SceneGram: Conceptualizing and Describing Tangrams in Scene Context
Authors: Simeon Junker, Sina Zarrieß
Accepted at: acl25

SCENEGRAM: Conceptualizing and Describing Tangrams in Scene Context

Simeon Junker and Sina Zarrieß
Computational Linguistics, Department of Linguistics
Bielefeld University, Germany
{simeon.junker,sina.zarriess}@uni-bielefeld.de

Abstract
Research on reference and naming suggests that humans can come up with very different ways of conceptualizing and referring to the same object, e.g. the same abstract tangram shape can be a “crab”, “sink”, or “space ship”. Another common assumption in cognitive science is that scene context fundamentally shapes our visual perception of objects and conceptual expectations. This paper contributes SCENEGRAM, a dataset of human references to tangram shapes placed in different scene contexts, allowing for systematic analyses of the effect of scene context on conceptualization. Based on this data, we analyze references to tangram shapes generated by multimodal LLMs, showing that these models do not account for the richness and variability of conceptualizations found in human references.1

1 Introduction
Reference to visual objects is an elementary component of language and situated interaction. Almost always, we can refer to something in many different ways, and the linguistic choices we make reflect the ways we categorize or conceptualize it. More often than not, we can have multiple conceptual perspectives (Clark, 1997) on the same things: For example, the same person could be referred to as a “human”, “woman”, “engineer”, “mom” or using their proper name, depending on what is deemed relevant, appropriate or useful in a given situation (e.g. Brown 1958; Graf et al. 2016). Even fewer limitations exist for abstract shapes such as tangrams: In Figure 1, the same shape can be seen as representing e.g., a “crab”, “sink”, or “space ship”, showcasing the flexibility and richness of human conceptualization and interpretation at the intersection of visual and semantic processing. Despite recent progress in multimodal language modeling (Zhang et al., 2024), current systems show mixed results in reproducing human variation in object naming (Testoni et al., 2024) and figurative descriptions for abstract stimuli remain a major challenge in vision and language (V&L) research (Ji et al., 2022; Gul and Artzi, 2024).

How can we navigate this complex web of many-to-many relationships between visual stimuli and possible conceptualizations and descriptions? A common assumption in linguistics, cognitive science, and psychology is that our cognition is highly “tuned” to the everyday contexts and situations we interact in. A well-researched instance of this is our visual perception of objects which is known to be fundamentally shaped by high-level, conceptual expectations on the level of scenes (Biederman et al. 1982; Greene 2013; Võ 2021, among many others). For example, at the beach, we would rather expect to see certain animals and plants than household items, whereas in a bathroom, it would be the other way around. It is well-researched that these processes facilitate, e.g., visual object recognition (e.g. Bar 2004), and it seems plausible that scene context also affects descriptions of objects which can be conceptualized in different ways. However, existing V&L datasets with real-world images make it difficult to study the same objects in different scenes: As objects often occur in typical contexts, the same regularities that are exploited for visual processing obstruct investigation with natural data.

In this paper, we contribute (i) a dataset of human references to tangram shapes placed in different scene contexts, allowing for systematic analyses of the effect of scene context on the conceptualization of the same shape, and (ii) analysis of references to tangram shapes generated by multimodal LLMs, showing that these models do not account for the richness and variability of conceptualizations found in human references. Our findings show that scene context affects tangram descriptions in a way, that speakers tend to verbalize conceptualizations that

1Data and code for this project are available at: github.com/clause-bielefeld/scenogram
human: sink (5); bowl (2); crab (2); bathtub shape (1)
LLaVA 7b: bathtub (6); rectangle (2); bathroom (2)

LLaVA 72b: house (8); boat (1); bathtub (1)

human: crab (7); bathtub (1); bowl (1); bull (1)
LLaVA 7b: sun (3); bird (2); diamond (2); boat (1); wave (1); house (1)

LLaVA 72b: sailboat (4); house (3); boat (3)

human: crab (4); bowl (2); dog (1); seal (1); letter c (1); space ship (1)
LLaVA 7b: house (3); square (2); diamond (2); triangle (1); parallelogram (1); box (1)
LLaVA 72b: house (7); boat (3)

Figure 1: A single tangram in bathroom, beach and baseline contexts with counts for labels in annotated or predicted descriptions. Human annotations commonly include labels which are coherent with scenes (“sink” and “crab”). LLaVA 7b and 72b show similar patterns, but also issues in differentiating between tangrams and scenes.

are consistent with the provided scene context. Experiments with MLLMs show similarities but also shed light on limitations and biases relevant to various topics in vision and language research.

2 Background

Human scene understanding  Research on human vision and perception has shown that when viewing a scene, humans perceive it as a coherent whole instead of mere collections of objects (Võ, 2021). Capturing the gist of a scene is a rapid process (Oliva and Torralba, 2006), and while incongruent context can also be misleading (Zhang et al., 2020; Gupta et al., 2022), scene-level information has been demonstrated to facilitate e.g., visual object recognition in both human cognition (Palmer 1975; Oliva and Torralba 2007; Parikh et al. 2012; Lauer et al. 2018, among others) and computer vision systems (Divvala et al. 2009; Galleguillo and Belongie 2010, see Wang and Zhu 2023 for a survey). For this, humans and machines can exploit learned knowledge about regularities of the visual word for visual processing (Biederman, 1972; Bar, 2004; Greene, 2013; Pereira and Castelhano, 2014; Sadeghi et al., 2015), e.g. semantic rules that certain objects tend to occur in some contexts rather than others (Biederman et al., 1982; Võ, 2021; Turini and Võ, 2022).

Context, Conceptual Perspective and Referential Choice  Verbal reference to visual objects requires making linguistic choices, as the same things can be called and described in many different ways (Brown, 1958; Graf et al., 2016; Davies et al., 2019). Research in Referring Expression Generation has modeled these choices as a function of context (Schüz et al., 2023), i.e., objects co-occurring with the target are factored in to determine which properties have to be realized to make a description unambiguous in a given scenario (see Kraemer and van Deemter 2012 for a survey). More generally, however, speakers can take on different conceptual perspectives on referents, highlighting different (often not mutually exclusive) facets and aspects of referents, guided by principles beyond pragmatic informativeness (Clark and Svaib, 1997; Gatt and van Deemter, 2006, 2007; Gatt, 2007; van Deemter, 2016). Importantly, different conceptualizations can be reflected in object labels or names (Clark, 1997; Gualdoni et al., 2023), which have been shown to be highly varied and flexible (Ordonez et al., 2016; Zarrieh and Schlangen, 2017; Silberer et al., 2020a,c; Gualdoni et al., 2022a,b, 2023). Recent work in vision and language research has started to model this variation (Ilinykh and Dobnik, 2023; Testoni et al., 2024), but general questions remain about how visual context affects conceptualization and naming in humans and generation systems.

Tangrams in linguistic research  Tangrams are abstract figures, which are constructed from a small set of geometric primitives and can be more or less nameable, i.e., easy or hard to describe (Zettersten and Lupyan, 2020). Unlike natural objects, tangrams lack established naming conventions, triggering diverse figurative descriptions and making them suitable as stimuli to investigate linguistic ref-
erence in humans (Clark and Wilkes-Gibbs, 1986; Schober and Clark, 1989; Wilkes-Gibbs and Clark, 1992; Brennan and Clark, 1996; Murfitt and McAllister, 2001; Hawkins et al., 2020; Bangerter et al., 2020; Fasquel et al., 2022; Sudo et al., 2022) and vision-language systems (Skantze and Willemsen, 2022; Ji et al., 2022; Gul and Artzi, 2024). Shore et al. (2018); Ji et al. (2022) released crowdsourced datasets using tangram figures as stimuli, we use tangrams from the latter for our work.

Research Gap While e.g. MANY NAMES (Silberer et al., 2020a,c) quantifies naming variation for objects in photographs, object types are often bound to certain contexts (reflecting real-life patterns), and most objects are highly nameable and easy to identify, limiting the range of different conceptualizations. In contrast to this, tangram datasets like KILOGRAM (Ji et al., 2022) offer rich variation in conceptualizations, but do not account for contextual influences, as items are described in isolation. In this work, we take a different approach and pair abstract tangram shapes with generated images representing a taxonomy of scene contexts. In this way, we collect diverse descriptions of visual items, which we subsequently analyze for context effects and compare with the predictions of multi-modal LLMs.

3 Data Collection

We combine tangram figures with images depicting different types of scenes, and crowdsource annotations to investigate how context affects the conceptualization of inherently ambiguous shapes.

Formally, each item in our dataset \( i \in I \) is defined as a tangram \( t_i \in T \) with a scene \( s_i \in S \) as visual context, i.e. a tuple \( i = \langle t_i, s_i \rangle \). For each item, we collect a set of \( D_i = D_{t_i,s_i} \) descriptions in English. From each annotated description, we extract an object label and the corresponding WordNet (Miller, 1995) synset, to reduce ontological variation and facilitate taxonomy-based analyses.

3.1 Item Design and Generation

As the tangrams for our dataset, we use half of the items from the dense split in KILOGRAM (Ji et al. 2022, \( |T| = 37 \)), which come with rich annotations that can be used for comparison.

Scene images (\( |S| = 11 \)) are generated for a set of scene categories using SDXL-Lightning (Lin et al., 2024) as a state of the art text-to-image model.2 We generate three images for each of the 8 basic level scene categories in Lauer et al. (2018), which include various indoor scenes (kitchen, bathroom, bedroom, office), for which we expect a particular influence due to their relation to common everyday objects, but also a broad selection of typical outdoor scenes (forest, mountain, beach, street). In addition to this, we include sky and sea bottom as additional outdoor scene categories, which are associated with certain objects that would be less expected in the remaining scenes (e.g., fish or birds). We prompt the model to generate “a photograph of a [SCENE]”, where [SCENE] is replaced with the respective scene category label. We also add none as the baseline scene condition \( s_b \) with neutral context, i.e., uniform color patches.

Our final items (\( n = |T| \times |S| = 407 \)) combine tangrams and scene images by arranging them into a \( 2 \times 2 \) grid of random order. Here, one tile is always occupied by a tangram shape and the remaining three tiles by images depicting a specific type of scene (cf. Figure 1). This procedure is intended to combine tangrams with contextual information, without evoking unwanted inferences about, e.g., size and location relations, which might occur if the tangram were placed directly in or overlaying scene images.

3.2 Data Collection

We collect our data using the Argilla framework3 with crowdworkers from Prolific (\( n = 110 \)). Annotators are instructed to locate the tangram and describe what kind of object it depicts. With this, we ensure that participants pay attention to the scene images at least briefly while locating the tangram, given preceding work which indicates that the gist of a scene is processed very quickly, cf. Oliva and Torralba 2006. We collect 10 annotations per item, i.e., a total of 4070 annotation points. Every annotator is assigned 37 items, which include exactly one item for each tangram in our data. 100 annotators cover scene conditions, i.e., tangram images are paired with random scene categories. Separate from this, 10 annotators provide descriptions for the baseline condition, i.e., tangrams are coupled with uniform color patches. Workers are paid according to the local minimum wage.

2huggingface.co/ByteDance/SDXL-Lightning, accessed via the provided Huggingface Space
3https://argilla.io/
3.3 Post Processing
We process the collected annotations using a combination of automatic tools and human validation or refinement. Every item in our dataset contains four annotations (raw, label, synset, normalized label), which are derived as follows: First, we reduce the raw annotations to labels, i.e., nouns or compounds that denote the type of object the tangram is thought of depicting. If annotators provide more than one interpretation for a given tangram, we select only the first. After this, we map the labels to WordNet synsets and select the first lemma from each synset as the normalized label with reduced onomasiological variation. We use spaCy (Honnibal et al., 2020) and NLTK (Bird et al., 2009) for label extraction and WordNet mapping. Both steps are manually validated and corrected.

4 Data Analysis

4.1 Research Questions and Hypotheses
In our analysis, we investigate whether people categorize and name tangrams differently if they co-occur with images of different scenes, i.e., if scene context affects the choice between alternative conceptual perspectives in tangram descriptions. Previous work has shown that tangrams vary in their conceptual flexibility, i.e., there are different degrees of naming consensus for different tangram shapes, cf. Ji et al. 2022. For scene context effects on tangram descriptions, we expect the following patterns:

H1 Scene context affects variation in tangram descriptions.

H2 Tangram descriptions elicited in context will be conceptually more coherent with this context as compared to descriptions elicited out of context.

H3 Context effects are more pronounced for certain combinations of tangrams and scenes, i.e. tangrams vary in their conceptual compatibility to certain scenes.

4.2 Analysis methods
Shape Naming Divergence (SND) and % Top
We rely on SND (Ji et al., 2022) and the inter-annotator agreement (% Top, Silberer et al. 2020b) to estimate the degree of variation in our data. SND quantifies variability between annotations by measuring if tokens are used in multiple descriptions for the same item or are specific to individual descriptions. Following Ji et al. 2022, we use SND to analyze phrase-level tangram descriptions. To this end, we calculate SND scores for each set of raw descriptions for individual items. We also correlate SND scores in our data with SND in KILOGRAM, using Kendall’s tau (Kendall, 1938). % Top is calculated for normalized object labels, i.e., lemmas of extracted synsets, by obtaining the relative frequency of the most frequent label for each item.

Lexical Overlap with KILOGRAM and Mean Reciprocal Rank (MRR)
We use annotations from the dense split in KILOGRAM as a benchmark for our data. Since we are particularly interested in the conceptualization of tangrams in terms of depicted object types, we extract the object labels from the phrases of the KILOGRAM annotations using spaCy. We then calculate the lexical overlap, i.e. the proportion of unique labels in our annotations that are also found in the KILOGRAM annotations for the same tangram, and the MRR of labels in our data and KILOGRAM ranked by frequency.

Label frequency
To get interpretable estimates of scene effects on tangram descriptions, we compute the occurrence frequency of normalized object labels in the annotations, aggregating all tangram descriptions for each scene type. To identify context effects, we test the most frequent labels in all context conditions for significant deviation from the baseline condition using a chi-squared test.

Label-Scene similarity
To quantify conceptual coherence and shifts in our data, we analyze the similarities between tangram descriptions and the scene context in which they were elicited, and compare this to the similarities between scene contexts and the baseline annotations, which are elicited without meaningful context (see Figure 1). We test text-image similarity using CLIP (Radford et al., 2021), and text-text similarity using GloVe (Pennington et al., 2014) and ConceptNet Numberbatch (Speer et al. 2017, henceforth Numberbatch). With CLIP, we encode the textual object labels extracted from the annotations and the images used as scene context and compute similarities between the labels and the mean representations of all three scene images used in each scene condition. For GloVe and Numberbatch, we replace the image vector with embeddings for the respective scene category label. Following Hessel et al. 2021; Takmaz et al. 2022, we report coherence between tangram descriptions
<table>
  <tr>
    <th rowspan="2">scene</th>
    <th colspan="3">Variation</th>
    <th colspan="2">KILOGRAM comparison</th>
  </tr>
  <tr>
    <th>SND mean/std</th>
    <th>SND corr. / KILOGRAM</th>
    <th>% Top mean/std</th>
    <th>overlap</th>
    <th>MRR</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>0.92±0.09</td>
    <td>0.42***</td>
    <td>27.0±16.1</td>
    <td>37.4</td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>0.92±0.13</td>
    <td>0.46***</td>
    <td>26.8±17.2</td>
    <td>38.3</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>0.93±0.11</td>
    <td>0.38**</td>
    <td>25.4±14.8</td>
    <td>40.0</td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>0.94±0.06</td>
    <td>0.51***</td>
    <td>25.4±11.2</td>
    <td><b>40.3</b></td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>0.92±0.11</td>
    <td>0.37**</td>
    <td><b>27.8±17.0</b></td>
    <td>34.6</td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td>0.92±0.13</td>
    <td>0.41***</td>
    <td>24.9±12.4</td>
    <td>39.2</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>office</td>
    <td>0.91±0.14</td>
    <td>0.51***</td>
    <td>25.7±18.2</td>
    <td>35.0</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>sea_bottom</td>
    <td><b>0.95±0.05</b></td>
    <td>0.47***</td>
    <td>22.2±8.5</td>
    <td>35.5</td>
    <td>0.24</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>0.91±0.13</td>
    <td>0.33**</td>
    <td>27.0±16.3</td>
    <td>34.0</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>street</td>
    <td>0.93±0.10</td>
    <td>0.51***</td>
    <td>25.4±14.5</td>
    <td>37.9</td>
    <td>0.27</td>
  </tr>
  <tr>
    <td>none</td>
    <td>0.91±0.13</td>
    <td><b>0.61***</b></td>
    <td>27.6±18.0</td>
    <td>38.7</td>
    <td><b>0.30</b></td>
  </tr>
</table>

Table 1: Variation and overlap results for human annotations. SND correlations, overlap, and Mean Reciprocal Rank (MRR) are calculated with respect to annotations and scores for the same tangrams in KILOGRAM, asterisks denote significance levels with Kendall’s tau (*p<0.05; **p<0.01; ***p<0.001). Highest and lowest scores are highlighted.

and scenes as scaled cosine similarities, i.e.,

\[
sim(\vec{d}, \vec{s}) = 2.5 * max(cos(\vec{d}, \vec{s}), 0)
\]

and

\[
\text{COHERENCE}(D, s) = \frac{\sum_{d \in D} sim(\vec{d}, \vec{s})}{|D|}
\]

, where \( \vec{d} \) and \( \vec{s} \) are vector representations for tangram descriptions (labels) and scene contexts (images or labels), and \( d \) is a single label in the set of annotated labels \( D \) for an item. We especially look at <i>in-context coherence</i> \( \text{COHERENCE}(D_{t_i,s_i}, s_i) \), that is coherence between descriptions for tangrams in a certain scene context and the respective scenes, and <i>baseline coherence</i> \( \text{COHERENCE}(D_{t_i,s_b}, s_i) \), i.e. coherence between tangrams described in the baseline condition \( s_b \) and certain scenes. To quantify conceptual shifts in scene context, we report the difference between these scores, that is

\[
\text{SHIFT}(i, s) = \text{COHERENCE}(D_{t_i,s_i}, s_i) - \text{COHERENCE}(D_{t_i,s_b}, s_i)
\]

, representing the increase in scene coherence for the descriptions of an item \( i = \langle t_i, s_i \rangle \) as compared to baseline descriptions of the same tangram. \( \text{SHIFT} > 0 \) indicates that tangrams are interpreted more coherently to the scenes they are placed in.

![SND scores broken down for tangrams, markers indicate the mean. The overall distribution is skewed right, showing high variation in descriptions.](page_1012_1012_388_388.png)

Figure 2: SND scores broken down for tangrams, markers indicate the mean. The overall distribution is skewed right, showing high variation in descriptions.

with scene context, indicating that scenes can bring in additional ways of interpreting tangram shapes that are less accessible without context. Correlating the SND scores for different scene conditions with the scores provided in KILOGRAM reveals significant correlations in all cases, i.e., similar variance patterns for the same tangrams with or without context. However, the highest correlation can be seen for the baseline condition, whereas patterns in certain scene conditions deviate more from KILOGRAM (Table 1). This again points to general context effects at the level of variation patterns, supporting H1 in this regard.

Aggregating scores over tangrams shows large differences between shapes, in line with Ji et al. (2022). However, mean SND scores are mostly close to the upper bound, indicating rich variation (Figure 2). Interestingly, lower scores tend to be associated with higher variance, suggesting that for
<table>
  <tr>
    <th></th>
    <th>#1</th>
    <th>#2</th>
    <th>#3</th>
    <th>#4</th>
    <th>#5</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>person (7.57 %)</td>
    <td>dog (3.78 %)</td>
    <td><b>sink (3.24 %)</b></td>
    <td>table (2.16 %)</td>
    <td>wrench (2.16 %)</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>person (5.41 %)</td>
    <td>dog (4.32 %)</td>
    <td>crab (3.51 %)</td>
    <td>horse (2.43 %)</td>
    <td>K (2.16 %)</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>person (6.49 %)</td>
    <td>dog (4.32 %)</td>
    <td><b>bed (3.78 %)</b></td>
    <td><b>lamp (3.51 %)</b></td>
    <td>table (2.16 %)</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>person (7.3 %)</td>
    <td>dog (4.05 %)</td>
    <td>bird (2.43 %)</td>
    <td>house (2.16 %)</td>
    <td><b>forest (1.89 %)</b></td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>person (5.68 %)</td>
    <td>dog (5.68 %)</td>
    <td><b>cabinet (2.7 %)</b></td>
    <td>table (2.43 %)</td>
    <td>bird (2.43 %)</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td><b>mountain (6.22 %)</b></td>
    <td>person (5.14 %)</td>
    <td>bird (2.97 %)</td>
    <td><b>dog (2.43 %)</b></td>
    <td><b>rock (1.89 %)</b></td>
  </tr>
  <tr>
    <td>office</td>
    <td>person (7.84 %)</td>
    <td>desk (4.32 %)</td>
    <td>table (3.24 %)</td>
    <td>dog (2.97 %)</td>
    <td>lamp (2.43 %)</td>
  </tr>
  <tr>
    <td>sea bottom</td>
    <td>person (4.59 %)</td>
    <td>fish (4.59 %)</td>
    <td>turtle (2.43 %)</td>
    <td><b>dog (2.43 %)</b></td>
    <td>table (2.16 %)</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>person (5.41 %)</td>
    <td>bird (4.86 %)</td>
    <td>dog (4.05 %)</td>
    <td><b>cloud (3.24 %)</b></td>
    <td>mountain (2.43 %)</td>
  </tr>
  <tr>
    <td>street</td>
    <td>person (7.57 %)</td>
    <td>dog (4.59 %)</td>
    <td>crab (2.7 %)</td>
    <td>house (1.89 %)</td>
    <td>bird (1.89 %)</td>
  </tr>
  <tr>
    <td>none</td>
    <td>person (7.03 %)</td>
    <td>dog (5.68 %)</td>
    <td>horse (2.97 %)</td>
    <td>bird (2.43 %)</td>
    <td>snake (2.16 %)</td>
  </tr>
</table>

Table 2: Occurrence frequencies for WordNet Lemmas in the annotations for all tangrams in the given scene. Labels printed bold deviate significantly from the occurrence frequencies in the baseline condition (none).

the same tangrams, annotators are more aligned in certain scenes than in others.

The % Top scores, calculated for labels normalized via WordNet, generally resemble the findings of the SND analysis (Table 1). Broken down for scenes, the high agreement can be seen in cases where SND scores indicate low variation (e.g., kitchen or the baseline condition), and low agreement aligns with high variation (sea bottom).

Lexical Overlap and MRR The overlap scores between labels in our annotations and the dense split in KILOGRAM reveal no clear patterns, with the highest overlap in the forest and the lowest in the sky condition (see Table 1). However, our data still contains a high proportion of labels not included in the extensive KILOGRAM annotations, highlighting the high variability in tangram descriptions. Taking into account the counts and frequency ranks of labels in both datasets, the MRR results in Table 1 reveal similar patterns as SND and % Top: Labels in the baseline condition resemble KILOGRAM annotations the most, and scores are generally lower with scene context.

Label frequency In Table 2, we show the five most frequent object labels in all tangram descriptions for each scene condition, normalized via WordNet. Although certain labels are frequent in all conditions (e.g. “person”, “dog”), others occur more frequently in conceptually related scenes. In particular, most labels whose frequency deviates significantly from the baseline condition (\( p < 0.05 \) in the chi-squared test) denote objects typically occurring in the respective scenes, e.g. “sink” / bathroom, “bed” / bedroom, “cabinet” / kitchen and, most prominently, “mountain” / mountain. This supports our hypothesis H2, i.e., the significantly higher frequency of conceptually related labels indicates preferences towards tangram conceptualizations coherent with scene context.

Label-Scene similarity The results of the conceptual coherence analysis are illustrated in Figure 3. Across all encoding methods and scenes, in-context coherence surpasses baseline coherence, i.e., labels that are elicited in a given scene context show higher similarity to corresponding scene representations than baseline annotations for the same tangrams. Generally, this indicates that annotators tend to produce descriptions for tangrams that align with scenes displayed to them, supporting H2.

Interestingly, the degree of conceptual shift depends on the embedding space used to encode labels and scenes. CLIP, which scores visual similarities, predicts smaller shifts than GloVe and Numberbatch, which encode more generic and conceptual similarities. This supports the interpretation that conceptual shifts triggered by scene context represent genuine conceptual variation and changes in perspective, rather than mere visual associations. The largest differences can be seen with Numberbatch embeddings, possibly as a result of the explicit semantic relations included in the underlying ConceptNet meaning representation.

To illustrate conceptual shifts for individual tangrams, Figure 4 shows the SHIFT scores for all tangram/scene combinations, computed with Numberbatch. For each tangram, a single data point represents the conceptual SHIFT with respect to a single scene, i.e., the difference between in-context and baseline coherence for this combination of tangram and scene. Again, there is a clear trend towards annotations that are coherent to the respective context, i.e. \( \text{SHIFT} > 0 \) in the majority of cases. However, the degree of conceptual shifts
Figure 3: Mean COHERENCE scores between tangram annotations and scenes (images for CLIP, labels for GloVe and Numberbatch) for all scene categories. In-context coherence consistently surpasses baseline coherence, indicating that scene context causes semantically related conceptualizations in descriptions.

Figure 4: SHIFT scores broken down for tangrams. SHIFT > 0 (red line) indicate that scene context leads to coherent tangram conceptualizations. The marked item is displayed in Figure 1 (bathroom condition).

varies between tangrams: whereas for some tangrams, descriptions in context show marginally higher scene coherence than baseline annotations, conceptualizations of other shapes seem to be more adaptable to different scenes. In particular, right outliers in this graph mark individual scenes for which tangram annotations elicited in context show a much higher similarity than the baseline annotations. This can be seen as cases where tangrams are conceptually compatible with certain scenes, with regard to interpretations that are less accessible without context, supporting hypothesis H3.

Qualitative Examples Figure 1 shows labels for a single tangram in three scene conditions (bathroom, beach, and none). With bathroom context, this item has one of the highest SHIFT scores in our data (0.65, cf. Figure 4), as it includes a high rate of conceptually related labels ("sink", "bathtub"), none of which occurs in the baseline condition. For beach, annotations also have high relatedness to the scene, but lower SHIFT (0.17) since related labels are also included in the baseline condition.

<table>
  <tr>
    <th>system</th>
    <th>% Top</th>
    <th colspan="2">Overlap</th>
  </tr>
  <tr>
    <th></th>
    <th></th>
    <th>SCENEGRAM</th>
    <th>KILOGRAM</th>
  </tr>
  <tr>
    <td>LLaVA-7b</td>
    <td>58.50</td>
    <td>26.61</td>
    <td>34.99</td>
  </tr>
  <tr>
    <td>LLaVA-13b</td>
    <td>36.71</td>
    <td>21.13</td>
    <td>34.42</td>
  </tr>
  <tr>
    <td>LLaVA-34b</td>
    <td>59.17</td>
    <td>27.64</td>
    <td>50.22</td>
  </tr>
  <tr>
    <td>LLaVA-72b</td>
    <td>79.46</td>
    <td>26.00</td>
    <td>54.52</td>
  </tr>
</table>

Table 3: % Top and % Overlap with our human annotations (same item, normalized labels) and the KILOGRAM annotations (same tangram), global mean.

5 Modelling Experiments

Analyzing human annotations has shown that scene context affects tangram descriptions, i.e., tangrams are often interpreted in ways that align with the scenes they are placed in. In this section, we explore the tangram descriptions in context generated by off-the-shelf multimodal LLMs. For this, we generate tangram descriptions using the 7b, 13b, 34b and 72b parameter variants of LLaVA-NeXT (Liu et al., 2023, 2024)4, and test the outputs for variation, alignment with human data and conceptual shifts, using methods from the preceding analysis.

5.1 Method

We use a two-step inference process to collect sets of tangram descriptions: First, we prompt our models to predict the location of the tangram in the item grid, i.e., top/bottom and left/right. After this, akin to Testoni et al. (2024) and keeping the location prediction as context, we repeatedly prompt our systems to generate descriptions of the tangram using nucleus decoding (Holtzman et al., 2019) with \( p=0.5 \). To facilitate subsequent analysis, we

4accessed via huggingface, the latter using Int-8 quantization due to resource limitations.
Figure 5: COHERENCE scores (Numberbatch) for LLaVA 7b (smallest) and 72b (largest). LLaVA 72b shows the higher overall scores (especially baseline coherence), but 7b has higher differences between in-context and baseline coherence. In both cases, in-context coherence spikes in the mountain condition.

restrict responses to WordNet lemmas, i.e., after each response, we use SpaCy to extract the head noun from the generated description, assert that it is included in WordNet, and repeat the prediction process a maximum of 10 times if this is not the case. Inference terminates after 10 valid responses.

We calculate the relative frequency of the most frequent label in each response set (% Top) to assess the variation in generated descriptions, and the overlap to extracted labels in KILOGRAM dense and our data. Finally, we use COHERENCE scores to test for conceptual shifts. Accuracies for location predictions and frequency tables for scene conditions are included in the appendix.

5.2 Results

% Top As we repeatedly sample the output distributions of the same models,% top indicates if the systems are able to conceptualize the same tangrams in different ways. The results in Table 3 show that apart from LLaVA 13b, scores increase with size, i.e., models converge on a limited set of interpretations per item. Although the exact scores depend on decoding parameters, we note that on average, the most frequent labels occur much more often than in the human data (between 20 and 30 %, see Table 1), indicating that individual systems cannot capture the range of human tangram conceptualizations.

Lexical Overlap With less than 30 % of the generated labels also occurring in human annotations for the same items, the overlap between system responses and annotations in our data is surprisingly small (Table 3). This indicates that system predictions seldom coincide with labels produced by humans, raising doubts about their general capability of replicating human-like conceptualization of abstract depictions. However, overlap with KILOGRAM is higher, possibly due to their extensive annotations (\( n \geq 50 \) per item).

Label-Scene Similarity The COHERENCE scores in Figure 5 suggest that our systems generate labels with higher coherence to scenes if tangrams are paired with the respective images, similar to human annotations. However, coherence scores and differences between baseline and in-context predictions are often much higher, especially for mountain scenes. Occurrence counts of generated labels show that here, systems generate the label “mountain” in up to 74 % of cases (cf. Appendix I), raising doubts about whether models rather describe scenes than tangrams, i.e., fail to parse the visually complex items.

6 Discussion and Conclusion

The role of context in the conceptualization of visual objects and its interactions with variability and creativity in referring are notoriously hard to grasp for (computational) linguistic research. SCENEGRAM addresses this gap, proposing a controlled paradigm that elicits descriptions of conceptually ambiguous tangram shapes in scene context. Our results underpin a common theoretical assumption that has, however, been rarely tested “in the wild”, especially not in language & vision research: scene context can fundamentally shape speakers’ conceptual perspectives when describing a visual stimulus. SCENEGRAM shows that tangram descriptions in context remain highly diverse while becoming conceptually more coherent with the scene context. Experiments with off-the-shelf multimodal LLMs indicate that the systems cannot reproduce human variance and conceptualizations of tangrams, but demonstrate general effects of scene context. Overall, our results highlight the importance of scene context on object naming at the level of conceptu-
alization, pointing to weaknesses of current multimodal language models in this regard.

For future work, our data can be used to, e.g., probe more general mechanisms of visuo-linguistic processing in multimodal LLMs, similar to work in cognitive science or psycholinguistics, where using abstract visual stimuli is a well-established paradigm. At the same time, our approach also raises new questions and directions. More work is needed to understand how exactly context modulates the variance of conceptualizations. Data in SCENEGRAM suggests that context could prime humans for certain interpretations, effectively reducing variance, or could evoke entirely new interpretations without blocking preexisting ones, pointing to interesting connections to creativity. Further work is also needed to understand how conceptual perspective and scene context interact when the communicative effectiveness of object descriptions is at stake, e.g. in a reference game. Here, the creative use of language is a necessity in the first place. Combining abstract stimuli with contextual information or communicative demands could be a valuable tool for future research to study creativity and linguistic variability in humans and language models.

Limitations

We identify the following limitations in our study:

First, we note that the annotation procedure could be further refined. In particular, more advanced setups with defined communicative objectives as in e.g. reference games could further ensure high-quality descriptions for tangrams and elicit tangram descriptions that are not only creative, but also effective if communication. Our primary interest here is to collect and analyze general descriptions for tangrams in varying scene contexts, but we see potential for further insights by adapting and improving our methods.

Second, the robustness of our findings could be further improved by scaling up the data collection. By focusing on a subset of the tangrams in KILOGRAM, we were able to achieve an acceptable sample size of 10 annotations for each combination of tangrams and scenes within our financial and time limits. However, due to the high variance in our data, a larger pool of annotations per item could allow for more reliable or comprehensive conclusions.

Finally, for the modelling experiments, further system architectures and hyperparameter configurations could be added for more comprehensive insights, possibly including commercial systems such as ChatGPT, Gemini, DeepSeek and Claude. Due to space and time constraints, we leave this for future research.

Acknowledgments

This research has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – CRC-1646, project number 512393437, project B02.

References

Adrian Bangerter, Eric Mayor, and Dominique Knutsen. 2020. Lexical entrainment without conceptual pacts? revisiting the matching task. Journal of Memory and Language, 114:104129.

Moshe Bar. 2004. Visual objects in context. Nature Reviews Neuroscience, 5(8):617–629. Congruent vs incongruent context.

Irving Biederman. 1972. Perceiving real-world scenes. Science, 177(4043):77–80.

Irving Biederman, Robert J. Mezzanotte, and Jan C. Rabinowitz. 1982. Scene perception: Detecting and judging objects undergoing relational violations. Cognitive Psychology, 14(2):143–177. Congruent vs incongruent context.

Steven Bird, Edward Loper, and Ewan Klein. 2009. Natural Language Processing with Python. O’Reilly, Beijing [u.a.].

Susan E. Brennan and Herbert H. Clark. 1996. Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(6):1482–1493.

Roger Brown. 1958. How shall a thing be called? Psychological Review, 65(1):14–21.

Eve V Clark. 1997. Conceptual perspective and lexical choice in acquisition. Cognition, 64(1):1–37.

Eve V. Clark and Trisha A. Svaib. 1997. Speaker perspective and reference in young children. First Language, 17(49):057–74.

Herbert H. Clark and Deanna Wilkes-Gibbs. 1986. Referring as a collaborative process. Cognition, 22(1):1–39.

Catherine Davies, Jennifer E. Arnold, book editor Cummins, Chris, and book editor Katsos, Napoleon. 2019. Reference and informativeness. The Oxford Handbook of Experimental Semantics and Pragmatics:, 2019.
Santosh K. Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros, and Martial Hebert. 2009. An empirical study of context in object detection. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE.

Alicia Fasquel, Angèle Brunellière, and Dominique Knutsen. 2022. A modified procedure for naming 332 pictures and collecting norms: Using tangram pictures in psycholinguistic studies. Behavior Research Methods, 55(5):2297–2319.

Carolina Galleguillos and Serge Belongie. 2010. Context based object categorization: A critical survey. Computer Vision and Image Understanding, 114(6):712–722.

Albert Gatt. 2007. Generating coherent references to multiple entities. Ph.D. thesis, University of Aberdeen, UK. Http://staff.um.edu.mt/albert.gatt/pubs/thesis.pdf.

Albert Gatt and Kees van Deemter. 2006. Conceptual coherence in the generation of referring expressions. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 255–262, Sydney, Australia. Association for Computational Linguistics.

Albert Gatt and Kees van Deemter. 2007. Lexical choice and conceptual perspective in the generation of plural referring expressions. Journal of Logic, Language and Information, 16(4):423–443.

Caroline Graf, Judith Degen, Robert X. D. Hawkins, and Noah D. Goodman. 2016. Animal, dog, or dalmatian? level of abstraction in nominal referring expressions. In CogSci.

Michelle R. Greene. 2013. Statistics of high-level scene context. Frontiers in Psychology, 4.

Eleonora Gualdoni, Thomas Brochhagen, Andreas Mädebach, and Gemma Boleda. 2022a. Woman or tennis player? visual typicality and lexical frequency affect variation in object naming. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 44.

Eleonora Gualdoni, Thomas Brochhagen, Andreas Mädebach, and Gemma Boleda. 2023. What’s in a name? a large-scale computational study on how competition between names affects naming variation. Journal of Memory and Language, 133:104459.

Eleonora Gualdoni, Andreas Madebach, Thomas Brochhagen, and Gemma Boleda. 2022b. Horse or pony? Visual typicality and lexical frequency affect variability in object naming. In Proceedings of the Society for Computation in Linguistics 2022, pages 241–243, online. Association for Computational Linguistics.

Mustafa Omer Gul and Yoav Artzi. 2024. CoGen: Learning from feedback with coupled comprehension and generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12966–12982, Miami, Florida, USA. Association for Computational Linguistics.

Vipul Gupta, Zhuowan Li, Adam Kortlewski, Chenyu Zhang, Yingwei Li, and Alan Yuille. 2022. Swapmix: Diagnosing and regularizing the over-reliance on visual context in visual question answering. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.

Robert D. Hawkins, Michael C. Frank, and Noah D. Goodman. 2020. Characterizing the dynamics of learning in repeated reference games. Cognitive Science, 44(6).

Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. 2021. CLIPScore: A reference-free evaluation metric for image captioning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7514–7528, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.

Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration.

Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020. spacy: Industrial-strength natural language processing in python.

Nikolai Ilinykh and Simon Dobnik. 2023. Context matters: evaluation of target and context features on variation of object naming. In Proceedings of the 1st Workshop on Linguistic Insights from and for Multimodal Language Processing, pages 12–24, Ingolstadt, Germany. Association for Computational Linguistics.

Anya Ji, Noriyuki Kojima, Noah Rush, Alane Suhr, Wai Keen Vong, Robert Hawkins, and Yoav Artzi. 2022. Abstract visual reasoning with tangram shapes. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 582–601, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Maurice G Kendall. 1938. A new measure of rank correlation. Biometrika, 30(1-2):81–93.

Emiel Krahmer and Kees van Deemter. 2012. Computational generation of referring expressions: A survey. Computational Linguistics, 38(1):173–218.

Tim Lauer, Tim H. W. Cornelissen, Dejan Draschkow, Verena Willenbockel, and Melissa L.-H. Võ. 2018. The role of scene summary statistics in object recognition. Scientific Reports, 8(1). Congruent vs incongruent context.

Shanchuan Lin, Anran Wang, and Xiao Yang. 2024. Sdxl-lightning: Progressive adversarial diffusion distillation. Preprint, arXiv:2402.13929.
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024. Llava-next: Improved reasoning, ocr, and world knowledge.

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual instruction tuning. In Advances in Neural Information Processing Systems, volume 36, pages 34892–34916. Curran Associates, Inc.

George A Miller. 1995. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41.

Tara Murfitt and Jan McAllister. 2001. The effect of production variables in monolog and dialog on comprehension by novel listeners. Language and Speech, 44(3):325–350.

Aude Oliva and Antonio Torralba. 2006. Chapter 2 building the gist of a scene: the role of global image features in recognition. In Progress in Brain Research, pages 23–36. Elsevier.

Aude Oliva and Antonio Torralba. 2007. The role of context in object recognition. Trends in Cognitive Sciences, 11(12):520–527. Congruent vs incongruent context.

Vicente Ordonez, Wei Liu, Jia Deng, Choi Yejin, Alexander Berg, and Tamara Berg. 2016. Learning to name objects. Communications of the ACM, 59:108–115.

Stephen E. Palmer. 1975. The effects of contextual scenes on the identification of objects. Memory & Cognition, 3(5):519–526.

Devi Parikh, C. Lawrence Zitnick, and Tsuhan Chen. 2012. Exploring tiny images: The roles of appearance and contextual information for machine and human object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10):1978–1991.

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543.

Effie J. Pereira and Monica S. Castelhano. 2014. Peripheral guidance in scenes: The interaction of scene context and object content. Journal of Experimental Psychology: Human Perception and Performance, 40(5):2056–2072.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 8748–8763. PMLR.

Zahra Sadeghi, James L. McClelland, and Paul Hoffman. 2015. You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes. Neuropsychologia, 76:52–61.

Michael F Schober and Herbert H Clark. 1989. Understanding by addressees and overhearers. Cognitive Psychology, 21(2):211–232.

Simeon Schüz, Albert Gatt, and Sina Zarrieß. 2023. Rethinking symbolic and visual context in referring expression generation. Frontiers in Artificial Intelligence, 6.

Todd Shore, Theofronia Androulakaki, and Gabriel Skantze. 2018. KTH tangrams: A dataset for research on alignment and conceptual pacts in task-oriented dialogue. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).

Carina Silberer, Sina Zarrieß, and Gemma Boleda. 2020a. Object naming in language and vision: A survey and a new dataset. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5792–5801, Marseille, France. European Language Resources Association.

Carina Silberer, Sina Zarrieß, and Gemma Boleda. 2020b. Object naming in language and vision: A survey and a new dataset. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5792–5801, Marseille, France. European Language Resources Association.

Carina Silberer, Sina Zarrieß, Matthijs Westera, and Gemma Boleda. 2020c. Humans meet models on object naming: A new dataset and analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1893–1905, Barcelona, Spain (Online). International Committee on Computational Linguistics.

Gabriel Skantze and Bram Willemsen. 2022. Collie: Continual learning of language grounding from language-image embeddings. Journal of Artificial Intelligence Research, 74:1201–1223.

Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. ConceptNet 5.5: An open multilingual graph of general knowledge. pages 4444–4451.

Saki Sudo, Kyoshiro Asano, Koh Mitsuda, Ryuichiro Higashinaka, and Yugo Takeuchi. 2022. A speculative and tentative common ground handling for efficient composition of uncertain dialogue. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3150–3157, Marseille, France. European Language Resources Association.

Ece Takmaz, Sandro Pezzelle, and Raquel Fernández. 2022. Less descriptive yet discriminative: Quantifying the properties of multimodal referring utterances
via CLIP. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 36–42, Dublin, Ireland. Association for Computational Linguistics.

Alberto Testoni, Juell Sprott, and Sandro Pezzelle. 2024. Naming, describing, and quantifying visual objects in humans and LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 547–557, Bangkok, Thailand. Association for Computational Linguistics.

Jacopo Turini and Melissa Le-Hoa Võ. 2022. Hierarchical organization of objects in scenes is reflected in mental representations of objects. Scientific Reports, 12(1).

Kees van Deemter. 2016. Computational models of referring : a study in cognitive science. The MIT Press, Cambridge, Massachusetts.

Melissa Le-Hoa Võ. 2021. The meaning and structure of scenes. Vision Research, 181:10–20.

Xuan Wang and Zhigang Zhu. 2023. Context understanding in computer vision: A survey. Computer Vision and Image Understanding, 229:103646.

Deanna Wilkes-Gibbs and Herbert H Clark. 1992. Coordinating beliefs in conversation. Journal of Memory and Language, 31(2):183–194.

Sina Zarriß and David Schlangen. 2017. Obtaining referential word meanings from visual and distributional information: Experiments on object naming. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 243–254, Vancouver, Canada. Association for Computational Linguistics.

Martin Zettersten and Gary Lupyan. 2020. Finding categories through words: More nameable features improve category learning. Cognition, 196:104135.

Duzhen Zhang, Yahan Yu, Jiahua Dong, Chenxing Li, Dan Su, Chenhui Chu, and Dong Yu. 2024. MMLMs: Recent advances in MultiModal large language models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12401–12430, Bangkok, Thailand. Association for Computational Linguistics.

Mengmi Zhang, Claire Tseng, and Gabriel Kreiman. 2020. Putting visual object recognition in context. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12982–12991.

A Crowdsourcing Procedure

For our data collection, we recruited 110 annotators, all of which are located in the United States and have stated English as their primary language. Workers were paid according to the local minimum wage, and the intended purpose of the data was explained. We recruited the crowdworkers via Prolific, and used Argilla (hosted on Hugging-face Spaces) to collect annotations. The annotators are heterogeneous in terms of their age (18-29: 25.3%; 30-39: 34.3%; 40-49: 22.2%; 60+: 18.2%), ethnicity (White: 58.6%; Black: 18.2%; Asian: 11.1%; other: 12.1%) and sex (female: 51.5%; male: 48.5%). A screenshot of the annotation instructions can be seen in Figure 6. The annotation interface can be seen in Figure 7.

To validate the automatic processing steps, we provided student assistants with the automatically processed data and instructed them to manually check whether the correct labels were extracted and valid WordNet synsets were selected, and update labels and synsets if necessary.

B Scientific Artifacts

In our work, we mainly use scientific artifacts in the form of publicly available datasets and model implementations, as well as Python frameworks and modules (cf. Appendix E). In all cases, we are confident that our work is consistent with their intended use. Most importantly, our work builds on the KILOGRAM dataset as a source of tangram figures. The dataset is available on GitHub. To generate scene images we rely on SDXL-Lightning (Lin et al., 2024), accessed via the provided Hugging-face Space. The model is available on huggingface. Our data and code for this project are available at github.com/clause-bielefeld/scenegram.

C Risks and Ethical Considerations

We do not believe that there are significant risks associated with this work, as we work with descriptions of abstract items which are not believed to be perceived as hurtful, and release data with limited scale. No ethics review was required. Our data does not contain any protected information and is fully anonymized.

D Prompts and Model Inference

Our model prompts consist of two parts. First, we instruct the systems to predict the locations of tangrams in the item grids, using greedy decoding:

“In this 2 by 2 grid, exactly one tile contains a tangram figure. In which grid cell is it? Pick your response from the following options: Top left, top right, bottom left, bottom right.”

After this, keeping the location prompt and predictions as context, we instruct the models to gen-
Content

Annotation Guidelines
The task for this annotation series is to name tangrams in image grids.
Tangrams are abstract figures composed of simple shapes that can be interpreted in different ways. For example, the following tangram could be interpreted as a table, turtle, or bathtub, among other possibilities:

![A tangram figure composed of triangles, interpreted as a table, turtle, or bathtub](page_324_186_377_153.png)

In the dataset 02_annotation_sws18_0 you will find a set of items, each of which consists of a 2 by 2 grid. Each grid contains exactly one tangram file.
Locate the tangram in the grid and describe what it looks like in the text field. Use short descriptions such as bathtub or swimming turtle.
After reading this, please continue to the 02_annotation_sws18_0 dataset by clicking on sws18_0 at the top of the screen.
After you’ve finished the annotations in 02_annotation_sws18_0, move on to 03_completion_sws18_0, where you’ll find the completion code for Prolific.
Please indicate that you have read these instructions by selecting the Yes label and enter your Prolific ID in the text field. After this, proceed to 02_annotation_sws18_0.

Figure 6: Screenshot of the annotation instructions

Content

![A kitchen interior with a tangram figure composed of triangles overlaid on the image](page_324_186_377_153.png)

Figure 7: Screenshot of the annotation setup
erate tangram descriptions:
“Describe what this tangram looks like. Ignore the other tiles. Keep your answer short and concise. Give your answer in the form: The tangram depicts a _.”

As described in Section 5.1, we repeatedly prompt our systems using the last prompt, to collect a diverse set of responses (using nucleus decoding with \( p = 0.5 \)). For each generated response, we extract the head noun from the model response using spaCy, assert that it is included in WordNet, and repeat the prediction for a maximum of 10 times if this is not the case. Inference terminates after 10 valid responses.

E Implementation Details

For our experiments we rely on models from huggingface. In detail, we used the following models:

• llava-hf/llava-v1.6-vicuna-7b-hf

• llava-hf/llava-v1.6-vicuna-13b-hf

• llava-hf/llava-v1.6-34b-hf

• llava-hf/llava-next-72b-hf (quantized using the bitsandbytes library)

To generate responses with our models, we used Python 3.9.20 with the following libraries: torch (2.5.1), transformers (4.46.2), bitsandbytes (0.44.1). For data analysis we used Python 3.10.9, mostly using the following frameworks: nltk (3.8.1), numpy (1.23.5), pandas (1.5.2), scikit-learn (1.2.0), scipy (1.9.3), seaborn (0.12.2), spacy (3.5.3 and the en_core_web_sm model).

We used three NVIDIA RTX A6000 GPUs for inference for LLaVA 72b, two GPUs of the same type for LLaVA 34b and a single GPU of the same type for the remaining models. Depending on model size, generating responses took between 50 min (LLaVA 7b) and 7h (LLaVA 72b).

F Further Examples

Figure 8 contains further qualitative examples for items with high SHIFT scores for human annotations.

G Location determination accuracies

In Table 4 we report accuracy scores for target locations in the item grids as predicted by our systems. The results indicate that most models handle the location task without major problems, with the exception of LLaVa 7b. Whereas all larger variants produce no or only singular mistakes, this system struggles especially in the baseline condition, where the remaining grid cells are filled with uniform colors. While this does not mean that it fails to describe the tangram, the low location determination scores point to parsing difficulties which should be taken into consideration.

<table>
  <tr>
    <th rowspan="2">scene</th>
    <th colspan="4">LLaVA</th>
  </tr>
  <tr>
    <th>7b</th>
    <th>13b</th>
    <th>34b</th>
    <th>72b</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>86.5</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>83.8</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>78.4</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>89.2</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>75.7</td>
    <td>97.3</td>
    <td>100.0</td>
    <td>97.3</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td>91.9</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>office</td>
    <td>83.8</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>97.3</td>
  </tr>
  <tr>
    <td>sea_bottom</td>
    <td>73.0</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>94.6</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>83.8</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>street</td>
    <td>86.5</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
  <tr>
    <td>none</td>
    <td>56.8</td>
    <td>100.0</td>
    <td>100.0</td>
    <td>100.0</td>
  </tr>
</table>

Table 4: Location determination accuracies (%)

H Lexical Classes in Scene Contexts

Using WordNet, we are able to abstract the label frequency analysis to more general sets of lexical items. For this, we map our annotation synsets to a pre-defined set of reference synsets (artifact.n.01, animal.n.01, person.n.01, geological_formation.n.01, written_symbol.n.01 and entity.n.01 as a generic fallback), selecting the reference synset with the highest distance from the WordNet root to which the annotated synset is a recursive hyponym as the lexical category.

Figure 9 shows that rankings are similar between scene conditions, i.e., artifact and animal are ranked first and second in all cases. However, there are differences in the frequency of occurrence: While artifacts is especially common for indoor scenes (bathroom, bedroom, kitchen, office), animals is slightly more frequent in natural or outdoor scenes (beach, forest, mountain, sea bottom, sky). geological_formation, with labels like “mountain” or “hill”, is especially frequent in the mountain condition, reflecting our label frequency results.

I Label Frequencies for Generated Descriptions

Table 5 shows occurrence frequencies for labels in tangram descriptions as generated by LLaVA variants, aggregated over scenes.
human: wrench (5); pipe wrench (2); hook (1); building (1); faucet (1)

LLaVA 7b: bathtub (3); chair (2); house (2); triangle (2); diamond (1)
LLaVA 72b: house (6); cross (2); cat (1); bird (1)

human: streetlamp (1); bird (1); drill (1); screw (1); spike (1); street sign (1); rose (1); bird talon (1); wrench (1); person (1)
LLaVA 7b: diamond (3); house (3); shape (1); triangle (1); figure (1); staircase (1)
LLaVA 72b: bird (5); cross (3); house (2)

human: monkey wrench (1); deer (1); tree (1); fishhook (1); wrench (1); sword (1); person (1); cactus (1); totem pole (1); chicken (1)
LLaVA 7b: diamond (5); house (2); triangle (2); figure (1)
LLaVA 72b: house (6); bird (3); person (1)

human: turtle (2); sailboat (1); wing suit (1); origami (1); airplane (1); kite (1); box kite (1); tie (1); map (1)
LLaVA 7b: beach (5); sunset (2); triangle (2); sun (1)
LLaVA 72b: house (10)

human: stingray (2); person (1); radio (1); iceberg (1); turtle (1); pyramids (1); water fountain (1); sea ray (1); duck (1)
LLaVA 7b: diamond (6); square (2); hexagon (2)
LLaVA 72b: house (10)

human: pyramids (1); monkey face (1); aeroplane (1); kite (1); shell (1); airplane (1); stealth bomber (1); tissue box (1); stingray (1); spaceship (1)
LLaVA 7b: diamond (5); square (2); cloud (2); triangle (1)
LLaVA 72b: house (10)

human: toilet (2); lightbulb (1); fountain (1); sink (1); bathtub (1); brush (1); water fountain (1); rocketship (1); rose (1)
LLaVA 7b: house (5); triangle (3); bathtub (2)
LLaVA 72b: house (7); pyramid (3)

human: pot (1); lighthouse (1); light (1); candle (1); torch (1); dagger (1); vase (1); house (1); lamp (1); mirror (1)
LLaVA 7b: house (6); pyramid (3); diamond (1)
LLaVA 72b: house (7); pyramid (2); triangle (1)

human: candle (2); library (1); lighthouse top (1); sword (1); torch (1); pot (1); corn (1); flower bud (1); altar (1)
LLaVA 7b: pyramid (7); triangle (3)
LLaVA 72b: house (6); pyramid (4)

Figure 8: Further examples with human annotations and model predictions. Humans often produce labels which are coherent to scenes; whereas models are considerably less creative.
<table>
  <tr>
    <th></th>
    <th>#1</th>
    <th>#2</th>
    <th>#3</th>
    <th>#4</th>
    <th>#5</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>bathrub (28.65 %)</td>
    <td>house (19.19 %)</td>
    <td>diamond (7.03 %)</td>
    <td>person (6.76 %)</td>
    <td>rectangl (5.95 %)</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>sun (14.59 %)</td>
    <td>house (12.43 %)</td>
    <td>bird (10.27 %)</td>
    <td>triangle (9.46 %)</td>
    <td>beach (9.19 %)</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>house (28.11 %)</td>
    <td>bed (14.59 %)</td>
    <td>diamond (10.54 %)</td>
    <td>bird (8.38 %)</td>
    <td>chair (7.3 %)</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>diamond (14.32 %)</td>
    <td>house (11.35 %)</td>
    <td>triangle (10.27 %)</td>
    <td>forest (8.65 %)</td>
    <td>tree (7.57 %)</td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>house (28.65 %)</td>
    <td>bird (12.16 %)</td>
    <td>diamond (7.57 %)</td>
    <td>chair (7.3 %)</td>
    <td>triangle (7.3 %)</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td>mountain (74.05 %)</td>
    <td>triangle (4.05 %)</td>
    <td>figure (3.78 %)</td>
    <td>person (3.24 %)</td>
    <td>pyramid (2.97 %)</td>
  </tr>
  <tr>
    <td>office</td>
    <td>chair (14.86 %)</td>
    <td>house (14.05 %)</td>
    <td>diamond (8.92 %)</td>
    <td>person (7.3 %)</td>
    <td>triangle (7.3 %)</td>
  </tr>
  <tr>
    <td>sea bottom</td>
    <td>triangle (9.19 %)</td>
    <td>diamond (8.65 %)</td>
    <td>square (7.57 %)</td>
    <td>fish (7.03 %)</td>
    <td>letter (7.03 %)</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>triangle (14.32 %)</td>
    <td>bird (13.78 %)</td>
    <td>house (11.35 %)</td>
    <td>diamond (10.27 %)</td>
    <td>letter (7.84 %)</td>
  </tr>
  <tr>
    <td>street</td>
    <td>house (22.7 %)</td>
    <td>triangle (11.35 %)</td>
    <td>diamond (10.81 %)</td>
    <td>person (8.65 %)</td>
    <td>dog (7.57 %)</td>
  </tr>
  <tr>
    <td>none</td>
    <td>diamond (17.3 %)</td>
    <td>house (14.05 %)</td>
    <td>triangle (10.54 %)</td>
    <td>square (8.38 %)</td>
    <td>bird (7.84 %)</td>
  </tr>
</table>

(a) LLaVA 7b

<table>
  <tr>
    <th></th>
    <th>#1</th>
    <th>#2</th>
    <th>#3</th>
    <th>#4</th>
    <th>#5</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>house (18.11 %)</td>
    <td>bird (16.22 %)</td>
    <td>person (13.78 %)</td>
    <td>square (10.81 %)</td>
    <td>tree (9.19 %)</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>bird (19.73 %)</td>
    <td>person (14.05 %)</td>
    <td>house (11.35 %)</td>
    <td>tree (10.27 %)</td>
    <td>a (7.57 %)</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>house (20.81 %)</td>
    <td>bird (17.84 %)</td>
    <td>person (12.7 %)</td>
    <td>square (7.84 %)</td>
    <td>a (7.57 %)</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>bird (26.22 %)</td>
    <td>person (18.92 %)</td>
    <td>house (15.14 %)</td>
    <td>tree (8.65 %)</td>
    <td>animal (4.86 %)</td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>house (20.81 %)</td>
    <td>bird (18.65 %)</td>
    <td>person (15.95 %)</td>
    <td>square (8.38 %)</td>
    <td>tree (5.41 %)</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td>mountain (35.41 %)</td>
    <td>house (16.76 %)</td>
    <td>bird (12.7 %)</td>
    <td>person (10.81 %)</td>
    <td>landscape (6.49 %)</td>
  </tr>
  <tr>
    <td>office</td>
    <td>house (18.92 %)</td>
    <td>bird (16.49 %)</td>
    <td>person (14.86 %)</td>
    <td>square (11.35 %)</td>
    <td>tree (5.41 %)</td>
  </tr>
  <tr>
    <td>sea_bottom</td>
    <td>bird (21.35 %)</td>
    <td>person (14.86 %)</td>
    <td>fish (9.19 %)</td>
    <td>house (8.92 %)</td>
    <td>square (8.38 %)</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>bird (24.86 %)</td>
    <td>house (18.11 %)</td>
    <td>person (14.32 %)</td>
    <td>square (7.03 %)</td>
    <td>tree (6.49 %)</td>
  </tr>
  <tr>
    <td>street</td>
    <td>bird (20.54 %)</td>
    <td>person (18.11 %)</td>
    <td>house (15.68 %)</td>
    <td>square (11.35 %)</td>
    <td>a (5.95 %)</td>
  </tr>
  <tr>
    <td>none</td>
    <td>bird (24.86 %)</td>
    <td>person (19.19 %)</td>
    <td>house (10.81 %)</td>
    <td>square (10.27 %)</td>
    <td>shape (6.76 %)</td>
  </tr>
</table>

(b) LLaVA 13b

<table>
  <tr>
    <th></th>
    <th>#1</th>
    <th>#2</th>
    <th>#3</th>
    <th>#4</th>
    <th>#5</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>bird (25.14 %)</td>
    <td>house (14.86 %)</td>
    <td>triangle (14.32 %)</td>
    <td>dog (8.92 %)</td>
    <td>man (4.86 %)</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>bird (24.05 %)</td>
    <td>triangle (15.68 %)</td>
    <td>dog (13.51 %)</td>
    <td>house (10.0 %)</td>
    <td>man (3.78 %)</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>bird (20.54 %)</td>
    <td>house (18.11 %)</td>
    <td>dog (14.05 %)</td>
    <td>triangle (12.97 %)</td>
    <td>horse (4.05 %)</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>bird (21.62 %)</td>
    <td>triangle (19.73 %)</td>
    <td>dog (10.81 %)</td>
    <td>house (9.73 %)</td>
    <td>man (4.32 %)</td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>bird (22.43 %)</td>
    <td>triangle (16.49 %)</td>
    <td>house (13.78 %)</td>
    <td>dog (12.7 %)</td>
    <td>square (4.05 %)</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td>triangle (21.08 %)</td>
    <td>mountain (16.22 %)</td>
    <td>bird (12.16 %)</td>
    <td>dog (8.92 %)</td>
    <td>house (7.84 %)</td>
  </tr>
  <tr>
    <td>office</td>
    <td>bird (21.62 %)</td>
    <td>triangle (17.57 %)</td>
    <td>dog (11.62 %)</td>
    <td>house (11.62 %)</td>
    <td>person (5.68 %)</td>
  </tr>
  <tr>
    <td>sea_bottom</td>
    <td>triangle (20.27 %)</td>
    <td>bird (19.46 %)</td>
    <td>dog (7.3 %)</td>
    <td>house (6.76 %)</td>
    <td>man (4.59 %)</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>bird (23.78 %)</td>
    <td>triangle (17.03 %)</td>
    <td>dog (10.0 %)</td>
    <td>house (9.73 %)</td>
    <td>man (5.95 %)</td>
  </tr>
  <tr>
    <td>street</td>
    <td>triangle (20.27 %)</td>
    <td>bird (19.19 %)</td>
    <td>dog (12.16 %)</td>
    <td>house (11.62 %)</td>
    <td>diamond (5.14 %)</td>
  </tr>
  <tr>
    <td>none</td>
    <td>bird (17.03 %)</td>
    <td>triangle (14.86 %)</td>
    <td>dog (11.08 %)</td>
    <td>house (8.92 %)</td>
    <td>person (3.78 %)</td>
  </tr>
</table>

(c) LLaVA 34b

<table>
  <tr>
    <th></th>
    <th>#1</th>
    <th>#2</th>
    <th>#3</th>
    <th>#4</th>
    <th>#5</th>
  </tr>
  <tr>
    <td>bathroom</td>
    <td>house (71.08 %)</td>
    <td>bird (6.76 %)</td>
    <td>letter (5.14 %)</td>
    <td>person (3.51 %)</td>
    <td>figure (1.89 %)</td>
  </tr>
  <tr>
    <td>beach</td>
    <td>house (64.86 %)</td>
    <td>bird (7.3 %)</td>
    <td>letter (5.68 %)</td>
    <td>person (5.14 %)</td>
    <td>dog (3.51 %)</td>
  </tr>
  <tr>
    <td>bedroom</td>
    <td>house (69.73 %)</td>
    <td>letter (6.22 %)</td>
    <td>person (4.59 %)</td>
    <td>bird (4.32 %)</td>
    <td>bed (3.24 %)</td>
  </tr>
  <tr>
    <td>forest</td>
    <td>house (63.78 %)</td>
    <td>person (8.38 %)</td>
    <td>dog (5.14 %)</td>
    <td>bird (4.59 %)</td>
    <td>letter (3.51 %)</td>
  </tr>
  <tr>
    <td>kitchen</td>
    <td>house (68.92 %)</td>
    <td>person (4.59 %)</td>
    <td>bird (4.05 %)</td>
    <td>letter (3.78 %)</td>
    <td>dog (3.78 %)</td>
  </tr>
  <tr>
    <td>mountain</td>
    <td>mountain (64.86 %)</td>
    <td>house (19.19 %)</td>
    <td>person (5.95 %)</td>
    <td>pyramid (2.43 %)</td>
    <td>letter (2.16 %)</td>
  </tr>
  <tr>
    <td>office</td>
    <td>house (57.3 %)</td>
    <td>person (7.84 %)</td>
    <td>letter (7.84 %)</td>
    <td>bird (4.86 %)</td>
    <td>pyramid (3.24 %)</td>
  </tr>
  <tr>
    <td>sea_bottom</td>
    <td>house (54.32 %)</td>
    <td>bird (7.3 %)</td>
    <td>boat (6.76 %)</td>
    <td>person (4.86 %)</td>
    <td>letter (4.05 %)</td>
  </tr>
  <tr>
    <td>sky</td>
    <td>house (62.43 %)</td>
    <td>bird (9.19 %)</td>
    <td>person (5.14 %)</td>
    <td>horse (4.59 %)</td>
    <td>letter (3.51 %)</td>
  </tr>
  <tr>
    <td>street</td>
    <td>house (64.59 %)</td>
    <td>person (8.11 %)</td>
    <td>letter (7.3 %)</td>
    <td>bird (5.41 %)</td>
    <td>dog (2.7 %)</td>
  </tr>
  <tr>
    <td>none</td>
    <td>house (58.92 %)</td>
    <td>person (6.49 %)</td>
    <td>None (5.41 %)</td>
    <td>bird (4.86 %)</td>
    <td>horse (4.05 %)</td>
  </tr>
</table>

(d) LLaVA 72b

Table 5: Occurrence frequencies for labels in tangram descriptions generated by LLaVA variants, aggregated over scenes.
Figure 9: Occurrence frequencies for labels in different lexical classes for human annotations, calculated via WordNet.

![Bar chart showing occurrence frequencies for labels in different lexical classes for human annotations, calculated via WordNet. The x-axis lists scene categories such as bathroom, beach, bedroom, forest, kitchen, mountain, office, sea bottom, sky, street, and none. The y-axis shows frequency (%) ranging from 0 to 50. Different colored bars represent various lexical classes: artifact.n.01 (blue), animal.n.01 (orange), person.n.01 (green), written_symbol.n.01 (red), geological_formation.n.01 (purple), entity.n.01 (brown).](page_420_670_601_312.png)

Reviewer paper summaries: This paper proposes a method for investigating the influence of contextual scenes on human conceptualization and naming of abstract shapes (e.g., tangrams) and introduces the SCENE-GRAM dataset. By embedding tangram shapes within diverse scenes (e.g., bathroom, beach, forest), the dataset systematically collects human descriptions of these shapes, enabling an analysis of how contextual scenes guide concept selection.
Summary of strengths: 1.By employing a “tangram + scene” combination to generate controlled experimental data, this study overcomes the strong object-scene correlation in natural image datasets, enabling the first systematic analysis of how an identical abstract shape is described across different scenes.

2.The study introduces quantitative metrics based on text-image similarity (CLIP) and lexical semantic similarity (GloVe, ConceptNet) to assess the conceptual consistency between scenes and descriptions.
Summary of weaknesses: 1.Unclear application value. The practical significance of the dataset is not convincingly demonstrated. The paper does not provide sufficient evidence that the lack of human-like conceptualization in models poses a fundamental limitation in multimodal reasoning tasks, nor does it establish the necessity of human conceptualization for such tasks.

2.Ambiguous or missing criteria for scene selection. The paper lacks a clearly defined standard for selecting scenes, which may affect the dataset’s effectiveness in evaluation. For instance, it remains unclear whether the authors deliberately selected semantically distinct scenes (e.g., an open natural environment like a beach vs. an enclosed artificial environment like a bathroom) to maximize conceptualization differences. A more comprehensive rationale for scene selection is needed to enhance the dataset’s robustness in assessing model scene understanding.

3.Insufficient transparency in dataset construction and validation. The paper does not provide enough details regarding the SCENE-GRAM dataset, such as annotator background (e.g., cultural diversity, age, participant selection criteria), which is crucial for evaluating potential biases.

4.Lack of baseline comparisons in experimental design. The experimental section appears to rely solely on LLaVA and its variants, which limits insights into how other multimodal models—both open-source and proprietary (e.g., GPT-4)—understand contextual scenes. A broader comparison would strengthen the study’s claims.
Comments, suggestions and typos: see above.
Overall assessment scores: 2.5