Temporal Slowness in Central Vision Drives Semantic Object Learning
Authors: Timothy Schaumlöffel
Arthur Aubret
Gemma Roig
Jochen Triesch
Venue: ICLR 2026 Poster (2026)
Keywords: bio-inspired learning
human vision
egocentric learning
self-supervised learning
Primary area: applications to neuroscience & cognitive science
TL;DR: Prioritizing central, gaze-predicted regions and temporal context in egocentric videos improves semantic object representations in self-supervised models, aligning them more closely with human visual learning.

Abstract: Humans acquire semantic object representations from egocentric visual streams with minimal supervision, but the underlying mechanisms remain unclear. Importantly, the visual system only processes the center of its field of view with high resolution and it learns similar representations for visual inputs occurring close in time. This emphasizes slowly changing information around gaze locations.
This study investigates the role of central vision and slowness learning in the formation of semantic object representations from human-like visual experience. We simulate five months of human-like visual experience using the Ego4D dataset and a state-of-the-art gaze prediction model. We extract image crops around predicted gaze locations to train a time-contrastive Self-Supervised Learning model.
Our results show that exploiting temporal slowness when learning from central visual field experience improves the encoding of different facets of object semantics. Specifically, focusing on central vision strengthens the extraction of foreground object features, while considering temporal slowness, especially in conjunction with eye movements, allows the model to encode broader semantic information about objects. These findings provide new insights into the mechanisms by which humans may develop semantic object representations from natural visual experience. Our code will be made public upon acceptance. Code is available at https://github.com/t9s9/central-vision-ssl.

Published as a conference paper at ICLR 2026
TEMPORAL SLOWNESS IN CENTRAL VISION DRIVES SEMANTIC OBJECT LEARNING
Timothy Schaumlöffel Arthur Aubret Gemma Roig! Jochen Triesch 1223,1 Department of Computer Science, Goethe University Frankfurt, Frankfurt am Main, Germany Thh Hessian Center for Artificial Intelligence (hessian.Al) Darmstadt, Germany Frankfurt Institute for Advanced Studies, Frankfurt Main, Germany
*Xidian-FIAS international Joint Research Center, Frankfurt am Main, Germany (schaumloeffel, roignogueral @em. uni-frankfurt.cc laubret, triesch] 8fias. uni uni--reaal frankfurt.de
ABSTRACT
Humans acquire semantic object representations from egocentric visual streams with minimal supervision, but the underlying mechanisms remain unclear, Impor- tantly, the visual system only processes the center of its field of view with high resolution and it learns similar representations for visual inputs occurring close time. This emphasizes slowly changing information around gaze locations. This study investigates the role of central vision and slowness learning in the formation of semantic object representations from human-like visual experience. We simu- late five months of human-like visual experience using the Ego4D dataset and a state-of-the art gaze prediction model. We extract image erops around predicted gaze locations to train a time-contrastive Self-Supervised Learning model. Our results show that exploiting temporal slowness when learning from central visual field experience improves the encoding of different facets of object semantics. Specifically, focusing on central vision strengthens the extraction of foreground object features, while considering temporal slowness, especially in conjunction with eye movements, allows the model encode broader semantic information about objects. These findings provide new insights into the mechanisms by which humans may develop semantic object representations from natural visual expe- rience. Our code will be made public upon acceptance. Code is available at
INTRODUCTION
Humans develop strong semantic object representations from egocentric visual stream with only little supervision. These semantic representations reflect different non- non-perceptual facets of an object, such its identity, fine-graing category. basic category its typical context of occurrence. Models trained with self-supervised learning (SSL) are reasonably good models of biological vision (Zhuang et al. 202T), but they are poor models of visual learning, they rely different training data and learning mechanisms than humans. This may be the reason why they fail to model human semantic object similarity judgments (Mahner al. 2025) and under-perform at recognizing objects when trained on visual experience similar to that of humans (Orhan 2023 Orhan & Lake 2024).
To learn semantic representations from their natural visual experience, humans may rely on two aspects of biological vision that are typically neglected in current models. First, the stimuli received by the human visual cortex differ structurally from egocentric videos. The human retina samples information from the center of the field of view more densely (Anstis) 1974 Wässle et al. 1989), such that high fidelity information only available within several degrees from the center the visual field, i.e., in central vision. As consequence, central vision plays crucial role in the formation of object-level representations in areas of the visual cortex related to semantic information (Quaia
& Krauzlis 2024 Yu et al. 2015). To compensate for the relatively low acuity in peripheral vision,
"Equal contribution
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humans their gaze around times per second to parse their environment. Second, key principle of biological learning states that biological systems assign similar representations to close-ing in-time visual inputs (Miyashita 1988). This may be important when learning from central visual experience. For instance, observing objects from different viewpoints may favor viewpoint-invarie representations (Aubret et 2022a). In addition, consecutive scanning of objects within the same context may support object representations that encode their context of occurrence (Aubret et al. 2024a), a feature of human semantic object perception (Turini & Vo 2022 Yeh al. (025). For instance, the presence of a knife often reflects "kitchen" context. At present, it is unclear how self-super with slowness objective and human- focus on central vision may shape the semantic properties of learned representations.
object
Estimate location
Extimation
Extract central vision
Algn
Algg
Separate
Alig
Alig
Time- augmented
SSL
Figure 1: Illustration of data generation and model training approach. 1) We extract frames from the egocentric dataset Ego4D (Grauman et al., 2022). 2) We predict the human gaze location (red dot) using state-of-the-ar model (Lai al.) 2024). We train time-augmented SSL model to
align representations of gaze-centered crops (red rectangle) extracted from close-in mose--n-time frames.
In this paper, we investigate the combined role of emphasizing central vision and slowness learning for the formation of semantic object representations from human-like visual experience. We simulate months of human-like visual experience using Ego4D (Grauman et al. 2022), a dataset that contains 3,670 hours of videos collected with head-mounted cameras. This dataset contains gaze locations a subset of videos (45 hours). For the rest, we apply a state-of-the-an human gaze prediction model (Lai et al., 2024). To simulate the preferential sampling of the central visual field, we crop image regions centered on the measured/predicted gaze locations. Finally, we train a biologically inspired SSL model that learns visual representations which change slowly on the resulting visual sequence for one single epoch. Figure summarizes our approach.
Our experiments demonstrate that learning slowly changing representations from central visual experience leads to more semantic object representations compared to standard training on the whole field of view. Our analysis shows that this stems from a combined effect emphasizing central vision, exploiting human eye movement patterns and the temporal slowness learning objective: while emphasizing central vision favors the extraction of foreground object information versus background information, learning with temporal slowness from human eye movements distills semantic information into the object representations. Overall, our work sheds light on how humans may build semantic object representations from natural visual experience. Furthermore, approach may inspire more efficient learning strategies in embodied Al that produce more human-like semantic representations.
2 RELATED WORKS
Egocentric SSL. The increased availability of datasets collected with head mounted cameras
(Grauman al. 2022 Sullivan et al. 202T Long al. 2024 Greene et al. 2024 Ma al. 2024
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recently induced surge for training egocentric SSL models (Orhan et al. 2024 He al. 2022 Zhou at. 2022 Emin Orhan 2024). To the best of our knowledge, all these approaches use the entire high-resolution field of view captured by head-mounted cameras. Previous work found that endowing SSL. models with representations that slowly change over time can slightly boost object learning (Orhan et al. (020). A related line of work leverages egocentric data train vision models useful for solving robotic tasks. VC-1 MMjjumdar et al. 2023) is trained on egocentric and third-person videos. R3M (Narr al. 2023) and VIP (Ma al. (023) both notably learn slowly changing representations gaze-based central vision elicits better object representations. Other works try to extract the correspondences between objects' views in videos
on Ego4D. We show in Section that training
to learn visual representations (Jabri et al. 2020; Venkataramanan et al. 2024 Salehi et al.) 2023
Parthasarathy et al., 2023 Gordon et al.] 2020). Here, we are rather interested in understanding how
focusing on central Vision may impact egocentric SSL.,
Time-based SSL. Many previous works have proposed to learn similar representations for close-
in-time visual inputs (Wiskott & Sejnowski 2002: Földiák 1991). More recently, this learning principle has been integrated into mainstream SSL methods (Schneider et al., 202T Aubret et al.
2022a However, these works do not leverage in-the-wild egocentric data, but synthetic data (Aubret ct al. 2022a; Schaumlöffel et al., (023) or curated visual sequences of interactions with objects (Aubret et al.) 2024b; (Snyya et al. 2023 Aubret et al. 2024a). Other works use third-person videos Sermanet et al. 2018), videos recorded by a car (Jayaraman & Grauman, 2013 2016). movie video
clips (Jayaraman & Grauman 2016), the egocentric perspective of chicks (Pandey al. 2024) or
object-tracking datasets (Xu & Wang 2021).
Central and peripheral vision in deep learning. Many studies have modeled the variant resolution of the retina in the context of artificial vision systems. Previous works have shown that this can make representations acquired via supervised learning more adversarially robust (Vuyyuru jet al. 2020), improve the computational efficiency of the training process (Lukanov et al. 2021 and induce stronger center bias (Deza & Konkle, 2020). Other works combined a bio-inspired focus on central vision with attention mechanisms for temporally extended image recognition (e.g. Almeida al. 2018)). In the context of SSL, Wang et al. (2021a) argues that foveation can mimic the impact of the Crop/Resize data data-augmentation, which is widespread in SSL. In line with our work, recent studies have combined retina modeling with time-based SSL. Aubret al. (2022b) showed that a progressive blur towards the visual periphery can make visual representations slightly more transferable across backgrounds, and Yu al. (2024) showed that gaze patterns in central vision may support the learning of view-invariant object representations. However, these two works trained SSL models with a tiny number of objects (10 and 24, respectively). In contrast, using human data at scale allows us study the role of bio-inspired learning with respect to semantic recognition abilities.
Learning context-wise object representations. Only few works studied the emergence similar visual representations for objects that co-occur in the same context. One work (Bonner & Epstein 2021) proposed to learn similar representations for objects that co-occur in individual images of natural scenes. They did not study temporal relations between objects across time. The impact of such temporal relations on learning more semantic representations was studied by AAbret et al. (2024a) with curated dataset showing egocentric rotations around images and hand-made statisties of object transitions. Thus, it remains unclear how and whether the natural experience of humans supports the construction of object representations reflecting their typical context of appearance.
3 METHOD
We aim to study the combined impact of high-resolution central vision and the slowness principle on visual learning in humans. We use the largest-to-date dataset of egocentric videos (Ego4D) and estimate human gaze locations with a state-of-the-ar model of human gaze prediction (Section 3.1.. To simulate the biological importance of central vision, we simply crop the visual area around a gaze location. To model biological learning, the created sequence of visual inputs feeds an SSL model that learns slowly changing representations, which is described in Section 3.2 Figure [ illlsstrates the main steps of the pipeline. Finally. we evaluate the ability of our model to capture different semantic
facets of objects, using the approaches detailed in Section 3.3 and Section 3.4
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3.1 DATASET
To simulate the visual experience of humans, we use the Ego4D dataset (Grauman et al. 2022). This dataset contains 3,600 hours of videos collected through head-mounted cameras, which corresponds to approximately 5 months of visual experience. This data was recorded by 931 participants coming from 74 worldwide locations wore camera for one ten hours. Thus, Ego4D arguably represents much more than 5 months of experience for single average human in terms of diversity, although it is hard to make precise estimates, We use videos with a resolution of 540 x 540 pixels and extract their frames approximately 5 fps. following previous findings that higher fis do not boost the learning process (Sheybami et al., 2024p.
During frame extraction, create small clips of 5 seconds (25 frames) that sequentially load into memory. We gather 24 frames of these 25 frames and split them into three sequences of frames. For Ego4D videos recorded with eye-tracker (45 hours), do not further process the frames and associate them with ground-truth gaze locations. For all other videos, we feed each sequence into GLC, state-of-the-an model of human gaze prediction trained on the Ego4D subset that contains gaze locations (Lai et al. 2024). This model uses spatio-temporal information to generate saliency map for each the 8 frames. For each frame, we take the position of the most salient pixel as gaze location (zg. Ug). Even when the saliency map contains multiple peaks, the strongest peak is typically stable across adjacent frames, yielding reliable and temporally consistent gaze trajectory. Compared to single-image saliency models (Riche & Mancas 2016), processing multiple frames allows the model to generate a temporally consistent gaze location and leverage more cues (c.g. motion). Our final preprocessed dataset contains 64,380,024 images.
To simulate the importance of central vision in humans, we crop a x N square region centered on the gaze location in each frame. If the crop extends beyond the image boundaries, we apply minimal shift to keep fully within the image.
3.2 BIO-INSPIRED LEARNING
Since most of the human visual experience is unsupervised. we train SSL models on the simulated experience in central vision. These models learn high-level visual representations without any explicit supervision, like human-providee labels. In this work, we focus specifically on the third version of Momentum Contrast (M)CoV3) (Chen et al. 2021). which is one of the best SSL models in the literature. The original MoCoV3 works by learning invariant representations to color- and spatial-based transformations of an image (e.g. horizontal flip, color jittering ...). To implement the biological principle of temporal slowness, further adapt the model to also learn slowly changing visual representations, following (Aubret et al.) 2022a Pandey et al. 2024).
For given imput image 2, in batch, randomly sample indirect temporal neighbor zy within a temporal window AT, from the same video recording. The two images capture the same scene different moments in time, providing temporally varied view. We compute the embeddings of images qe fa (zz) and by- fix (z() using a query feature extractor fa and a momentum feature extractor fk. both implemented as neural networks. Finally, for pair (ge, ke), the query encoder updated by minimizing the InfoNCE loss (van den Oord et al. 2019):
exp(sim(gr, kr)/7)
C..
log ΣK expppii(qe.)).. (k)m(g)))))/)) kz)/r)
where sim denotes cosine similarity, 7 is a temperature hyperparameter, and K represents the outputs of fir from the same training batch. Intuitively, the objective increases the similarity between representations of temporally close views (I, and Zr) while enhancing the dissimilarity between all views (x, and r,). The momentum encoder parameters 02 are updated via an exponential moving average of the query encoder 0y: 0% + m& + m)0g, with momentum coefficient m.
3.3 EVALUATION OF IMAGE RECOGNITION ABILITIES
We follow standard SSL transfer protocols, evaluating frozen representations via linear probing across diverse downstream tasks grouped by semantic focus (see below). For each dataset, we train a linear classifier top of the frozen features of the pre-trained encoder for 100 epochs. We apply the
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standard crop/resize and horizontal flip augmentations during training and report the accuracy center crop of validation images.
Object categorization: To assess the categorization ability of the models, consider the ImageNet- 1k (Russakovsky et al. 2015). ImageNet 100 (Tian et al. 2020) and CIFAR (Krizhevsky et al.
2009) datasets, including reduced, balanced subsets of ImageNet- Ik (1 %, 10%) (Chen et al. 2020). We also analyze object categorization tasks that contain tiny number of classes in Appendix D.3
Fine-grained object categorization: Most classes in the two previous groups for basic-level category recognition (e.g. car, trucks, bananas ...). Here, we instead assess categorization at the subordinate level (e.g. for cars, differentiating 2012 VW Polo from 2012 BMW M3). This requires model to extract finer details about an object. We consider wide range of subordinate
categories: Flowers 101 (Nilsback & Zisserman 2008). Stanford Cars (Krause et al. 2013). Oxford
Pet (Parkhi et al. 2012). FGVC-Aircratt (Maji et al. 2013), DTD (Cimpoi et al. 2014).
Instance-level object recognition: We evaluate object instance recognition when exposed in front of different backgrounds with varying orientations. We use ToyBox (Wang et al., (017). COIL100 (ene et al. 1996), Core50 (Lomonaco & Maltoni, 2017). Core50 mostly allows us to assess the robustness of the representation to changing backgrounds, while ToyBox and COIL110 present objects in different positions and orientations. We explain Appendix B how we split the training and testing splits. We do not apply center crop COIL100.
Scene recognition: For scene recognition, we focus on Places365-standard (Zhou et al. 70) This dataset contains 1.8 million images from 365 scene categories and is commonly used to probe scene-level representations.
3.4 EVALUATION OF THE CONTEXT-WISE ORGANIZATION OF OBJECT REPRESENTATIONS
In order to evaluate whether the knowledge about 3D object co-occurrences can naturally emerge from our models, we compare the representations of our models with representations specifically built to encode objects' co-occurrence structure.
Object Co-occurrence Representations: To model the latent semantic structure of natural scenes, we extract object co-occurrence statistics from three large-scale image datasets: COCO (Lnn et al. 2014), ADE20K (Zhou et al., 2017b), and Visual Genome (VG) (Krishna al.) (017). These datasets vary in label density and semantic granularity. COCO contains coarse object categories with dense instance annotations: ADE has finer- mner-grained, segmentation-lever labels; and VG offers a rich, albeit noisy, semantic graph structure. For each dataset, we construct a co-occurrence matrix X E N.N. where No is the number of object classes and Xi, counts how often the object appears with the object j in the same image. We train GloVe (Global Vectors for Word Representation) (Pennington et al.) 2014) on these matrices to derive low-dimensional representations that encode this co-occurrence structure. We refer to Appendix for more details.
Moddel-to-Semantic Alignment: To assess whether neural network representations encode seman- tic structure similar to the co-occurrence embeddings. we perform a representation similarity analysis using Centered Kernel Alignment (CKA) (Kornblit et 2019).
We map object classes from the co-occurrence matrices to their corresponding WordNet synsets (Miller 1995). Then, we retrieve representative images from the THINGS dataset (Hebart al. 2023). which contains isolated object instances with a naturalistic appearance. For each object, we extract activations from all layers of given model and average across object images, resulting in single feature vector per object and layer. We then compute the linear CKA score between each layer's object representation matrix and the GloVe embedding matrix. Alternatively, we concatenate the representations across layers to compute global CKA score, which serves summary measure of semantic alignment for the entire model. We study the robustness of the vocabulary mapping and the selection of object images in Appendix D.7
We repeat all evaluations across 100 GloVe seeds. We perform paired t-test across the seeds to compare the CKA scores of each model under identical co-occurrence conditions and assess statistical significance between models. Throughout the experiment section, we report mean scores, standard deviations, and significance levels.
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3.5 IMPLEMENTATION DETAILS
We adapt the solo-learn training pipeline and implementation of the MoCoV3 model (da Costa et al. 2022), with ResNet-50 (He al. 2016 and ViT-B/16 (Dosovitskiy et al. 2020 backbones. For the MoCoV3 loss, a two-layer MLP (hidden dimension 4096) projects features into a 256-dimensional embedding space. Models are trained for one epoch on Ego4D: training longer yielded only minor gains (x= +0.5%) at substantial computational cost, due to the large but redundant dataset sampled at fps. Full hyperparameters are given Appendix
EXPERIMENTS
We aim assess the impact of bio-inspired learning emphasizing central vision and harnessing temporal slowness objective on the resulting visual representations. To this end, we compare our model "Bio-inspired Learning" to baseline from previous work, "Frames Learning", which uses the full field of view and omits the slowness objective during training (Orhan & Lake 2024).
4.1 BIO-INSPIRED LEARNING FROM NATURAL EXPERIENCE BOOSTS OBJECT RECOGNITION.
Table I: Linear probe accuracy on various datasets across two architectures, grouped by semantic category. For each semantic group, report the average recognition accuracy. For bio-inspired vision, we use AT 3s for ResNet50 and AT = 1s for ViT.
ResNet50
ViT-B/16
Dataset
Frames Learning
Bio-inspired Learning
Frames Learning
Bio-inspired Learning
Category recognition
ImgNet-14
49.50
49.58
49.47
49.86
ImgNet-1k 10%
35.53
35.34
37.65
38.10 10
ImgNet-Ik 1%
19.23
20. 25
19.51
20.10
ImgNet-100
70.44
70.34
70.04
70.12
CIFARI00
53.53
59
61.73
62.67
Average
45.65
46 94
47.68
48 17
Fine-grained recognition
DTD
17.21
57.06
59.89
62.23 22.23
FGVCAireraft
12.83
15.77
28.87
28.60
Flowers101
43.72
49.01
76.35
77.05
OxfordlITPPt
47.03
54.41
56.26
StanfordCars
18.70
23.25
33.30
33.26
Average
33.84
38.42 42
50.56
51.58
Instance recognition
ToyBox
89.75
92.61
92.94
95.03
COIL100
64.53
80.12
79.24
86.94
Core50
22.82
28.26
24.02
23.77
Average 59.03
67.00
65.40
68.58
Scene recognition
Places365
43.02
12.95
44.49
39.84
We investigate whether bio-inspired learning improves object recognition abilities by comparing
against SSL models learning without temporal slowness objective and from raw frames. In Table I
we observe that bio-inspired learning promotes category recognition, fine-grained recognition and
object instance recognition. The improvement is particularly strong for fine-graineds and object instance recognition. For scene recognition, training on full frames consistently leads to better results. We conclude that bio-inspired learning from a natural visual experience promotes better object recognition, but comes at the cost of impaired scene recognition. In Appendix D. that these results generalize to additional model architectures.
we show
Emphasizing Central vision makes representations more object-centered. To investigate why bio-inspired learning impacts scene and object recognition differently, we first study the impact

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of the size of the gaze-based crops N learning. In Figure we observe sweet spot for the intermediate crop sizes N = 224 and = 336 for all object-centered datasets. This sweet spot is located at N 336 for category and instance recognition, while N 224 seems to be better for fine-grained recognition. N = 112 gives the worst semantic recognition accuracies, indicating that it probably dismisses too much information about the image. Interestingly, the scene recognition accuracy consistently increases as we enlarge the crop size.
Average image recognition improvement ResNet50
Average image recognition improvement VIT-B/18
Accuracy improvereett (%)
Agcuracy improvement (9)
Scana
Calegory
2-
Fine grained
Instanoe
0-
100
200
300
400
500
100
200
300
400
500
Gaze crop size N (pixets)
Gaze size (pixels)
Figure 2: Impact of the gaze-bace gaze-based crop size on different groups of semantic image recognition tasks for ResNet50 and ViT-B/16. We compute the average improvement relative to N 112. We use temporal window of AT 3s. We omit error bars as variance across datasets is largely driven by differences in dataset difficulty rather than model instability; individual results are provided in the Appendix Table
could be that expanding the field of view mostly incites the model to extract more background features; background features may be more important for recognizing scenes than objects. To investigate this, we take the ImageNet-9 dataset, a dataset of natural images designed to investigate the background sensitivity of models (Xiao al. 202T). The dataset provides different versions of the images, without background and with different ways to substitute the most salient object with background information. We compute the category recognition accuracy of our model with linear probe trained on ImageNet- Ik. We first find that training on central vision also benefits category recognition on normal images for this dataset (80% versus 75% on ResNet50). Then, we compute the recognition accuracy when removing the background (Missing background) and when removing the object (Missing object). When removing the object, we average the recognition accuracies the different ways of removing the foreground object (cf. Xiao al. 2 To obtain measure of background and object sensitivity irrespective the raw performance of the model, we subtract the recognition accuracy on normal images from those for the missing object and missing background images.
ImageNet-9 average robusthass ResNet50
ImagaNet-9 average robustness on VIT-B/16
Missing Object
1.4
Relalive accurscy (86)
Missing Background
1.2
0.8
3-
0.6
2-
0.4
0.2
Missing Object Missing Background
300
300
Gaze crop size (pixels)
Gaze crop size N (pixels)
Figure 3: ImageNet-9 recognition sensitivity to missing background or missing foreground object. We show the relative improvement with respect to the worst model for the two settings. The higher, the more relatively robust is the representation to missing backgrounds or missing objects. We use temporal window AT' = 3s.
In Figure 3 we clearly observe that training on an intermediate size of central vision (N E {224, 336)) allows the model to rely more on the foreground object (missing background), and less the
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background (missing object). We assumee this is because training on input from the central visual field removes much of the background information while often keeping the foreground object intact. For N 112, there is opposite trend, presumably because objects are often strongly cropped. Overall, we conclude that prioritizing central visual input leads to better object-centered representations because it reduces the amount of extracted background information.
Slowness objective supports object learning. Previous work on self-superviser learning from standard videos suggests that learning representations with a slowness objective can be beneficial if the representations of images that are around L = 1 second apart are made more similar (Xu & Wang 202T). However, focusing on gaze-based central vision during egocentric learning provides semantically different temporal dynamics compared to using the whole field of view of, c.g., movie clip. In Figure 4 we present the impact of the level of temporal slowness on visual representations acquired from such natural egocentric input. We observe that temporal slowness is critical for learning representations with respect to all the semantic aspects investigated (AT = 0 versus AT = 3 for ResNer50 and AT for ViT). We note exception for category recognition with ViT-B/16, for which the reason is currently unclear to us. We provide detailed results in Appendix E which further show that the best temporal window AT is overall consistent for datasets within a group of semantic tasks.
Average image recognilion improvement on ResNel50
Average image recognilion improvement VIT-B/16
Accuraccy improvement (%)
Accuracy improverent (%)
Sowne
6-
Calegory
Fine-grained
2-
2
Temporal window AT (seconds)
Temporal window AT (seconds)
Figure 4: Impact of the temporal window of slowness learning on different groups of semantic image recognition tasks. We compute the average improvement for each group of tasks with respect to AT = 0 seconds. We use crop of size N 224. We omit error bars variance across datasets is largely driven by differences in dataset difficulty rather than model instability; individual results are provided in the Appendix Table TO
4.2 HUMAN EYE MOVEMENT PATTERNS SUPPORT THE LEARNING OF OBJECT SEMANTICS
Humans actively look at salient objects inside a scene, raising the question of whether these specific eye movements are important for visual learning (Yu et al. 2024). To test this, we compare bio-inspired model to baselines that ignore eye movements. The first baseline always crops the center of the frame, while the second uses fixation points predicted by a classical saliency model (Iti) et al. 1998). Note that the resulting visual sequences still vary over time due to head movements of the camera wearer. In Table - (GC) outperforms both center crops and saliency- -bssed crops in most cases. Notably, for instance recognition, gaze-crops yield substantial improvements of +4.17% for ResNet and +1.14% for ViT over CC, while saliency-based crops close only small fraction of this gap. The difference is even more pronounced for fine- Appendix D.I for Ct these findings indicate that using simulated gaze locations of humans (weakly) boosts semantic object learning.
we observe that bio-inspired training with estimated human gaze
fine-graing grained and category recognition. Together with additional results in
Human gaze behaviors are characterized by relatively long fixations interleaved by short saccadic eye movements, a pattern captured by the gaze estimation model (cf. Appendix [D44). To systematically investigate the relative importance of fixations versus saccades for object learning, we segment the data into alternations of likely fixations and saccades using a simple velocity threshold. In particular, we define fixation consecutive sequence of frames during which the velocity of the gaze location in the image remains less than P/200 px ms -1 where P is a parameter that denotes the maximum allowed gaze movement in 200 ms. Then, during training, we ensure that two image frames forming
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Table 2: Average recognition accuracies within semantic groups when pre-training with center-crop (Center), gaze-based visual cropping (Gaze) and saliency-based cropping (Saliency). We use a crop of size N 224 and AT 3s.
ResNet50
ViT-B/16
Semantic Group
Center
Saliency
Gaze
Center
Gaze
Category recognition
46.58
45.36
46.94
46.53
46.76
Fine teme-graied recognition
37.77
36.28
38.42
50.31
50.35
Instance recognition
62.83
65.92
67.00
67.99
69.03
a positive pair during contrastive learning, even spaced in time, belong to the same fixation. We train the model for different movement thresholds P {5, 15, 30,44).... chosen expose the model to varying proportions of fixations vs. saccades, following the statistics reported in Appendix [.4 In Figure [ we observe that the model can indeed learn better object representations with respect to all object recognition abilities, when the fastest gaze movements are removed from the training sequence (i.e., when P 00). Under normal conditions, humans are aware of whether their eye movements are fixational or saccadic (O"Regan & Noëj 2001). Our results suggest that explicitly leveraging this distinction, i.e., suppressing the learning signal during saccades, may enhance the emergence of semantic object representation.
Average image recognition improvement ResNet50
Average image recognition improvement ViT-B/16
12
Accuracy improvement
Accuracy improvement
15
Category Fine-graired Instance
10
6-
15
30
45
30 Maximum fication speed
15
45
Maximum fixation speed (P)
Figure 5: Impact of varying the gaze velocity threshold for identifying likely fixations. We report the average improvement for each group of semantic tasks relative to the 0 baseline, which corresponds to AT 0. All models are trained under the same settings the bio-inspired models described in section
4.3 STOWWNSSS LEARNING BETTER ASSOCIATES OBJECTS THAT OCCUR IN THE SAME CONTEXT
In this section, explore how slowness learning and central vision jointly shape the semantic similarity between categories of objects. In Table bio-inspired learning yields object representations whose inter-object similarities more closely align with object co-occurrence statistics, capturing contextual relationships more effectively than "Frames learning". This effect is moderate with ResNet50 (+0.010 CKA score), but large with ViT (+0.028 CKA score). These improvements correspond to large effect sizes, indicated by Cohen's 's d = 2.5 for ResNet50 and d 7.0 for ViT-B, We further observe that models trained with slowness learning produce significantly higher CKA similarity with co-occurrence embeddings than static models. This suggests that temporal slowness is essential for semantic learning.
In practice, the context-based organization of objects may be captured by learning both spatial and temporal co-oceurrences. The former may be particularly pronounced when the image contains several objects, such in the full frame. The latter may result from slowness learning when consecutively observing different objects (Aubret et al. 2024a). Thus, we investigate their relative role in shaping context-based object representations. In Table remove the focus on central vision (w/o Central vision), presumably because co-occure co-occurring objects often spatially distributed and may not appear within the crop. However, observe minor
observe increase in CKA similarity

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gap between "Bio-inspired Learning" and "without Slowness" suggesting that the integration of central vision over time partly compensates for the limited field of view. These findings generalize across different datasets (Appendix D.2) Overall, conclude that slowness learning promotes the formation of context-aware object representations.
Table 3: CKA similarity between learned representations and GloVe-hased object co-occurrence embeddings, computed on the COCO dataset. Higher values indicate stronger semantic alignment. w/o Slowness and w/o Central Vision correspond to the models labeled AT' = 0 in Figure and N 540 in Figure respectively.
Frames Learning
Bio-inspired Learning
w/o Slowness
w/o Central Vision
ResNet50
0.315 + 0.004
0.325 + 0.004
0.320 + 0.004
0.335 + 0.003
ViT-B/16
0.453 + 0.004
0.481 0.004
0.406 + 0.004
0.487 + 0.003
5 CONCLUSION
Humans remain far more efficient in learning semantic visual representations than current machine
learning systems. For example, the accuracy of the Top-5 linear probe with ImageNet-Ik 1k 1 1% barely
goes beyond 40%. compared to about 90% for humans (Russakovsky et al. 2015 Orhan 2023)
Here, we investigated whether the relative importance of central vision (vs. peripheral vision) and the biological learning principle of slowness jointly support the learning of more semantic object representations from human-like visual experience. To this end, we simulated humans' gaze locations on the largest-to-date dataset of egocentric videos and extracted image regions surrounding the gaze locations. Then, we trained a biologically inspired SSL model that learns slowly changing visual representations on these data. Our extensive experiments demonstrate that extracting slowly changing information from the central visual field allows visual representations to better encode different facets of human object semantics. This includes the between-object similarity based on their context of co-occurrences, their basic category, fine-grained (or subordinate) category and their instance identity, Our analysis shows that emphasizing central vision elicits the extraction of more object-related features than background features. In addition, we found that fixational eye movements support such bio-inspired learning.
Our work has several limitations. First, the visuo-motor experience of infants during early develop- ment differs from the experience of adults modeled in the present work (Ayzenberg & Behrmann 2024). Future work will have to investigate whether the conclusions drawn here generalize to visual experience modeled after that young infants. Second, using a gaze estimation model sidesteps the question of how the developing visual system learns when and where to move its eyes. A complete model of visual development requires also modeling the learning of eye movement control strategies. Third, our model could be improved by incorporating more realistic retinal processing (Wang et al.) 2021a), with a more gradual attenuation of the sampling of visual information towards the periphery. Yet, our work shows that prioritizing slow information from the central visual field permits the learning of strong semantic representations, marking a step toward a better understanding of the emergence of semantic object representations during human development.
ACKNOWLEDGMENTS
This work was funded by the Deutsche Forscungsgemeinnchaft:: DFG project 5368 ("Abstract REpresentations in Neural Architectures (ARENA)") and DFG project 539642788, RO 6458/5-1 ("Learning from the Environment Through the Eyes of Children (LEECHI)"). This work was also supported by the Deutsche Forschungseemeincchatf (German Research Foundation, DFG) under Germany's ' Excellence Strategy (EXC 3066/1 "The Adaptive Mind", Project No. 533717223). The authors acknowledge the ANR FRANCE (French National Research Agency) for its financial support of the McSMERise project ANR-23 23--C23--00222 1-01. The authors gratefully acknowledge the computing time provided to them at the NHR Center NHR @SW at Goethe University Frankfurt (project autolearn). This is funded by the Federal Ministry of Education and Research, and the state governments participating on the basis of the resolutions of the GWK for national high performance
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computing at universities (www.nhr- part by computational resources provided by the MaSC high-performance computing cluster at Callipps-Universitee Marburg, the Hessian Center For Artificial Intelligence and GENCI-IDRIS (Grant 2026-AD01 1017150).
(www.ahr.vercindd//nneee sareeemdde/uusse--prrtneee This work was also supported in
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Reviews:
[{"review_id": "cYh8OXHOV5", "reviewer": "Reviewer_KB1f", "summary": "In this paper, the authors explore the contribution of central vision and slowness learning in visual representations of objects within the human brain via bio-inspired self-supervised learning. Using gaze locations as estimated by a gaze prediction model, the method extracts crops that resemble human central vision and train a time-augmented SSL model that aligns crops from the same short time segments within Ego4D recordings. Through several experiments on different granularities of object and scene recognition using both ResNet and VIT architectures, the authors present evidence for their claim that central vision and slowness learning has a strong effect on learning semantic object-level visual representations.", "questions": "1. The authors talk about central vision, i.e., the part of the scene that is fixated, and in L.449, they mention that the crop is parafovea-sized can they elaborate how they map the gaze crop size in pixels to visual angle to infer that it is parafovea-sized?\n\n2. What are the linear probe accuracies as reported in Table 1 for the ablation \"w/o Central Vision\" (provided in Table 2)?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-written and easy to follow.\n\n2. The set of experiments and analyses done to present evidence of the claims is quite meticulous and impressive. I want to particularly appreciate the analysis on CKA similarity between learned representations and Glove-based object co-occurrence embeddings.\n\n3. The performance metrics for some of the tasks like fine-grained and instance-level recognition are quite impressive.", "weaknesses": "1. Even though the paper focuses on central vision, simply ignoring peripheral vision might overlook many insights to the human visual system.\n\n2. Since the gaze crop consists of almost half of the scene (224/336), and the gaze location is heavily biased to the center (Fig. 10 of the appendix), I am not sure if a gaze location is necessary to crop the frame. What would the results look like if the crop was a 224X224 or 336X336 box centered in the frame?\n\n3. I am not convinced that a biologically plausible method would excel at fine-grained recognition but not as much at recognizing the coarser class labels. The analysis of the same is very hand-wavy in L.304-310, and overlooks the very small or non-existent gains in performance with the bio-inspired learning method.", "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": "Ys06K6fGIb", "reviewer": "Reviewer_pvCu", "summary": "The paper investigates whether combining central vision via gaze-centered crops with temporal slowness in self-supervised learning improves object-centric representation learning from long egocentric video. The pipeline predicts gaze where ground truth is unavailable, constructs gaze-centered crops, and trains a time-augmented SSL variant that aligns crops across nearby timesteps. Linear-probe evaluations indicate consistent gains for category, fine-grained, and instance recognition, while scene recognition can favor full-frame training. The analysis further explores context learning by comparing representation similarity to object co-occurrence embeddings.", "questions": "1. How does representation quality change on the eye-tracked subset when training with ground-truth gaze, predicted gaze, fixed center crops, and saliency-only crops? Providing accuracy deltas with confidence intervals would clarify the specific contribution of human-like fixation information.\n\n2. What are the calibration and error properties of the large-scale gaze predictor (e.g., angular error, spatial bias, per-activity breakdown), and how do these correlate with downstream linear-probe metrics when substituting ground truth?\n\n3. Were mixed training regimes attempted (for example, combining full-frame and gaze-centered crops within a batch or varying crop scale for temporally paired views), and how did these affect object-centric and scene-centric metrics?\n\n4. How sensitive are results to the fixation threshold and grouping rules? Histograms of fixation/saccade durations and speeds, plus an ablation over the threshold, would make the dependence more transparent.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The integration of central vision with temporal slowness is a focused, biologically motivated idea that appears to yield practical improvements on object-centric tasks, suggesting value for egocentric and embodied learning communities.\n- The empirical exploration includes reasonable sweeps over crop sizes and temporal pairing that reveal interpretable behavior and replicate across backbones.\n- The end-to-end pipeline is presented clearly with intuitive figures that explain how gaze-centered cropping and temporal pairing interact.", "weaknesses": "- There is a heavy reliance on the predicted gaze rather than ground-truth gaze, and it is not accompanied by any calibration/error metrics. The paper could benefit from reporting prediction error characteristics and relating them to performance when ground-truth gaze is substituted for the eye-tracked subset.\n- It remains unclear whether the gains arise from human-like fixations or from general object-biased views. The paper could benefit from controls using fixed center crops, saliency-only crops without temporal constraints, and motion-centric crops that ignore gaze, matched for compute and augmentations.\n-  Important details such as batch sizes, optimizer and learning-rate schedules, EMA settings, total updates, and effective tokens/pixels are not consolidated, making it difficult to rule out under- or over-training effects. The paper could benefit from a unified table and training curves across methods.\n- The method appears less favorable for scene recognition. The paper could benefit from experiments that blend full-frame and gaze-centered crops within batches or adjust crop scales during temporal pairing to probe whether object gains can be retained while narrowing the scene gap.\n- Effect sizes, multiple-comparison adjustments, and potential vocabulary-mapping biases are not fully discussed. The paper could benefit from explicit reporting and robustness checks that vary the mapping pipeline.", "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": "brOgXVdAXI", "reviewer": "Reviewer_Qmz9", "summary": "This paper investigates the role of central vision and temporal slowness in the formation of semantic object representations in humans. It collects video data from Ego4D dataset and generates gaze coordinates with a existing gaze prediction model. It then crops image regions around gaze coordinates (central vision) and train a time-contrastive self-supervised learning model (temporal slowness).  The experiments assess the impact of learning visual representations with bio-inspired central vision and temporal slowness.", "questions": "1) Current gaze prediction methods based on video input are more like saliency prediction which produces heatmap for each image frame. How do you crop the image when such heatmap is distributed like multi-Gaussians?  \n2) Have you considered the cases of gaze transition in a video clip which causes dissimilarity of visual appearance between adjacent framews.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "1) The paper aims to study the role of central vision and temporal slowness in the formation of semantic object representations in humans. The motivation is interesting.  \n2) Extensive experiments are conducted to evaluate effectiveness of the proposed feature learning on four downstream vision recognition tasks.", "weaknesses": "1) **Lack of technical novelty**. The technical approach of the paper, that is the extraction of gaze-centered image crops with existing gaze prediction model and the self-supervised constrastive learning guided by temporal distance, are very basic technical processes. Thus there is lack of sufficient technical contributions of the paper.  \n2) **Lack of sufficient performance comparison**.  The paper didn't make sufficient comparison with SOTA feature learning methods to validate the superior performance of the proposed method. There are not a few work each year focusing on self-supervised feature learning in the computer vision community, however, the paper only compares with a baseline of “Frames Learning”.", "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": "unFSVU7ymG", "reviewer": "Reviewer_mtJa", "summary": "This paper investigates the role of central vision and slowness learning in semantic object learning. Specifically, it simulates the human-like visual experience using ego videos. The experimental results show that the combination of temporal slowness and central vision can improve the encoding of different semantic facets of object representations. Experiments are conducted on various tasks, including object categorization, Fine-grained object categorization, instance-level object recognition, and scene recognition, to validate the image recognition capabilities of the proposed approach.", "questions": "1. Would the accuracy of gaze estimation affect the performance of the proposed method? Furthermore, if the salient regions were cropped instead to construct the central vision data, would the method still perform well?\n\n2. The proposed bio-inspired learning is inferior to frame-based learning on scene recognition under both the ResNet and ViT backbones. Could the authors provide a comprehensive discussion of this?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The authors conducted extensive experiments to validate the effectiveness of the proposed bio-inspired learning strategy.\n\n2. The utilization fo Ego4D dataset and gaze coordinates to mimic central vision is a kind of novel\n\n3. This paper is well organized and easy to read.", "weaknesses": "1. The proposed bio-inspired learning is inferior to frames learning on scene recognition under both the ResNet and ViT backbones. This result is not well discussed.\n\n2. The influence of gaze estimation is not thoroughly discussed. The authors leverage a state-of-the-art gaze estimation model to obtain cropped regions that mimic central vision. Would the accuracy of gaze estimation affect the performance of the proposed method? Furthermore, if the salient regions were cropped instead to construct the central vision data, would the method still perform well?\n\n3. This paper contains some typos, such as a missing period in line 093.", "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)