Search

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. The results showed that meaning accounted for significantly greater variance in fixation densities than image salience, both overall and in early attention across both studies. Here, meaning explained 58% and 63% of the theoretical ceiling of variance in attention across both studies, respectively. Furthermore, both studies demonstrated that fast initial saccades were not more likely to be directed to higher salience regions than slower initial saccades, and initial saccades of all latencies were directed to regions containing higher meaning than salience. Together, these results demonstrated that even though meaning was task-neutral, the visual system still selected meaningful over salient scene regions for attention during search.

Humans rapidly process and understand real-world scenes with ease. Our stored semantic knowledge gained from experience is thought to be central to this ability by organizing perceptual information into meaningful units to efficiently guide our attention in scenes. However, the role stored semantic representations play in scene guidance remains difficult to study and poorly understood. Here, we apply a state-of-the-art multimodal transformer trained on billions of image-text pairs to help advance our understanding of the role semantic representations play in scene understanding. We demonstrate across multiple studies that this transformer-based approach can be used to automatically estimate local scene meaning in indoor and outdoor scenes, predict where people look in these scenes, detect changes in local semantic content, and provide a human-interpretable account of why one scene region is more meaningful than another. Taken together, these findings highlight how multimodal transformers can advance our understanding of the role scene semantics play in scene understanding by serving as a representational framework that bridges vision and language.

Global and local semantic information are thought to influence where attention is allocated in real-world scene images. How the two interact to guide attention and what subsequent effects each has on memory for a viewed scene is unknown. We used the BubbleView paradigm (Kim, Bylinskii, et al., 2017) to monitor movements of attention via participants’ clicks during a scene-viewing task. We manipulated access to scene gist by providing a preview of the BubbleView scene on some trials. ‘Click Maps’ were compiled from participants’ click locations. These were compared to fixation density maps for the same scenes and to meaning maps. Click maps were similar to fixation density maps, suggesting that clicks in the BubbleView paradigm approximate the locus of overt attention. In both a between-subjects (Experiment 1) and a within-subjects comparison (Experiments 2 and 3), access to gist guided attention to locally meaningful scene regions. Local meaning also guided attention, even in the absence of gist. These results support theories describing cognitive guidance of attention in natural scene images. Access to gist and attention to meaning were also important for participants’ memory for the scene images: when participants had access to gist and when they had clicked more meaningful scene regions, they were more likely to remember the image at test. Taken together, our results suggest that global (scene gist) and local scene semantics influence both where we attend in a scene and our subsequent memory for it.

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. Because grasping is but one of many object interactions, previous work may have downplayed the influence of object affordances on attention. The current study investigated the relationship between overt visual attention and object affordances versus broadly construed semantic information in scenes as speakers describe or memorize scenes. In addition to meaning and grasp maps—which capture informativeness and grasping object affordances in scenes, respectively—we introduce interact maps, which capture affordances more broadly. In a mixed-effects analysis of 5 eyetracking experiments, we found that meaning predicted fixated locations in a general description task and during scene memorization. Grasp maps marginally predicted fixated locations during action description for scenes that depicted reachable spaces only. Interact maps predicted fixated regions in description experiments alone. Our findings suggest observers allocate attention to scene regions that could be readily interacted with when talking about the scene, while general informativeness preferentially guides attention when the task does not encourage careful consideration of objects in the scene. The current study suggests that the influence of object affordances on visual attention in scenes is mediated by task demands.

Prior research on film viewing has demonstrated that participants frequently fail to notice spatiotemporal disruptions, such as scene edits in the movies. Whether such insensitivity to spatiotemporal disruptions extends beyond scene edits in film viewing is not well understood. Across three experiments, we created spatiotemporal disruptions by presenting participants with minute long movie clips, and occasionally jumping the movie clips ahead or backward in time. Participants were instructed to press a button when they noticed any disruptions while watching the clips. The results from experiments 1 and 2 indicate that participants failed to notice the disruptions in continuity about 10% to 30% of the time depending on the magnitude of the jump. In addition, detection rates were lower by approximately 10% when the videos jumped ahead in time compared to the backward jumps across all jump magnitudes, suggesting a role of knowledge about the future affects jump detection. An additional analysis used optic flow similarity during these disruptions. Our findings suggest that insensitivity to spatiotemporal disruptions during film viewing is influenced by knowledge about future states.

The continued warming of the Arctic could release vast stores of carbon into the atmosphere from high-latitude ecosystems, especially from thawing permafrost. Increasing uptake of carbon dioxide (CO2) by vegetation during longer growing seasons may partially offset such release of carbon. However, evidence of significant net annual release of carbon from site-level observations and model simulations across tundra ecosystems has been inconclusive. To address this knowledge gap, we combined top-down observations of atmospheric CO2 concentration enhancements from aircraft and a tall tower, which integrate ecosystem exchange over large regions, with bottom-up observed CO2 fluxes from tundra environments and found that the Alaska North Slope is not a consistent net source nor net sink of CO2 to the atmosphere (ranging from −6 to +6 Tg C yr−1 for 2012–2017). Our analysis suggests that significant biogenic CO2 fluxes from unfrozen terrestrial soils, and likely inland waters, during the early cold season (September–December) are major factors in determining the net annual carbon balance of the North Slope, implying strong sensitivity to the rapidly warming freeze-up period. At the regional level, we find no evidence of the previously reported large late-cold-season (January–April) CO2 emissions to the atmosphere during the study period. Despite the importance of the cold-season CO2 emissions to the annual total, the interannual variability in the net CO2 flux is driven by the variability in growing season fluxes. During the growing season, the regional net CO2 flux is also highly sensitive to the distribution of tundra vegetation types throughout the North Slope. This study shows that quantification and characterization of year-round CO2 fluxes from the heterogeneous terrestrial and aquatic ecosystems in the Arctic using both site-level and atmospheric observations are important to accurately project the Earth system response to future warming.

Physically salient objects are thought to attract attention in natural scenes. However, research has shown that meaning maps, which capture the spatial distribution of semantically informative scene features, trump physical saliency in predicting the pattern of eye moments in natural scene viewing. Meaning maps even predict the fastest eye movements, suggesting that the brain extracts the spatial distribution of potentially meaningful scene regions very rapidly. To test this hypothesis, we applied representational similarity analysis to ERP data. The ERPs were obtained from human participants (N= 32, male and female) who viewed a series of 50 different natural scenes while performing a modified 1-back task. For each scene, we obtained a physical saliency map from a computational model and a meaning map from crowd-sourced ratings. We then used representational similarity analysis to assess the extent to which the representational geometry of physical saliency maps and meaning maps can predict the representational geometry of the neural response (the ERP scalp distribution) at each moment in time following scene onset. We found that a link between physical saliency and the ERPs emerged first (∼78 ms after stimulus onset), with a link to semantic informativeness emerging soon afterward (∼87 ms after stimulus onset). These findings are in line with previous evidence indicating that saliency is computed rapidly, while also indicating that information related to the spatial distribution of semantically informative scene elements is computed shortly thereafter, early enough to potentially exert an influence on eye movements.SIGNIFICANCE STATEMENTAttention may be attracted by physically salient objects, such as flashing lights, but humans must also be able to direct their attention to meaningful parts of scenes. Understanding how we direct attention to meaningful scene regions will be important for developing treatments for disorders of attention and for designing roadways, cockpits, and computer user interfaces. Information about saliency appears to be extracted rapidly by the brain, but little is known about the mechanisms that determine the locations of meaningful information. To address this gap, we showed people photographs of real-world scenes and measured brain activity. We found that information related to the locations of meaningful scene elements was extracted rapidly, shortly after the emergence of saliency-related information.

When viewing scenes, observers differ in how long they linger at each fixation location and how far they move their eyes between fixations. What factors drive these differences in eye-movement behaviors? Previous work suggests individual differences in working memory capacity may influence fixation durations and saccade amplitudes. In the present study, participants (N = 98) performed two scene-viewing tasks, aesthetic judgement and memorization, while viewing 100 photographs of real-world scenes. Working memory capacity, working memory processing ability, and fluid intelligence were assessed with an operation span task, a memory updating task, and Raven’s Advanced Progressive Matrices respectively. Across participants, we found significant effects of task on both fixation durations and saccade amplitudes. At the level of each individual participant, we also found a significant relationship between memory updating task performance and participants’ fixation duration distributions. However, we found no effect of fluid intelligence and no effect of working memory capacity on fixation duration or saccade amplitude distributions, inconsistent with previous findings. These results suggest that the ability to flexibly maintain and update working memory is strongly related to fixation duration behavior.

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.

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. We quantified image salience, meaning, and graspability for real-world scenes. In 3 eyetracking experiments, native English speakers described possible actions that could be carried out in a scene. We hypothesized that graspability would preferentially guide attention due to its task-relevance. In 2 experiments using stimuli from a previous study, meaning explained visual attention better than graspability or salience did, and graspability explained attention better than salience. In a third experiment we quantified image salience, meaning, graspability, and reach-weighted graspability for scenes that depicted reachable spaces containing graspable objects. Graspability and meaning explained attention equally well in the third experiment, and both explained attention better than salience. We conclude that speakers use object graspability to allocate attention to plan descriptions when scenes depict graspable objects within reach, and otherwise rely more on general meaning. The results shed light on what aspects of meaning guide attention during scene viewing in language production tasks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The complexity of the visual world requires that we constrain visual attention and prioritize some regions of the scene for attention over others. The current study investigated whether verbal encoding processes influence how attention is allocated in scenes. Specifically, we asked whether the advantage of scene meaning over image salience in attentional guidance is modulated by verbal encoding, given that we often use language to process information. In two experiments, 60 subjects studied scenes (N(1) = 30, N(2) = 60) for 12 seconds each in preparation for a scene recognition task. Half of the time, subjects engaged in a secondary articulatory suppression task concurrent with scene viewing. Meaning and saliency maps were quantified for each of the experimental scenes. In both experiments, we found that meaning explained more of the variance in visual attention than image salience did, particularly when we controlled for the overlap between meaning and salience, with and without the suppression task. Based on these results, verbal encoding processes do not appear to modulate the relationship between scene meaning and visual attention. Our findings suggest that semantic information in the scene steers the attentional ship, consistent with cognitive guidance theory.

When viewing scenes, observers differ in how long they linger at each fixation location and how far they move their eyes between fixations. What factors drive these differences in eye-movement behaviors? Previous work suggests individual differences in working memory capacity may influence fixation durations and saccade amplitudes. In the present study, participants (N = 98) performed two scene-viewing tasks, aesthetic judgment and memorization, while viewing 100 photographs of real-world scenes. Working memory capacity, working memory processing ability, and fluid intelligence were assessed with an operation span task, a memory updating task, and Raven's Advanced Progressive Matrices, respectively. Across participants, we found significant effects of task on both fixation durations and saccade amplitudes. At the level of each individual participant, we also found a significant relationship between memory updating task performance and participants' fixation duration distributions. However, we found no effect of fluid intelligence and no effect of working memory capacity on fixation duration or saccade amplitude distributions, inconsistent with previous findings. These results suggest that the ability to flexibly maintain and update working memory is strongly related to fixation duration behavior.

Semantic guidance theories propose that attention in real-world scenes is strongly associated with semantically informative scene regions. That is, we look where there are recognizable and informative objects that help us make sense of our visual environment. In contrast, image guidance theories propose that local differences in semantically uninterpreted image features such as luminance, color, and edge orientation primarily determine where we look in scenes. While it is clear that both semantic guidance and image guidance play a role in where we look in scenes, the degree of their relative contributions and how they interact with each other remains poorly understood. In the current study, we presented real-world scenes in upright and inverted orientations and used general linear mixed effects models to understand how semantic guidance, image guidance, and observer center bias were associated with fixation location and fixation duration. We observed distinct patterns of change under inversion. Semantic guidance was severely disrupted by scene inversion, while image guidance was mildly impaired and observer center bias was enhanced. In addition, we found that fixation durations for semantically rich regions decreased when viewing inverted scenes relative to upright scene viewing, while fixation durations for image salience and center bias were unaffected by inversion. Together these results provide important new constraints on theories and computational models of attention in real-world scenes.

Meaning mapping uses human raters to estimate different semantic features in scenes, and has been a useful tool in demonstrating the important role semantics play in guiding attention. However, recent work has argued that meaning maps do not capture semantic content, but like deep learning models of scene attention, represent only semantically-neutral image features. In the present study, we directly tested this hypothesis using a diffeomorphic image transformation that is designed to remove the meaning of an image region while preserving its image features. Specifically, we tested whether meaning maps and three state-of-the-art deep learning models were sensitive to the loss of semantic content in this critical diffeomorphed scene region. The results were clear: meaning maps generated by human raters showed a large decrease in the diffeomorphed scene regions, while all three deep saliency models showed a moderate increase in the diffeomorphed scene regions. These results demonstrate that meaning maps reflect local semantic content in scenes while deep saliency models do something else. We conclude the meaning mapping approach is an effective tool for estimating semantic content in scenes.

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N=54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is.

We extend decades of research on infants' visual processing by examining their eye gaze during viewing of natural scenes. We examined the eye movements of a racially diverse group of 4- to 12-month-old infants (N = 54; 27 boys; 24 infants were White and not Hispanic, 30 infants were African American, Asian American, mixed race and/or Hispanic) as they viewed images selected from the MIT Saliency Benchmark Project. In general, across this age range infants' fixation distributions became more consistent and more adult-like, suggesting that infants' fixations in natural scenes become increasingly more systematic. Evaluation of infants' fixation patterns with saliency maps generated by different models of physical salience revealed that although over this age range there was an increase in the correlations between infants' fixations and saliency, the amount of variance accounted for by salience actually decreased. At the youngest age, the amount of variance accounted for by salience was very similar to the consistency between infants' fixations, suggesting that the systematicity in these youngest infants' fixations was explained by their attention to physically salient regions. By 12 months, in contrast, the consistency between infants was greater than the variance accounted for by salience, suggesting that the systematicity in older infants' fixations reflected more than their attention to physically salient regions. Together these results show that infants' fixations when viewing natural scenes becomes more systematic and predictable, and that predictability is due to their attention to features other than physical salience. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Although the physical salience of objects has previously been demonstrated to guide attention in real-world scene perception, it is unknown whether objects are also prioritized based on their meaning. To answer this question, we computed the average meaning and the average physical salience of objects in scenes. 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. The results demonstrated that fixations are more likely to be directed to high meaning objects than low meaning objects regardless of object salience. Furthermore, the influence of object salience was progressively reduced as object meaning increased and was eliminated at the highest levels of meaning. Overall, these findings provide the first evidence that objects are prioritized by meaning for attentional selection during active scene viewing.

How do we determine where to focus our attention in real-world scenes? Image saliency theory proposes that our attention is 'pulled' to scene regions that differ in low-level image features. However, models that formalize image saliency theory often contain significant scene-independent spatial biases. In the present studies, three different viewing tasks were used to evaluate whether image saliency models account for variance in scene fixation density based primarily on scene-dependent, low-level feature contrast, or on their scene-independent spatial biases. For comparison, fixation density was also compared to semantic feature maps (Meaning Maps; Henderson & Hayes, Nature Human Behaviour, 1, 743-747, 2017) that were generated using human ratings of isolated scene patches. The squared correlations (R2) between scene fixation density and each image saliency model's center bias, each full image saliency model, and meaning maps were computed. The results showed that in tasks that produced observer center bias, the image saliency models on average explained 23% less variance in scene fixation density than their center biases alone. In comparison, meaning maps explained on average 10% more variance than center bias alone. We conclude that image saliency theory generalizes poorly to real-world scenes.

Schema knowledge can dramatically affect how we encode and retrieve memories. Current models propose that schema information is combined with episodic memory at retrieval to influence memory decisions, but it is not known how the strength or type of episodic memory (i.e., unconscious memory versus familiarity versus recollection) influences the extent to which schema information is incorporated into memory decisions. To address this question, we had participants search for target objects in semantically expected (i.e., congruent) locations or in unusual (i.e., incongruent) locations within scenes. In a subsequent test, participants indicated where in each scene the target had been located previously, then provided confidence-based recognition memory judgments that indexed recollection, familiarity strength, and unconscious memory for the scenes. In both an initial online study (n = 133) and replication (n = 59), target location recall was more accurate for targets that had been located in schema-congruent rather than incongruent locations; importantly, this effect was strongest for new scenes, decreased with unconscious memory, decreased further with familiarity strength, and was eliminated entirely for recollected scenes. Moreover, when participants recollected an incongruent scene but did not correctly remember the target location, they were still biased away from congruent regions-suggesting that detrimental schema bias was suppressed in the presence of recollection even when precise target location information was not remembered. The results indicate that episodic memory modulates how schemas are used: Schema knowledge contributes to spatial memory judgments primarily when episodic memory fails to provide precise information, and recollection can override schema bias completely.

As we age, we accumulate a wealth of information about the surrounding world. Evidence from visual search suggests that older adults retain intact knowledge for where objects tend to occur in everyday environments (semantic information) that allows them to successfully locate objects in scenes, but may over-rely on semantic guidance. We investigated age differences in the allocation of attention to semantically informative and visually salient information in a task in which the eye movements of younger (N=30, aged 18-24) and older (N=30, aged 66-82) adults were tracked as they described real-world scenes. We measured the semantic information in scenes based on "meaning map" ratings from a norming sample of young and older adults, and image salience as Graph-Based Visual Saliency. Logistic mixed-effects modeling was used to determine whether, controlling for center bias, fixated scene locations differed in semantic informativeness and visual salience from locations that were not fixated, and whether these effects differed for young and older adults. Semantic informativeness predicted fixated locations well overall, as did image salience, although unique variance in the model was better explained by semantic informativeness than image salience. Older adults were less likely to fixate informative locations in scenes than young adults were, though the locations older adults’ fixated were independently predicted well by informativeness. These results suggest young and older adults both use semantic information to guide attention in scenes, and that older adults do not over-rely on semantic information across the board.

Deep saliency models represent the current state-of-the-art for predicting where humans look in real-world scenes. However, for deep saliency models to inform cognitive theories of attention, we need to know how deep saliency models predict where people look. Here we open the black box of deep saliency models using an approach that models the association between the output of 3 prominent deep saliency models (MSI-Net, DeepGaze II, and SAM-ResNet) and low-, mid-, and high-level scene features. Specifically, we measured the association between each deep saliency model and low-level image saliency, mid-level contour symmetry and junctions, and high-level meaning by applying a mixed effects modeling approach to a large eye movement dataset. We found that despite different architectures, training regimens, and loss functions, all three deep saliency models were most strongly associated with high-level meaning. These findings suggest that deep saliency models are primarily learning image features associated with scene meaning.

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.

The memories we form are determined by what we attend to, and conversely, what we attend to is influenced by our memory for past experiences. Although we know that shifts of attention via eye movements are related to memory during encoding and retrieval, the role of specific memory processes in this relationship is unclear. There is evidence that attention may be especially important for some forms of memory (i.e., conscious recollection), and less so for others (i.e., familiarity-based recognition and unconscious influences of memory), but results are conflicting with respect to both the memory processes and eye movement patterns involved. To address this, we used a confidence-based method of isolating eye movement indices of spatial attention that are related to different memory processes (i.e., recollection, familiarity strength, and unconscious memory) during encoding and retrieval of real-world scenes. We also developed a new method of measuring the dispersion of eye movements, which proved to be more sensitive to memory processing than previously used measures. Specifically, in 2 studies, we found that familiarity strength-that is, changes in subjective reports of memory confidence-increased with (a) more dispersed patterns of viewing during encoding, (b) less dispersed viewing during retrieval, and (c) greater overlap in regions viewed between encoding and retrieval (i.e., resampling). Recollection was also related to these eye movements in a similar manner, though the associations with recollection were less consistent across experiments. Furthermore, we found no evidence for effects related to unconscious influences of memory. These findings indicate that attentional processes during viewing may not preferentially relate to recollection, and that the spatial distribution of eye movements is directly related to familiarity-based memory during encoding and retrieval. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Abstract According to the Gricean Maxim of Quantity, speakers provide the amount of information listeners require to correctly interpret an utterance, and no more (Grice in Logic and conversation, 1975). However, speakers do tend to violate the Maxim of Quantity often, especially when the redundant information improves reference precision (Degen et al. in Psychol Rev 127(4):591–621, 2020). Redundant (non-contrastive) information may facilitate real-world search if it narrows the spatial scope under consideration, or improves target template specificity. The current study investigated whether non-contrastive modifiers that improve reference precision facilitate visual search in real-world scenes. In two visual search experiments, we compared search performance when perceptually relevant, but non-contrastive modifiers were included in the search instruction. Participants (NExp. 1 = 48, NExp. 2 = 48) searched for a unique target object following a search instruction that contained either no modifier, a location modifier (Experiment 1: on the top left, Experiment 2: on the shelf), or a color modifier (the black lamp). In Experiment 1 only, the target was located faster when the verbal instruction included either modifier, and there was an overall benefit of color modifiers in a combined analysis for scenes and conditions common to both experiments. The results suggest that violations of the Maxim of Quantity can facilitate search when the violations include task-relevant information that either augments the target template or constrains the search space, and when at least one modifier provides a highly reliable cue. Consistent with Degen et al. (2020), we conclude that listeners benefit from non-contrastive information that improves reference precision, and engage in rational reference comprehension. Significance statement This study investigated whether providing more information than someone needs to find an object in a photograph helps them to find that object more easily, even though it means they need to interpret a more complicated sentence. Before searching a scene, participants were either given information about where the object would be located in the scene, what color the object was, or were only told what object to search for. The results showed that providing additional information helped participants locate an object in an image more easily only when at least one piece of information communicated what part of the scene the object was in, which suggests that more information can be beneficial as long as that information is specific and helps the recipient achieve a goal. We conclude that people will pay attention to redundant information when it supports their task. In practice, our results suggest that instructions in other contexts (e.g., real-world navigation, using a smartphone app, prescription instructions, etc.) can benefit from the inclusion of what appears to be redundant information.

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. In the present study, participants maintained a visual or verbal working memory load as they simultaneously viewed scenes while their eye movements were recorded. Because eye movements and visual working memory are closely linked, we anticipated the visual load would interfere with scene-viewing (and vice versa), while the verbal load would not. Surprisingly, both visual and verbal memory loads interfered with scene-viewing behavior, while eye movements during scene-viewing did not significantly interfere with performance on either memory task. These results suggest that a verbal working memory load can interfere with eye movements in a visual task.

When we look at repeated scenes, we tend to visit similar regions each time—a phenomenon known as resampling. Resampling has long been attributed to episodic memory, but the relationship between resampling and episodic memory has recently been found to be less consistent than assumed. A possibility that has yet to be fully considered is that factors unrelated to episodic memory may generate resampling: for example, other factors such as semantic memory and visual salience that are consistently present each time an image is viewed and are independent of specific prior viewing instances. We addressed this possibility by tracking participants’ eyes during scene viewing to examine how semantic memory, indexed by the semantic informativeness of scene regions (i.e., meaning), is involved in resampling. We found that viewing more meaningful regions predicted resampling, as did episodic familiarity strength. Furthermore, we found that meaning interacted with familiarity strength to predict resampling. Specifically, the effect of meaning on resampling was attenuated in the presence of strong episodic memory, and vice versa. These results suggest that episodic and semantic memory are each involved in resampling behavior and are in competition rather than synergistically increasing resampling. More generally, this suggests that episodic and semantic memory may compete to guide attention.

Human infection with the trematode Fasciola occurs with a worldwide prevalence of up to 17 million. Sheep and cattle are the normal host. Infection typically results in hepatobiliary disease, but extrahepatic manifestations are occasionally reported. Here, we present the case of a previously healthy 31-year-old Kurdish woman, admitted to hospital with a subarachnoid hemorrhage, eosinophilic meningitis, and lung and liver disease. A diagnosis of Fasciola infection was made based on strongly positive serology in blood and cerebrospinal fluid. The patient improved following treatment with triclabendazole and prednisolone.

Perception of a complex visual scene requires that important regions be prioritized and attentionally selected. What is the basis for this selection? Although much research has focused on the spatial distribution of image salience as an important factor guiding attention, relatively little work has focused on the spatial distribution of semantic features (meaning) across a scene. To address this imbalance, we have recently developed a new method for measuring, representing, and evaluating the spatial distribution of meaning in scenes and its influence on attention. In this method, the spatial distribution of meaning is represented as a meaning map. Meaning maps are generated from crowd-sourced responses given by naive subjects who rate a large number of scene patches drawn from each scene. Meaning maps are coded in the same format as traditional saliency maps, and therefore both types of maps can be directly compared and evaluated against the spatial distribution of attention derived from viewers' eye fixations. In this review I provide an overview of my lab's research comparing the influences of meaning and image salience on attentional guidance in real-world scenes. Overall, we have found that both the spatial distribution of meaning and physical salience across a scene predict the spatial distribution of attention, but when the correlation between meaning and image salience is statistically controlled, only meaning uniquely accounts for variance in attention. I discuss the theoretical implications of these findings and point to new questions for the future.