Evidence map›Paper›PMID 32269790›Full record

ArticleRoyal Society open science2020

The interaction between map complexity and crowd movement on navigation decisions in virtual reality.

Hantao Zhao, Tyler Thrash, Armin Grossrieder, Mubbasir Kapadia, Mehdi Moussaïd, Christoph Hölscher, Victor R Schinazi

Abstract read
In one paragraph

Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hantao ZhaoChair of Cognitive Science, ETH Zürich, Zürich, Switzerland.ORCID 0000-0003-0398-3842
Tyler ThrashChair of Cognitive Science, ETH Zürich, Zürich, Switzerland.ORCID 0000-0002-3011-7029
Armin GrossriederChair of Cognitive Science, ETH Zürich, Zürich, Switzerland.
Mubbasir KapadiaDepartment of Computer Science, Rutgers University, New Brunswick, NJ, USA.ORCID 0000-0002-3501-0028
Mehdi MoussaïdCenter for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.ORCID 0000-0002-1268-6332
Christoph HölscherChair of Cognitive Science, ETH Zürich, Zürich, Switzerland.ORCID 0000-0002-7520-9146
Victor R SchinaziChair of Cognitive Science, ETH Zürich, Zürich, Switzerland.ORCID 0000-0002-2345-2806

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A carefully designed map can reduce pedestrians' cognitive load during wayfinding and may be an especially useful navigation aid in crowded public environments. In the present paper, we report three studies that investigated the effects of map complexity and crowd movement on wayfinding time, accuracy and hesitation using both online and laboratory-based networked virtual reality (VR) platforms. In the online study, we found that simple map designs led to shorter decision times and higher accuracy compared to complex map designs. In the networked VR set-up, we found that co-present participants made very few errors. In the final VR study, we replayed the traces of participants' avatars from the second study so that they indicated a different direction than the maps. In this scenario, we found an interaction between map design and crowd movement in terms of decision time and the distributions of locations at which participants hesitated. Together, these findings can help the designers of maps for public spaces account for the movements of real crowds.

Indexed as

cartographycrowd-sourcedecision sciencenavigationnetworked virtual environmentvirtual reality

Identifiers

PMID32269790
PMCPMC7137954

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.