Get Your Guidance Going: Investigating the Activation of Spatial Priors for Efficient Search in Virtual Reality

Scene semantics affects allocentric spatial coding for action in naturalistic (virtual) environments

Interacting with objects in our environment requires determining their locations, often with respect to surrounding objects (i.e., allocentrically). According to the scene grammar framework, these usually small, local objects are movable within a scene and represent the lowest level of a scene's hierarchy. How do higher hierarchical levels of scene grammar influence allocentric coding for memory-guided actions? Here, we focused on the effect of large, immovable objects (anchors) on the encoding of local object positions. In a virtual reality study, participants (n = 30) viewed one of four possible scenes (two kitchens or two bathrooms), with two anchors connected by a shelf, onto which were presented three local objects (congruent with one anchor) (Encoding). The scene was re-presented (Test) with 1) local objects missing and 2) one of the anchors shifted (Shift) or not (No shift). Participants, then, saw a floating local object (target), which they grabbed and placed back on the shelf in its remembered position (Response). Eye-tracking data revealed that both local objects and anchors were fixated, with preference for local objects. Additionally, anchors guided allocentric coding of local objects, despite being task-irrelevant. Overall, anchors implicitly influence spatial coding of local object locations for memory-guided actions within naturalistic (virtual) environments.

Where was this thing again? Evaluating methods to indicate remembered object positions in virtual reality

A current focus in sensorimotor research is the study of human perception and action in increasingly naturalistic tasks and visual environments. This is further enabled by the recent commercial success of virtual reality (VR) technology, which allows for highly realistic but well-controlled three-dimensional (3D) scenes. VR enables a multitude of different ways to interact with virtual objects, but only rarely are such interaction techniques evaluated and compared before being selected for a sensorimotor experiment. Here, we compare different response techniques for a memory-guided action task, in which participants indicated the position of a previously seen 3D object in a VR scene: pointing, using a virtual laser pointer of short or unlimited length, and placing, either the target object itself or a generic reference cube. Response techniques differed in availability of 3D object cues and requirement to physically move to the remembered object position by walking. Object placement was the most accurate but slowest due to repeated repositioning. When placing objects, participants tended to match the original object's orientation. In contrast, the laser pointer was fastest but least accurate, with the short pointer showing a good speed-accuracy compromise. Our findings can help researchers in selecting appropriate methods when studying naturalistic visuomotor behavior in virtual environments.

Using a flashlight-contingent window paradigm to investigate visual search and object memory in virtual reality and on computer screens

A popular technique to modulate visual input during search is to use gaze-contingent windows. However, these are often rather discomforting, providing the impression of visual impairment. To counteract this, we asked participants in this study to search through illuminated as well as dark three-dimensional scenes using a more naturalistic flashlight with which they could illuminate the rooms. In a surprise incidental memory task, we tested the identities and locations of objects encountered during search. Importantly, we tested this study design in both immersive virtual reality (VR; Experiment 1) and on a desktop-computer screen (Experiment 2). As hypothesized, searching with a flashlight increased search difficulty and memory usage during search. We found a memory benefit for identities of distractors in the flashlight condition in VR but not in the computer screen experiment. Surprisingly, location memory was comparable across search conditions despite the enormous difference in visual input. Subtle differences across experiments only appeared in VR after accounting for previous recognition performance, hinting at a benefit of flashlight search in VR. Our findings highlight that removing visual information does not necessarily impair location memory, and that screen experiments using virtual environments can elicit the same major effects as VR setups.

PTVR - A software in Python to make virtual reality experiments easier to build and more reproducible

Researchers increasingly use virtual reality (VR) to perform behavioral experiments, especially in vision science. These experiments are usually programmed directly in so-called game engines that are extremely powerful. However, this process is tricky and time-consuming as it requires solid knowledge of game engines. Consequently, the anticipated prohibitive effort discourages many researchers who want to engage in VR. This paper introduces the Perception Toolbox for Virtual Reality (PTVR) library, allowing visual perception studies in VR to be created using high-level Python script programming. A crucial consequence of using a script is that an experiment can be described by a single, easy-to-read piece of code, thus improving VR studies' transparency, reproducibility, and reusability. We built our library upon a seminal open-source library released in 2018 that we have considerably developed since then. This paper aims to provide a comprehensive overview of the PTVR software for the first time. We introduce the main objects and features of PTVR and some general concepts related to the three-dimensional (3D) world. This new library should dramatically reduce the difficulty of programming experiments in VR and elicit a whole new set of visual perception studies with high ecological validity.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

The impact of different distractions on outdoor visual search and object memory

We investigated whether and how different types of search distractions affect visual search behavior and target memory while participants searched in a real-world environment. They searched either undistracted (control condition), listened to a podcast (auditory distraction), counted down aloud at intervals of three while searching (executive working memory load), or were forced to stop the search on half of the trials (time pressure). In line with findings from laboratory settings, participants searched longer but made fewer errors when the target was absent than when it was present, regardless of distraction condition. Furthermore, compared to the auditory distraction condition, the executive working memory load led to higher error rates (but not longer search times). In a surprise memory test after the end of the search tasks, recognition was better for previously present targets than for absent targets. Again, this was regardless of the previous distraction condition, although significantly fewer targets were remembered by the participants in the executive working memory load condition than by those in the control condition. The findings suggest that executive working memory load, but likely not auditory distraction and time pressure affected visual search performance and target memory in a real-world environment.

Good-enough attentional guidance

Theories of attention posit that attentional guidance operates on information held in a target template within memory. The template is often thought to contain veridical target features, akin to a photograph, and to guide attention to objects that match the exact target features. However, recent evidence suggests that attentional guidance is highly flexible and often guided by non-veridical features, a subset of features, or only associated features. We integrate these findings and propose that attentional guidance maximizes search efficiency based on a 'good-enough' principle to rapidly localize candidate target objects. Candidates are then serially interrogated to make target-match decisions using more precise information. We suggest that good-enough guidance optimizes the speed-accuracy-effort trade-offs inherent in each stage of visual search.

Eye and head movements while encoding and recognizing panoramic scenes in virtual reality

One approach to studying the recognition of scenes and objects relies on the comparison of eye movement patterns during encoding and recognition. Past studies typically analyzed the perception of flat stimuli of limited extent presented on a computer monitor that did not require head movements. In contrast, participants in the present study saw omnidirectional panoramic scenes through an immersive 3D virtual reality viewer, and they could move their head freely to inspect different parts of the visual scenes. This allowed us to examine how unconstrained observers use their head and eyes to encode and recognize visual scenes. By studying head and eye movement within a fully immersive environment, and applying cross-recurrence analysis, we found that eye movements are strongly influenced by the content of the visual environment, as are head movements-though to a much lesser degree. Moreover, we found that the head and eyes are linked, with the head supporting, and by and large mirroring the movements of the eyes, consistent with the notion that the head operates to support the acquisition of visual information by the eyes.

Active visual search in naturalistic environments reflects individual differences in classic visual search performance

Visual search is a ubiquitous activity in real-world environments. Yet, traditionally, visual search is investigated in tightly controlled paradigms, where head-restricted participants locate a minimalistic target in a cluttered array that is presented on a computer screen. Do traditional visual search tasks predict performance in naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality technology to test the degree to which classic and naturalistic search are limited by a common factor, set size, and the degree to which individual differences in classic search behavior predict naturalistic search behavior in a large sample of individuals (N = 75). In a naturalistic search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic search task, participants searched for a target within a simple array of colored letters using only eye-movements. In each task, we found that participants' search performance was impacted by increases in set size-the number of items in the visual display. Critically, we observed that participants' efficiency in classic search tasks-the degree to which set size slowed performance-indeed predicted efficiency in real-world scenes. These results demonstrate that classic, computer-based visual search tasks are excellent models of active, real-world search behavior.

Context matters during pick-and-place in VR: Impact on search and transport phases

When considering external assistive systems for people with motor impairments, gaze has been shown to be a powerful tool as it is anticipatory to motor actions and is promising for understanding intentions of an individual even before the action. Up until now, the vast majority of studies investigating the coordinated eye and hand movement in a grasping task focused on single objects manipulation without placing them in a meaningful scene. Very little is known about the impact of the scene context on how we manipulate objects in an interactive task. In the present study, it was investigated how the scene context affects human object manipulation in a pick-and-place task in a realistic scenario implemented in VR. During the experiment, participants were instructed to find the target object in a room, pick it up, and transport it to a predefined final location. Thereafter, the impact of the scene context on different stages of the task was examined using head and hand movement, as well as eye tracking. As the main result, the scene context had a significant effect on the search and transport phases, but not on the reach phase of the task. The present work provides insights into the development of potential supporting intention predicting systems, revealing the dynamics of the pick-and-place task behavior once it is realized in a realistic context-rich scenario.

Auxiliary Scene-Context Information Provided by Anchor Objects Guides Attention and Locomotion in Natural Search Behavior

Successful adaptive behavior requires efficient attentional and locomotive systems. Previous research has thoroughly investigated how we achieve this efficiency during natural behavior by exploiting prior knowledge related to targets of our actions (e.g., attending to metallic targets when looking for a pot) and to the environmental context (e.g., looking for the pot in the kitchen). Less is known about whether and how individual nontarget components of the environment support natural behavior. In our immersive virtual reality task, 24 adult participants searched for objects in naturalistic scenes in which we manipulated the presence and arrangement of large, static objects that anchor predictions about targets (e.g., the sink provides a prediction for the location of the soap). Our results show that gaze and body movements in this naturalistic setting are strongly guided by these anchors. These findings demonstrate that objects auxiliary to the target are incorporated into the representations guiding attention and locomotion.

The importance of peripheral vision when searching 3D real-world scenes: A gaze-contingent study in virtual reality

Visual search in natural scenes is a complex task relying on peripheral vision to detect potential targets and central vision to verify them. The segregation of the visual fields has been particularly established by on-screen experiments. We conducted a gaze-contingent experiment in virtual reality in order to test how the perceived roles of central and peripheral visions translated to more natural settings. The use of everyday scenes in virtual reality allowed us to study visual attention by implementing a fairly ecological protocol that cannot be implemented in the real world. Central or peripheral vision was masked during visual search, with target objects selected according to scene semantic rules. Analyzing the resulting search behavior, we found that target objects that were not spatially constrained to a probable location within the scene impacted search measures negatively. Our results diverge from on-screen studies in that search performances were only slightly affected by central vision loss. In particular, a central mask did not impact verification times when the target was grammatically constrained to an anchor object. Our findings demonstrates that the role of central vision (up to 6 degrees of eccentricities) in identifying objects in natural scenes seems to be minor, while the role of peripheral preprocessing of targets in immersive real-world searches may have been underestimated by on-screen experiments.