Look at what I can do: Object affordances guide visual attention while speakers describe potential actions

The label-feedback effect is influenced by target category in visual search

The label-feedback hypothesis states that language can modulate visual processing. In particular, hearing or reading aloud target names (labels) speeds up performance in visual search tasks by facilitating target detection and such advantage is often measured against a condition where the target name is shown visually (i.e. via the same modality as the search task). The current study conceptually complements and expands previous investigations. The effect of a multimodal label presentation (i.e., an audio+visual, AV, priming label) in a visual search task is compared to that of a multimodal (i.e. white noise+visual, NV, label) and two unimodal (i.e. audio, A, label or visual, V, label) control conditions. The name of a category (i.e. a label at the superordinate level) is used as a cue, instead of the more commonly used target name (a basic level label), with targets belonging to one of three categories: garments, improper weapons, and proper weapons. These categories vary for their structure, improper weapons being an ad hoc category (i.e. context-dependent), unlike proper weapons and garments. The preregistered analysis shows an overall facilitation of visual search performance in the AV condition compared to the NV condition, confirming that the label-feedback effect may not be explained away by the effects of multimodal stimulation only and that it extends to superordinate labels. Moreover, exploratory analyses show that such facilitation is driven by the garments and proper weapons categories, rather than improper weapons. Thus, the superordinate label-feedback effect is modulated by the structural properties of a category. These findings are consistent with the idea that the AV condition prompts an "up-regulation" of the label, a requirement for enhancing the label's beneficial effects, but not when the label refers to an ad hoc category. They also highlight the peculiar status of the category of improper weapons and set it apart from that of proper weapons.

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.

Exploring the Semantic-Inconsistency Effect in Scenes Using a Continuous Measure of Linguistic-Semantic Similarity

Viewers use contextual information to visually explore complex scenes. Object recognition is facilitated by exploiting object-scene relations (which objects are expected in a given scene) and object-object relations (which objects are expected because of the occurrence of other objects). Semantically inconsistent objects deviate from these expectations, so they tend to capture viewers' attention (the semantic-inconsistency effect). Some objects fit the identity of a scene more or less than others, yet semantic inconsistencies have hitherto been operationalized as binary (consistent vs. inconsistent). In an eye-tracking experiment (N = 21 adults), we study the semantic-inconsistency effect in a continuous manner by using the linguistic-semantic similarity of an object to the scene category and to other objects in the scene. We found that both highly consistent and highly inconsistent objects are viewed more than other objects (U-shaped relationship), revealing that the (in)consistency effect is more than a simple binary classification.

Speakers prioritise affordance-based object semantics in scene descriptions

This work investigates the linearisation strategies used by speakers when describing real-world scenes to better understand production plans for multi-utterance sequences. In this study, 30 participants described real-world scenes aloud. To investigate which semantic features of scenes predict order of mention, we quantified three features (meaning, graspability, and interactability) using two techniques (whole-object ratings and feature map values). We found that object-level semantic features, namely those affordance-based, predicted order of mention in a scene description task. Our findings provide the first evidence for an object-related semantic feature that guides linguistic ordering decisions and offer theoretical support for the role of object semantics in scene viewing and description.