Literature DB >> 35262897

A practical guide for studying human behavior in the lab.

Joao Barbosa1,2, Heike Stein3,4, Sam Zorowitz5, Yael Niv5,6, Christopher Summerfield7, Salvador Soto-Faraco8, Alexandre Hyafil9.   

Abstract

In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.
© 2022. The Psychonomic Society, Inc.

Entities:  

Keywords:  10 rules; Good practices; Human behavioral experiments; Open science; Study design

Year:  2022        PMID: 35262897     DOI: 10.3758/s13428-022-01793-9

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  59 in total

1.  The staircrase-method in psychophysics.

Authors:  T N CORNSWEET
Journal:  Am J Psychol       Date:  1962-09

2.  Publish your computer code: it is good enough.

Authors:  Nick Barnes
Journal:  Nature       Date:  2010-10-14       Impact factor: 49.962

3.  A power primer.

Authors:  J Cohen
Journal:  Psychol Bull       Date:  1992-07       Impact factor: 17.737

4.  Human-level saccade detection performance using deep neural networks.

Authors:  Marie E Bellet; Joachim Bellet; Hendrikje Nienborg; Ziad M Hafed; Philipp Berens
Journal:  J Neurophysiol       Date:  2018-12-19       Impact factor: 2.714

Review 5.  Power failure: why small sample size undermines the reliability of neuroscience.

Authors:  Katherine S Button; John P A Ioannidis; Claire Mokrysz; Brian A Nosek; Jonathan Flint; Emma S J Robinson; Marcus R Munafò
Journal:  Nat Rev Neurosci       Date:  2013-04-10       Impact factor: 34.870

6.  Adaptive gain control during human perceptual choice.

Authors:  Samuel Cheadle; Valentin Wyart; Konstantinos Tsetsos; Nicholas Myers; Vincent de Gardelle; Santiago Herce Castañón; Christopher Summerfield
Journal:  Neuron       Date:  2014-03-19       Impact factor: 17.173

7.  Adaptable history biases in human perceptual decisions.

Authors:  Arman Abrahamyan; Laura Luz Silva; Steven C Dakin; Matteo Carandini; Justin L Gardner
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-02       Impact factor: 11.205

8.  How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables.

Authors:  Marc Brysbaert
Journal:  J Cogn       Date:  2019-07-19

9.  Power contours: Optimising sample size and precision in experimental psychology and human neuroscience.

Authors:  Daniel H Baker; Greta Vilidaite; Freya A Lygo; Anika K Smith; Tessa R Flack; André D Gouws; Timothy J Andrews
Journal:  Psychol Methods       Date:  2020-07-16
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