Objects are selected for attention based upon meaning during passive scene viewing.

While object meaning has been demonstrated to guide attention during active scene viewing and object salience guides attention during passive viewing, it is unknown whether object meaning predicts attention in passive viewing tasks and whether attention during passive viewing is more strongly related to meaning or salience. To answer this question, we used a mixed modeling approach where we computed the average meaning and physical salience of objects in scenes while statistically controlling for the roles of object size and eccentricity. Using eye-movement data from aesthetic judgment and memorization tasks, we then tested whether fixations are more likely to land on high-meaning objects than low-meaning objects while controlling for object salience, size, and eccentricity.

Searching for meaning: Local scene semantics guide attention during natural visual search in scenes.

Models of visual search in scenes include image salience as a source of attentional guidance. However, because scene meaning is correlated with image salience, it could be that the salience predictor in these models is driven by meaning. To test this proposal, we generated meaning maps that represented the spatial distribution of semantic informativeness in scenes, and salience maps which represented the spatial distribution of conspicuous image features and tested their influence on fixation densities from two object search tasks in real-world scenes.

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

As we act on the world around us, our eyes seek out objects we plan to interact with. A growing body of evidence suggests that overt visual attention selects objects in the environment that could be interacted with, even when the task precludes physical interaction. In previous work, objects that afford grasping interactions influenced attention when static scenes depicted reachable spaces, and attention was otherwise better explained by general informativeness.

Gaze dynamics are sensitive to target orienting for working memory encoding in virtual reality.

Numerous studies have demonstrated that visuospatial attention is a requirement for successful working memory encoding. It is unknown, however, whether this established relationship manifests in consistent gaze dynamics as people orient their visuospatial attention toward an encoding target when searching for information in naturalistic environments. To test this hypothesis, participants' eye movements were recorded while they searched for and encoded objects in a virtual apartment (Experiment 1).

Meaning and expected surfaces combine to guide attention during visual search in scenes.

How do spatial constraints and meaningful scene regions interact to control overt attention during visual search for objects in real-world scenes? To answer this question, we combined novel surface maps of the likely locations of target objects with maps of the spatial distribution of scene semantic content. The surface maps captured likely target surfaces as continuous probabilities. Meaning was represented by meaning maps highlighting the distribution of semantic content in local scene regions.

Meaning maps capture the density of local semantic features in scenes: A reply to Pedziwiatr, Kümmerer, Wallis, Bethge & Teufel (2021).

Pedziwiatr, Kümmerer, Wallis, Bethge, & Teufel (2021) contend that Meaning Maps do not represent the spatial distribution of semantic features in scenes. We argue that Pesziwiatr et al. provide neither logical nor empirical support for that claim, and we conclude that Meaning Maps do what they were designed to do: represent the spatial distribution of meaning in scenes.

Center Bias Does Not Account for the Advantage of Meaning Over Salience in Attentional Guidance During Scene Viewing.

Studies assessing the relationship between high-level meaning and low-level image salience on real-world attention have shown that meaning better predicts eye movements than image salience. However, it is not yet clear whether the advantage of meaning over salience is a general phenomenon or whether it is related to center bias: the tendency for viewers to fixate scene centers. Previous meaning mapping studies have shown meaning predicts eye movements beyond center bias whereas saliency does not.

Visual and verbal working memory loads interfere with scene-viewing.

Working memory is thought to be divided into distinct visual and verbal subsystems. Studies of visual working memory frequently use verbal working memory tasks as control conditions and/or use articulatory suppression to ensure that visual load is not transferred to verbal working memory. Using these verbal tasks relies on the assumption that the verbal working memory load will not interfere with the same processes as visual working memory.

Where the action could be: Speakers look at graspable objects and meaningful scene regions when describing potential actions.

The world is visually complex, yet we can efficiently describe it by extracting the information that is most relevant to convey. How do the properties of real-world scenes help us decide where to look and what to say? Image salience has been the dominant explanation for what drives visual attention and production as we describe displays, but new evidence shows scene meaning predicts attention better than image salience. Here we investigated the relevance of one aspect of meaning, graspability (the grasping interactions objects in the scene afford), given that affordances have been implicated in both visual and linguistic processing.

Meaning and Attentional Guidance in Scenes: A Review of the Meaning Map Approach.

Perception of a complex visual scene requires that important regions be prioritized and attentionally selected for processing. What is the basis for this selection? Although much research has focused on image salience as an important factor guiding attention, relatively little work has focused on semantic salience. To address this imbalance, we have recently developed a new method for measuring, representing, and evaluating the role of meaning in scenes.

The role of meaning in attentional guidance during free viewing of real-world scenes.

In real-world vision, humans prioritize the most relevant visual information at the expense of other information via attentional selection. The current study sought to understand the role of semantic features and image features on attentional selection during free viewing of real-world scenes. We compared the ability of meaning maps generated from ratings of isolated, context-free image patches and saliency maps generated from the Graph-Based Visual Saliency model to predict the spatial distribution of attention in scenes as measured by eye movements.

Correction to: Meaning guides attention during scene viewing, even when it is irrelevant.

A minor coding error slightly affected a few originally reported values.

Meaning guides attention during scene viewing, even when it is irrelevant.

During real-world scene viewing, humans must prioritize scene regions for attention. What are the roles of low-level image salience and high-level semantic meaning in attentional prioritization? A previous study suggested that when salience and meaning are directly contrasted in scene memorization and preference tasks, attentional priority is assigned by meaning (Henderson & Hayes in Nature Human Behavior, 1, 743-747, 2017). Here we examined the role of meaning in attentional guidance using two tasks in which meaning was irrelevant and salience was relevant: a brightness rating task and a brightness search task.

Verbal cues flexibly transform spatial representations in human memory.

Humans possess a unique ability to communicate spatially-relevant information, yet the intersection between language and navigation remains largely unexplored. One possibility is that verbal cues accentuate heuristics useful for coding spatial layouts, yet this idea remains largely untested. We test the idea that verbal cues flexibly accentuate the coding of heuristics to remember spatial layouts via spatial boundaries or landmarks.

Electrophysiological correlates of encoding processes in a full-report visual working memory paradigm.

Why are some visual stimuli remembered, whereas others are forgotten? A limitation of recognition paradigms is that they measure aggregate behavioral performance and/or neural responses to all stimuli presented in a visual working memory (VWM) array. To address this limitation, we paired an electroencephalography (EEG) frequency-tagging technique with two full-report VWM paradigms. This permitted the tracking of individual stimuli as well as the aggregate response.

Encoding-Stage Adaptation Effects: Long-Term Memory.

Given adaptation changes perceptual experience, it probably shapes long-term memory (LTM). Across four experiments, participants were adapted to strongly gendered (male, female: Experiments 1 and 2) or aged faces (old, young: Experiments 3 and 4) before LTM encoding and later completed an LTM test in which the encoded faces were morphed with the opposite end of the relevant continuum. At retrieval, participants judged whether probe faces were more or less male or female or young or old than when presented during encoding.