Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Authors: Declan Iain Campbell
Sunayana Rane
Tyler Giallanza
C. Nicolò De Sabbata
Kia Ghods
Amogh Joshi
Alexander Ku
Steven M Frankland
Thomas L. Griffiths
Jonathan D. Cohen
Taylor Whittington Webb
Venue: NeurIPS 2024 poster (2024)
Keywords: visual reasoning
foundation models
multi-object reasoning
cognitive science
Primary area: neuroscience_and_cognitive_science
TL;DR: We show that vision language models exhibit human-like capacity constraints in multi-object visual reasoning and image generation.

Abstract: Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks -- such as counting, localization, and simple forms of visual analogy -- that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures, we turn to theoretical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when a shared set of representational resources must be used to represent distinct entities (e.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar to the limitations exhibited by rapid, feedforward processing in the human brain.

Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Declan Camphell', Sunayana Rane', Tyler Giallanza', Nicolò De Sabbata', Kia Ghods', Amogh Joshi', Alexander Ku², Steven M. Frankland', Thomas L. Griffiths Jonathan Cohen 1.2" and Taylor Webb
Princeton Neuroscience Institute
Department of Psychology. Princeton University DDeparmment of Computer Science, EPFL
4 Department of Cognitive Science, Dartmouth College Department of Computer Science, Princeton University Miirosooft Research
Equal contribution
Abstract
Recent work has documented striking heterogeneity in the performance of state- of-the-art vision language models (VLMs), including both multimodal language models and skx-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures basic multi-object reasoning tasks such as counting. localization, and simple forms of visual analogy that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures. turn theo- retical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when shared must be used to represent distinct entities (c.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-aa art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar the limitations exhibited by rapid, feedforward processing in the human brain.
representational resources
1 Introduction
Recent progress in training large-scale neural networks on internet-scale datasets has led to the creation of AI systems with capabilities rivaling human performance across broad range of complex tasks. Most recently, this has given rise to an array of vision language models (VLMs), including multimodal language models such GPT-4y that generate text descriptions of multimodal text and image inputs [1] and text-to-image models such as DALL-E 3 that can generate images from natural language descriptions (24] However, despite the considerable success of VLMs across many tasks, these models still perform poorly on several surprisingly simple multi-object reasoning tasks such counting 23 25 40) relational image generation 77. relational scene understanding [15 31)
and simple visual analogy tasks 20 38) on which humans achieve near perfect accuracy.
Drawing from thcoretical work both in cognitive science and neuroscience, turn the binding problem 9 10 29
33
36) as a potential explanation for these limitations. 'Binding" refers to the
feature of an object (e.g., its color) with the other features of that object (e.g., its shape and location), and the 'binding problem' refers to the question of how the brain accomplishes this without interference between the features for different objects. It is widely recognized that the
ability associate
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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human visual system relies on serial processing to solve this problem. iteratively directing attention to and that binding errors arise when is forced
individual objects so as avoid interference 28
33
to rely on rapid, parallel visual processing [1 19 33 For example, when human participants are not
able to effectively deploy serial processing (e.g. because attention overloaded, or because speeded judgments are required), they are susceptible to so-called illusory conjunctions (e.g., mistakenly identifying a red square in an image that contains a green square and a red circle) [32].
In this work, we test the hypothesis that the failures exhibited by VLMs on multi-object reasoning tasks are due to representational interference resulting from an inability to manage the binding problem. We first investigate two classic tasks from the cognitive science literature, visual search [3] and numerical estimation [14] (i.e.. counting), finding that a wide range of VLMs (including multimodal language models and 4 eoxt-to-image models) exhibit stark capacity constraints similar to those displayed by human observers when forced to make speeded responses. Importantly, although these effects are more pronounced for scenes with more objects, they cannot be explained entirely as a function of the number of objects by the probability of interference given the specific distribution of features and their conjunctions within a scene. Motivated by this observation, we develop a novel scene description benchmark that systematically varies the likelihood of interference, finding that this quantity is highly predictive of binding errors.
scene. Instead, we find that performance is best explained
We also apply these insights to better understand the limitations of VLMs in visual analogy tasks, introducing simple imput pre-processing technique reduce the potential for representational inter- ference. We show that this technique improves the performance of VLMMs on the task, suggesting that their original limitation on this task may be due to more basic difficulty with processing multi-object scenes, rather than an inability to process relations. Finally, we discuss the normative factors that underlic the binding problem 22 g 222, highlighting the role of compositional representations, which are useful for generalization, but introduce the potential for interference when shared representations are used to process multiple objects at the same time. We argue that, surprisingly, the binding failures exhibited by VLMs imply the presence of compositional representations. Overall, these results highlight the usefulness of cognitive science in helping to understand the limits of large-scale generative models, and suggest the presence of common set of principles that govern information processing in both artificial systems and human cognition.
Visual Search
Extensive prior work in cognitive psychology has tested how people process scenes involving multiple objects and under what conditions their performance degrades. These studies demonstrate that performance is not driven solely by the number of objects present in scene, but also depends on the likelihood of interference among objects given the specific distribution of features and feature conjunctions from which they are composed. This can be seen most directly in rescarch on visual search, where participants are typically tasked with identifying specific object within multi-object array. A classic pattern of results arises from a comparison of two conditions: disjunctive and conjunctive search 33 In disjunetive search (depicted of distractor objects that share one feature with the target (e.g., the distractors are all circles) but differ in second feature (e.g., the distractors are all red circles in the 2D task variant). Since one of the feature values (the color green) is uniquely assigned to the target object, the distractors present little interference and therefore task performance is invariant to the number of distractors. This condition is therefore sometimes referred to as "popout" search, as the target immediately stands out from the distractors, and the task can thus be performed rapidly without the need for serial processing. Conversely, in conjunctive search (depicted in the middle of Figure[]. there are two types of distractor objects that each share one feature with the target (e.g., half of the distractors are red L-shapes and the other half are green T-shapes). In this case, the target (a green L-shape) possesses no unique feature that easily distinguishes from the distractors, leading to a significant degree of interference between the distractors and the target. One way to mitigate this is the use of serial search to identify the target. This is suggested by ubiquitiously observed increases in reaction time as a function of the number of distractors, as well as the observation that when participants are prevented from engaging in serial search (e.g., by forcing participants to respond quickly), task performance degrades rapidly as more objects are added to the scene.
the left side of Figure [) the array consists
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Figure 1: Visual search tasks and results. Example trials for the 2D (top) and 3D (bottom) variants of the disjunctive (left/red column) and conjunctive (middle/blue column) search conditions. Perfor- mance for 2D and 3D task variants are plotted on the right. Results reflect aggregate performance for all four VLMs (GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5; see Supplementary Figure for separate model results). Error bars denote 95% binomial confidence intervals.
2.1 Methods
We tested the extent to which VLMs demonstrate similar capacity constraints to humans in visual search tasks. We evaluated four multimodal language models GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5 a task involving disjunctive and conjunctive search conditions We generated datasets involving either 2D sprites or 3D scenes created Blender (6 (similar to those designed evaluate the ability of the model to detect the presence of a target object among multiple distractors. In half the images, a target was present, while the other half, no target was present.
found in the CLEVR dataset [K]) The datasets
Each image contained between 4 and 50 distractors. For the disjunctive search task, these consisted of non-overlapping red circles (for the 2D dataset) green spheres (for the 3D dataset) of uniform size. Half of the images additionally contained target object, which was green circle (for the 2D dataset) red sphere (for the 3D dataset), For the conjunctive search task, the 2D dataset consisted of images in which the distractors were either red L-shapes or green T-shapes (randomly selected with equal probability). Half of the images additionally contained target object, which was green L-shape. The 3D dataset consisted of images in which the distractors were either green spheres or red cubes (randomly selected with equal probability). Half of the images additionally contained target object, which was a red sphere. Each of the datasets (2D disjunctive, 2D conjunctive, 3D disjunctive, 3D conjunctive) contained 1000 images.
2.2 Results
We measured the performance of each model by calculating, for each condition, how detection accuracy varied function of the number of distractor The results indicate that performance in the disjunctive search (i.c., popout) condition was perfect, and invariant to the number of distractors. That is, regardless of the number of distractors, all models achieved perfect accuracy in this condition.
We also evaluated an open-source multimodal language model - Llava 1.5 but performance was very low for these tasks. These results are presented in Supplementary Figure
"When studying visual search in human participants, reaction time (RT) paradigm is typically employed, measuring the time required to locate the target object. Because RT measures cannot be straightforwardly obtained from VLMs. we instead measure accuracy. which is sometimes used in human behavioral studies that employ speeded responses. [18]

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In contrast, in the conjunctive search condition performance was inversely related to the number of objects: for objects, all models displayed an accuracy of ~90%, but the number of objects increased, performance dropped substantially. These results were consistently observed for both the 2DD (top panel of Figure T and 3D (bottom panel of Figure ! datasets. These results were also replicated in an alternative version of the disjunctive search task, in which target and distractor colors were varied between trials (Supplementary Figure 7
The results of these experiments suggest that multimodal language models demonstrate human-like capacity constraints their ability to perform visual search multi-object settings. It is important to emphasize that these capacity constraints are not driven solely by the number of objects present within a scene. Like humans, these models demonstrate capacity constraints only in the task conditions that are impacted by interference between the target and distractor objects, consistent with the hypothesis that these capacity constraints arise as a consequence of the binding problem.
Numerical Estimation
To assess the generality of the human-like capacity constraints observed for VLMS in visual process- ing, we investigated simple numerical estimation task (i.e., counting) that has been widely studied in cognitive psychology. Although human observers can precisely count a very large number of items when allowed to explicitly process those items one at a time, their ability to rapidly estimate the number of items in a display is subject to a severe capacity constraint. Studies have found that the number of objects that can be reliably estimated without explicit serial counting (sometimes referred to as "subitizing")
somewhere between 4 and 6 [4 19 27 To determine whether VLMs are subject to similar constraints, we evaluated both multimodal language models (GPT-4v, GPT-4o, Gemini Ultra 1.5, Claude Sonnet 3.5, and Llava 1.5) and ext-to--mmae models (Stable Diffusion Ultra, DALL-E 3, Google Parti, and Google Muse) on numerical estimation task involving variations of both the number and type of objects. We found that VLMs, across a variety of stimulus and model types, display strikingly similar quantitative capacity limits to those observed in human vision. We also found that these capacity constraints were strongly affected by the variability of features present in image. This effect is consistent with the hypothesis that these constraints arise due to representational interference: given that objects are represented with shared set of representational resources, greater feature variability leads to less overlap in the use of these resources, and therefore less opportunities for interference and binding errors.
3.1 Methods
We generated datasets involving both 2D sprites and 3D objects, varying the number of objects per image between 1 and 20. We explored four conditions with varying levels of feature entropy (i.e., feature variability): a low-entropy condition in which all objects in an image had the same color and shape; two medium-entropy conditions in which all objects in an image had the same shape but unique colors, vice versa; and high-entropy condition in which all objects in image had unique colors and shapes. We prompted the multimodal language models to describe the image and then state the number of objects present in it. To test the text-to-image models, comprising 100 distinct categories, evenly split between common foods (50 categories) and animals (50 categories). We tasked these models with generating images from each category, for which the number of instances of each object ranged from I to 10. To assess their ability to generate images with the exact number of objects requested, we conducted human evaluation study. Participants were asked to count and report the number of objects visible in each generated image. The collected human judgments were then used quantify the model's accuracy. See Appendix C for further details on human evaluations.
generated dataset
3.2 Results
We measured performance by calculating, for each condition, how accuracy varied with the number of objects present in the scene. The results indicated that, regardless of the type of stimuli used (2D vs. 3D shapes, or animals vs. food), and across two fundamentally different types of vision language model (multimodal language models and text-to-image models), VLMs displayed human-like capacity limits (Figure 2). For both multimodal language models and T2I models, accuracy was very high for scenes involving a relatively small number of objects (1-5), but dropped sharply for scenes involving

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Figure 2: Numerical estimation tasks and results. Top left: Examples of images generated by text-to-image (T2I) models for different numbers and categories of objects. Top right: Performance of T2I models as a function of the number and category of objects. Results reflect an aggregate of four models (Stable Diffusion Ultra, DALL-E 3, Google Parti, and Google Muse). Bottom left: Examples of images (featuring either 2D or 3D objects) used to evaluate numerosity estimation. Feature entropy was varied four conditions (low entropy, high entropy, and two medium entropy conditions). Bottom middle: Numerosity estimation results for four multimodal language models (GPT-4v, GPT-4o, Gemini Ultra 1 1.5, Claude Sonnet 3.5; see Supplementary Figure 6 for results with Llava-1.5). Bottom right: Numerosity estimation results plotted as function of the number of objects in an image, aggregated across all four models (see Supplementary Figure S for individual model results). Error bars for all plots reflect 95% binomial confidence intervals.
more objects. Moreover, the multimodal language models exhibited performance consistent with our hypothesis that capacity limits arise due to representational interference across objects (i.e., the binding problem), with overall performance highest in the high-entropy condition (lowest interference), lowest in the low-entropy condition (highest interference), and intermediate in between these two extremes in the medium-entropy conditions. Though there are slight differences between the capacity limits exhibited by these two classes of models, it is striking that they both fall within the subitizing limit of human vision, especially when considering the significant differences both architecture and training procedures. Furthermore, the effect of feature entropy these capacity

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limits strongly suggests that they are driven by representational interference. arising due an inability to manage the binding problem. To further investigate this hypothesis, we next turned novel scene description benchmark that allowed us to systematically investigate the likelihood of representational interference, thus enabling a direct test of the extent to which this factor is responsible for the shortcomings of VLMs.
4 Scene Description
Theoretical accounts of the binding problem g 33 posit that capacity limits in rapid visual processing arise as consequence of interference between representations. Given scene containing multiple objects, and a set of shared features with which to represent those objects, the likelihood of interference will tend to increase as a function of the number of objects in the scene (without the availability of a mechanism for binding features together, e.g., serial processing). However, as emphasized in our experiments on visual search and numerosity estimation, interference is not driven solely by the number of objects, but is also strongly influenced by the specific feature conjunctions present within a scene.
We developed a novel scene description task to further investigate the extent to which VLM per- formance is driven by representational interference. The task is illustrated in Figure Ba. For each image, the likelihood of representational interference was quantified as the number of feature triplets present in that image. A feature triplet is defined as any set of three objects for which one pair shares feature, and another pair shares a different feature. For instance, [green X, green triangle, yellow triangle) is a feature triplet, because the feature 'green' is shared by two objects (the green X and the green triangle), and the feature 'triangle' is shared by two objects (the green triangle and the yellow triangle). Without the ability to accurately bind these features together at the level of objects, such feature triplets create opportunities for representational interference, and thus lead to illusory conjunctions. For instance, the feature triplet (green X, green triangle, yellow triangle) may lead to the erroneous identification of a yellow X. We studied the extent to which the presence of such feature triplets can account for scene description performance in VLMs.
4.1 Methods
As in the previous tasks, we generated datasets involving either 2D sprites or 3D objects. Each scene contained a variable number of objects (10-15 objects for the 2D dataset and 8-12 objects for the 3D dataset), and systematically varied the number of feature triplets present in each scene. For example, the scene depicted in Figure 33 contains three feature triplets (illustrated by the dashed lines). VLMs (GPT-4v, GPT-4o. Gemini Ultra 1.5, and Claude Sonnet 3.5) were prompted to provide a description of the objects in JSON format (see Appendix B for more details). We also generated prompts describing similar scenes (but involving real-worls objects) and tested the ability of the T2I (Stable Diffusion Ultra, DALL-E 3, Google Parti, and Google Muse) to accurately generate these scenes (as assessed by human evaluation; Appendix C for more details). To obtain representative sampling of scenes with different triplet counts, we systematically varied the diversity of colors and shapes across trials. This approach ensured adequate sampling of trials with different feature combinations and their associated triplet counts. To ensure reliable performance estimates, we excluded from analysis any triplet counts represented by fewer than 20 trials across all conditions. For the T2I experiments, we additionally excluded trials where models generated more than three extraneous objects not specified in the prompt, as these represented significant deviations from the intended scene structure.
4.2 Results
We measured scene description performance by calculating how the number of errors (quantified as the edit distance between the true description of the scene and the model's description of the scene) varies as a function of the number of objects present in the scene, and the number of feature triplets. The results (Figure 3 confirmed our prediction that performance should vary as a function of the number of triplets. Across multiple stimulus types (2D and 3D objects), and model types (both multimodal language models and text-to-image models), the largest number of errors occurred in the trials where the risk of binding errors was highest (i.e., the trials with the largest number of feature triplets), consistent with the hypothesis that errors would be driven primarily by the formation

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Figure 3: Scene description task and results. A) Example image used in 2D scene description lask, illustrating the concept of feature triplets: sets of three objects where one pair of objects shares a feature, and another pair shares a different feature. This example contains three feature triplets, demarcated by the dashed lines. 3D scenes were also investigated. B) Scene description results for text-to-image (T2I) models) function of the number of feature triplets. C) 2D scene description results for multimodal language models as function of the number of feature triplets. Left panel illustrates the results aggregated across four models (GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5). Right panel illustrates the results aggregated across scenes with different numbers of objects. D) 3D scene description results, Error bars represent the standard error of the mean.
of illusory conjunctions. These results also showed pattern of increasing errors function of the number of objects, consistent with a general capacity limit on the number of objects that can be accurately represented at the same time. Overall, these results suggest that the capacity limits displayed by VLMs are best explained by an inability to manage the binding problem. In the next section, we apply these insights better understand the limited visual reasoning capabilities of VLMs.
5
Visual Analogy
An open question in studying the performance VLMs is the extent to which these models can solve analogical reasoning tasks. These tasks are of particular interest given their centrality in human higher-order cognition [1] and their use as measures of human intelligence (30. Recent work has demonstrated that LLMs have impressive ability solve range of text-based analogical reasoning tasks [37], but initial tests of VLMs have suggested that they often struggle to solve comparable visual forms of these tasks, sometimes performing well below human participants 20) 38)
This leads to the question of why, given the success LLMSs on text-based problems in this domain, VLMs do not display comparable success in solving analogy tasks. One possible explanation,
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Source Pair
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Figure 4: Visual analogy task. The Unified and Decomposed conditions present the same object pairs, but in the Decomposed condition is broken up across three images. The correct target pair must share both relations (shape and color) with the source pair, so the correct answer in this example Target Pair 2 because satisfies both the 'same shape' and 'different color' relations.
suggested by our investigation of the binding problem. is that the difficulty displayed by VLMs on visual reasoning tasks may stem from more basic difficulty with processing multi-object scenes. In other words, regardless of whether VLMMs have the capacity for abstract reasoning necessary to solve analogy tasks, they will likely struggle to solve visual analogy tasks simply because these tasks involve processing multi-object scenes. To distinguish between these two failure modes (inability to process abstract relations vs. inability to process multi-object scenes), we performed experiments using visual analogy task in which the visual processing demands were explicitly manipulated, and measured the ability of multimodal language models to perform both abstract relational tasks and basic object-level tasks.
5.1 Methods
We generated 200 trials from a simple relational match-to -a-mple (RMTS) task (Figure 4 using the same 2D sprite stimuli from the previous experiments. We selected subset 8 easily recognizable shapes and colors to generate a set of 64 stimuli. For each trial, we chose the source pair by sampling two objects that shared at least one of the two feature dimensions. We then selected the target pairs by sampling two pairs of objects: one which matched the source pair exactly along its relations (the correct target) and one which shared only one of the relations with the source pair (the incorrect target). We manipulated visual processing demands by investigating two conditions, one in which the source and target pairs were presented in a single image (the "unified" condition), and one in which the source and target pairs were presented separate images presented in sequence (the "decomposed" condition), thereby reducing the chance of binding errors.
We assessed the performance of four VLMMs (GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5) four tasks: identification of the correct target pair the full RMTS task (Analogy), decoding of single features from specific individual objects (Single Feature Decoding Task), comprehensive decoding of all features given problem (Full Feature Decoding), and decoding of relations between object pairs (Relation Decoding).
5.2 Results
We found that performance on this task was highly variable aross VLMs, with some models (Claude Sonnet) showing nearly perfect performance on all tasks (Table and other models (Gemini Ultra) showing poor performance on most tasks (Table 4 These results are consistent with recent work showing mixed success on visual analogy problems [88) and at odds with work claiming that VLMs have capacity for visual analogy (00 Interestingly, we also found that many models also struggled on more basic tasks such as identifying the features the objects present in the image, identifying the relations for individual pairs of objects.

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Table 1: Visual analogy results: GPT-4v.
Unitied Accuracy
Decomposed Accuracy
Accuracy
95% CI
Accuracy
95% CI
Analogy
91% %
(86%, 94%)
99%
(97%, 99%)
Relation decoding
80%
(73%, 84%)
91%
(86%, 94%)
Full feature decoding
85%
(79%, 89%)
100%
(100%, 100%)
Single feature decoding
95%
(92%, 98%)
98%
(96%, 99%)
Table 2: Visual analogy results: GPT-4o.
Unified Accuracy
Decomposed Accuracy
Accuracy
95% CI
Accuracy
95% CI
Analogy
99%
(96%, 100%)
100%
(100%, 100%)
Relation decoding
88%
(82%, 92%)
98%
(95%, 99%)
Full feature decoding
97%
(84%, 100%)
100%
(100%, 100%)
Single feature decoding
86%
(81%, 91%)
94%
(81 %. 91%)
Table 3: Visual analogy results: Claude Sonnet 3.5.
Unified Accuracy
Decomposed Accuracy
Accuracy
95% CI
Accuracy
95%
Analogy
100%
(100%, 100%)
100%
(100%, 100%)
Relation decoding
92%
(88%, 95%)
99%
(97%, 100%)
Full feature decoding
100%
(100%, 100%)
100%
(100%, 100%)
Single feature decoding
100%
(100%. 100%)
100%
(100%, 100%)
Table
Visual analogy results: Gemini Ultra 1.5.
Unified Accuracy
Decomposed Accuracy
Accuracy
95% CI
Accuracy
95% CI
Analogy
56%
(49% 64%)
60%
(53%, 67%)
Relation decoding
84%
(79%, 89%)
89%
(83%, 93%)
Full feature decoding
100%
(100%, 100%)
100%
(100%, 100%)
Single feature decoding
(74%, 85%)
83%
(78%, 88%)
Most importantly, found that performance was significantly improved in the Decomposed con- dition (involving separate images for each pair of objects) as compared with the Unified condition (involving a single image with all object pairs). This was the case across all tasks, and for all models, except for the cases in which performance was already at ceiling for the Unified condition. Taken together, these results suggest that the poor performance of VLMs on visual analogy tasks may be a consequence of more general limitation with processing multi-object scenes, arising due to an inability to manage the binding problem.
Discussion
We have presented a series of experiments aimed at understanding the limits of vision language models in processing multi-object scenes. Our results suggest that these limitations can all be understood as arising from an inability to manage the binding problem, a fundamental problem associated with compositional coding identified by classic work in cognitive science 10 33].
Recent theoretical work has formalized this problem within a normative framework 1 sug- gesting that it arises due to a tension between the learning of compositional representations, and the shared use of such representations to encode multiple objects the same time. To illustrate this, consider two different schemes for representing multi-object scenes: conjunc-

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rive scheme, involving dedicated representations for every possible conjunction of features (e.g.. fredsquare, greencircle, bluetriangle, greensquare ...)) vs. a compositional scheme, involving the dynamic combination of shared features across different dimensions (e.g., combining the features {red] and (triangle) to represent red triangle). The compositional scheme enables efficient use of finite neural resources, and offers major benefits in terms of generalization (e.g., anything learned about red triangles can then be readily generalized to support inferences about other red objects), but, without a mechanism for dynamically keeping track of the bindings between features, this scheme leads to severe interference, giving rise to the capacity limits observed in cognitive processing. One surprising implication of this perspective is that the binding errors exhibited by VLMs suggest that they have developed compositional representations, perhaps as a consequence of being forced to generalize by their immense and highly diverse training corpora. Without the use of such compo- sitional representations (i.e., if VLMS employed a conjunctive coding scheme), there would be no interference between the representations for different objects, and thus there would be no binding problem. The presence of the binding problem, therefore, implies the presence of compositional representations in VLMs.
is worth considering how VLMs might be improved so as to enable them to cope with the binding problem. One might think that this could be accomplished through additional fine-tuning multi- object tasks. However, the theoretical perspective outlined above suggests that, to the extent this alleviates the binding problem, it would do so by eliminating the use of compositional representations, which would then have negative impacts on generalization. The question then is how VLMs might be enhanced to solve the binding problem, while preserving the benefits of compositional representations. The most obvious possibility here is to augment VLMs with mechanisms for serial processing of images, of the sort that enable human reasoners to manage the binding problem by selectively attending to individual objects one at time 22 33). A number of methods have been proposed for sequential reasoning over images 212 35 though none of these methods have yet been deployed at the scale of VLMs. An alternative approach (which may be unique to artificial systems) involves the use of slot-based methods for object-centric representation learning [ 10 16, which have been shown to dramatically improve performance in visual reasoning tasks without requiring sequential processing of images 8 (2), but which have also not been scaled to the level of current VLMs. It remains to be seen whether and how these techniques might contribute to improved reasoning in future VLMs, or whether new approaches will be needed to enable human-like visual reasoning.
6.1 Limitations& & Future Directions
This study has several limitations that should be considered when interpreting the results. First, we limited our analysis a relatively small set of tasks. The tasks in our study were selected illustrate the different settings in which the binding problem may impact performance, while grounding our analysis in well known tasks from cogmitive science that have been used to index such capacity constraints in humans. Future work may examine broader set of tasks such as matrix reasoning tasks B 26 39 that are more diagnostic of the reasoning failures arising due issues with binding. Second, we primarily investigated propietary VLMs, for which we do not have detailed knowledge of their training data or architecture, or the ability to directly investigate their internal representations. We chose to focus on these models because they reflect the best- best-performing current set of VLMs (our experiments with the open-source Llava-1.5 yielded very poor performance on all tasks), but continued progress in the development of open-source VLMs should make it possible to investigate open-source models in future work. Finally, our work is focused particularly on characterizing the capacity constraints of VLMs arising due to issues with feature binding. While propose a naive approach for improving performance by selectively processing sub-images independently, future work may explore more flexible methods for decomposing complex, multi-object reasoning tasks, especially by exploiting methods for object-centric representation learning [4| 5 10 16].
7 Acknowledgements
This work was supported in part by Microsoft Azure credits provided Princeton University. D.C. and T.G. are supported by the National Science Foundation Graduate Research Fellowship Program (NSF GRFP).
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A Appendix: Supplementary Figures
2D Results
Claude Sonnet 3.5
GPT-4o
Propertin cornect
Proportion cornect
0.0-
2.2
2.8-
Conjunctive Search
2.5
Disjunctive
0.7-
9.7
Conjunctive Search
Disjunctive Search
0.5
35
0.
20 Number objects
25
35
Number objects
Gemini Ultra 1.5
GPT-4V
Proportion correct
2.3m
Proportonn
0.8-
Conjunctive
hve Search
9.84
Disjunctive Search
Conjunctive Search
0.6
2.6
35
40
20
25
30
35
Number objects
Number objects
3D Results
Claude Sonnet 3.5
GPT-4o
1.0
0.9
0.81
Proportion
0.8
vonjunctive Search
Corjunctive Searth
0.6
Disjunctive Search
6.6
Disjunctive Search
objects
Number objects
Gemini Ultra 1.5
GPT-4v
Preportion correct
3.0
0.0
Proportion
0.81
0.7
6.7-
Conjunctive Search
Corjunctive Search
Disuncttvv
Diglunctive
0.5
20
25 10
0.5
20
25
30
Number objects
Number of objects
Figure 5: Visual search model results. Individual model performance for 2D and 3D visual search tasks. Error bars denote 95% binomial confidence intervals.
Numerosity Estimation
Visual Search
Propertion correct
1.0
high entragy medium entropy
Proortion correct
1.0
Conjunctive Search
Disjunctive Search
0.9
0.6-
0.2
0.6
0.0
15
20
0.5
25
30
50
Number of objects
Number of objects
Figure 6: LLa VA 1.5 Performance. Performance of LLaVA 1.5 on the visual search and numerosity
estimation tasks. Performance substantially weaker than all other models evaluated.
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Claude Sonnet 3.5 Control
1.0
Proportion correct
0.9
0.8
0.7
Disjucctiv Search
0.6
Conjunctive Search
Disjunctive Search Control
0.5
20 Number of objects
Figure 7: Visual search results with additional control experiment. Results for Claude Sonnet 3.5 on 2D visual search tasks, including disjunctive and conjunctive conditions, and an additional disjunctive search condition ("Disjunctive Search Control") in which target and distractor colors were varied between trials.
2D Results
3D Results
Cloude Sonnet 3.5
GPT46
Claude Sonnet 3.5
GP7-6o
Propottin conect.
Proportonn convet
Proportion correc.
moddan cntray enEr3y
mecke entioes
medLE erisgyy
esropp
estrops
Number of objects
Number objects
Number of abjects
Number objects
Gemini Ultra 1.5
GPT-4v
Gemini Ulbra 1.5
GPT-4V
Proportiin correc.
Proportion connet
Procortion correct
Fand poreery entraty
Werk retries
2
motkey criogyy
entopo
extrpe
Number of objects
Number of objects
Number of objects
Number objects
Figure 8: Numerosity estimation model results. Individual model performance for 2D and 3D numerosity estimation tasks. Error bars denote 95% binomial confidence intervals.
14

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B Appendix: Prompts for Vision-Language Model Experiments
B.1 Numerical Estimation
1. 2D VLM Prompt
presented sccurately count the number of objects in the image. Follow these instructions carefully:
image containing
objects.
should enclosed in square by describing each objact the image.
2. Conclude your reaponse by providing the total count of objects as an integer in square brackets.
Only
2. 3D VLM Prompt
image containing several shapes. accurately count the number of shapes in the image. Follow these instructions carefully:
presented
shape 2. Conclude your reeponse by providing the total count of shapes as an integer, enclosed square brackets. Only brackets.
Begin by describing
should enclosed square inage.
3. T2I Prompt
Render an image with exactly (ame), each distinetly separated and easily countable, arranged againet unifern background in photorealistic style.
B.2 Visual Search
1. 2D Disjunctive Search Prompt
You are presented with an image containing zeveral shapes. Your task is to daternine there any green shapes the image. Follaw these steps carefully:
image, noting 2. Conclude reaponse stating [True] if there are green shapes, False] there are none. Enclose your final anaver in square brackete, as shown.
1.
shape
2. 3D Disjunctive Search Prompt
You are presented with an image containing saveral objacta. Your task is to deternine there any objecta carefully:
image. Follow steps
inage, noting 2. Conclude your reeponse stating [True] if there False] there are none. Enclose your final ansver in square brackete, as shoun.
Describe object
objects,
3. 2D Disjunctive Search (Variable Color) Prompt
You are presented with an image containing several shapes. Your task is to the color. Follox steps carefully: 1. Describe each shape in the image, noting their coler.
determine
shapes
Conclude your response by stating [True] [Falae] if there is "oddball" shape that is different coler. Enclose your final ansver in aquare bracketa, as shavn.
shapes are sane color,
4. 2D Conjunctive Search
image contalning letters "L and 'T', These letters sill appear in either red or green. inage. Follox
presented
lettere, specifically
deternine these steps carefully:
are any green
1. Describe each shape ln the image, noting their color.
2. Conclude response stating [frue] if the letter " appears green, or [False] there are no green "Ls. Enclore your final anszer in square brackete,
15

---

5. 3D Conjunctive Search Prompt
You are presented with an image containing a set of objects, specifically apherea cubes. These abjects will appear in either red or green.
Your tack is to deternine if there any red spheres in the image. Follov these steps carefully:
Describe
object
inage, neting their
2. Conclude your response by stating [True] if a red sphere is present, False]
none. Enclose your
in square brackets,
shoun.
B.3 Scene Description
1.
2D VLM Prompt
The folloving image
multiple simple, colored objects. The possible shapes that nay be present in the inage are: <airplane, triangle, star, circle, square, spade,
cloud, X-shape, unbrella, pentagon,
aciasors, infinity, check nark, right-
The possible colors that may be present the inage are: <red, nagents, salmon green, line, olive, blue, teal, yellow, purple, broun, gray, black, eyan, orange?.
Dascribe each object the image the of J8ON objact datailing the color and shape of each iten. must
enly
jeon array of objecte, without any additional
information text.
For example, if the imsge containe a purple check mark, to green sciesore, one orange right-arron and teal infinity you would write:
("shape": 'check mark", "color"; "purple"), ("shape": "scissore", "coler": "green"), ('shape": "scissors", "color": "green"), ("shape": 'right-arror" "color"; "orange") ("shape"; "infinity", "coler"; "teal") ]
2. 3D VLM Prompt
The folloving image contains multiple simple, colored objects. The possible shapes that donut, sphere, cube, droplet, walling-pin, coil, creen, snomman, spikey ball>.
present in the
<eone, cylinder, boul
The aet of colors that may be present in the inage are: <red, green, blue, yellov, purple, light grean, gray, black, light blus, pink, teal, broun?. Describe each object in the image in the form of JSDN object, detailing the color and shape of each iten. You must information or text. For example,
only
the
array of objects, without additional
inage contains apikey-ball, you would write:
cube, green donuta, and cyan
("shape": "cube", "color"; "broxn"). ("shape": "donut", "colar": "green"), (*shapa": "donut", "color": "graan"), (*shape"; "spixey-ball", "color"; "cyan") ]
3. T21 Prompt
Render an image in photoroalistic style with exactly (objecta_strian) arranged againat uniforn background, each distinctly saparated. Include only these objecta in the image and nothing elae.
16

---

B.4 Relational Match to Sample (RMTS)
1. Full Task Unified Condition
Iolloving inage depicta tuo features: shape and color.
sample with
There
pairs objects relevant your task: source pair top pair of objacta), target pair #1 (the pair of objects botton left), and target pair (the pair of objects on the botton right). pair and the target pair, To accomplish this taak, you can use the folloving ateps: Identify the features of
Now, the
paire, identify the natching
objects each pair
shape color).
2. Identify the relations over the features of each pair.
3. Determine
target pair exactly one target pair will ahare the same relations with the source. Conclude with the integer of the correct target pair wrapped in equare brackets. Fer example,
relations
source pair
4.
pair #1 natches the source return (1).
2. Full Task Decomposed Condition
The follaving images depict trial of relational match to sample task with tuo features: shape and color.
There threa pairs objects relevant your task: the pair #1, and target pair #2.
pair,
target paire, identify natching target pair, To aecomplich this tack, you can use the folleving steps: objects 2. Identify the relations over the featurea of each pair
given
pair
1. Identify the features of
pair (i.e. shape color).
3. Determine which target pair shares the sane relations with the source pair exactly
target pair will 4. Conclude with the integer of the correct target pair krapped in aquare brackets. example, target pair
the relations
the
pair,
(1).
3. Relation Decoding- Unified condition
Are the objects in the (pair) (the (pair_loc) pair) the (relation }?
ansver should [True] if they have different (relation). Ensure your ansver wrapped in square (relation) [False
objecta
4. Relation Decoding- Decomposed condition
Are objecta }?
{pair) (the {pair_loc} pair) (relation ansver should [True] if they have different (relation).
(relation) [Palse Ensure that your final answer is wrapped in square brackets.
objects
5. Single Feature Decoding Unified condition
{object_loc) ((object_ind}) object in the {pair) pair? Unly provide the (feature).
{feature)
Your response should be a single word ansver wrapped in square brackets.
For instance, if ask for the coler of red object, you should and nothing else. If ask for the shape of an object that is circle, you [circle].
[red]
should
Valid shapes include: triangle, cloud, cross, heart, circle, square. Valid include: red, green, blue, darkorange, purple, gray.
6. Single Feature Decoding Decomposed condition
What the (feature) of the (abject_lac) ((object_ind)) object in the {pair) pair? Only provide the (feature). response should For instance, if ask far the calor of red object, should return [red] and nothing elee. If ask for the shape of an object that is circle, you should return [circle].
single word anseer urapped square
Valid shapes include: triangle, cloud, cross, heart, circle, square. Valid colors include: red, green, blue, darkorange, purpla, gray.
17

---

7. All Feature Decoding Unified condition
Examine the image provided, which depicts six basic, colored shapes arranged into three distinct pairs objecta: the source pair at the top, target pair #1 on the botton left, and target pair #2 the betton right. For each pair, identify the shapes as follous: "objecti" refers to the l right- object in
object pair.
pair, and the "object2"
the color shape
object
joon
deacribed belox.
Valid shapes: triangle, cloud, cross, heart, circle, square. Valid colors: red, green, blue, darkorange, purple, and gray.
response should
folloving
source: oburce_objecti: (shape: circle, color: purple), source_object2: (shape: circle, color: purple)
targetl: {
targatl_object:: {shape: triangle, color: broun), targeti_object2: (shape: triangle, color: broun)
terget2.objocti: (ehape: equare, color: green), torjet2_ (shape: square, color: black)
Reaponse:
8. All Feature Decoding- Decomposed condition
inages provided,
depict
shapee
arranged into three distinct pairs of objects: the source pair, target pair #1, target pair
For each pair, identify the shapes follous: "objecti" refere to the left- most object in the pair, and the "object2" to the right- object in the pair.
Return the color and shape of each object in the trial in the joon format described belox.
Valid shapes: triangle, cloud, cross, heart, circle, square.
Valid colors: red, green, blue, darkorange, purpla, gray.
Your reeponse should be the folloving format:
source: {
color: purple), source_objact2: (shape: circle, color: purple]
oburct_objecti;; (shape:
target1: {
tarjoti.objocti:: (chape: triangle, color: brown), targeti_object2: (shapes triangle, color: brown)
obrett? {shape: square, color: green), obtett2 {shape: square, color: black)
Reaponse:
18

---

C
Appendix: Human Evaluations
Round 2/ /60
aideccntiiee of mufik cevepodddegg
Count the rumber poilos the image Pooce ontor an iigge vilvo.
euorpe Eirannn mew iive uen ont
Binding task evaluation
(h) Counting task evaluation
Figure 9: Human Evaluation
Human evaluations of the eexttto-image variants of the counting and binding tasks were conducted on Prolific. Participants were asked to count the number of objects in each T2I counting task image, or asked to match objects to provided labels in each T2I scene description task image. They were also asked to list extraneous objects (generated objects not in the input prompt) when evaluating the binding images. Participants were paid a total estimated wage of approximately $12/hour, and total compensation for the entirety of the human evaluations approximately $600. All participants provided informed consent. The study was approved by the Princeton University IRB.
D Appendix: Computational Resources
Experiments were conducted on closed source models, and therefore did not require any specialized hardware. Generation of the 3D variants of the task were more computationally intensive and were performed in parallel on local cluster, with 16GB RAM allocated per CPU per job.
19

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Justification:
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Reviews:
[{"review_id": "X8qYKlfYYO", "reviewer": "Reviewer_N3JA", "summary": "The paper puts forth the hypothesis that VLMs suffer from similar constraints as humans in multi-object visual processing, especially\nwith respect to \"binding\".  Results from cognitive science show human constraints in numerical estimation, visual search, and relation\nidentification when not able to do serial processing, which is required for \"binding\" different object features together. The paper hypothesizes that lack of serial processing and binding in VLMs is responsible for failures in these tasks.  The paper gives experimental\nresults that support this hypothesis.", "questions": "Figure 2 caption: \"Performance for 2D and 3D task variants\" -- performance of what?  GPT-4v?  I also suggest in that caption that you\nshould say what the targets are for each search (red circle, red \"L\", red sphere, red sphere).\n\nIn Section 3.1., explain what the task is for GPT-4V: \"Is there a red circle?\" \"Is there a red L?  This is explained in the appendix (A.2) but should be explained here in the main paper.\n\nLooking at the visual search task as described in A.2, it seems that the task wasn't actually \"search\" in the sense that, unlike humans, GPT-4V doesn't have to localize the target object.  In these kinds of tasks, aren't humans asked to localize the target?  How does this affect the comparability of the results between GPT-4V and humans on this task?\n\nIn Section 3.1, explain why in half the images, targets were left out. This doesn't make sense unless readers know what the specific task is.\n\nIn Section 3.1 you say \"VLMs demonstrate similar capacity constraints to humans.\"  What do you mean by \"similar\"?  Do humans show a similar success/failure curve as those in Figure 2? Or is the similarity just that humans perform well on disjunctive search and badly on\nconjunctive search (if not enough time for serial processing)?\n\nIn Section 3.2, explain how the \"accuracy\" of the model is calculated.\n\nIn Section 4.2 you refer to \"the true description of the scene\". Where did this \"true description\" come from?  Also, define \"edit\ndistance\".  I was also confused because each scene could have different correct descriptions with different levels of detail.  Was it possible that the VLM gave one version of a correct description but it was counted as wrong because it included details that the \"true description\" didn't contain?\n\nI was also confused by the visual analogy task in Figure 4.  It seems to me that both Target Pair 1 and Target Pair 2 could be considered\nthe correct answer.  The source pair has two obvious relations: \"same star shape\" and \"different color\".  The former would map to Target 1 and the latter would map to Target 2.  Which Target did you consider correct, and why?", "limitations": "The authors do a good job of discussing limitations.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "This is a good paper -- it gives a cognitive-science-based approach to understanding VLM behavior. It is well-written and has a mostly clear discussion of hypotheses, experiments, and results.", "weaknesses": "The paper leaves out some important information.  For each task, the authors prompted GPT-4v to describe the input images, but as far as I can tell, they did not give any evaluation of how well GPT-4v was able to describe them.  Other work (e.g., http://www.arxiv.org/abs/2407.06581) has shown that GPT-4v has trouble describing such synthetic images and often will hallucinate.\n\nHere, the authors should include the results on how well GPT-4v can even parse what is in the images (including shapes, colors, etc.) and the authors should assess whether that failure to describe simple images is related to its failure on these tasks.  To what extent are the failures a matter of lack of binding features, or of missing or hallucinating features?\n\nA couple of typos/corrections:\n\nIn Section 3, \"Figure 1\" should be \"Figure 2\".\n\nIn the caption for Figure 2, \"blue column\" should be \"red column\" and \"red column\" should be \"blue column\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:\n\n\n**GPT-4v’s ability to describe synthetic images, and relationship to concurrent work (‘Vision language models are blind’)**\n\nThank you for highlighting this highly relevant concurrent work, which we have now cited in the revised manuscript. We completely agree that VLMs struggle to accurately describe multi-object scenes. In this work, we propose an explanation for why VLMs show this difficulty. Specifically, we propose that it results primarily from an inability to accurately bind features at the object level. This is distinct from explanations that involve hallucinating features that aren’t present in the image (see additional results in section below for an analysis of these failure modes).\n\n\n**Analysis of binding failures vs. hallucinations**\n\nWe agree that it is important to distinguish binding failures from the hallucination of individual features that aren’t actually present in the image. We have now included an analysis to account for this. We found that  the model rarely (<25% of errors) generates descriptions containing features not present in the trial. When this does happen, it is typically due to the misidentification of a feature with a close neighbor (e.g., attributing the color cyan to a blue shape). Most errors in this task can be attributed to binding errors where the model incorrectly attributes a feature to the wrong object. \n\n\n**Clarifications regarding visual search task**\n\nIn these tasks, subjects are typically asked to identify whether the target is present, so the task is comparable. It is called ‘search’ in the literature, but we agree that it doesn’t necessarily involve localization. We have clarified this in the revised manuscript. We have also clarified why half of the images do not involve targets (because it is really a detection task, there must be some trials without targets in order to obtain a meaningful accuracy estimate).\n\nRegarding the similarity between human and VLM capacity constraints, it is difficult to perform a direct comparison because the original tasks involve a response time measure, and there is no clear analog of response time in VLMs. The classic finding is that response time increases as a function of the number of objects in the conjunctive search task, but is unaffected by the number of objects in the disjunctive search task. Because we do not have a response time measure for VLMs, we instead evaluate accuracy. We find a similar qualitative pattern (accuracy varies as a function of the number of objects in the conjunctive search task, but is unaffected in the disjunctive search task). \n\nWe have also added an explanation of how accuracy is calculated in this task. Accuracy is computed as the proportion of trials that are correct. The following accuracy equation has been included in the supplemental for clarity: $acc = \\frac{nCorrect_{target} + nCorrect_{distractors}}{nTrials}$\n\n\n**Clarification regarding ‘true description’ and ‘edit distance’ in the scene description task**\n\nTo address these concerns, in the most recent version of the paper, we have updated the prompts so that the model directly returns a json of the trial objects’ shapes and colors (rather than an open-ended description of the scene). Edit distance here is defined as the number of objects that the model failed to identify from the ground truth json plus number of objects that the model erroneously identified (conjunction of shape/color not present in the ground truth json). We have added a description of the updated prompting strategy and a definition of edit distance in the revised manuscript.\n\n\n**Clarification regarding correct answer in example visual analogy problem**\n\nWe have clarified that the correct target pair must share both relations (shape and color) with the source pair. The correct answer in the example is Target Pair 2 because it satisfies both the ‘same shape’ and ‘different color’ relations. Target Pair 1 only satisfies one relation. We did not observe any evidence that the task was ambiguous for the VLMs. This clarification has been added to the revised manuscript.\n\nWe have also corrected typos identified, and implemented other clarifications (in the caption for figure 2, and the description of the visual search task).\n\nWe hope these revisions and clarifications address your concerns. Thank you again for your insightful feedback.", "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": "KqPt9lZJdA", "reviewer": "Reviewer_gAnB", "summary": "**Summary:**\n\tThe paper investigates the counting abilities of VLMs. It puts forward a cognitive science inspired perspective, where VLMs are fundamentally constrained in their processing of multi-object scenes due to a lack of serial processing mechanisms.\n\n**Decision:**\n\tThe paper is well structured and investigates an interesting (albeit a bit constrained) question concerning VLMs object reasoning abilities. While some of the conclusions seem overstated to me, I nonetheless recommend accept, since the authors present novel findings that improve the research community's understanding of VLMs visual reasoning abilities.", "questions": "- Figure 1: There is a spike at 9 objects for both models. Do you have an idea on why this might be the case? [1] (which you already cite) also find the same spike and offer some potential explanations for this (see Figure 5).\n- Answer parsing is again conducted by a large language model. I am wondering how accurate this parsing is. Did you compute any metrics to check the accuracy of the final parsed output or run some qualitative checks?\n-  In line 189 you write \"GPT-4v demonstrates human-like capacity constraints in its ability to perform visual search in multi-object settings.\" I was wondering what human level performance is in this task? It would be nice to have some more information to compare the model performance to. The same is true for line 127, where I'm not sure this is a fair comparison. Sure, the number of objects the models can count well are somewhat similar to the subitizing limit of humans, but this task should not require rapid estimation of the number of objects and therefore we would expect humans to be able to count well above 6 objects here anyways.\n- The sentence in line 303 reads a bit odd, it should probably read \"facilitate generalization\" instead of facilitation and there might be a comma missing before?\n- If I understand correctly you describe a different measure for the model performance for the last task in Section 4.2, would it not be possible to again report the proportion of correct answers here? I find it hard to compare this experiment with the previous experiments.\n- To me the take aways seem overstated. What does lack of serial processing for VLMs mean? I don't think VLMs are fundamentally unable to sequentially process an image and therefore they should in theory be able to \"selectively attend to individual objects one at a time\". This is also why I don't completely follow the conclusion that the VLMs show \"human-like capacity constraints\". I would argue they should be able to do sequential processing and should therefore not be measured against the human subitizing limit anyways. Maybe you can offer some more details on this argument.\n\n[1] Rane, S., Ku, A., Baldridge, J., Tenney, I., Griffiths, T., & Kim, B. (2024). Can Generative Multimodal Models Count to Ten? Proceedings of the Annual Meeting of the Cognitive Science Society, 46. Retrieved from https://escholarship.org/uc/item/8kz5787g", "limitations": "The paper is well structured and investigates an interesting (albeit a bit constrained) question concerning VLMs object reasoning abilities. While some of the conclusions seem overstated to me, I nonetheless recommend accept, since the authors present novel findings that improve the research community's understanding of VLMs visual reasoning abilities.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "*Originality*\n\t\tThe underlying question is timely and well motivated. The cognitive science derived approach is principled and the authors perform a number of interesting experiments to further delineate the issues underlying counting in VLMs.\n\n*Quality*\n\t\tThe submission is technically sound. I don't agree with all resulting claims, but the methods are appropriate.\n\n*Clarity*\n\t\tThe submission is clearly written and follows a good outline.\n\n*Significance*\n\t\tThe paper yields new data which allows for unique conclusions, however the scope of the investigation is somewhat limited.", "weaknesses": "I have brought it up in the questions sections.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:\n\n**Clarification on human-like capacity limits and the logic of our experiments**\n\nThe reviewer has asked for clarification regarding human performance in the visual search task, and raised the concern that humans are indeed able to count beyond the subitizing limit when given enough time. Before directly addressing these issues, let us first briefly explain the types of capacity limits that we are interested in, and the conditions under which they arise in human cognition. It is widely acknowledged in cognitive science that there is a distinction between parallel (automatic) and serial (controlled, deliberative) modes of processing (Posner et al. 1980; Schneider et al. 1977; Treisman & Gelade 1980). The parallel mode of processing is dominant when people make rapid judgments, and displays classic capacity limits, such as difficulty counting beyond the subitizing limit, and difficulty in conjunctive search tasks. The serial mode of processing requires temporally extended processing, and is able to overcome these capacity limits, enabling precise counting for large numbers of objects, and serial search to identify specific feature conjunctions. \n\nOur hypothesis in this work is that the current generation of VLMs is primarily limited to the parallel mode of processing. We explain below some reasons for thinking that this might be the case (based on current understanding of the architectures and algorithms used), but, given the close-sourced nature of most current VLMs, our approach in this work is to test whether VLMs display behavioral signatures that resemble rapid parallel processing in human cognition. We find that VLMs do indeed display several specific phenomena that are diagnostic of parallel processing, including 1) difficulty counting beyond the subitizing limit, 2) greater difficulty in counting arrays of objects with uniform features than arrays with variable features (see new results in global rebuttal), 3) performance on disjunctive search tasks that is invariant to the number of objects, combined with decreased performance on conjunctive search tasks as a function of the number of objects, and 4) binding errors in scene description. Taken together, these phenomena provide indirect, but nevertheless highly specific, evidence that VLMs are limited to parallel processing. The fact that humans can complement this form of processing with serial processing – as in counting and serial search – is not at odds with our conclusions. Rather, it suggests a way that we can imagine augmenting VLMs to bring them closer to human performance.\n\n**Potential reasons for limited serial processing in current VLMs**\n\nWe do not claim that serial processing is beyond the ability of any type of vision-language model, including future improvements of these systems, but there are at least two major reasons to suspect that the current generation of VLMs may suffer from difficulty with serial processing:\n\n1. VLM image encoders may struggle to maintain distinct object representations within a single embedding. Compressing multi-object scenes while preserving accurate feature bindings is challenging, potentially limiting the model's ability to process objects individually.\n2. In autoregressive transformers, it is possible to perform some degree of serial processing through the output of the model (i.e. chain-of-thought), but in order to use this to sequentially attend to individual objects, there must be some clear way to index these objects in the model's output, which is often difficult for multi-object scenes.\n\nThese are certainly not fundamental limitations. It is easy to imagine that serial processing can be improved in VLMs through the use of object-centric visual representations, or the use of recurrent rather than feedforward transformers, and there is indeed some recent work in this direction (You et al. 2023; Webb et al. 2024). Our results suggest that VLMs will benefit from the incorporation of these stronger inductive biases for serial processing. We have added further discussion of these issues to the paper.\n\n**Possibility of errors in LLM answer parsing**\n\nThis is an excellent point. To avoid concerns over response parsing, we have updated our prompts so that VLMs directly return their responses in a form that we can evaluate without a secondary parsing model. We find that all of the same binding errors are present without secondary response parsing (see global rebuttal).\n\n**Spike in counting performance for 9 objects**\n\nThanks for highlighting this. We've run extended analysis across more models (see global rebuttal) and don't universally observe this trend. We agree it's interesting – like Rane et al. 2024, we hypothesize it may result from 3x3 grid arrangements in training data.\n\n\n**Citations:**\n\nPosner, M. I., Snyder, C. R., & Davidson, B. J. (1980). Attention and the detection of signals. *Journal of experimental psychology: General*, 109(2), 160.\n\nRane, S., Ku, A., Baldridge, J., Tenney, I., Griffiths, T., & Kim, B. (2024). *Can Generative Multimodal Models Count to Ten?*. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).\n\nSchneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. *Psychological review*, 84(1), 1.\n\nTreisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. *Cognitive psychology*, 12(1), 97-136.\n\nWebb, T., Mondal, S. S., & Cohen, J. D. (2024). Systematic visual reasoning through object-centric relational abstraction. *Advances in Neural Information Processing Systems*, 36.\n\nYou, Haoxuan, et al. \"Ferret: Refer and ground anything anywhere at any granularity.\" *arXiv preprint* arXiv:2310.07704(2023).", "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": "87rr2TTe3U", "reviewer": "Reviewer_MYwe", "summary": "This work investigates the performance of the vision language model (VLM) GPT-4v and text-to-image model DALL-E-3 on multi-object reasoning tasks. According to the literature, humans perform well on multi-object tasks because they learn a compositional representation of the world and use serial processing to dynamically combine object features, overcoming the binding problem. However, in time-constrained tasks, they fail to use serial processing and rely on parallel processing, leading to illusory conjunction. In this work, the authors hypothesize, without testing, that VLMs learn a compositional representation for the sake of generalization but are not compelled to develop a serial-processing mechanism. According to the authors, this leads to failure in multi-object reasoning tasks, thus exhibiting the same limitations as humans in time-constrained tasks. The authors test these limitations on four multi-object reasoning tasks: counting, visual search, scene description, and visual analogy. In these tasks, the authors show that the performance of VLMs decreases as the number of objects increases, supporting the conclusion that VLMs are not able to solve the binding problem because they lack the mechanism to perform serial processing.", "questions": "If we carefully engineer prompts for VLMs to sequentially focus and analyze each object in an image, considering they can learn a compositional representation as claimed, thus they should theoretically possess all the elements necessary to solve the binding problem? Can you elaborate on that?", "limitations": "See “Weaknesses”", "rating": 7, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper is straightforward and easy to understand. The authors did a good job making it accessible, and the presentation and sectioning help in grasping the content.\n- This work seeks to understand the limitations of VLMs through the lens of human cognition, which could provide valuable insights. \n- Originality in the problem statement: In terms of performance, VLMs in their normal regime are equivalent to humans in time-limited tasks.\n- The investigation utilizes various types of experiments, each guided by studies conducted on humans, and they look easy to redo and the empirical results seem relevant.", "weaknesses": "My score would increase if the following weaknesses were addressed:\n- The hypothesis that VLMs learn a compositional representation for generalization purposes could benefit from a reference if possible, since the discussion depends on that hypothesis.\n- Despite conducting various types of experiments (four in total), each individual experiment lacks sufficient variability. For instance, in the counting experiment, although they vary the colors of the circles, they do not vary their sizes or shapes. Another example, for the poppout task it would have been nice to also alternate the color of target and distractors.\n- The authors carried out the experiment on a very small group of language models (two).\n\nSome typos: \n- Figure 1, top right: the x-axis should be integers in the plot of GPT-4v counting performance\n- Line 156, 188: should be ‘Figure 2’ and not ‘Figure 1’", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:\n\n**Concerns about generalizability and scope of our results**\n\nWe thank the reviewer for suggesting to run additional control experiments and to analyze a broader range of models. We have run the following additional experiments to strengthen our work.\n\n1. We have expanded our analysis to include five VLMs: GPT-4v, GPT-4o, Claude Sonnet 3.5, Google Gemini Ultra 1.5, and LLaVA 1.5. Additionally, we have expanded the text-to-image model experiments to include four models: DALL-E-3, Stable Diffusion Ultra, Google Parti, and Google Muse. The general effects are present across all models, with some minor deviations for LLaVA, which we suspect is due to its specific limitations relative to stronger open source models.\n\n2. For the visual search task, we included a condition where the color of the target and distractors varies across each trial. The results show that model performance is comparable to the original experiment, with the same qualitative effect. Please refer to sections E and F of the attached PDF for the figure of results in an example model.\n\n3. For the counting task, we introduced conditions where we manipulate the color and shape dimensions of the stimuli. Our findings support our hypothesis that capacity limits arise due to issues with representational interference. Performance is generally highest in the highest entropy condition (unique colors and shapes across all objects), lowest in the low entropy condition (uniform colors and shapes), and intermediate for the medium entropy conditions (either unique colors or shapes, but not both). Please refer to section C of the attached PDF for the figures of the updated results.\n\n**Evidence for compositional representations in VLMs/LLMs**\n\nThe reviewer has requested that we cite evidence in support of the claim that VLMs learn compositional representations. We have included references to the following papers, which provide evidence that LLMs/VLMs form compositional representations (Lepori et al. 2023; Lewis et al. 2022; McCoy 2022; Yun et al. 2022, Yu et al. 2023), and agree that future work should directly investigate the representations themselves. We additionally note that if VLMs did not learn compositional representations, we would not expect to observe a different pattern of errors in the model behavior. The binding problem arises only when a system uses compositional representations, thus evidence for the binding problem can be viewed as indirect evidence for the presence of compositional representations. \n\n**Solving the binding problem with improved prompting?**\n\nIt is difficult to say for sure, given the lack of transparency about closed-source model architectures, whether there might be some prompting approach that improves results. However, there are reasons to believe that prompting alone cannot resolve the binding problem in current models. First, we have already attempted to prompt the models to enumerate objects one at a time, which did not improve performance (indeed, the performance reported in both counting and search tasks is prompting the model to explicitly enumerate all objects). Second, there are architectural limitations: \n\n1. VLM image encoders may struggle to maintain distinct object representations within a single embedding. Compressing multi-object scenes while preserving accurate feature bindings is challenging, potentially limiting the model's ability to process objects individually.\n\n2. In autoregressive transformers, it is possible to perform some degree of serial processing through the output of the model (i.e. chain-of-thought), but in order to use this to sequentially attend to individual objects, there must be some clear way to index these objects in the model's output, which is often difficult for multi-object scenes.\n\nWe have added an explanation of these factors to the revised manuscript and included relevant citations. \n\n\nWe hope these revisions and clarifications address your concerns. Thank you again for your insightful feedback.\n \n**Citations**:\n\nLepori, M., Serre, T., & Pavlick, E. (2023). Break it down: Evidence for structural compositionality in neural networks. *Advances in Neural Information Processing Systems*, 36, 42623-42660.\n\nLewis, M., Nayak, N. V., Yu, P., Yu, Q., Merullo, J., Bach, S. H., & Pavlick, E. (2022). Does clip bind concepts? probing compositionality in large image models. *arXiv preprint* arXiv:2212.10537.\n\nMcCoy, R. T. (2022). Implicit Compositional Structure in the Vector Representations of Artificial Neural Networks (Doctoral dissertation, Johns Hopkins University).\n\nYun, T., Bhalla, U., Pavlick, E., & Sun, C. (2022). Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?. *arXiv preprint* arXiv:2203.17271.\n\nYu, D., Kaur, S., Gupta, A., Brown-Cohen, J., Goyal, A., & Arora, S. (2023). Skill-Mix: A flexible and expandable family of evaluations for AI models. *arXiv preprint* arXiv:2310.17567.", "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": "Scxf62cIp2", "reviewer": "Reviewer_Ga5j", "summary": "Vision Language Models (VLMs), including models that are able to describe images, such as GPT-4v, and models that generate images given a text description, such as DALL-E, display several failures in tasks that humans are able to perform effortlessly. Such tasks include counting, localizing a given object in an image, and visual analogy. This paper interprets these failures as resulting from a trade-off between representational flexibility (the ability to generalize compositionally to novel combinations of features) and channel capacity (the number of entities that can be represented simultaneously). This trade-off corresponds to a well-established phenomenon in cognitive science, the binding problem. The binding problem identifies a series of failures in human visual perception, that arise in many situations in which human subjects need to process multi-object scenes under time constraints. Such constraints prevent them from processing those scenes in a sequential way, and force them to rely on parallel processing, which incurs into capacity limits. This leads to phenomena such as illusory conjunctions, in which a feature is bound to the wrong object: for example, a green triangle might be falsely detected in a scene comprising red triangles, green circles and red circles. The failures shown by VLMs are remarkably similar, suggesting that they might result from the lack of a serial processing mechanism like the one humans can rely on in the absence of time constraints. To test this hypothesis, in this work VLMs are tested on two classic cognitive tasks (counting and visual search), showing error patterns similar to time-constrained human subjects. In counting, a sharp drop in performance is observed with scenes comprising more than 4 to 6 objects, a similar range to that beyond which humans are unable to “subitize” (reliably count objects in parallel, without serial processing). In visual search, conjunctive search, in which a unique combination of features needs to be detected, led to a sharp drop in performance with increasing numbers of objects, while disjunctive search, in which targets can be detected based on a single feature, did not. These results are remarkably similar to the symptoms of the binding problem in humans. The authors further posit that these failures result from interference between target and distractor objects, and not by the number of objects alone. They test this hypothesis by parametrically generating scenes that vary in their feature entropy: the amount of variation in the two features, shape and color, across all objects. Using a scene description task, they find that errors were most frequent when the feature entropy was intermediate, leading to a combination of diversity in features and sharing of features between objects. Finally, the VLMs are tested on a visual analogy task. The known failures of these models in solving these tasks is hypothesized to also depend on limitations in the number of objects that can be processed in parallel. To alleviate this problem, the authors propose a simple “decomposed condition”: feeding the target stimuli (pairs of objects) to the models in sequence, rather than as part of a single image (”unified condition”). The decomposed condition mimicks the serial processing that humans adopt in similar tasks. In both the analogy task and extraction of individual object features, VLMs perform better in the decomposed condition than the unified condition, providing further evidence that VLMs’ failures are due to capacity limits in binding features to multiple objects in parallel.", "questions": "No particular questions. I have mentioned a few points that I believe should be clarified in the \"weaknesses\" section, as I believe the paper would benefit from the addition of their answers.", "limitations": "One limitation which has not been acknowledged is the use of closed-source models. This could compromise the reproducibility of this work, and will limit the ability of follow-up works to investigate the mechanisms underlying these findings.", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- The paper presents a simple framework to explain failure cases of VLMs, directly inspired by a long tradition of research in cognitive science. It is a great example of how cognitive science research can inform the study of widespread deep learning models, with many potential applications in evaluating and improving such models.\n- The research methods are sound: the link between the theoretical question and the experiments is clear, and models are tested across multiple tasks and conditions (e.g. 2D and 3D scenes, different object categories) to ensure that results are robust.\n- Creative, but at the same time simple and elegant, ways to measure the relation between the binding problem and VLM failures (feature entropy) and to alleviate the binding problem in VLMs (decomposed condition) are proposed.\n- The interpretation of the results is fair and clear.", "weaknesses": "The weaknesses are mostly related to the lack of detail concerning some of the methods, or the lack of explanations about certain choices. No major shortcoming was found by this reviewer.\n\n- In some of the prompts used to run experiments on the models (e.g. for counting), the model is first prompted to describe every object in the image, and then to perform the task. In others (e.g. disjunctive visual search) the model is directly asked to answer the question of interest (e.g. whether a target object is present in the image or not). Moreover, prompts from some tasks (e.g. 2D conjunctive search) include a form of role-playing \"You have perfect vision and pay great attention to detail...\". Since it is well known that similar techniques can have a great impact on the quality of language models' responses, the authors should provide an explanation of why these techniques were used in certain conditions and not others. Otherwise, performance comparisons between conditions (e.g. conjunctive vs. disjunctive search) can become harder to interpret.\n- In the scene description task, a language model is used to parse the descriptions. These are, I suppose, harder to parse than the simple answers provided in other tasks, such as the number of objects in the counting task or True or False in the visual search task. The authors should show that the language model was accurate in parsing these descriptions. Manually validating all of them would be infeasible, but at least a few examples to show the language models' accuracy should be provided in the supplementary materials.\n- Also in the scene description task, the generation of scenes with different feature entropies should be explained in more detail. Did the feature entropy manipulation simply consist in setting a particular number of shapes and features, and assigning them to a fixed number of objects? Or was the feature entropy computed in a different way? Making this clear would also help interpreting the values on the feature entropy that are shown in Figure 3.\n- In Figure 3, it is a bit hard to tell how many levels of feature entropy each number of objects has. For example, the 10 objects condition has two small bends that make it look like there are only three levels. For visualization purposes, I would recommend adding markers to the plot. Additionally, the values on the x-axis don't seem to align with the actual levels of feature entropy that were used. I understand that different number of objects had different levels, making it impossible to accomodate all possible levels, but if at least some of them could be aligned that would make the plot easier to read.\n- Also related to the scene description task, an explanation should be added of how the \"edit distance between the true description of the scene and the model's description\", the measure of errors, was computed. Does this mean that, for each (ground-truth) object in the scene, the most similar object (in terms of shape and color) in the description was chosen, and differences between that object and the ground truth were counted (e.g. color different = 1 error, shape different = 1 error)? How was, for example, a missing object in the description counted? As 2 errors, as both the color and shape were missing? A detailed explanation should be added.\n- The visual analogy task is similarly missing a detailed explanation of the pair generation process. In particular, what were the relations included in the task? Figure 4 apparently shows a \"sameness\" relation, in which target pair 2 is more similar to the source pair as they both feature objects that differ in color. Was this the only relation included? Also, on page 8, line 272 you write that the incorrect target \"shared only one of the relations\" with the source pair. I assume that this means that in figure 4, for example, TP2 shares both the \"same shape\" and the \"different color\" relation with the source pair, while TP1 only shares \"same shape\". Making this explicit and clarifying with an example would make this part easier for the reader.\n- The decomposed condition was only applied to the analogy task: was there a reason for this? As it's proposed as a general recipe for alleviating feature binding problems in VLMs, it would be more convincing if it was shown to work on a variety of tasks. The other tasks in the paper, in particular, seem to me like they would be amenable to be presented sequentially to the models. For example, a sequence of images containing only a few items at a time could be presented in visual search, simulating the way in which human participants serially scan such displays with eye movements in visual search experiments. If I am missing some practical reasons why conducting these experiments with a decomposed condition would be infeasible, or uninformative, I would be grateful to the authors if they could explain them.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and valuable feedback. We appreciate the opportunity to address your comments and clarify our work. Below is our point-by-point response:\n\n**Concerns about response parsing**\n\nWe thank the reviewer for pointing out this important concern. We have now eliminated the intermediate parsing step and ask the models to directly provide its response as output. We find that all of the same binding errors are present without secondary response parsing (see global rebuttal).\n\n**Explanation of metrics used in Scene Description task**\n\nWe have updated our scene description task to explicitly evaluate the relationship between feature interference across objects and model performance. Instead of using entropy, we now estimate the \"likelihood of a swap error,\" defined as the number of triplet pairs in the color/shape feature matrix where swapping two of the triplet features results in a binding error. We find that this new metric is significantly correlated with task performance, and provides a more elegant account of model behavior. See section A and B of the attached PDF for the updated results. Our dataset is generated by balancing the number of trials across each object count and feature diversity condition. Detailed descriptions of how this novel metric is computed and how our scene description dataset was generated are provided in the updated manuscript.\n\nEdit distance is defined as the number of objects the model failed to identify from the ground truth set of objects plus the number of objects erroneously identified (conjunction of shape/color for identified object not present in the ground truth). We have added a detailed description of the updated prompting strategy and a definition of edit distance in the revised manuscript.\n\n**Improved plotting for scene description task:**\n\nWe appreciate the suggestion. We have implemented these changes in the updated plots to improve clarity (see section A of the attached PDF).\n\n**Explanation of RMTS task:**\n\nWe have clarified that the relations are ‘same shape, different color’. The correct answer is Target Pair 2 because it satisfies both relations, while Target Pair 1 only satisfies one. We did not observe any evidence that the task was ambiguous for the VLMs. This clarification has been added to the revised manuscript.\n\n**Feasibility of running decomposed trial conditions on tasks other than RMTS**\n\nFor counting and scene description, we believe that the decomposed condition would likely improve performance, but we did not test this because we felt that it may be viewed as trivial. For example, in the counting task, this would just amount to counting the number of images that are presented. For the search task, the number of objects exceeds the context window size for images (10 images max for GPT-4V).\n\n**Limitations of Closed Source Models**\n\nWe acknowledge this limitation and have added experiments with LLaVA 1.5. While LLaVA did not respond consistently with our task instructions using the original prompts, modified prompts show that the subitizing limit in the counting task is preserved (see section C in attached PDF). However, the model struggles with search tasks (section F) and visual analogy. The absence of effects in LLaVA may be due to performance differences or prompting strategy.\n\nWe hope these revisions and clarifications address your concerns. Thank you again for your insightful feedback.", "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:
Dear Reviewers,

We sincerely thank you for your thoughtful and constructive feedback. In response to your comments, we have conducted several new analyses that we believe have significantly strengthened our work:

1. **Extended Model Analysis**: We expanded our study to include five VLMs (GPT-4v, GPT-4o, Claude Sonnet 3.5, Google Gemini Ultra 1.5, and LLaVA 1.5) and four text-to-image models (DALL-E-3, Stable Diffusion Ultra, Google Parti, and Google Muse). This broader analysis demonstrates the generalizability of our findings across different model architectures.
2. **Additional Counting Experiments**: We introduced new conditions manipulating color and shape dimensions of stimuli. We explored four conditions with varying levels of feature entropy: a low-entropy condition where both color and shape were uniform across all stimuli; two medium-entropy conditions where either color or shape were uniform while the other varied uniquely across stimuli; and a high-entropy condition where both color and shape were unique for each stimulus. Results support our hypothesis that capacity limits arise due to issues managing representational interference across objects, with performance highest in high-entropy conditions and lowest in low-entropy conditions, with graded levels of performance in between. 
3. **Refined Scene Description Task**: We developed a novel metric, 'number of triplets', which is directly predicted by our theory of object representation in neural networks. This metric quantifies the potential for binding errors in multi-object scenes by counting feature triplets. A triplet consists of three objects where:  two objects share one feature (such as shape), two objects share a different feature (such as color), and critically, one object possesses both of these shared features. Our theory predicts that these triplets create opportunities for feature misattribution, potentially leading to incorrect object descriptions. For example, a scene containing a red circle, red square, and blue square could erroneously yield a description including a non-existent blue circle. The number of triplets in a scene directly corresponds to the likelihood of binding errors: more triplets create more opportunities for feature swaps, increasing error potential. This metric, grounded in our theoretical framework, provides a direct evaluation of model performance in maintaining correct feature bindings. We find across all models evaluated that performance is strongly predicted by this metric (see sections A and B in attached PDF), further confirming that binding errors are one of the primary limitations in these models.
4. **Additional Control Experiments**: For the visual search task, we included a condition where target and distractor colors vary across trials (last subplot in section E of attached PDF). Results show comparable performance to the original experiment, reinforcing the robustness of our findings.
5. **Clarified Methodology**: We've updated our prompting strategies to remove any post-hoc response parsing with a secondary model to address the reviewer concerns.
6. **Analysis of Binding Failures vs. Hallucinations**: We found that <25% of errors involve hallucinating features not present in the image, with most errors attributable to binding failures.

We provide detailed responses to the concerns and questions identified by each reviewer in the individual responses below.

Meta-review: The paper takes a cognitive science perspective to investigate the ability of modern VLMs on multi-object reasoning tasks. The key result is that models perform similar to humans when they are processing is forced to be time-constrained, ie they have to rely on parallel as opposed to serial processing. The implication is that models limitations stem from an inability to leverage serial as opposed to parallel processing. The reviewers unanimously found the paper clear, insightful and all experiments to be carried out well. The AC thus recommends the paper to be accepted for rigorously testing limitations of current VLMs.
Decision: Accept (poster)