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
Autor*in: |
Donkin, Chris [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2014transfer abstract |
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11 |
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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 |
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Übergeordnetes Werk: |
volume:62 ; year:2014 ; pages:22-32 ; extent:11 |
Links: |
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DOI / URN: |
10.1016/j.jmp.2014.09.006 |
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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. | ||
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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 |
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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 |
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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 |
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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 |
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Cognitive decline and defects in neuroplasticity are reversible in an inducible mouse model expressing pro-aggregant full-length human tau |
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Piéron’s Law is not just an artifact of the response mechanism |
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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. |
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Piéron’s Law is not just an artifact of the response mechanism |
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https://doi.org/10.1016/j.jmp.2014.09.006 |
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