Re: [AUDITORY] SDT with continuous ratings ("Oberfeld-Twistel, Daniel" )

Subject: Re: [AUDITORY] SDT with continuous ratings From: "Oberfeld-Twistel, Daniel" <oberfeld@xxxxxxxx> Date: Fri, 23 Feb 2018 08:08:44 +0000 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY> --_000_ac7eaa4ed5fa45ba9e1641379e4fd097unimainzde_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Samuel, (quasi) continuous ratings are used a lot in radiology - you could use this= paper as a starting point: Wagner, R. F., Beiden, S. V., & Metz, C. E. (2001). Continuous versus categ= orical data for ROC analysis: Some quantitative considerations. Academic Ra= diology, 8(4), 328-334. doi: 10.1016/s1076-6332(03)80502-0 Best Daniel --------------------------------- Dr. Daniel Oberfeld-Twistel Associate Professor Johannes Gutenberg - Universitaet Mainz Institute of Psychology Experimental Psychology Wallstrasse 3 55122 Mainz Germany Phone ++49 (0) 6131 39 39274 Fax ++49 (0) 6131 39 39268 http://www.staff.uni-mainz.de/oberfeld/ https://www.facebook.com/WahrnehmungUndPsychophysikUniMainz From: AUDITORY - Research in Auditory Perception [mailto:AUDITORY@xxxxxxxx= ILL.CA] On Behalf Of Samuel Mathias Sent: Thursday, February 22, 2018 1:41 PM To: AUDITORY@xxxxxxxx Subject: SDT with continuous ratings Dear list, does anyone know of any work that has formally applied signal de= tection theory or a related framework to continuous confidence ratings? By = continuous, I mean a rating scale that is not discretized. For example, say= we have a recognition memory task in which subjects are presented with old= or new stimuli. The discretized version might have response options "defin= itely old", "maybe old", "maybe new" "definitely new", whereas the continuo= us version might require subjects to use a slider going from 0 to 1. By for= mally, I mean that the model was actually fitted to the continuous ratings = and not to the data after artificially discretizing them, for example into = quartiles. -- Samuel R. Mathias, Ph.D. Associate Research Scientist (ARS) Neurocognition, Neurocomputation and Neurogenetics (n3) Division Yale University School of Medicine 40 Temple Street, Room 694 New Haven CT 06510 http://www.srmathias.com --_000_ac7eaa4ed5fa45ba9e1641379e4fd097unimainzde_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable <html xmlns:v=3D"urn:schemas-microsoft-com:vml" xmlns:o=3D"urn:schemas-micr= osoft-com:office:office" xmlns:w=3D"urn:schemas-microsoft-com:office:word" = xmlns:m=3D"http://schemas.microsoft.com/office/2004/12/omml" xmlns=3D"http:= //www.w3.org/TR/REC-html40"> <head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Dus-ascii"= > <meta name=3D"Generator" content=3D"Microsoft Word 15 (filtered medium)"> <style></style> </head> <body lang=3D"DE" link=3D"blue" vlink=3D"purple"> <div class=3D"WordSection1"> = Hi Samuel,<o:p></o= :p> = <o:p> </o:p><= /span> = (quasi) continuous= ratings are used a lot in radiology – you could use this paper as a starting point:<o:p></o:p> = <o:p> </o:p><= /span> <div> Wagner, R. F., B= eiden, S. V., & Metz, C. E. (2001). Continuous= versus categorical data for ROC analysis: Some quantitative considerations= . Acad= emic Radiology, 8<span= style=3D"font-size:9.0pt;font-family:"Segoe UI",sans-serif">(4), 328-334. doi: 10.1016/s1076-6332(03)80502-0<o:p></o:p> = <o:p> </o:p> = Best<o:p></o:p> = <o:p> </o:p> = Daniel <o:p></o:p> = <o:p> </o:p> = ---------------------------------<o:p></o:p></= p> = Dr. Daniel Oberfeld-Twistel<o:p></o:p>= = Associate Professor<o:p></o:p><= /p> = Johannes Gutenberg - Universitaet Mainz<o:p></o:p></f= ont> = Institute of Psychology<o:p></o:p> = Experimental Psychology<o:p></o:p> = Wallstrasse 3<o:p></o:p> = 55122 Mainz<o:p></o:p> = Germany<o:p></o:p> = <o:p> </o:p> = Phone ++49 (0) 6131 39 39274 <o:p></o:p> = Fax   ++49 (0) 6131 39 39268<o:p></o:p></s= pan> = http://www.staff.uni-mainz.de/oberfeld/<o:p></o:p></f= ont> = https://www.facebook.com/WahrnehmungUndPsychophysikUniMainz<= o:p></o:p> </div> = <o:p> </o:p></= p> <div style=3D"border:none;border-left:solid blue 1.5pt;padding:0cm 0cm 0cm = 4.0pt"> <div> <div style=3D"border:none;border-top:solid #E1E1E1 1.0pt;padding:3.0pt 0cm = 0cm 0cm"> From: AUDITORY - Research in Auditory Perception [mailto:AUDITORY@xxxxxxxx <sp= an style=3D"font-weight:bold">On Behalf Of Samuel Mathias Sent: Thursday, February 22,= 2018 1:41 PM To: AUDITORY@xxxxxxxx= Subject: SDT with continuous= ratings<o:p></o:p> </div> </div> <o:p> </o:p> <div id=3D"compose-container"> <div> <div> Dear list, does anyone know of any work that has for= mally applied signal detection theory or a related framework to continuous = confidence ratings? By continuous, I mean a rating scale that is not discretized. For example, say we have a recogni= tion memory task in which subjects are presented with old or new stimuli. T= he discretized version might have response options “definitely old&#8= 221;, “maybe old”, “maybe new” “definitely ne= w”, whereas the continuous version might require subjects to use a slider goin= g from 0 to 1. By formally, I mean that the model was actually fitted to th= e continuous ratings and not to the data after artificially discretizing th= em, for example into quartiles.<o:p></o:p> </div> <div> <o:p> </o:p> </div> <div> <div> --<o:p></o:p> </div> <div> Samuel R. Mathias, Ph.D.<o:p></o:p></p= > </div> <div> Associate Research Scientist (ARS)<o:p></o:p>= </div> <div> Neurocognition, Neurocomputation and Neurogenetics (= n3) Division<o:p></o:p> </div> <div> Yale University School of Medicine<o:p></o:p>= </div> <div> 40 Temple Street, Room 694<o:p></o:p><= /p> </div> <div> New Haven CT 06510<o:p></o:p> </div> <div> <a href=3D"http://www.srmathias.com">http://www.srma= thias.com</a><o:p></o:p> </div> </div> </div> </div> </div> </div> </body> </html> --_000_ac7eaa4ed5fa45ba9e1641379e4fd097unimainzde_--

This message came from the mail archive
../postings/2018/
maintained by:

DAn Ellis <dpwe@ee.columbia.edu>
Electrical Engineering Dept., Columbia University