Evidence map›Paper›PMID 32979183›Full record

ArticlePsychometrika2020

Flexible Utility Function Approximation via Cubic Bezier Splines.

Sangil Lee, Chris M Glaze, Eric T Bradlow, Joseph W Kable

Abstract read
In one paragraph

Article in Psychometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Sangil LeeDepartment of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA. sangillee3rd@gmail.com.ORCID 0000-0002-4443-9926
Chris M GlazeDepartment of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
Eric T BradlowDepartment of Marketing, Wharton School, University of Pennsylvania, Philadelphia, USA.
Joseph W KableDepartment of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Neuroscience-based Interventions for Cancer Risk Behavior ChangeR35CA197461 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LERMAN, CARYN · 2015 to 2021
$6.0M
Individual, age-dependent differences in ACC-mediated adaptive decision makingR01MH098899 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GOLD, JOSHUA I, KABLE, JOSEPH W · 2012 to 2014
$2.0M
Retraining Neurocognitive Mechanisms of Cancer Risk Behavior (PQ4)R01CA170297 · NCI · UNIVERSITY OF PENNSYLVANIA · PI KABLE, JOSEPH W, LERMAN, CARYN · 2012 to 2015
$2.0M
NCI NIH HHS R01 CA170297NCI NIH HHS R01-CA170297NCI NIH HHS R35 CA197461NCI NIH HHS R35-CA197461NIMH NIH HHS R01 MH098899NIMH NIH HHS R01-MH098899
6 · The paper itself

Abstract

In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals' impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions.

Indexed as

PsychometricsResearch DesignHumansLinear ModelsLogistic Modelsflexible modelinggeneralized utility functionsheterogeneityintertemporal choicerisky choice

Identifiers

PMID32979183
PMCPMC7599200

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.