word recognition relies on an orthographic prediction error signal

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 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.

neuroscience↗

bioRxiv2018

Dissociable prelexical and lexical contributions to visual word recognition and priming: Evidence from MEG and behavior

Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/ phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N=38 human participants) while Experiment 2 assessed behavioral facilitation effects (N=24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context based-facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in pseudowords familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context based-facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism. Significance StatementThe goal of reading is to derive meaning from script. This highly automatized process benefits from facilitation depending on word familiarity and text context. Facilitation might occur exclusively within each level of word processing (i.e., visual, pre-lexical, and/or lexical-semantic) but could alternatively also propagate in a top-down manner from higher to lower levels. To test the relevance of these two alternative accounts at each processing level, we combined a pseudoword learning approach controlling for letter string familiarity with repetition priming. We found enhanced context-based facilitation at the lexical-semantic but not pre-lexical processing stage, and no evidence of top-down facilitation from lexical-semantic to earlier word recognition processes. We also identified predictive coding as the most likely mechanism underlying within-level context-based facilitation.

neuroscience↗

bioRxiv2018

Reading at the speed of speech: the rate of eye movements aligns with auditory language processing

Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3-5.5Hz, reflecting the production and processing of linguistic information chunks (syllables, words) every [~]200ms. Interestingly, [~]200ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at [~]5Hz. A subsequent meta-analysis with 142 studies from 14 languages replicates this result, but also shows that sampling frequencies vary across languages between 3.9Hz and 5.2Hz, and that this variation systematically depends on the complexity of the writing systems (character-based vs. alphabetic systems, orthographic transparency). Finally, we demonstrate empirically a positive correlation between speech spectrum and eye-movement sampling in low-skilled readers. Based on this convergent evidence, we propose that during reading, our brains linguistic processing systems imprint a preferred processing rate, i.e., the rate of spoken language production and perception, onto the oculomotor system.

neuroscience↗

bioRxiv2016

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

To characterize the left-ventral occipito-temporal cortex (lvOT) role 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 filter out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging. Empirically, using functional magnetic resonance imaging, we demonstrate that quantitative LCM simulations predict lvOT activation across three studies better than alternative models. Besides, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT. In contrast, a dichotomous word/non-word contrast, which is assumed as the LCMs output, could be localized to upstream frontal brain regions. Finally, we found that training lexical categorization results in more efficient reading. Thus, we propose a ventral-visual-stream processing framework for reading involving word-likeness extraction followed by lexical categorization, before meaning extraction.

neuroscience↗