Grasping behavior does not recover after sight restoration from congenital blindness.

We investigated whether early visual input is essential for establishing the ability to use predictions in the control of actions and for perception. To successfully interact with objects, it is necessary to pre-program bodily actions such as grasping movements (feedforward control). Feedforward control requires a model for making predictions, which is typically shaped by previous sensory experience and interaction with the environment.1 Vision is the most crucial sense for establishing such predictions.2,3 We typically rely on visual estimations of the to-be-grasped object's size and weight in order to scale grip force and hand aperture accordingly.4,5,6 Size-weight expectations play a role also for perception, as evident in the size-weight illusion (SWI), in which the smaller of two equal-weight objects is misjudged to be heavier.7,8 Here, we investigated predictions for action and perception by testing the development of feedforward controlled grasping and of the SWI in young individuals surgically treated for congenital cataracts several years after birth. Surprisingly, what typically developing individuals do easily within the first years of life, namely to adeptly grasp new objects based on visually predicted properties, cataract-treated individuals did not learn after years of visual experience. Contrary, the SWI exhibited significant development. Even though the two tasks differ in substantial ways, these results may suggest a potential dissociation in using visual experience to make predictions about an object's features for perception or action. What seems a very simple task-picking up small objects-is in truth a highly complex computation that necessitates early structured visual input to develop.

Did it move? Humans use spatio-temporal landmark permanency efficiently for navigation.

Visual landmarks provide crucial information for human navigation. But what characteristics define a landmark? To be uniquely recognized, a landmark should be distinctive and salient, while providing precise and accurate positional information. It should also be permanent. For example, to find back to your car, a nearby church seems a better landmark compared with a distinct truck or bicycle, because you learned that there is a chance that these objects might move. To this end, we investigated human learning of landmark permanency for navigation while treating spatiotemporal permanency as a probabilistic property. We hypothesized that humans will be able to learn the probabilistic nature of landmark permanency and assign higher weight to more permanent landmarks. To test this hypothesis, we designed a homing task where participants had to return to a position that was surrounded by three landmarks. In the learning phase we manipulated the spatiotemporal permanency of one landmark by secretly repositioning it before participants returned home. In the test phase, we investigated the weight allocated to the nonpermanent landmark by analyzing its influence on the navigational performance during homing. We conducted four experiments: In the first two experiments we altered the statistics of permanency and accordingly found an influence on participants' behavior, nonpermanent objects were used less for finding home. In the last two experiments we investigated the role of short-term learning of novel statistics versus long-term knowledge about such statistics. No carry-over effects in Experiment 3 and very little influence of object identity with different long-term permanency characteristics in Experiment 4 revealed a dominance of short-term learning over the use of long-term a priori knowledge about object permanency. This indicates that long-term prior beliefs are quickly updated by the current permanency statistics. Taken together, consistent with a Bayesian account for navigation these results indicate that humans quickly learn and update the statistics of landmark permanency and use it in an effective way, assigning gradually more weight to the more permanent landmark and making it more important for navigation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

ReActLab: A Custom Framework for Sensorimotor Experiments "in-the-wild".

Over the last few years online platforms for running psychology experiments beyond simple questionnaires and surveys have become increasingly popular. This trend has especially increased after many laboratory facilities had to temporarily avoid in-person data collection following COVID-19-related lockdown regulations. Yet, while offering a valid alternative to in-person experiments in many cases, platforms for online experiments are still not a viable solution for a large part of human-based behavioral research. Two situations in particular pose challenges: First, when the research question requires design features or participant interaction which exceed the customization capability provided by the online platform; and second, when variation among hardware characteristics between participants results in an inadmissible confounding factor. To mitigate the effects of these limitations, we developed ReActLab (Remote Action Laboratory), a framework for programming remote, browser-based experiments using freely available and open-source JavaScript libraries. Since the experiment is run entirely within the browser, our framework allows for portability to any operating system and many devices. In our case, we tested our approach by running experiments using only a specific model of Android tablet. Using ReActLab with this standardized hardware allowed us to optimize our experimental design for our research questions, as well as collect data outside of laboratory facilities without introducing setup variation among participants. In this paper, we describe our framework and show examples of two different experiments carried out with it: one consisting of a visuomotor adaptation task, the other of a visual localization task. Through comparison with results obtained from similar tasks in in-person laboratory settings, we discuss the advantages and limitations for developing browser-based experiments using our framework.

Semantic Entity-Component State Management Techniques to Enhance Software Quality for Multimodal VR-Systems.

Modularity, modifiability, reusability, and API usability are important software qualities that determine the maintainability of software architectures. Virtual, Augmented, and Mixed Reality (VR, AR, MR) systems, modern computer games, as well as interactive human-robot systems often include various dedicated input-, output-, and processing subsystems. These subsystems collectively maintain a real-time simulation of a coherent application state. The resulting interdependencies between individual state representations, mutual state access, overall synchronization, and flow of control implies a conceptual close coupling whereas software quality asks for a decoupling to develop maintainable solutions. This article presents five semantics-based software techniques that address this contradiction: Semantic grounding, code from semantics, grounded actions, semantic queries, and decoupling by semantics. These techniques are applied to extend the well-established entity-component-system (ECS) pattern to overcome some of this pattern's deficits with respect to the implied state access. A walk-through of central implementation aspects of a multimodal (speech and gesture) VR-interface is used to highlight the techniques' benefits. This use-case is chosen as a prototypical example of complex architectures with multiple interacting subsystems found in many VR, AR and MR architectures. Finally, implementation hints are given, lessons learned regarding maintainability pointed-out, and performance implications discussed.