Location probability learning in 3-dimensional virtual search environments

Research output: Contribution to journalArticlepeer-review

Abstract

When a visual search target frequently appears in one target-rich region of space, participants learn to search there first, resulting in faster reaction time when the target appears there than when it appears elsewhere. Most research on this location probability learning (LPL) effect uses 2-dimensional (2D) search environments that are distinct from real-world search contexts, and the few studies on LPL in 3-dimensional (3D) contexts include complex visual cues or foraging tasks and therefore may not tap into the same habit-like learning mechanism as 2D LPL. The present study aimed to establish a baseline evaluation of LPL in controlled 3D search environments using virtual reality. The use of a virtual 3D search environment allowed us to compare LPL for information within a participant’s initial field of view to LPL for information behind participants, outside of the initial field of view. Participants searched for a letter T on the ground among letter Ls in a large virtual space that was devoid of complex visual cues or landmarks. The T appeared in one target-rich quadrant of the floor space on half of the trials during the training phase. The target-rich quadrant appeared in front of half of the participants and behind the other half. LPL was considerably greater in the former condition than in the latter. This reveals an important constraint on LPL in real-world environments and indicates that consistent search patterns and consistent egocentric spatial coding are essential for this form of visual statistical learning in 3D environments.

Original languageEnglish (US)
Article number21
JournalCognitive Research: Principles and Implications
Volume6
Issue number1
DOIs
StatePublished - Mar 24 2021

Bibliographical note

Funding Information:
CS was supported by the National Science Foundation’s Graduate Research Fellowships Program and NRT Training Grant. This study was supported in part by the Engdahl Family Research Fund. We thank Hunter Schouviller and Julie Jia for help with data collection.

Publisher Copyright:
© 2021, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.

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