Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.

Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, they cannot accumulate knowledge relevant to the upcoming visual input. Consequently, the influence of such knowledge on how this input is processed remains underexplored. Here, we investigated this influence in the context of gaze control. We used sequences of static film frames arranged in a way that allowed us to compare eye movements to identical frames between two groups: a group that accumulated prior knowledge relevant to the situations depicted in these frames and a group that did not. We used a machine learning approach based on hidden Markov models fitted to individual scanpaths to demonstrate that the gaze patterns from the two groups differed systematically and, thereby, showed that recently accumulated prior knowledge contributes to gaze control. Next, we leveraged the interpretability of hidden Markov models to characterize these differences. Additionally, we report two unexpected and interesting caveats of our approach. Overall, our results highlight the importance of recently acquired prior knowledge for oculomotor control and the potential of hidden Markov models as a tool for investigating it.

Prior knowledge about events depicted in scenes decreases oculomotor exploration.

The visual input that the eyes receive usually contains temporally continuous information about unfolding events. Therefore, humans can accumulate knowledge about their current environment. Typical studies on scene perception, however, involve presenting multiple unrelated images and thereby render this accumulation unnecessary. Our study, instead, facilitated it and explored its effects. Specifically, we investigated how recently-accumulated prior knowledge affects gaze behavior. Participants viewed sequences of static film frames that contained several 'context frames' followed by a 'critical frame'. The context frames showed either events from which the situation depicted in the critical frame naturally followed, or events unrelated to this situation. Therefore, participants viewed identical critical frames while possessing prior knowledge that was either relevant or irrelevant to the frames' content. In the former case, participants' gaze behavior was slightly more exploratory, as revealed by seven gaze characteristics we analyzed. This result demonstrates that recently-gained prior knowledge reduces exploratory eye movements.

Knowledge-driven perceptual organization reshapes information sampling via eye movements.

Humans constantly move their eyes to explore the environment. However, how image-computable features and object representations contribute to eye-movement control is an ongoing debate. Recent developments in object perception indicate a complex relationship between features and object representations, where image-independent object knowledge generates objecthood by reconfiguring how feature space is carved up. Here, we adopt this emerging perspective, asking whether object-oriented eye movements result from gaze being guided by image-computable features, or by the fact that these features are bound into an object representation. We recorded eye movements in response to stimuli that initially appear as meaningless patches but are experienced as coherent objects once relevant object knowledge has been acquired. We demonstrate that fixations on identical images are more object-centered, less dispersed, and more consistent across observers once these images are organized into objects. Gaze guidance also showed a shift from exploratory information sampling to exploitation of object-related image areas. These effects were evident from the first fixations onwards. Importantly, eye movements were not fully determined by knowledge-dependent object representations but were best explained by the integration of these representations with image-computable features. Overall, the results show how information sampling via eye movements is guided by a dynamic interaction between image-computable features and knowledge-driven perceptual organization. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.

Semantic information is important in eye movement control. An important semantic influence on gaze guidance relates to object-scene relationships: objects that are semantically inconsistent with the scene attract more fixations than consistent objects. One interpretation of this effect is that fixations are driven toward inconsistent objects because they are semantically more informative. We tested this explanation using contextualized meaning maps, a method that is based on crowd-sourced ratings to quantify the spatial distribution of context-sensitive "meaning" in images. In Experiment 1, we compared gaze data and contextualized meaning maps for images, in which objects-scene consistency was manipulated. Observers fixated more on inconsistent versus consistent objects. However, contextualized meaning maps did not assign higher meaning to image regions that contained semantic inconsistencies. In Experiment 2, a large number of raters evaluated image-regions, which were deliberately selected for their content and expected meaningfulness. The results suggest that the same scene locations were experienced as slightly less meaningful when they contained inconsistent compared to consistent objects. In summary, we demonstrated that - in the context of our rating task - semantically inconsistent objects are experienced as less meaningful than their consistent counterparts and that contextualized meaning maps do not capture prototypical influences of image meaning on gaze guidance.

There is no evidence that meaning maps capture semantic information relevant to gaze guidance: Reply to Henderson, Hayes, Peacock, and Rehrig (2021).

The concerns raised by Henderson, Hayes, Peacock, and Rehrig (2021) are based on misconceptions of our work. We show that Meaning Maps (MMs) do not predict gaze guidance better than a state-of-the-art saliency model that is based on semantically-neutral, high-level features. We argue that there is therefore no evidence to date that MMs index anything beyond these features. Furthermore, we show that although alterations in meaning cause changes in gaze guidance, MMs fail to capture these alterations. We agree that semantic information is important in the guidance of eye-movements, but the contribution of MMs for understanding its role remains elusive.

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.

Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed to support the hypothesis that meaning rather than image features guides human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II - a deep neural network trained to predict fixations based on high-level features rather than meaning - outperforms MMs. Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images.