Biology subjects

Rajaei, K.

Publications and source records attributed to Rajaei, K..

5 recordsLinked to original sources

bioRxiv2026

Local recurrence accounts for extended processing during occluded-object recognition

Recognizing objects from incomplete visual input often requires processing beyond the initial feedforward sweep, but the relative contributions of local recurrence and long-range top-down feedback remain unclear. We combined source-localized magnetoencephalography (MEG), time-resolved decoding, backward masking, representational Granger causality, and computational modeling to examine these mechanisms during occluded-object recognition. We characterized neural dynamics in early visual cortex (V1-3), the lateral occipital complex (LOC), and inferotemporal-parahippocampal cortex (IT-PHC). Occlusion delayed the emergence of category information and elicited a late component that was selectively disrupted by backward masking, consistent with dependence on continued processing. These temporal changes occurred without detectable changes in the relative timing of regional responses or directed interareal interactions, including no measurable occlusion-related increase in feedback among the regions examined. To assess candidate computational mechanisms, we compared three nested model variants sharing a trained backbone: feedforward, local recurrent, and local recurrent with added long-range top-down feedback. Local recurrence improved recognition under occlusion and increased model-brain correspondence during the late, mask-sensitive interval, particularly in V1-3. Adding the implemented top-down pathway provided no consistent further benefit. Together, these findings favor prolonged local recurrent processing as an account of the additional computation supporting occluded-object recognition under the conditions tested.

neuroscience↗

bioRxiv2026

Language-aligned models and structured scene descriptions reveal sensitivity to compositional scene structure in the high-level visual cortex

Natural scenes are defined not only by their contained objects, but also by the structured relations among those objects. Using 7T fMRI data from the Natural Scenes Dataset, we tested whether the high-level visual cortex is sensitive to this compositional scene structure. We related narrative scene descriptions to cortical responses in an encoding framework, contrasting intact narrative descriptions with lexical control descriptions that preserved word content while disrupting compositional structure via word randomization. Intact narrative descriptions better predicted cortical responses in high-level visual cortex, indicating sensitivity to structured scene information beyond lexical content alone. This advantage was particularly pronounced for scenes with richer compositional structure. Further, a language-aligned vision model outperformed a self-supervised vision-only model, and this advantage showed a similar high-level cortical distribution. These findings suggest that the high-level visual cortex represents structured scene information beyond simple entity co-occurrence and language supervision may help induce similar representations in artificial vision models.

neuroscience↗

bioRxiv2026

Coherent scene context accelerates and reshapes neural object representations

Coherent scenes facilitate object recognition, but the representational basis of this facilitation and its temporal evolution in the brain remain unclear. We tested this question using EEG and multivariate pattern analysis while 15 participants categorized objects from five semantic categories after a 500-ms preview of either an intact rendered scene or a phase-scrambled version of the same background. Reliable object decoding emerged earlier in intact scenes than scrambled scenes (142 {+/-} 5 vs. 162 {+/-} 10 ms), with higher decoding for intact scenes from 124 to 268 ms after object onset. Cross-condition decoding object information that generalized across scene formats, whereas subtracting cross-condition from within-condition decoding identified an earlier and stronger context-dependent component when scene structure was coherent. Cross-temporal representational similarity analysis (RSA) further showed that representational structure established during late scene preview generalized to early object processing only for intact scenes, linking contextual facilitation to anticipatory scene-derived representations. Finally, model-to-brain RSA showed that a language-aligned model explained neural representational geometry in intact scenes better than vision-only models, an advantage attenuated by scene scrambling. These findings indicate that coherent scene context shapes object coding by accelerating object-selective processing and contributing context-dependent representational structure beyond a context-invariant object code.

neuroscience↗

bioRxiv2025

Does Human-Like Contextual Object Recognition Emerge from Language Supervision and Language-Guided Inference?

Human vision is an active, context-sensitive process that interprets objects in relation to their surroundings. While behavioral research has long shown that scene context facilitates object recognition, the underlying computational mechanisms--and the extent to which artificial vision models replicate this ability--remain unclear. Here, we addressed this gap by combining human behavioral experiments with computational modeling to investigate how structured scene context influences object recognition. Using a novel 3D simulation framework, we embedded target objects into indoor scenes, and manipulated contextual coherence between objects and scenes by using either intact scenes or their phase-scrambled versions. Humans showed a robust object recognition advantage in coherent scenes, particularly under challenging conditions such as occlusion, crowding, or non-canonical viewpoints. Conventional vision models--including convolutional neural networks (CNNs) and vision transformers (ViTs)--failed to replicate this effect. In contrast, vision-language models (VLMs), particularly those using ViT architectures and trained with language supervision (e.g., CLIP), approached human-like accuracy. This shows that semantically rich and category-structured representations are required for modelling context sensitivity. Notably, context sensitive behavior was closest to humans in VLMs when using language-guided inference at test time. This suggests that how a model accesses its representations during inference is relevant for enabling context-sensitive behavior. Together, this work offers steps towards a computational account of contextual facilitation of objects by scenes, and highlights zero-shot inference as an interesting alignment metric when benchmarking artificial and biological vision.

neuroscience↗

bioRxiv2018

Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion

Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem. Author SummaryIn recent years, deep-learning-based computer vision algorithms have been able to achieve human-level performance in several object recognition tasks. This has also contributed in our understanding of how our brain may be solving these recognition tasks. However, object recognition under more challenging conditions, such as occlusion, is less characterized. Temporal dynamics of object recognition under occlusion is largely unknown in the human brain. Furthermore, we do not know if the previously successful deep-learning algorithms can similarly achieve human-level performance in these more challenging object recognition tasks. By linking brain data with behavior, and computational modeling, we characterized temporal dynamics of object recognition under occlusion, and proposed a computational mechanism that explains both behavioral and the neural data in humans. This provides a plausible mechanistic explanation for how our brain might be solving object recognition under more challenging conditions.

neuroscience↗