How big should this object be? Perceptual influences on viewing-size preferences

Investigating canonical size phenomenon in drawing from memory task in different perceptual conditions among children

The canonical size phenomenon refers to the mental representation of real-object size information: the objects larger in the physical world are represented as larger in mental spatial representations. This study tested this phenomenon in a drawing-from-memory task among children aged 5, 7, and 9 years. The participants were asked to draw objects whose actual sizes varied at eight size rank levels. Drawings were made on regular paper sheets or special foils to produce embossed drawings. When drawing from memory, the participants were either sighted or blindfolded (to prevent visual feedback). We predicted that the drawn size of objects would increase with increasing size rank of objects. The findings supported the hypothesis concerning the canonical size effect among all age groups tested. This means that children aged 5 to 9 represent real-world size information about everyday objects and are sensitive to their size subtleties. Moreover, the drawn size increased with increasing size ranks both within sighted and blindfolded perceptual conditions (however, the slope of functions that best explain the relation between size rank and drawn size varied between the perceptual conditions). This finding further supports the recent evidence of the spatial character of the canonical size phenomenon.

Object-based attention during scene perception elicits boundary contraction in memory

Boundary contraction and extension are two types of scene transformations that occur in memory. In extension, viewers extrapolate information beyond the edges of the image, whereas in contraction, viewers forget information near the edges. Recent work suggests that image composition influences the direction and magnitude of boundary transformation. We hypothesize that selective attention at encoding is an important driver of boundary transformation effects, selective attention to specific objects at encoding leading to boundary contraction. In this study, one group of participants (N = 36) memorized 15 scenes while searching for targets, while a separate group (N = 36) just memorized the scenes. Both groups then drew the scenes from memory with as much object and spatial detail as they could remember. We asked online workers to provide ratings of boundary transformations in the drawings, as well as how many objects they contained and the precision of remembered object size and location. We found that search condition drawings showed significantly greater boundary contraction than drawings of the same scenes in the memorize condition. Search drawings were significantly more likely to contain target objects, and the likelihood to recall other objects in the scene decreased as a function of their distance from the target. These findings suggest that selective attention to a specific object due to a search task at encoding will lead to significant boundary contraction.

Drawing as a tool for investigating the nature of imagery representations of blind people: The case of the canonical size phenomenon

Several studies have shown that blind people, including those with congenital blindness, can use raised-line drawings, both for "reading" tactile graphics and for drawing unassisted. However, research on drawings produced by blind people has mainly been qualitative. The current experimental study was designed to investigate the under-researched issue of the size of drawings created by people with blindness. Participants (N = 59) varied in their visual status. Adventitiously blind people had previous visual experience and might use visual representations (e.g., when visualising objects in imagery/working memory). Congenitally blind people did not have any visual experience. The participant's task was to draw from memory common objects that vary in size in the real world. The findings revealed that both groups of participants produced larger drawings of objects that have larger actual sizes. This means that the size of familiar objects is a property of blind people's mental representations, regardless of their visual status. Our research also sheds light on the nature of the phenomenon of canonical size. Since we have found the canonical size effect in a group of people who are blind from birth, the assumption of the visual nature of this phenomenon - caused by the ocular-centric biases present in studies on drawing performance - should be revised.

Finding the meaning in meaning maps: Quantifying the roles of semantic and non-semantic scene information in guiding visual attention

In real-world vision, people prioritise the most informative scene regions via eye-movements. According to the cognitive guidance theory of visual attention, viewers allocate visual attention to those parts of the scene that are expected to be the most informative. The expected information of a scene region is coded in the semantic distribution of that scene. Meaning maps have been proposed to capture the spatial distribution of local scene semantics in order to test cognitive guidance theories of attention. Notwithstanding the success of meaning maps, the reason for their success has been contested. This has led to at least two possible explanations for the success of meaning maps in predicting visual attention. On the one hand, meaning maps might measure scene semantics. On the other hand, meaning maps might measure scene features, overlapping with, but distinct from, scene semantics. This study aims to disentangle these two sources of information by considering both conceptual information and non-semantic scene entropy simultaneously. We found that both semantic and non-semantic information is captured by meaning maps, but scene entropy accounted for more unique variance in the success of meaning maps than conceptual information. Additionally, some explained variance was unaccounted for by either source of information. Thus, although meaning maps may index some aspect of semantic information, their success seems to be better explained by non-semantic information. We conclude that meaning maps may not yet be a good tool to test cognitive guidance theories of attention in general, since they capture non-semantic aspects of local semantic density and only a small portion of conceptual information. Rather, we suggest that researchers should better define the exact aspect of cognitive guidance theories they wish to test and then use the tool that best captures that desired semantic information. As it stands, the semantic information contained in meaning maps seems too ambiguous to draw strong conclusions about how and when semantic information guides visual attention.

Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing this ability from other visual perceptions, like depth, has been challenging. Using the THINGS EEG2 dataset with high time-resolution human brain recordings and more ecologically valid naturalistic stimuli, our study uses an innovative approach to disentangle neural representations of object real-world size from retinal size and perceived real-world depth in a way that was not previously possible. Leveraging this state-of-the-art dataset, our EEG representational similarity results reveal a pure representation of object real-world size in human brains. We report a representational timeline of visual object processing: object real-world depth appeared first, then retinal size, and finally, real-world size. Additionally, we input both these naturalistic images and object-only images without natural background into artificial neural networks. Consistent with the human EEG findings, we also successfully disentangled representation of object real-world size from retinal size and real-world depth in all three types of artificial neural networks (visual-only ResNet, visual-language CLIP, and language-only Word2Vec). Moreover, our multi-modal representational comparison framework across human EEG and artificial neural networks reveals real-world size as a stable and higher-level dimension in object space incorporating both visual and semantic information. Our research provides a detailed and clear characterization of the object processing process, which offers further advances and insights into our understanding of object space and the construction of more brain-like visual models.

Mid-level Feature Differences Support Early Animacy and Object Size Distinctions: Evidence from Electroencephalography Decoding

Responses to visually presented objects along the cortical surface of the human brain have a large-scale organization reflecting the broad categorical divisions of animacy and object size. Emerging evidence indicates that this topographical organization is supported by differences between objects in mid-level perceptual features. With regard to the timing of neural responses, images of objects quickly evoke neural responses with decodable information about animacy and object size, but are mid-level features sufficient to evoke these rapid neural responses? Or is slower iterative neural processing required to untangle information about animacy and object size from mid-level features, requiring hundreds of milliseconds more processing time? To answer this question, we used EEG to measure human neural responses to images of objects and their texform counterparts-unrecognizable images that preserve some mid-level feature information about texture and coarse form. We found that texform images evoked neural responses with early decodable information about both animacy and real-world size, as early as responses evoked by original images. Furthermore, successful cross-decoding indicates that both texform and original images evoke information about animacy and size through a common underlying neural basis. Broadly, these results indicate that the visual system contains a mid-level feature bank carrying linearly decodable information on animacy and size, which can be rapidly activated without requiring explicit recognition or protracted temporal processing.