SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning.
This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli--cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Comp...
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Public Library of Science (PLoS)
2014
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oai:doaj.org-article:2263064ee6014b4ca468e3c0202cde292021-11-25T06:07:25ZSSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning.1932-620310.1371/journal.pone.0102469https://doaj.org/article/2263064ee6014b4ca468e3c0202cde292014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25054799/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli--cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Compound Stimuli Temporal Difference model (SSCC TD) can model both simultaneous and serial stimulus compounds, as well as compounds including the experimental context. This modification significantly broadens the range of phenomena which the TD paradigm can explain, and allows it to predict phenomena which traditional TD solutions cannot, particularly effects that depend on compound stimuli functioning as a whole, such as pattern learning and serial structural discriminations, and context-related effects.Esther MondragónJonathan GrayEduardo AlonsoCharlotte BonardiDómhnall J JenningsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e102469 (2014) |
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Medicine R Science Q Esther Mondragón Jonathan Gray Eduardo Alonso Charlotte Bonardi Dómhnall J Jennings SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
description |
This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli--cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Compound Stimuli Temporal Difference model (SSCC TD) can model both simultaneous and serial stimulus compounds, as well as compounds including the experimental context. This modification significantly broadens the range of phenomena which the TD paradigm can explain, and allows it to predict phenomena which traditional TD solutions cannot, particularly effects that depend on compound stimuli functioning as a whole, such as pattern learning and serial structural discriminations, and context-related effects. |
format |
article |
author |
Esther Mondragón Jonathan Gray Eduardo Alonso Charlotte Bonardi Dómhnall J Jennings |
author_facet |
Esther Mondragón Jonathan Gray Eduardo Alonso Charlotte Bonardi Dómhnall J Jennings |
author_sort |
Esther Mondragón |
title |
SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
title_short |
SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
title_full |
SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
title_fullStr |
SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
title_full_unstemmed |
SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
title_sort |
sscc td: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2014 |
url |
https://doaj.org/article/2263064ee6014b4ca468e3c0202cde29 |
work_keys_str_mv |
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_version_ |
1718414150991872000 |