Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc

Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpreta...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Stuit, S. M. [verfasserIn] Paffen, C. L. E. Van der Stigchel, S.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Image Statistics Machine Learning Toolbox Matlab

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Behavior research methods, instruments & computers - Austin, Tex. : Psychonomic Society Publ., 1984, 54(2021), 5 vom: 16. Dez., Seite 2422-2432
Übergeordnetes Werk:	volume:54 ; year:2021 ; number:5 ; day:16 ; month:12 ; pages:2422-2432

Links:	Volltext

DOI / URN:	10.3758/s13428-021-01737-9

Katalog-ID:	SPR048391093

Internformat


LEADER	01000caa a22002652 4500
001	SPR048391093
003	DE-627
005	20230509114116.0
007	cr uuu---uuuuu
008	221019s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3758/s13428-021-01737-9 \|2 doi
035			\|a (DE-627)SPR048391093
035			\|a (SPR)s13428-021-01737-9-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	1		\|a Stuit, S. M. \|e verfasserin \|0 (orcid)0000-0003-3891-2171 \|4 aut
245	1	0	\|a Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2021
520			\|a Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example.
650		4	\|a Image Statistics \|7 (dpeaa)DE-He213
650		4	\|a Machine Learning \|7 (dpeaa)DE-He213
650		4	\|a Toolbox \|7 (dpeaa)DE-He213
650		4	\|a Matlab \|7 (dpeaa)DE-He213
700	1		\|a Paffen, C. L. E. \|4 aut
700	1		\|a Van der Stigchel, S. \|4 aut
773	0	8	\|i Enthalten in \|t Behavior research methods, instruments & computers \|d Austin, Tex. : Psychonomic Society Publ., 1984 \|g 54(2021), 5 vom: 16. Dez., Seite 2422-2432 \|w (DE-627)32998067X \|w (DE-600)2048669-8 \|x 1532-5970 \|7 nnns
773	1	8	\|g volume:54 \|g year:2021 \|g number:5 \|g day:16 \|g month:12 \|g pages:2422-2432
856	4	0	\|u https://dx.doi.org/10.3758/s13428-021-01737-9 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_224
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2014
951			\|a AR
952			\|d 54 \|j 2021 \|e 5 \|b 16 \|c 12 \|h 2422-2432

Indexfelder

author_variant	s m s sm sms c l e p cle clep d s s v dss dssv
matchkey_str	article:15325970:2021----::nrdcnterttpcltmlshrcei
hierarchy_sort_str	2021
publishDate	2021
allfields	10.3758/s13428-021-01737-9 doi (DE-627)SPR048391093 (SPR)s13428-021-01737-9-e DE-627 ger DE-627 rakwb eng Stuit, S. M. verfasserin (orcid)0000-0003-3891-2171 aut Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. Image Statistics (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Toolbox (dpeaa)DE-He213 Matlab (dpeaa)DE-He213 Paffen, C. L. E. aut Van der Stigchel, S. aut Enthalten in Behavior research methods, instruments & computers Austin, Tex. : Psychonomic Society Publ., 1984 54(2021), 5 vom: 16. Dez., Seite 2422-2432 (DE-627)32998067X (DE-600)2048669-8 1532-5970 nnns volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432 https://dx.doi.org/10.3758/s13428-021-01737-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 AR 54 2021 5 16 12 2422-2432
spelling	10.3758/s13428-021-01737-9 doi (DE-627)SPR048391093 (SPR)s13428-021-01737-9-e DE-627 ger DE-627 rakwb eng Stuit, S. M. verfasserin (orcid)0000-0003-3891-2171 aut Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. Image Statistics (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Toolbox (dpeaa)DE-He213 Matlab (dpeaa)DE-He213 Paffen, C. L. E. aut Van der Stigchel, S. aut Enthalten in Behavior research methods, instruments & computers Austin, Tex. : Psychonomic Society Publ., 1984 54(2021), 5 vom: 16. Dez., Seite 2422-2432 (DE-627)32998067X (DE-600)2048669-8 1532-5970 nnns volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432 https://dx.doi.org/10.3758/s13428-021-01737-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 AR 54 2021 5 16 12 2422-2432
allfields_unstemmed	10.3758/s13428-021-01737-9 doi (DE-627)SPR048391093 (SPR)s13428-021-01737-9-e DE-627 ger DE-627 rakwb eng Stuit, S. M. verfasserin (orcid)0000-0003-3891-2171 aut Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. Image Statistics (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Toolbox (dpeaa)DE-He213 Matlab (dpeaa)DE-He213 Paffen, C. L. E. aut Van der Stigchel, S. aut Enthalten in Behavior research methods, instruments & computers Austin, Tex. : Psychonomic Society Publ., 1984 54(2021), 5 vom: 16. Dez., Seite 2422-2432 (DE-627)32998067X (DE-600)2048669-8 1532-5970 nnns volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432 https://dx.doi.org/10.3758/s13428-021-01737-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 AR 54 2021 5 16 12 2422-2432
allfieldsGer	10.3758/s13428-021-01737-9 doi (DE-627)SPR048391093 (SPR)s13428-021-01737-9-e DE-627 ger DE-627 rakwb eng Stuit, S. M. verfasserin (orcid)0000-0003-3891-2171 aut Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. Image Statistics (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Toolbox (dpeaa)DE-He213 Matlab (dpeaa)DE-He213 Paffen, C. L. E. aut Van der Stigchel, S. aut Enthalten in Behavior research methods, instruments & computers Austin, Tex. : Psychonomic Society Publ., 1984 54(2021), 5 vom: 16. Dez., Seite 2422-2432 (DE-627)32998067X (DE-600)2048669-8 1532-5970 nnns volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432 https://dx.doi.org/10.3758/s13428-021-01737-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 AR 54 2021 5 16 12 2422-2432
allfieldsSound	10.3758/s13428-021-01737-9 doi (DE-627)SPR048391093 (SPR)s13428-021-01737-9-e DE-627 ger DE-627 rakwb eng Stuit, S. M. verfasserin (orcid)0000-0003-3891-2171 aut Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. Image Statistics (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Toolbox (dpeaa)DE-He213 Matlab (dpeaa)DE-He213 Paffen, C. L. E. aut Van der Stigchel, S. aut Enthalten in Behavior research methods, instruments & computers Austin, Tex. : Psychonomic Society Publ., 1984 54(2021), 5 vom: 16. Dez., Seite 2422-2432 (DE-627)32998067X (DE-600)2048669-8 1532-5970 nnns volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432 https://dx.doi.org/10.3758/s13428-021-01737-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 AR 54 2021 5 16 12 2422-2432
language	English
source	Enthalten in Behavior research methods, instruments & computers 54(2021), 5 vom: 16. Dez., Seite 2422-2432 volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432
sourceStr	Enthalten in Behavior research methods, instruments & computers 54(2021), 5 vom: 16. Dez., Seite 2422-2432 volume:54 year:2021 number:5 day:16 month:12 pages:2422-2432
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image Statistics Machine Learning Toolbox Matlab
isfreeaccess_bool	true
container_title	Behavior research methods, instruments & computers
authorswithroles_txt_mv	Stuit, S. M. @@aut@@ Paffen, C. L. E. @@aut@@ Van der Stigchel, S. @@aut@@
publishDateDaySort_date	2021-12-16T00:00:00Z
hierarchy_top_id	32998067X
id	SPR048391093
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR048391093</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509114116.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221019s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3758/s13428-021-01737-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048391093</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13428-021-01737-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Stuit, S. M.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3891-2171</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image Statistics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine Learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Toolbox</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Matlab</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Paffen, C. L. E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Van der Stigchel, S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Behavior research methods, instruments & computers</subfield><subfield code="d">Austin, Tex. : Psychonomic Society Publ., 1984</subfield><subfield code="g">54(2021), 5 vom: 16. Dez., Seite 2422-2432</subfield><subfield code="w">(DE-627)32998067X</subfield><subfield code="w">(DE-600)2048669-8</subfield><subfield code="x">1532-5970</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:54</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:5</subfield><subfield code="g">day:16</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:2422-2432</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.3758/s13428-021-01737-9</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">54</subfield><subfield code="j">2021</subfield><subfield code="e">5</subfield><subfield code="b">16</subfield><subfield code="c">12</subfield><subfield code="h">2422-2432</subfield></datafield></record></collection>
author	Stuit, S. M.
spellingShingle	Stuit, S. M. misc Image Statistics misc Machine Learning misc Toolbox misc Matlab Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc
authorStr	Stuit, S. M.
ppnlink_with_tag_str_mv	@@773@@(DE-627)32998067X
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1532-5970
topic_title	Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc Image Statistics (dpeaa)DE-He213 Machine Learning (dpeaa)DE-He213 Toolbox (dpeaa)DE-He213 Matlab (dpeaa)DE-He213
topic	misc Image Statistics misc Machine Learning misc Toolbox misc Matlab
topic_unstemmed	misc Image Statistics misc Machine Learning misc Toolbox misc Matlab
topic_browse	misc Image Statistics misc Machine Learning misc Toolbox misc Matlab
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Behavior research methods, instruments & computers
hierarchy_parent_id	32998067X
hierarchy_top_title	Behavior research methods, instruments & computers
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)32998067X (DE-600)2048669-8
title	Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc
ctrlnum	(DE-627)SPR048391093 (SPR)s13428-021-01737-9-e
title_full	Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc
author_sort	Stuit, S. M.
journal	Behavior research methods, instruments & computers
journalStr	Behavior research methods, instruments & computers
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
container_start_page	2422
author_browse	Stuit, S. M. Paffen, C. L. E. Van der Stigchel, S.
container_volume	54
format_se	Elektronische Aufsätze
author-letter	Stuit, S. M.
doi_str_mv	10.3758/s13428-021-01737-9
normlink	(ORCID)0000-0003-3891-2171
normlink_prefix_str_mv	(orcid)0000-0003-3891-2171
title_sort	introducing the prototypical stimulus characteristics toolbox: protosc
title_auth	Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc
abstract	Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. © The Author(s) 2021
abstractGer	Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. © The Author(s) 2021
abstract_unstemmed	Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example. © The Author(s) 2021
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014
container_issue	5
title_short	Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc
url	https://dx.doi.org/10.3758/s13428-021-01737-9
remote_bool	true
author2	Paffen, C. L. E. Van der Stigchel, S.
author2Str	Paffen, C. L. E. Van der Stigchel, S.
ppnlink	32998067X
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3758/s13428-021-01737-9
up_date	2024-07-03T18:54:22.857Z
_version_	1803585179206287360
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR048391093</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509114116.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221019s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3758/s13428-021-01737-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048391093</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13428-021-01737-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Stuit, S. M.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3891-2171</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Many studies use different categories of images to define their conditions. Since any difference between these categories is a valid candidate to explain category-related behavioral differences, knowledge about the objective image differences between categories is crucial for the interpretation of the behaviors. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning performance as a tool, that have predictive value over the category the images belong to. In other words, we describe a means to find the features of a group of images by which the categories can be objectively and quantitatively defined. Note that we are not aiming to provide a means for the best possible decoding performance; instead, our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based toolbox that performs such an analysis and aids the user in visualizing the features of relevance. We first applied the toolbox to a mock data set with a ground truth to show the sensitivity of the approach. Next, we applied the toolbox to a set of natural images as a more practical example.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image Statistics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine Learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Toolbox</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Matlab</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Paffen, C. L. E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Van der Stigchel, S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Behavior research methods, instruments & computers</subfield><subfield code="d">Austin, Tex. : Psychonomic Society Publ., 1984</subfield><subfield code="g">54(2021), 5 vom: 16. Dez., Seite 2422-2432</subfield><subfield code="w">(DE-627)32998067X</subfield><subfield code="w">(DE-600)2048669-8</subfield><subfield code="x">1532-5970</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:54</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:5</subfield><subfield code="g">day:16</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:2422-2432</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.3758/s13428-021-01737-9</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">54</subfield><subfield code="j">2021</subfield><subfield code="e">5</subfield><subfield code="b">16</subfield><subfield code="c">12</subfield><subfield code="h">2422-2432</subfield></datafield></record></collection>
score	7.39966

Nicht das Richtige dabei?

Schreiben Sie uns!

Introducing the Prototypical Stimulus Characteristics Toolbox: Protosc

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?