Reinforcement Learning In The Acquisition Of Spatial Graph Knowledge: The Impact Of Making Explicit Predictions On Active And Passive Navigation - Info and Reading Options

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Successful navigation in day-to-day activities fundamentally requires the encoding of the spatial layout of one’s surroundings. When arriving at a new location, making decisions about how or where to explore could play a key role in aiding the translation of one’s physical surroundings into an internal spatial representation that can be modified and refined with further navigation experience. We previously found an advantage of active decision-making (vs. being guided through a new environment) in the acquisition of spatial graph knowledge (i.e., a flexible understanding of specific locations within an environment and the path connections between them; Chrastil & Warren, 2015), but not for map-like survey knowledge (Chrastil & Warren, 2013). However, more research is warranted to understand the specific cognitive advantages behind active decision-making in the acquisition of spatial graph knowledge. We theorize that making decisions allows people to make small predictions about what they think they will encounter during exploration, and then get feedback on those decisions. This reinforcement learning-inspired hypothesis suggests that those who are guided could benefit from making explicit predictions. Alternatively, it is possible that simply the act of choosing ones path is the strongest factor in active decision-making. The following study aims to systematically investigate the effects of decision-making on spatial graph learning during spatial exploration by including the factor of making explicit predictions about the locations of salient features in an environment, followed by feedback, and the subsequent reinforcement learning that ensues from this. We examine the interplay between decision-making and reinforcement learning using a between-subjects, 2 (Decision-Making: Active Exploration, Passive Exploration) x 2 (Reinforcement Learning: Making Explicit Predictions and Feedback, Making Selections) Factorial Design throughout an exploration phase, followed by a testing (i.e., wayfinding trials) phase using immersive walking virtual reality with an HTC VIVE Pro Eye VR head-mounted display. First, participants are randomly assigned to one of four exploration phase groups: 1) active exploration, making predictions followed by feedback; 2) active exploration, making selections; 3) passive exploration, making predictions, followed by feedback; 4) passive exploration, making selections. Participants in the explicit prediction and feedback groups will first enter an alcove and only see a 3x3 selection grid (i.e., without seeing the hidden target object that is placed in this alcove). Each cell in the 3x3 grid displays an image and name tag of one of 9 possible target objects located in the maze. Using a handheld controller, these participants will click the controller on one of the 9 cells to predict what object they think is in the alcove. After making their prediction, the selection grid will disappear, and the hidden target object placed in this alcove will be revealed as feedback. Initially, participants may make unsystematic predictions of the target objects’ locations as they explore alcoves for the first time, as there are no explicit cues that would suggest one target object belonging to a certain alcove. Through further exploration of the virtual maze, these participants may begin to make more systematic predictions, which when followed by feedback may improve their learning of the 9 target objects’ locations. Conversely, participants in the selection group will see both a 3x3 selection grid and a visible target object when arriving at alcoves. Rather than making an explicit prediction and receiving feedback, these participants simply select the appropriate cell that matches the presented target object. With regards to decision-making factor, participants in one of the two active exploration conditions can freely explore without any bias or predetermined path, while participants in one of the two passive exploration conditions are yoked (i.e., an experimenter will guide passive participants using a handheld controller on the same exploration trajectory of an active explorer with their respective reinforcement learning condition, allowing them to experience the same visual stimuli). Participants in both passive groups are still instructed to use the 3x3 selection grid to either make predictions and receive feedback or make selections. Participants in all four groups are tasked with locating and learning the locations of 9 target objects for approximately seven minutes. Subsequently, all participants across the exploration groups are tested on the acquisition of their spatial graph knowledge in the test phase. On each trial, participants are instructed to travel from a starting object to a target object using the maze’s hallways, however, all of the objects are turned into red spheres to minimize feedback, so they do not necessarily know if they reached the correct target. There are 36 test trials, with a time limit for each trial. The primary dependent variable of interest is wayfinding success, which is the proportion of correct trials that participants correctly reach the specified target. Overall, reinforcement learning during the encoding of novel spatial environments may facilitate aspects of decision-making and hence represent one of the most critical components of active spatial learning that may contribute to better spatial knowledge acquisition. This knowledge can, in turn, serve as a method for training individuals to improve their navigation ability.

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