Behavior Stability and Individual Differences in Pavlovian Extended Conditioning
How stable and general is behavior once maximum learning is reached? To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends associative models of Pavlovian conditioning to a dynamical oscill...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyg.2020.00612/full |
| _version_ | 1818519863949262848 |
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| author | Gianluca Calcagni Ernesto Caballero-Garrido Ricardo Pellón |
| author_facet | Gianluca Calcagni Ernesto Caballero-Garrido Ricardo Pellón |
| author_sort | Gianluca Calcagni |
| collection | DOAJ |
| description | How stable and general is behavior once maximum learning is reached? To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends associative models of Pavlovian conditioning to a dynamical oscillatory model where subjects have a greater memory capacity than usually postulated, but with greater forecast uncertainty. This results in a greater resistance to learning in the first few sessions followed by an over-optimal response peak and a sequence of progressively damped response oscillations. We detected the first peak and trough of the new learning curve in our data, but their dispersion was too large to also check the presence of oscillations with smaller amplitude. We ran an unusually long experiment with 32 rats over 3,960 trials, where we excluded habituation and other well-known phenomena as sources of variability in the subjects' performance. Using the data of this and another Pavlovian experiment by Harris et al. (2015), as an illustration of the principle we tested the theory against the basic associative single-cue Rescorla–Wagner (RW) model. We found evidence that the RW model is the best non-linear regression to data only for a minority of the subjects, while its dynamical extension can explain the almost totality of data with strong to very strong evidence. Finally, an analysis of short-scale fluctuations of individual responses showed that they are described by random white noise, in contrast with the colored-noise findings in human performance. |
| first_indexed | 2024-12-11T01:29:45Z |
| format | Article |
| id | doaj.art-57a1eabca9c849699db30e7d3f3155b3 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-11T01:29:45Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-57a1eabca9c849699db30e7d3f3155b32022-12-22T01:25:23ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-04-011110.3389/fpsyg.2020.00612508495Behavior Stability and Individual Differences in Pavlovian Extended ConditioningGianluca Calcagni0Ernesto Caballero-Garrido1Ricardo Pellón2Instituto de Estructura de la Materia, CSIC, Madrid, SpainNational Association of Researchers, Twenty-First Century, Madrid, SpainFacultad de Psicología, Universidad Nacional de Educación a Distancia (UNED), Madrid, SpainHow stable and general is behavior once maximum learning is reached? To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends associative models of Pavlovian conditioning to a dynamical oscillatory model where subjects have a greater memory capacity than usually postulated, but with greater forecast uncertainty. This results in a greater resistance to learning in the first few sessions followed by an over-optimal response peak and a sequence of progressively damped response oscillations. We detected the first peak and trough of the new learning curve in our data, but their dispersion was too large to also check the presence of oscillations with smaller amplitude. We ran an unusually long experiment with 32 rats over 3,960 trials, where we excluded habituation and other well-known phenomena as sources of variability in the subjects' performance. Using the data of this and another Pavlovian experiment by Harris et al. (2015), as an illustration of the principle we tested the theory against the basic associative single-cue Rescorla–Wagner (RW) model. We found evidence that the RW model is the best non-linear regression to data only for a minority of the subjects, while its dynamical extension can explain the almost totality of data with strong to very strong evidence. Finally, an analysis of short-scale fluctuations of individual responses showed that they are described by random white noise, in contrast with the colored-noise findings in human performance.https://www.frontiersin.org/article/10.3389/fpsyg.2020.00612/fullPavlovian conditioningRescorla–Wagner modelassociative modelsextended trainingindividual differencesBayes information criterion |
| spellingShingle | Gianluca Calcagni Ernesto Caballero-Garrido Ricardo Pellón Behavior Stability and Individual Differences in Pavlovian Extended Conditioning Frontiers in Psychology Pavlovian conditioning Rescorla–Wagner model associative models extended training individual differences Bayes information criterion |
| title | Behavior Stability and Individual Differences in Pavlovian Extended Conditioning |
| title_full | Behavior Stability and Individual Differences in Pavlovian Extended Conditioning |
| title_fullStr | Behavior Stability and Individual Differences in Pavlovian Extended Conditioning |
| title_full_unstemmed | Behavior Stability and Individual Differences in Pavlovian Extended Conditioning |
| title_short | Behavior Stability and Individual Differences in Pavlovian Extended Conditioning |
| title_sort | behavior stability and individual differences in pavlovian extended conditioning |
| topic | Pavlovian conditioning Rescorla–Wagner model associative models extended training individual differences Bayes information criterion |
| url | https://www.frontiersin.org/article/10.3389/fpsyg.2020.00612/full |
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