Evidence map›Paper›PMID 36753556›Full record

ArticleScience advances2023

From scanner to court: A neuroscientifically informed "reasonable person" test of trademark infringement.

Zhihao Zhang, Maxwell Good, Vera Kulikov, Femke van Horen, Mark Bartholomew, Andrew S Kayser, Ming Hsu

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zhihao ZhangDarden School of Business, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0003-2643-6841
Maxwell GoodHaas School of Business, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0001-9573-9278
Vera KulikovHaas School of Business, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0001-6598-3843
Femke van HorenSchool of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.ORCID 0000-0001-8887-2695
Mark BartholomewSchool of Law, State University of New York at Buffalo, Buffalo, NY, USA.
Andrew S KayserHelen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0001-5102-2136
Ming HsuHaas School of Business, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0001-7195-3579

Funding

Higher-Order Visual Decision Making: Networks and MechanismsR01EY024554 · NEI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KAYSER, ANDREW S · 2014 to 2018
$1.7M
NEI NIH HHS R01 EY024554
6 · The paper itself

Abstract

Many legal decisions center on the thoughts or perceptions of some idealized group of individuals, referred to variously as the "average person," "the typical consumer," or the "reasonable person." Substantial concerns exist, however, regarding the subjectivity and vulnerability to biases inherent in conventional means of assessing such responses, particularly the use of self-report evidence. Here, we addressed these concerns by complementing self-report evidence with neural data to inform the mental representations in question. Using an example from intellectual property law, we demonstrate that it is possible to construct a parsimonious neural index of visual similarity that can inform the reasonable person test of trademark infringement. Moreover, when aggregated across multiple participants, this index was able to detect experimenter-induced biases in self-report surveys in a sensitive and replicable fashion. Together, these findings potentially broaden the possibilities for neuroscientific data to inform legal decision-making across a range of settings.

Identifiers

PMID36753556
PMCPMC9908014

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.