Inducing Dyslexia in Vision Language Models
Authors: Melika Honarmand
Ayati Sharma
Badr AlKhamissi
Johannes Mehrer
Martin Schrimpf
Venue: ICLR 2026 Poster (2026)
Keywords: VLMs
Dyslexia
Reading
Cognition
Causal hypothesis testing
NeuroAI
Primary area: applications to neuroscience & cognitive science
TL;DR: We model dyslexia in vision-language models by selectively perturbing word processing units, reproducing core reading deficits while preserving general language and visual abilities.

Abstract: Dyslexia, a neurodevelopmental disorder characterized by persistent reading difficulties, is often linked to reduced activity of the visual word form area (VWFA) in the ventral occipito-temporal cortex. Traditional approaches to studying dyslexia, such as behavioral and neuroimaging methods, have provided valuable insights but remain limited in their ability to test causal hypotheses about the underlying mechanisms of reading impairments. In this study, we use large-scale vision-language models (VLMs) to simulate dyslexia by functionally identifying and perturbing artificial analogues of word processing. Using stimuli from cognitive neuroscience, we identify visual-word-form-selective units within VLMs and demonstrate that they predict human VWFA neural responses.
Ablating model VWF units leads to selective impairments in reading tasks while general visual and language comprehension abilities remain intact. 
In particular, the resulting model matches dyslexic humans' phonological deficits without a significant change in orthographic processing, and mirrors dyslexic behavior in font sensitivity.
Taken together, our modeling results replicate key characteristics of dyslexia and establish a computational framework for investigating brain disorders.

Published as a conference paper at ICLR 2026
INDUCING DYSLEXIA IN VISION LANGUAGE MODELS
Melika Honarmand, Ayati Sharma, Badr AlKhamissi, Johannes Mehrer Martin Schrimpf"
École Polytechnique Fédérale de Lausanne (EPFL) Lausanne, Switzerland
badr. madr.alkhamisse www.aaaaaaasssepsiil.. jjohannes.mehrer,
mehrer, martin. stchiischhmmmffmcccc feefffllcmcefccccccc ayati. sha rma@berkeleyy edd
edu
ABSTRACT
Dyslexia, neurodevelopmmenal disorder characterized by persistent reading diffi- culties, is often linked to reduced activity of the visual word form area (VWFA) in the ventral occipito-temporal cortex. Traditional approaches studying dyslexia, such as behavioral and neuroimaging methods, have provided valuable insights but remain limited their ability to test causal hypotheses about the underlying mech- anisms of reading impairments. In this study, we use large-scale vision-language models (VLMs) to simulate dyslexia by functionally identifying and perturbing ar- tificial analogues of word processing. Using stimuli from cognitive neuroscience, we identify visual-word- wwwrd-form--o-- form-selecte units within VLMs and demonstrate that they predict human VWFA neural responses. Ablating model VWF units leads selective impairments in reading tasks while general visual and language compre- hension abilities remain intact. In particular, the resulting model matches dyslexic humans' phonological deficits without a significant change in orthographic pro- cessing, and mirrors dyslexic behavior in font sensitivity. Taken together, our modeling results replicate key characteristics of dyslexia and establish a compu- tational framework for investigating brain disorders.
INTRODUCTION
Dyslexia is a complex neurodevelopmental learning disorder that impairs person's 's ability to decode written language and to spell, despite normal intelligence and educational opportunity. Difficulties arise mainly in reading, spelling, accuracy, fluency, and decoding abilities (Snowling et al.) 2020
Kunwar & Sapkota 2022). Depending on the severity criteria used, dyslcxia is estimated to impact 6-17% of children the school-age population (Pennington al. 2009 Lyon al. 2003), with the
global prevalence reported to range from less than 5% to 20% of the entire population (Wagner et al,,
2020 The brain disorder is associated with atypical neural activity in regions involved in language processing and phonological representation, in particular the visual word form area (VWFA) (Brem)
al. 20200: Monzalvo al. 20012] Shaywitz al. 2002 Maurer al.) 2007 Boros et al. 2016
Kronbichler & Kronbichler 2018). While dyslexia has been linked to both genetic and neurocogni-
tive factors such as phonological abilities, its exact causal mechanisms remain unclear (Peterson &
Pennington 2015
Werth 2023).
Advances in Machine Learning have significantly enhanced our ability to simulate and understand neural processes by leveraging biologically inspired computational frameworks. These develop- ments have enabled the modeling of brain activity with increasing fidelity, capturing not only the structural characteristics but also the precise neural patterns of the human brain. Recent work has demonstrated that artificial neural networks can approximate the brain's mechanisms, aligning closely with neural responses observed in human cortical regions, such as in vision and language Yamins & DiCarlo 2016, Schrimpf et al. 2020 2021).
"Joint supervision
Code available via GitHub

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(a) Human impairment
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Visual Word Form Area Hypoactivations
Visual-Word- Forrdd-Forr--o Selective Units Hypoactivations
Figure 1: Modeling dyslexia via visual-worddforry hypoactivation. (a) In humans, reduced activ- ity in the visual word form area is thought to result in diminished performance on reading-related measures while sparing general visual intelligence.
(h) Testing this hypothesis in vision-language models, we find that ablating cooual-word-forrr form-selective units produces the same dissociation.
Building this foundation, research aims to extend computational approaches of the healthy brain to the modeling of specific brain disorders - here focusing on dyslexia. In particular, we develop computational framework to model dyslexia by functionally identifying and perturbing specific units within vision-language models. This approach simulates hypoactivations documented in the VWFA of dyslexic subjects (Brem et al., 2020), abstracting away genetic and other con- tributing factors. A successful model simulation of dyslexia should exhibit selective impairment of reading performance while preserving other cognitive functions such as general intelligence and reasoning.
We show that identifying and ablating sorael--ooo-fomm-seeeeee units in vision language model (such as Qwen (Yang al. (0224) indeed leads diminished performance on dyslexia screening assessments like the Rapid Online Assessment of Reading (ROAR) (Yeatman et al. (021), while leaving general visual intelligence measures unaffected, including Raven's s Progressive Matrices [Burke 1958 Zhang et al. 2019) and the Kempler Test (Kempler et al.) 1998) (Fig. D Ablating random units the other hand does not have a selective effect reading abilities and instead equally affects visual reasoning performance. An investigation into chosen hyperparameters reveals that the choice of layer type is critical while the effect varies more smoothly with the number and severity of affected units. Testing our model's s reading deficits in more detail we find that it mirrors phonological deficits in human dyslexic subjects, but shows no significant impairment on ortho- graphic stimuli. Overall, our computational results mimic empirical findings in dyslexic individuals, exhibiting selective reading-speciff impairments without corresponding deficits in general intelli- gence (Snowling et al. [2020; [Peterson & Pennington| 2015), and thus establish a computational framework for modeling the neural mechanisms underlying brain disorders.
BACKGROUND & RELATED WORK
Behavioral effects of dyslexia. Dyslexia is characterized by persistent difficulties in accurate and/or fluent word recognition and spelling, despite adequate education, normal intelligence, and intact sensory abilities (Lyon al. (003). These difficulties are closely linked to deficits in phonologi- cal awareness and processing, often manifesting as problems with decoding unfamiliar or nonsense words, rapid naming, and word finding. Further challenges may include variable difficulties with let- ter sound learning, oral reading accuracy, written composition, and reading comprehension (Roitsch) & Watson 2019) Importantly, studies indicate that the core mechanisms of dyslexia are consistent regardless of IQ (Stanovich) 2005; Tanaka et al. 2011).
Hypothesized neural substrate. A growing body of evidence highlights the central role of the Visual Word Form Area (VWFA) in the neurobiology of dyslexia. The VWFA, located in the left ventral occipitotemporal cortex, is critical for fluent reading as it specializes the rapid and auto- matic recognition of written words (Dehaene & Cohen 2011 McCandliss et al. 2003). In individ- uals with dyslexia, this region often shows functional and anatomical abnormalities Longitudinal studies have revealed that dyslexic readers consistently exhibit smaller and less selective VWFAs compared to typical readers, even after significant improvements in reading performance through targeted training (Mitchell al. 2025 Brem al. 2020). These persistent differences suggest that
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beef
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Figure 2: Identifying Ssuual-word-form-eeeeeety units in VLMs. (1) To identify VWF-selective units, we compare unit activations in response images of words versus images of non-words, and identify the units that exhibit the strongest word selectivity. (2) To model the reduced VWFA activity observed in dyslexic individuals, we ablate the localized units. As control, we ablate an equal number of randomly selected units. (3) To assess the impact of ablations, we evaluate model performance on dyslexia screening tasks (ROAR (Yeatman et al. (22I]) and the Lexical Decision benchmark (Luke et al., 2023)) as well as visual IQ and reasoning tasks (RAVEN (Burke 1958) and Kempler et al. 1998) sentence comprehension tasks).
VWFA atypicalities are not merely a consequence of poor reading experience but represent a sta- ble neurobiological trait of dyslexia. Empirical studies consistently report VWFA hypoactivation and structural differences in dyslexics across languages and cultures (Paulesu et al. 2001: Siilnii al. 2005), reinforcing its cross-linguistic relevance. Moreover, lesions in this region have been causally linked to acquired reading disorders (Turkeltaub et al. 2013)), underscoring the VWFA's es- sential role in both typical and atypical reading processes. Nevertheless, the causal status of VWFA abnormalities remains debated, with some studies suggesting that these differences may reflect consequence rather than a primary cause of dyslexia (Olulade et al. 2013 Valdois 2010).
Models of Brain Function. Computational neural networks have proven effective at predicting both behavioral performance and neural responses in healthy subjects, making them valuable tools for studying brain function. In the visual domain, deep convolutional as well as transformer-based models have successfully captured neural activity in the primate ventral visual stream across ob-
ject and scene recognition tasks (c.g.. Yamins al.) 2014; Khaligh-Razzz Razavi & Kriegeskorte 2014]
Schrimpl et al. 2018 2020; Cadena et al., 2019; Spocrer et al. 2020; Zhuang et al. [021]] Wang
al. 2023 Margalit et al. 2024] Gokce & Schrimpt [2024] Lonnqvist al., 2025 TTng et al.
2025
In the language domain, transformer-basel and recurrent models have accurately predicted neural responses related to semantic, syntactic, and phonological processing (e.g.. SSchiimp al,
2021
Caucheteux et al. 2022 Goldstein et al. [2022] Toneva et al. [0128008) 2018|Hooe Hosseini et al.] 2024 Aw
al.
2024 Tuckute et al. 2024 Rathi al. 2025 AlKhamissi et al.) 2025 Du al.) (025)).
Machine learning methods thus provide powerful platform for capturing the cognitive and neural
patterns associated with dyslexia. By enabling controlled experiments and hypothesis testing in silico, such models can offer new insights into the mechanisms underlying the disorder and might support the development of targeted diagnostic and intervention strategies.
Another approach to model brain disorders employs comnectivity-baset models, which simulate large-scale brain dynamics using empirically derived structural or functional connectomes. In the context of reading and dyslexia, such models have been used to relate altered white matter pathways and disrupted functional connectivity to deficits in phonological processing and reading performance (uüller et al. 2017 Sihvonen et al. 2202I), but these comparatively coarse-grain approaches remain limited in capturing the precise underlying neural mechanisms.
To the best of our knowledge, there is no prior work on modeling brain disorders using system-level neural models, particularly in the context of dyslexia. While there are studies that employ com- putational models to investigate aspects of dyslexia, such visual information processing (Ogawal ett al. 2023) and handwriting anomalies (Alevizos et al. 2024), these approaches do not simulate the complex neural activity changes associated with dyslexia. Therefore, our work represents first.
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Figure 3: Ablating visual-worrdffomm-seettev units in the model. (a) Increasing the number of
VWF ablated units translates into a severe monotonic performance decline in ROAR (blue), while RAVEN (gray) is only affected at larger mask sizes. We chose the first mask size (bold) where ROAR performance falls below the dyslexia threshold (blue dashed line). Shaded regions represent 95% confidence intervals. (b) Beyond full ablation (bold), scaling unit activity (with fixed mask size of 6.89%) has little effect for positive scaling, while negative scaling severely degrades outputs non-selcctively. (c) While ablations in all layer types substantially affect performance, only the MLP components of the language decoder showed selective effects, indicating its core involvement in reading (full trends in Fig. 11.. (d) Distribution of WWF-selective units across the 80 transformer blocks of the language decoder. Ratios are mean across 20 random seeds and resampling of the localizer stimuli; standard deviations never exceed 0.03%.
attempt to model brain disorders (here, dyslexia) through hypothesized neural activity changes in state-of-the-aa models.
BENCHMARKS
We evaluate model performance using a suite of standardized assessments originally designed for human subjects in clinical psychology and cognitive neuroscience (Fig. 2 right). These include tests of lexical decision-making (ROAR, Yeatman et al. (021). visual reasoning (RAVEN, Burke 1958 Zhang et al. 2019), sentence-level syntactic comprehension (Kempler Test, Kempler et al. 1998.. and dissociations between orthographic and phonological processing (Luke et al. (023)) By repurposing these human- designed benchmarks for VLMs, we enable a targeted and comparable evaluation of specific cognitive capacities and their impairments, providing a structured framework for interpreting model behavior in relation to established cognitive theories.
Rapid Online Assessment of Reading (ROAR) is a browser-based, self-administered lexical deci- sion task that evaluates core reading skills by measuring both accuracy and speed in distinguishing real words (e.g., "able", "animal") from pseudo words (e.g.. "ablood", "accastanct")) (Yeatman et al., 202T] more examples in Table D]. In our model, however, response time is not a consideration, so evaluate performance solely based on the accuracy of lexical decisions. On each trial, the model is shown an image of a letter string and must decide whether it is a real word or a pseudo word. We present 200 real words and 200 pseudo words drawn from the full ROAR-Word corpus the train set for finding the minimal VWFA mask, with the remaining 50 real words and 50 pseudo words serving as the test set for lexical evaluation. Dyslexia Threshold: We define 65% ROAR perfor- mance as the threshold at which subjects are considered as dyslexic. This number is one standard deviation below the mean ROAR-score of the human population, consistent with prior works in hu- mans (Wagner et al. 2020). This choice is also meaningful from an epidemiological perspective as dyslexia estimated to affect about 5-20% of people (Wagner al. 2020)), and this prevalence aligns well with scores in the range of [1.65, 0.95) standard deviations below the mean.
Raven's Progressive Matrices (RAVEN) is a nonverbal assessment of fluid intelligence in which participants must select, from five candidate panels, the one that correctly completes a 3x3 or 2x2 matrix by preserving an underlying pattern of shapes or spatial relations (Burke) 1958 Zhang et al. 2019 examples in Appendix Fig. 9). Because it minimizes linguistic and cultural biases, RAVEN serves as an ideal control for general visual-spatial reasoning in our study. In our experiments, we administered the easy section of the RAVEN clinical version which consists of twelve items. By assessing the model's 's accuracy on these twelve puzzles, we establish a baseline for general visual intelligence against which any reading-specific impairments can be contrasted.
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Reading-selective deficits from ablating VWF-selective units. (a) Ablating VWF- selective units led to a selective reading deficit below the dyslexia threshold (ROAR, p 0.012), while performance on visual IQ and reasoning benchmarks (RAVEN, Kempler) remained intact or was slightly enhanced. (b) Ablating equal number of randomly selected units from the same layers affected performance throughout, with ROAR remaining above the dyslexia threshold and significant impairments
Figure 4:
visual reasoning. Dark blue bars indicate ablated model accuracy, relative to the intact model (light blue bars). Error bars denote 95% confidence intervals, and signif- icance was assessed with one-sample, one-tailed Student's ' t-test.
Kempler's Sentence Comprehension Test is a clinical assessment designed to evaluate syntactic comprehension by presenting participants with pairs of images accompanied by spoken or written sentence (Kempler et al. 1998). The task is choose the image that correctly corresponds to the sentence, making it useful probe of grammatical understanding and sentence-level semantics. In our implementation, we adapt this test for model-based evaluation by using only the image pairs from the original stimulus set, while providing the sentence as a text prompt. The model is asked to decide which image matches given caption (examples in Appendix Fig. I0). This transforms the task into visual question answering (VQA) challenge, allowing us to assess the model's capacity for sentence comprehension grounded visual reasoning.
Lexical Decision with Orthographic and Phonological Manipulations. To further dissect read- ing impairments into orthographic and phonological components, we incorporated stimuli from a lexical decision task developed by Luke al. (2023). This benchmark includes four stimulus types: (1) homophones (e.g., "brake" and "break"), (2) pseudo-homophones (e.g., "beaf" and "birf"), (3) transposed-letter (TL.) neighbour words (e.g., "blots" and "bolts"), and (4) TL non-words (e.g., "golve" and "glove"). These stimuli were originally used to contrast phonological and orthographic theories of dyslexia in human readers. For analysis. we group the stimuli into two categories: chonology-sensitivv (homophones and sheudo-homophones and wrthography-sensingg and TL non- words). The model performs a lexical decision on each item (an image containing word or non-word), and accuracy serves as the primary outcome measure. By comparing perfor- mance across these two groups, we can determine whether the model finds phonologically demand- ing items orthographically demanding items more challenging.
(TL words
METHODOLOGY
Functional Localization of VWF-selective Units. Given the well-established role of the VWFA in dyslexia, we target sooulllwor--oom-seleeeed (VLMs) as regions of interest for artificial lesions. To reflect the dual involvement visual and linguistic processes in dyslexia, we focus computational models that process images (and text) as input which reflect the full processing chain from retina (pixel) input to language. These VLMs, by presenting visual stimuli image input and task instructions text tokens, allow visual processing impairments from general language deficiencies, and their integration of visual and linguistic modalities within single architecture provides unified framework for examining the
(VWF-selecting units in vision-language models
dissociate
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Figure 5: VWF-selective units align with human VWFA activity. Alignment between predicted and actual IMRI responses in the human VWFA (noise-normalizeed), computed across the top-k% of VWF-selective units of the model (dark blue bars) and randomly selected units (light blue bars) for 5 seeds. At small subset sizes, VWF-selective units show significantly higher alignment than the average of five randomly selected subsets. Error bars indicate 95% confidence intervals across seeds.
cortical processes underlying word recognition and reading impairments in dyslexia. Our method enables hypothesis-drive investigation of reading-specific mechanisms directly within artificial systems and provides scalable and manipulable alternative to costly human studies.
Using functional localizers to identify selective units in computational models is neuroscience- inspired approach rooted studies like Kanwisher et al. (1997), who used fMRI localizers to iden- tify specialized brain regions. (Saygin al. (2016) functionally localized the VWFA by contrasting responses to written words with visually matched control stimuli, including line drawings of faces, scrambled words, and line drawings of objects, enabling robust identification of word-selective re- gions in the ventrotemporal cortex. Recently, AIKhamissi et al. (2025) adapted such functional localization methods for large language models and identified the causal role of language-sell selectivee units through targeted ablations. We adopt a similar approach by applying functional localizer paradigm our vision language model, targeting VWF-selective units.
Specifically, we use classic fMRI localizer from neuroscience (Saygin et al., 2016 to identify VWF-selective units in computational models. The model presented with the four stimulus cate- gories from (Saygin et al. (2016): written words, scrambled words, faces, and objects. For each con- sidered model unit, we compute a t-statistic comparing responses to word images versus the three non-word control categories. The t-statistic quantifies how strongly unit prefers words relative to other stimuli by measuring the difference response means normalized by response variability. Units with higher t-statistics respond more selectively and reliably to words, helping identify candidate VWF-selective units in the model. Units are then ordered by descending t-statistic, and define the top k% of these units to be the model's VWF-selective units (Fig. 2
Minimal Subnetwork for Dyslexia Simulation. To induce a dyslexia-like impairment, we gradu- ally increase the fraction of the top k% VWF-selective units that are ablated (i.c., set to zero) within the model's language layers. We begin with no masking (0%) and repeatedly mask a larger pro- portion of these units, evaluating at each level the model's accuracy on the train subset of ROAR stimuli. We stop increasing the mask size soon as the ROAR score falls below 65%, which we defined as the ROAR dyslexia threshold (Section B} The smallest proportion of masked units that meets this criterion defines our VWF-selective units (see also Fig. 3a).
Model Details. We focused our analyses on Qwen2-VL-72BB Yang et al. 2024). chosen for its strong OCR and visual understanding performance. Qwen2-VL-72B is large-scale autoregressive transformer integrating visual and textual modalities. Within the model, we examined the MLP layers of the language decoder where the minimal subnetwork for dyslexia corresponds to approxi- mately 6.89% all units (see below for hyperparameter analyses). We successfully elicited reading deficits in all tested models (additionally Molmo-13 72B, Deitke et al. 2024; and ExxTral1222 Agrawal et al. 2024). Deficits were monding-specife with no significant effects on visual IQ bench- marks (Appendix Fig.1

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Figure 6: Model mirrors human phonological reading deficits. (a) Lexical decision accuracy on
the ROAR reading test before (light blue) and after (dark blue) ablation VWF-selective units. Following ablation, model lexical decision accuracy at real/pseudo word classification drops be- low the dyslexia threshold, paralleling dyslexic participants (dark green) relative to control subjects (light green). (b) Ablated model performance decreases for phonologically confusable stimuli (e.g. "beaf" which sounds the same as "beef" but a pseudo word), indicating a phonological deficit and mirroring observations in humans. (c) Ablated model performance is not significantly affected on orthographically confusable stimuli (e.g. "golve" which looks similar to "glove" but is a pseudo word), whereas dyslexic humans tend to be affected. Error bars denote 95% confidence intervals; model results are averaged over 20 random seeds, each corresponding to a different sample of the localizer; significance assessed via one-tailed Student's t-test (Appendix A.44.
Perturbation Strength. Beyond a full ablation (activity O), we additionally experimented with parametric perturbations of VWFA units by varying the scaling factor the activations of the selected VWF-selective units. Specifically, we tested scaling factors in the range [-2, 4]. including sublinear, neutral (1.0), and superlinear amplification. We observed that only complete ablation (scaling factor 0) reliably produced the selective impairments characteristic of dyslexia (Fig. Bp). Perturbations other scales either had negligible behavioral effects or resulted in severely degraded output coherence, producing nonsensical or empty responses (Table Consequently, we focus our analysis on full ablation as the most faithful computational analogue of focal VWFA disruption.
Layer Choice. To identify VWF-selective units, we evaluated subnetworks from different layer types across the model. The MI.P gate projection layers in the language decoder emerged as the most selective, aligning with prior findings that MLP layers exhibit strong knowledge- specific selectivity (Meng et al., 2022, Zhang et al. 2022). To support this interpretation, ablated minimal subnetworks from other components of the model, the vision encoder wiiuull.blocke (il.bblockkk)).aaa........ prooj, proj), the visual merger (visual. vrisual.merger.. www.ll.errer..mmmm (i))(i}), and the language decoder's self-attention outputs (model layers, {i} wel..atttnoo.rrr.. These ablations led to sharper declines in RAVEN performance relative to ROAR per- formance, indicating that the observed effects Based on these results, we define VWF-selective units as those located in the MLP layers (model, layers........ (ivers.(i)... www.mpp.p..e.....m.mmmmmmmmmmmee proj) across all 80 transformer blocks of the language de- coder (Fig.3d))
purely reading-selective (Fig. 3p).
RESULTS
We evaluated the impact of VWFA-targete ablation model performance across tasks that probe reading, language comprehension, and nonverbal reasoning. We examine how selective disruption of functionally localized units affects different cognitive domains.
Selective Reading Deficits, Preserved Reasoning Capabilities. Ablating visual-word wood-woor-foooo form (eleeiseeeetive units leads to substantial decline in lexical decision accuracy the ROAR lexical reading task (Fig. 4b). Table shows example responses of the ablated model. Performance fact dropped below the dyslexia threshold on the held-out test set, marked reduction from the model's

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Model Accuracyt
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Figure 7: Model predicts font-specific reading performance. Accuracy on ROAR lexical decision
items across fonts for intact (light blue) and ablated "dyslexic" (dark blue) model, averaged over 10 random seeds. The dyslexic model shows significantly improved performance for OpenDyslexic, OpenDyslexic on an orange background, Comic Sans, and KG Primary Penmanship (p < 0.001), well for Orange Arial (p < 0.01). while performance is significantly worse for Papyrus (p < 0.01). In contrast, the intact model's accuracy remains stable across fonts with no signifi- cant differences. Error bars denote 95% confidence intervals across seeds.
pre-ablation accuracy (-32% in normalized accuracy with p < 0.01 using a one-tailed Student's s H-tsst for whether ablated model performance was significantly lower than baseline performance on each of the tasks; see Appendix A.4 for more details). In contrast, performance on the RAVEN's Progressive Matrices task remained stable (p 0.75), indicating that the lesioned subnetwork selec- tively impairs reading ability while sparing broader visual reasoning processes. We further evaluated sentence comprehension and visual grounding using the Kempler Sentence Comprehension Test as another visual Q&A task. Following ablation, the model'ss accuracy the Kempler task did not decrease compared to the non-ablated baseline (p ~ one-tailed test; a two-tailed Student's t-test reveals a statistically significant increase in performance of +10%, p < 0.01). This pattern paral- lels empirical findings in some dyslexic individuals, where deficits in phonological or orthographic processing are accompanied by preserved or enhanced performance in visual-spatial reasoning
and comprehension tasks (von Károlyi et al. 2003 Stanovich 20055 Tanaka et al. 2011).
A Ablating Random Units Does Not Lead Selective Reading Impairments. To verify that the observed dyslexia-like effects were not simply due to the number or layer-wise distribution of ab- lated units, but rather depended on the specific identity of the VWF-selective units, we conducted control experiment. We preserved both the total number of ablated units and the selected layers, but randomly selected which specific units were ablated within each layer. This approach did not lead to selective reading impairments: the performance on all benchmarks dropped (-21% with p < 0.003 for ROAR, 22% with p- < 0.004 for RAVEN, and 13% with p < 0.042 for Kempler), and reading performance did not drop below the dyslexia threshold (p 0.87; Fig. 4b). Furthermore, when the same number of units was ablated but distributed randomly across the entire network rather than within the same layers, the model's outputs largely degraded, often generating inco- herent or empty responses, confirming that the ceading-specifii impairment depends on targeting VWF-selective units.
Human-Like Phonological Deficits. VWFA-targeted ablation selectively impairs reading perfor- mance, producing deficits reminiscent of those observed in dyslexic individuals. To assess whether the observed deficit was primarily phonological or orthographic, we evaluated model performance on the lexical decision benchmark from Luke al. (2023). Post-ablation, model accuracy dropped significantly on mhonology-sensitive items (Fig. 6 8% with p < 0.01), but remained relatively stable for orthography-sensitee stimuli (p > 0.059) indicating disproportionate disruption of phono- logical processing with preserved orthographic representations. Human behavioral data released by Luke et al. (2023) reveal a related dissociation: Dyslexie participants showed a pronounced accuracy items (-9% with < 0.01), but also demonstrated impairments on orthography-sensiiive items(-6% with p < 0.01) on average. Dyslexia often co-occurs with other learning and language disorders (Nicolson & Fawcett 2011 Chalikia al.) 2025). yet the available dataset does not report such metadata, which may contribute to heterogeneity and the observed orthographic impairments. The dominant view holds that dyslexia arising from VWFA- related impairments is primarily phonological, consistent with the selective phonological deficit observed in ablated model.(MMCandlinn al. 2003).
reduction on phonology-sensiccee
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Table 1: Common error patterns of the WWF-ablated model. Blank: Model gave no output at all (completely empty response). Misclassification: Model explicitly labeled real word as pseudo, or pseudo word as real. Contextual Over--nterpretaaion:: Model invoked external references (languages, acronyms, domains) to justify the wrong answer. Ambiguous Hedging: Model refused commit, saying the word could be real or pseudo depending on context. Gibberish / Corrupted: Output was meaningless fragments, random characters, non-English text, or truncated nonsense. More examples of each pattern in Table 2
Response type
Sample Word Image
Example Model Output
Blank Misclassification
accust (real) yammerring (real)
The word "yammerring" looks like a pseudo word.
Contextual Over Inter-
hus (pseudo)
The word "hus" in the image is a real word in English. It is which is type of edible corn.
pretation
that means "husk"
Ambiguous Hedging
dood (pseudo)
The word "dood" appears in the image. seems to be pseudo word because it has no meaning in English. However, could potentially be real word if it were part of a specific context or phonetic representa- tion, for which further investigation would be necessary.
Gibberish / Corrupted
imeyits (pscudo)
image of the image of the image of the im- age of the following.
Alignment Between VWF-seleetive Units and Human Brain Activity. Building the observed behavioral alignment, we next assessed whether selected units correspond to human neural activ- ity. We quantified how VWF-selective units align with human neural responses using standard encoding-mode: framework (Naselaris et al. [011]: Schrimpf et al. 2021). For each subject in the Marvi al. (2255) dataset, ridge-regression models were trained to predict voxel-wise fMRI responses from the model's 's video-evoked activations using 5-fold cross-validation. Pearson correla- tions between predicted and actual voxel responses were averaged across folds and divided by each voxel's 's noise ceiling to yield the alignment score. We evaluated model alignment using subsets of the most VWF-selective units, varying the subset size from 0.25% 5% of all units. For each subset size. we generated five VWF-seleetive masks by resampling the localizer stimuli with five different seeds. Each VWF-selective mask was paired with a size-matched random mask for comparison. At smaller percentages, -el-seleetive subsets showed significantly higher neural alignment than dom subsets, indicating that these units encode brain-relevant structure rather than arbitrary features and supporting their role both behavioral and neural correspondence to visual word-form pro- cessing (See Fig. 5). For larger percentages (above 1.25%), the same effect is not observed. This likely reflects differences in feature dependence; VWT-selective subsets are drawn from a ranked list and therefore contain correlated units, whereas random subsets contain more heterogeneous, less correlated features. As more units are included, these independent random features improve fMRI prediction, while correlated VWF-selective features add redundant information. This suggests that VWF-selective units form specialized subspace that is highly informative when the most selective units are sampled.
Predicting Dystexia-Friendii Fonts. Because the ablated model reliably reproduces behavioral patterns observed in dyslexic readers, it can be used to evaluate reading interventions and design supportive visual stimuli. To illustrate this, we presented both the intact and ablated models with the original ROAR lexical decision items in Arial (the standard font used in the benchmark), keep- ing the font size as similar as possible, and then systematically replaced the font with alternatives known from prior work and expert recommendations to be either challenging easier for dyslexic

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readers. Fonts tested included, Tahoma, OpenDyslexic (Gonzalez 2014). Comic Sans, KG Pri- mary Penmanship, Chiller, Papyrus, Onyx, and Ecofont. We also tested Arial and OpenDyslexic on dark orange background, grounded in personal reports from dyslexic individuals that colored backgrounds can aid reading. The ablated model mirrored human sensitivity and performance de- creased remained unchanged on fonts considered difficult, and improved remained stable on dyslexia-friendly fonts, whereas the intact model showed no systematic differences (See Fig. 7ig.7..m The ablated model showed significant improvement in lexical decision accuracy relative Arial for OpenDyslexic, OpenDyslexic on an orange background, Comic Sans, and KG Primary Penmanship (p & 0.001), and for Orange Arial (p < 0.01). contrast, performance significantly decreased for Papyrus (p < 0.01) compared to Arial. This demonstrates that the model can capture font-specific reading difficulties observed in dyslexic readers. Importantly, this approach opens a novel possibil- ity unique to computational modeling. Future work can design or optimize new fonts by identifying visual configurations that maximize performance in the ablated model while preserving readability in the intact model.
DISCUSSION
By inducing dyslexia-like behavior in vision language models, we can examine how specific changes affect performance and study the mechanisms involved. These models, though abstracted from biol- ogy, preserve key correspondences with brain- like processing enabling insight into brain disorders, prototyping interventions, and overcoming limitations of human data collection.
Interpretability. This work advances interpretability by identifying reading-selective subnetwork in VLMS reminiscent of the VWFA in humans. Targeted ablations within this subnetwork induce a phonological rather than orthographic deficit, despite the model never processing auditory input. These findings suggest that functional localization can be used to uncover mechanistic and causal links between specialized neural circuits and behavioral outcomes. We also observe performance increase on nonverbal benchmarks post-ablation, paralleling findings in some human dyslexics (von Károlyi et al., [003 Lam Tong 202T), which we plan to explore further in future work with the goal of enhancing learning via ablation of non-selective units.
Deficit Characterization. Hypoactivation of the VWFA is often linked to phonological deficits McCandliss et al., (2003). By ablating VWFA-selective units, we find that the resulting deficit is phonological, supporting the dominant view that phonological dysfunction is primary, at least within the model.
Broader Applicability. Despite differences from biological brains, brain-aligned models pro- vide useful proxies for identifying mechanisms underlying behavioral deficits. Models exhibiting dyslexia-like behavior can act as digital twins, enabling controlled causal experiments (c.g., tar- geted ablations) that are difficult in humans. The proposed Docalization-ablatii behavviora testing framework generalizes to other brain disorders where contrast stimuli identify neural substrates and behavioral benchmarks assess causal effects, offering flexible platform for studying psychiatric conditions beyond dyslexia and informing intervention strategies such as dyslexia-aware font de- sign to improve reading performance in dyslexic readers.
CONCLUSION
By localizing and lesioning soraal-woord-ro--seeeetttt units in vision-language models, this work provides a biologically inspired simulation of dyslexia, capturing its core reading-specific deficits while preserving broader cognitive functions. Our findings mirror empirical dissociations observed in dyslexic individuals and validate the functional relevance of the visual word form area. Our approach demonstrates how mechanistic manipulations of artificial neural network models can sim- ulate selective cognitive deficits, offering novel computational framework for testing hypotheses about the causal role of specific neural circuits reading. Via the identification of model compo- nents whose ablation mimics dyslexic-like impairments, such in-silico experiments could help test hypotheses about neural targets for early screening, suggest biomarkers for subtyping dyslexia (e.g., based on differential phonological versus orthographic deficits), and inform the development of tar- geted intervention strategies such as pesslxia-frieende fonts. This framework lays the groundwork for modeling not just the neurotypical but also the neurodivergent brain.
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A APPENDIX
A.1
OTHER MODELS
We applied our VWFA localization and ablation methodology across other vision-language models: Molmo-72B(Deitke et al., 2024), and PixTral-12B(Agraaal et al. 2024). Molmo-72B showed a baseline ROAR accuracy of 83%, providing suitable foundation for evaluating selective impairments. After ablating the VWFA- VWFA-analogous units, ROAR performance exhibited statistically significant decrease, whereas RAVEN performance did not show statistically significant change, indicating that the observed deficit is specific to reading- reading-related related processing (Fig. (h). PixTrral-12 12B demonstrated robust baseline performance on ROAR: 86. 66.75% After ablating the -ell-velee units, ROAR performance decreased, whereas the change in RAVEN performance was not statisti- cally significant, suggesting that the method can be generalized to other models.
A.2
VISUAL IQ AND REASONING BENCHMARKS
Figure shows an example of the non-verbal benchmarks. More examples of Kempler are shown in Figure 10
A.3 PROMPTS USED ACROSS ALL EXPERIMENTS
For transparency and reproducibility, we include the exact prompts used across all lexical, phono- logical, and nonverbal reasoning benchmarks. These prompts created with the explicit goal of mimicking how human participants are typically instructed to perform these tasks, ensuring that the model receives directions that parallel standard experimental protocols.
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Activation Extraction Prompt.
Describe the image.
Lexical Decision Prompt.
A real or pseudo word will be presented to you in an image. The pseudo words might look like English words, but they don't t mean anything in English. For example, laip, bove real words will be ones you recognize. They are real English words like is, basket, lion. Please answer the following question: Is the word in the image real word or pseudo word?
cigbert are pseudo words. The
KEMPLER Prompt.
In which image (DESCRIPTION OF TARGET IMAGE] After providing the reason, give your final answer in this format: "The answer is picture a" or "The answer is picture b".
RAVEN Prompt.
You will be presented with a nonverbal Raven's Progressive Matrices IQ puzzle. The puzzle will consist of a visual pattern with one missing element, by a question mark. Your task is to identify which of the five provided options best completes the pattern. Please choose the option that logically fits the sequence. Write the answer in one digit in [1, 2, 3, 4, 5].
(a) Molmo
(b) PixTral
n.s.
**
Normalized Accuracy (%)
**
Normalized Accuracy (%)
100
Dyslexia
80
Threshold
Dyslexia Threshold
n.s.
80
60
60
40
40
20
20
ROAR
Raven
ROAR
Raven
units in other vision-language models. Plots show accuracy of the intact and ablated models on the ROAR and RAVEN benchmarks. Bars indicate mean aceu- racy over 5 random seeds, with error bars representing 95% confidence intervals. For Molmo-72B (a), ablation of selective units significantly reduces ROAR performance from 83%, while RAVEN performance remains unchanged. For PixTral- 12B (b), ablation significantly reduces ROAR perfor- mance from 86% without affecting RAVEN, indicating that the deficits are selective for reading.
Figure 8: Ablating
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(a) RAVEN
(b) Kempler
?
A
B
b
Figure 9: Examples of IQ and visual reasoning benchmarks (a) Example item from the RAVEN visual reasoning test. The task is to identify the panel (from five candidates) that correctly completes the matrix by following the underlying visual pattern. In this example, the correct answer Option 3. RAVEN serves measure of general visual-spatio reasoning, independent of language ability.
(b) Example item from the Kempler's Sentence Comprehension Test used in our model evaluation.
Given a caption and pair of images (A: left, B: right), the model is prompted to identify which image correctly depicts the deseribed event. In this example, the model is asked: "In which image
(A or B) the dog that chases the girl is following the boy?" (correct answer option A.)
A.4 STATISTICAL TESTING
All model results are reported averages over 20 random seeds, each corresponding to a different random sample of the localizer stimuli. To evaluate whether ablations reduced performance, we compared the 20 ablated values (one per seed) against the intact model's baseline using a one-tailed Student's 's f-test. The null hypothesis was that mean ablated performance equals baseline, and the alternative was that mean ablated performance is lower. one-tailed test was chosen because our hypothesis was directional: ablations were expected to impair, but not improve, task performance. Reported /-statistics and p-values reflect this comparison for each benchmark. Intriguingly, a two- tailed Student's t-test, testing bidirectional changes of value distributions, revealed statistically significant increase on the Kempler visual reasoning task.
A.5
COMPUTATIONAL RESOURCES
All experiments were run on two NVIDIA A100 GPUs with 80GB memory each. The ablation implementation does not introduce meaningful overhead.
A.6 USE OF LLM ASSISTANCE
We used ChatGPT (GPT-5, OpenAl, 2025) to aid in structuring and polishing the writing of this document. The authors reviewed, edited, and are fully responsible for the final content.
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(1) The cow chases the pig and the duck
A
(2) The baby is pulling the mother
A
B
(3) The bear that watches the boy is watching the dad
A
B
(4) The dog that chases the cat is chasing the pig
A
B
Figure 10:
Examples of IQ and visual reasoning benchmarks Several items from Kempler's Sentence Comprehension Test, which evaluates the ability to map linguistic descriptions onto visual scenes. In each case, the model is given caption and two candidate images (labeled A: left, B: right) and must decide which picture matches the sentence. Shown here are four sample items with increasing syntactic complexity. The correct answers for Items 1-4 are: A. B. A,
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(a) Visual Merger
(b) Language Self-attention
(c) Vision Self-attention
58
Raven Accurslcy (%6
ROAR Aczuracy (%)
Raven Acturacy (%)
ROAR Accuracy (%)
Acc uraec (%)
40
10
ROAR Accurany
16
30
23
x
10
10
5.055
0.166 Mask Size (%)
0:113
0.27
Mask Size (56)
Mark Siz0 (56)
Figure 11: Ablating units in non-selective layers does not result in selective impairments. Test- ing other layers in the (a) visual merger (projection layers), (b) language decoder, and (c) vision encoder shows that RAVEN performance declines faster than ROAR performance indicating the effect not reading- reading-selective selective. Intriguingly, the self-attention layers show inverse effect, with po- tential selectivity for RAVEN over ROAR. Shaded regions around each curve represent the 95% confidence intervals.
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noun in Published as a conference paper at ICLR 2026
Table 2: Further examples of wrong model responses to ROAR when VWF-selective units are ablated. (Continued from Table [ Errors span misclassification, contextual over-interpretation, ambiguous hedging, and gibberish//orrupped outputs.
Response type
Sample word image
Example model output
Misclassification
charring (real)
The word in the image is a pseudo word.
creat (pseudo)
The word "creat" is real word in En- glish. It can be used as a verb, such as "to creat something."
rackle (pseudo)
The word in the image is real word.
Contextual Over-lnterpretation
che (pseudo)
The word "che" is a real word in En- glish. is used as conjunction in Italian grammar and also English, referring to a type of cheese.
hu (pseudo)
The word "hu" be considered real word in some contexts. in Hungarian names and titles, such as Budapest University of Economics (HU).
used
mich (pseudo)
The word "mich" in the image appears to be a real word... It seems to be a play on the word "Michigan."
a bust (pseudo)
The word "albust" is real word in En- glish, but it doesn't have any meaning associated with it. It appears to be a play on the word "albus."
Ambiguous Hedging
bi (pseudo)
The word "bi" can be a real word or pseudo word depending the con- text. It could mean "two" as a prefix, but alone it may not count word.
ase (pseudo)
The word "ase" can be interpreted as real word or a pscudo word depend- ing on its context. If it stands alone, it is a pseudo word, but words like "phrase" or "database" it is real.
yos (pseudo)
The word "yos" appears to be real word. It seems be part of sen- tence or phrase, suggesting it could be meaningful in English. However, its full meaning is unclear.
Gibberish/ / Corrupted
napery (real)
(Chinese characters).
pumer (pseudo)
(Chinese characters)
cowbim (pseudo)
real words will bepseudo words will bepscudo words
seashell (real)
the image of the image words
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Table 3: Examples of wrong model responses to ROAR when the VWF-Selective units are hyperactivated by 3.25 times. Sentences collapse with random numbers, irrelevant fragments, or broken logic, confirming that hyperactivating VWF-selective units does not replicate the patterns of impairment observed in humans.
Model input
Model output
find (real)
The word "find" cannot be found in the image, according see me more words 123 6th of March 3, 2027, 5:42: :00-05 no- 13 2016 12:00 am am very well-/.c.0 2.t.c02222221121 202.002222200000 100.000.0000000..11 5028 0.27. 0001 1 0_0000000.
in (real)
The word "in" is actually letters. To get the maximum number of letters, you would have to look the middle of the word. The text in the middle of this, we hope, can be as much as as the s. The machine can be working part of its body (p) or a part.pellp the verb 'to" in in(Ref#p=truly/500 word no string of fine, in which case it becomes verb in the text of the image content. 1/s=10000001/ <t>the act of
never (real)
The word "neve" completing the word 'never' is 5 characters long and 100000000 times as long as the word never, by welcome back after the 'cat" 'never')
telescoped (real)
The word "telescopically" is a word only one time. The word "tele- scoped" be seen in different order. How would you support this statement with less than 10000 words at a website with a word count factoring the perfect total of each and before it. 4.500 E-17. The last time with a similar tag and its own 2000.3999999999999 times [2000. 1804.880000.
everyone (real)
The word "everyone" is not real word. It was just a fun thing to think about. If word real word and it's word we' 'e all seen before and the "to" means that don't have to right now but it's real word because it can be seen and not for other people to be used, they can't t be naturally occurring the word "eves" can be word with one or two syllate, e.g., "be" can be used by the "best" and "you" (2
if even where none of the time. It's then
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Reviews:
[{"review_id": "yVOURiOead", "reviewer": "Reviewer_63KZ", "summary": "The paper presents a computational approach for simulating dyslexia within VLMs by identifying and ablating artificial analogues of the human visual word form area (VWFA). The authors use neuroscience-inspired functional localization to pinpoint visual-word-form-selective units inside transformer architectures and perform targeted ablation to mimic the neural disruptions observed in dyslexic individuals. Ablation leads to substantial reading deficits, measured by clinical benchmarks, while leaving general visual reasoning and sentence comprehension unaffected, thus reproducing the dissociation observed in human dyslexia. \n\nCritically, the computational lesion predominantly impairs phonological processing with minimal orthographic deficits, paralleling key aspects of developmental dyslexia in the clinic. The framework is benchmarked across multiple VLMs and analyzed for specificity with rigorous controls, and is offered as a platform for causally testing mechanisms of neurodevelopmental disorders using artificial neural networks.", "questions": "- Can the identification of VWF-selective units be validated by alignment with neural data, for example, through representational similarity analysis with fMRI or MEG signals from human readers? This would strengthen claims of biological realism.\n\n- The phonological deficit induced by ablation is robust, but the orthographic deficit is comparatively minor. Can the lesioning approach be modified to target orthographic processing more effectively, and what does this suggest about the separability of these mechanisms in transformer architectures?\n\n- Are the identified VWF-selective units spatially or functionally clustered within the model, and do they possess unique connectivity profiles? Are there architectural analogues to known properties of the VWFA (position, connectivity) in the cortex that can be drawn from transformer weights or activation maps?\n\n- The current approach is limited to the ablation (silencing) of units. Have the authors considered developmentally inspired interventions, such as training models under restricted input, gradual lesioning, or biased gradient propagation, to better capture the emergence and remediation of dyslexia-like deficits?\n\n- Benchmark choices are justified, but the absence of processing speed (a key diagnostic marker in dyslexia) is notable. Can future work incorporate time-dependent metrics?\n\n- Minor: Visualizations (activation maps via GradCAM, ablated unit distributions) could benefit from more direct comparison to human neuroimaging data or developmental profiles.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The work is original in proposing unit-level functional localization and lesioning inside VLMs to simulate neurobiological disorders. Unlike previous works, the method operates at the level of hypothesized neural substrates and performs causal manipulations aligned with neuroscience conventions rather than coarse-grained connectivity perturbations or behavioral proxies.​\n\n- The experimental design is precise: identification of VWF-selective units via well-established localizer paradigms from human neuroimaging is directly transposed to transformer architectures, and quantitative assessment is conducted with adapted human clinical tasks.​\n\n- Control experiments using random unit ablation convincingly show that only lesioning functionally localized word-selective units produces the desired dissociation, with random ablation causing global performance degradation, establishing the necessity of neuro-informed unit targeting.​\n\n- The work addresses not just cognitive outcomes but fidelity to documented human error types; response analyses detail patterns like misclassification, contextual over-interpretation, ambiguous hedging, and corrupted outputs, directly paralleling error modes seen in clinical dyslexia assessments.​\n\n- The quantitative analysis is comprehensive, including hyperparameter sweeps for mask size, scaling factor variations, and layer-type discrimination, allowing for mechanistic insights into where reading specialization resides within transformer blocks.​", "weaknesses": "- The identification of VWF-selective units depends on activation statistics from model internals in response to image categories, but does not incorporate human brain recordings! Without direct mapping between artificial and biological VWFA units, the correspondence remains functional rather than structural or neural, potentially limiting claims about mechanisms and transferability.​\n\n- The clinical profiles of dyslexia captured by the ablated models are more purely phonological than in human subjects, who often display mixed phonological and orthographic impairments. The model’s orthographic capacities are notably resistant to ablation, and the observed deficit distribution is less nuanced than seen in patients, raising questions as to whether this reflects architectural constraints or a misalignment in the simulated lesion procedure. The authors acknowledge this, but propose no practical avenues for resolving it within the current model scope?\n\n- The experimental controls, while thorough, operate entirely within model space. There is no demonstration that the artificial VWFA units share structural, developmental, or topological features with their biological counterpart. For example, are the localized units spatially clustered within the transformer, and do their connectivity patterns resemble cortical circuits?\n\n- Evaluation benchmarks are based on image-word discrimination, general nonverbal reasoning, and sentence comprehension tasks, all adapted from clinical settings. Although valid for cross-domain comparison, the complexity of the clinical phenotype is reduced by focusing only on limited aspects (speed of reading is omitted; real-life reading comprehension and development trajectory are not simulated). Dyslexia is developmental, but the ablation is static and does not reflect learning dynamics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OYIwpbAthJ", "reviewer": "Reviewer_RhN7", "summary": "This paper proposes a framework for simulating dyslexia in large-scale vision-language models (VLMs) by identifying and ablating visual-word-form-selective (VWF) units, inspired by the functional role of the human visual word form area (VWFA) in reading. Using cognitive neuroscience benchmarks and targeted interventions in VLMs, the study shows that ablation of selected units impairs reading and phonological performance on tasks such as ROAR, while generally sparing visual reasoning and sentence comprehension. These findings are mostly consistent with human experiments, suggesting the potential of the proposed framework as a tool for modeling brain disorders.", "questions": "1. In Figure 3a, RAVEN accuracy increases notably as the mask size increases from 0 to 6.08%, which seems counterintuitive. Have the authors examined this phenomenon or provided an explanation for this unexpected trend?\n2. In Appendix Figure 6b, PixTral exhibits substantial variation, particularly on the Raven benchmark. Could the authors clarify the cause of this high variability and whether it reflects instability in the model or differences in experimental setup?\n3. It would be helpful if the authors could specify the computational resources required to conduct the experiments. This information is important for assessing the scalability and reproducibility of the proposed approach.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is clearly written and easy to follow. The figures are well-designed and effectively support the reader’s understanding of the main ideas.\n2. Using VLMs for modeling brain disorders is a novel and interesting idea. To my knowledge, no previous studies have explored this direction.\n3. The cognitive benchmarks used in this study are carefully chosen and serve well-defined purposes. The adaptation of these human tests to VLMs is clearly described and supported by sound reasoning.\n4. The inclusion of multiple base models and control conditions (random ablations, model layers, etc) strengthens the study’s rigor, making the results convincing.", "weaknesses": "1. The core mechanisms in this paper, such as the use of VLMs and the ablation analyses, have been explored in previous neuroscience studies [1]. The authors are encouraged to cite and discuss relevant works to better situate their contributions within the existing literature.\n2. The paper lacks details about the prompts used in the cognitive experiments. This is a crucial omission, as the performance and interpretability of VLMs/LLMs are known to depend heavily on prompt design. The authors should include detailed descriptions or examples of the prompts to enhance transparency and reproducibility.\n3. The paper relies primarily on textual descriptions and lacks a clear mathematical formalization of the proposed framework. Incorporating explicit mathematical statements in the methodology section would improve both the scientific rigor and the reproducibility of the work.\n4. It's appreciated that the authors have provided an anonymous link their source code, but the files in the repository cannot be accessed.\n\n[1] Du, Changde, et al. \"Human-like object concept representations emerge naturally in multimodal large language models.\" Nature Machine Intelligence (2025): 1-16.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "djqmurw6Bp", "reviewer": "Reviewer_qoES", "summary": "This paper proposes a paradigm to localize and lesion VWF-selective units in vision-language models by exploring different ablation protocols, different ways of choosing model units, and benchmarking against different clinical assessments to qualitatively capture core reading-specific deficits while preserving broader cognitive functions--which are key hallmarks of dyslexia.", "questions": "Please see the weaknesses section above.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper does a mostly thorough job of systematically introducing the research question and explaining the various cognitive assessments used to benchmark the models, and it is easy to follow. Developing mechanistic computational models of brain function that represent a rich language of hypotheses that can narrow the scope of experiments to perform ex-silico and observe trends that might be difficult in humans is an important undertaking, and I appreciate the authors' efforts in doing so.", "weaknesses": "(1) While the paper recapitulates findings from the dyslexia literature on a qualitative basis--such as the selective impairments in reading tasks while comprehension capabilities remain unaffected--I am not entirely convinced if that is enough to validate the framework. I know that there is really less data on brain activity or assessment scores recorded longitudinally from dyslexic individuals, but a subsequent missing quantitative comparison between the models and that data feels like a test whose absence quite strongly undermines my ability to validate the framework. (2) Additionally, could the authors point out how different their work is from the work by AlKhamissi and colleagues (2025)? Is it simply the case that their approach of functional localization is being applied to the visual word form area here? (3) Both vision and language models have been validated, to a large extent, as decent models of the corresponding sensory systems they model due to being able to use model units to explain a large amount of variance in sensory neural responses under different transform classes. A discussion on the same for vision language models which are being used here seems like a pre-requisite before even considering them to analyze brain behavior under some form of dysfunction. For example, do the representation similarity matrices from the brain and these models using some stimulus set show a large degree of convergence? (4) I appreciate the fact that the authors try a variety of different ablation strategies. However, I am curious to know why the authors think that completely ablating model units (effectively, instant cell death) is the computational principle that matches how, say, focal lesions occur in the brain. In the conclusion, the authors state their simulation is \"biologically inspired\",  but the ablation strategy does not seem to reveal that. (5) The framework, as it is currently set up, misses some nuance associated with modeling brain dysfunction. Dysfunction such as from focal lesions spark a cascade of ischemic processes such as the expansion of the ischemic insult into the surrounding penumbra, plasticity mechanisms, behavioral attention changes, take into account the topographic layout on a systems level, etc. While I don't consider it necessary for the paper to have recapitulated observations around all of these, at least an attempt to think about these as they pertain to dysfunction modeling seems important, and is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FglvJ6Lwuv", "reviewer": "Reviewer_aZ8H", "summary": "This papers studies dyslexia and the supposed role of a specific brain area, VWFA, in this disorder, corresponding to impairments in language decoding but not in normal intelligence. To do so, the authors consider a neuronal model, the Vision Language Model, learning associations between words and their visual form. The approach is in three steps: localization, ablation, testing. Neurons with the same typical responses avec the VWFA are localized and the performance of the model with or without the ablation of these neurons are measured. Interestingly, the same deficits but also the same preservation of performances are observed.", "questions": "Could you develop more the limitations of your approach, what it suggests and which kind of conclusion must be considered with caution or associated with more experiments.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The presentation is clear, the state of the art is well presented. Applications to dyslexia are not so frequent. Results can indeed evoke a certain analogy between the artificial and natural cases.", "weaknesses": "Nevertheless, the approach is somewhat too superficial and does not discuss enough which conclusions can or cannot be drawn for these observations. Correlation does not mean causality.  In addition, the discussion makes perhaps a too rapid generalization by suggesting that this approach could be extended to other pathologies, including schizophrenia for example.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)