Piéron’s Law is not just an artifact of the response mechanism
Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Donkin, Chris [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
11 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau - 2011, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:62 ; year:2014 ; pages:22-32 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jmp.2014.09.006 |
|---|
| Katalog-ID: |
ELV023181966 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV023181966 | ||
| 003 | DE-627 | ||
| 005 | 20230625140904.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2014 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jmp.2014.09.006 |2 doi | |
| 028 | 5 | 2 | |a GBVA2014022000028.pica |
| 035 | |a (DE-627)ELV023181966 | ||
| 035 | |a (ELSEVIER)S0022-2496(14)00062-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 150 |a 510 | |
| 082 | 0 | 4 | |a 150 |q DE-600 |
| 082 | 0 | 4 | |a 510 |q DE-600 |
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 530 |q VZ |
| 084 | |a 52.56 |2 bkl | ||
| 100 | 1 | |a Donkin, Chris |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Piéron’s Law is not just an artifact of the response mechanism |
| 264 | 1 | |c 2014transfer abstract | |
| 300 | |a 11 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. | ||
| 520 | |a Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. | ||
| 650 | 7 | |a Response time |2 Elsevier | |
| 650 | 7 | |a Sequential sampling models |2 Elsevier | |
| 650 | 7 | |a Piéron’s Law |2 Elsevier | |
| 650 | 7 | |a Evidence accumulation models |2 Elsevier | |
| 700 | 1 | |a Van Maanen, Leendert |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Academic Press |t Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |d 2011 |g Orlando, Fla |w (DE-627)ELV010679790 |
| 773 | 1 | 8 | |g volume:62 |g year:2014 |g pages:22-32 |g extent:11 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jmp.2014.09.006 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_40 | ||
| 936 | b | k | |a 52.56 |j Regenerative Energieformen |j alternative Energieformen |q VZ |
| 951 | |a AR | ||
| 952 | |d 62 |j 2014 |h 22-32 |g 11 | ||
| 953 | |2 045F |a 150 | ||
| author_variant |
c d cd |
|---|---|
| matchkey_str |
donkinchrisvanmaanenleendert:2014----:iosaintutnriatfhrs |
| hierarchy_sort_str |
2014transfer abstract |
| bklnumber |
52.56 |
| publishDate |
2014 |
| allfields |
10.1016/j.jmp.2014.09.006 doi GBVA2014022000028.pica (DE-627)ELV023181966 (ELSEVIER)S0022-2496(14)00062-5 DE-627 ger DE-627 rakwb eng 150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl Donkin, Chris verfasserin aut Piéron’s Law is not just an artifact of the response mechanism 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Elsevier Van Maanen, Leendert oth Enthalten in Academic Press Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau 2011 Orlando, Fla (DE-627)ELV010679790 volume:62 year:2014 pages:22-32 extent:11 https://doi.org/10.1016/j.jmp.2014.09.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 52.56 Regenerative Energieformen alternative Energieformen VZ AR 62 2014 22-32 11 045F 150 |
| spelling |
10.1016/j.jmp.2014.09.006 doi GBVA2014022000028.pica (DE-627)ELV023181966 (ELSEVIER)S0022-2496(14)00062-5 DE-627 ger DE-627 rakwb eng 150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl Donkin, Chris verfasserin aut Piéron’s Law is not just an artifact of the response mechanism 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Elsevier Van Maanen, Leendert oth Enthalten in Academic Press Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau 2011 Orlando, Fla (DE-627)ELV010679790 volume:62 year:2014 pages:22-32 extent:11 https://doi.org/10.1016/j.jmp.2014.09.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 52.56 Regenerative Energieformen alternative Energieformen VZ AR 62 2014 22-32 11 045F 150 |
| allfields_unstemmed |
10.1016/j.jmp.2014.09.006 doi GBVA2014022000028.pica (DE-627)ELV023181966 (ELSEVIER)S0022-2496(14)00062-5 DE-627 ger DE-627 rakwb eng 150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl Donkin, Chris verfasserin aut Piéron’s Law is not just an artifact of the response mechanism 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Elsevier Van Maanen, Leendert oth Enthalten in Academic Press Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau 2011 Orlando, Fla (DE-627)ELV010679790 volume:62 year:2014 pages:22-32 extent:11 https://doi.org/10.1016/j.jmp.2014.09.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 52.56 Regenerative Energieformen alternative Energieformen VZ AR 62 2014 22-32 11 045F 150 |
| allfieldsGer |
10.1016/j.jmp.2014.09.006 doi GBVA2014022000028.pica (DE-627)ELV023181966 (ELSEVIER)S0022-2496(14)00062-5 DE-627 ger DE-627 rakwb eng 150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl Donkin, Chris verfasserin aut Piéron’s Law is not just an artifact of the response mechanism 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Elsevier Van Maanen, Leendert oth Enthalten in Academic Press Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau 2011 Orlando, Fla (DE-627)ELV010679790 volume:62 year:2014 pages:22-32 extent:11 https://doi.org/10.1016/j.jmp.2014.09.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 52.56 Regenerative Energieformen alternative Energieformen VZ AR 62 2014 22-32 11 045F 150 |
| allfieldsSound |
10.1016/j.jmp.2014.09.006 doi GBVA2014022000028.pica (DE-627)ELV023181966 (ELSEVIER)S0022-2496(14)00062-5 DE-627 ger DE-627 rakwb eng 150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl Donkin, Chris verfasserin aut Piéron’s Law is not just an artifact of the response mechanism 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Elsevier Van Maanen, Leendert oth Enthalten in Academic Press Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau 2011 Orlando, Fla (DE-627)ELV010679790 volume:62 year:2014 pages:22-32 extent:11 https://doi.org/10.1016/j.jmp.2014.09.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 52.56 Regenerative Energieformen alternative Energieformen VZ AR 62 2014 22-32 11 045F 150 |
| language |
English |
| source |
Enthalten in Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau Orlando, Fla volume:62 year:2014 pages:22-32 extent:11 |
| sourceStr |
Enthalten in Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau Orlando, Fla volume:62 year:2014 pages:22-32 extent:11 |
| format_phy_str_mv |
Article |
| bklname |
Regenerative Energieformen alternative Energieformen |
| institution |
findex.gbv.de |
| topic_facet |
Response time Sequential sampling models Piéron’s Law Evidence accumulation models |
| dewey-raw |
150 |
| isfreeaccess_bool |
false |
| container_title |
Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |
| authorswithroles_txt_mv |
Donkin, Chris @@aut@@ Van Maanen, Leendert @@oth@@ |
| publishDateDaySort_date |
2014-01-01T00:00:00Z |
| hierarchy_top_id |
ELV010679790 |
| dewey-sort |
3150 |
| id |
ELV023181966 |
| 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">ELV023181966</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625140904.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmp.2014.09.006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014022000028.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV023181966</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0022-2496(14)00062-5</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="082" ind1="0" ind2=" "><subfield code="a">150</subfield><subfield code="a">510</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.56</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Donkin, Chris</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Piéron’s Law is not just an artifact of the response mechanism</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Response time</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Sequential sampling models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Piéron’s Law</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Evidence accumulation models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Van Maanen, Leendert</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="t">Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau</subfield><subfield code="d">2011</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV010679790</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:22-32</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmp.2014.09.006</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</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_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.56</subfield><subfield code="j">Regenerative Energieformen</subfield><subfield code="j">alternative Energieformen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">62</subfield><subfield code="j">2014</subfield><subfield code="h">22-32</subfield><subfield code="g">11</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">150</subfield></datafield></record></collection>
|
| author |
Donkin, Chris |
| spellingShingle |
Donkin, Chris ddc 150 ddc 510 ddc 610 ddc 530 bkl 52.56 Elsevier Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Piéron’s Law is not just an artifact of the response mechanism |
| authorStr |
Donkin, Chris |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV010679790 |
| format |
electronic Article |
| dewey-ones |
150 - Psychology 510 - Mathematics 610 - Medicine & health 530 - Physics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl Piéron’s Law is not just an artifact of the response mechanism Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models Elsevier |
| topic |
ddc 150 ddc 510 ddc 610 ddc 530 bkl 52.56 Elsevier Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models |
| topic_unstemmed |
ddc 150 ddc 510 ddc 610 ddc 530 bkl 52.56 Elsevier Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models |
| topic_browse |
ddc 150 ddc 510 ddc 610 ddc 530 bkl 52.56 Elsevier Response time Elsevier Sequential sampling models Elsevier Piéron’s Law Elsevier Evidence accumulation models |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
m l v ml mlv |
| hierarchy_parent_title |
Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |
| hierarchy_parent_id |
ELV010679790 |
| dewey-tens |
150 - Psychology 510 - Mathematics 610 - Medicine & health 530 - Physics |
| hierarchy_top_title |
Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV010679790 |
| title |
Piéron’s Law is not just an artifact of the response mechanism |
| ctrlnum |
(DE-627)ELV023181966 (ELSEVIER)S0022-2496(14)00062-5 |
| title_full |
Piéron’s Law is not just an artifact of the response mechanism |
| author_sort |
Donkin, Chris |
| journal |
Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |
| journalStr |
Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
100 - Philosophy & psychology 500 - Science 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2014 |
| contenttype_str_mv |
zzz |
| container_start_page |
22 |
| author_browse |
Donkin, Chris |
| container_volume |
62 |
| physical |
11 |
| class |
150 510 150 DE-600 510 DE-600 610 VZ 530 VZ 52.56 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Donkin, Chris |
| doi_str_mv |
10.1016/j.jmp.2014.09.006 |
| dewey-full |
150 510 610 530 |
| title_sort |
piéron’s law is not just an artifact of the response mechanism |
| title_auth |
Piéron’s Law is not just an artifact of the response mechanism |
| abstract |
Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. |
| abstractGer |
Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. |
| abstract_unstemmed |
Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 |
| title_short |
Piéron’s Law is not just an artifact of the response mechanism |
| url |
https://doi.org/10.1016/j.jmp.2014.09.006 |
| remote_bool |
true |
| author2 |
Van Maanen, Leendert |
| author2Str |
Van Maanen, Leendert |
| ppnlink |
ELV010679790 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.jmp.2014.09.006 |
| up_date |
2024-07-06T18:13:14.054Z |
| _version_ |
1803854381376864256 |
| 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">ELV023181966</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625140904.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmp.2014.09.006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014022000028.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV023181966</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0022-2496(14)00062-5</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="082" ind1="0" ind2=" "><subfield code="a">150</subfield><subfield code="a">510</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.56</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Donkin, Chris</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Piéron’s Law is not just an artifact of the response mechanism</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Piéron’s Law, the power relation between mean RT and stimulus intensity or discriminability, has historically been understood to reflect a non-linear scaling between objective intensity and perception. More recently, Piéron’s Law was demonstrated to arise out of the architecture of rise-to-threshold decision-making models (Stafford and Gurney, 2004). Here we explicitly tested whether such an explanation would suffice to fit human data, or whether additional assumptions about the nature of perceptual processing are required. We fitted a simple rise-to-threshold model to full RT distributions and choice probabilities from three data sets that show Piéron’s Law. The model assumed that accumulation rate was linearly related to perceptual processing, leaving only the architecture of the model to produce Piéron’s Law. For two data sets, this linear rate model is unable to account for the data, suggesting that Piéron’s Law sometimes reflects additional perceptual scaling information. For the third data set, however, Piéron’s Law does appear to simply arise out of the rise-to-threshold architecture of decision-making models. Our results suggest that it is important to fit models to data in order to draw inference about the causes underlying Piéron’s Law.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Response time</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Sequential sampling models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Piéron’s Law</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Evidence accumulation models</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Van Maanen, Leendert</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="t">Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau</subfield><subfield code="d">2011</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV010679790</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:22-32</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmp.2014.09.006</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</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_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.56</subfield><subfield code="j">Regenerative Energieformen</subfield><subfield code="j">alternative Energieformen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">62</subfield><subfield code="j">2014</subfield><subfield code="h">22-32</subfield><subfield code="g">11</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">150</subfield></datafield></record></collection>
|
| score |
7.401719 |