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We check the robustness of a recently proposed dynamical model of associative Pavlovian learning that extends the Rescorla-Wagner (RW) model in a natural way and predicts progressively damped oscillations in the response of the subjects. Using the data of two experiments, we compare the dynamical oscillatory model (DOM) with an oscillatory model made of the superposition of the RW learning curve and oscillations. Not only do data clearly show an oscillatory pattern, but they also favor the DOM over the added oscillation model, thus pointing out that these oscillations are the manifestation of an associative process. The latter is interpreted as the fact that subjects make predictions on trial outcomes more extended in time than in the RW model, but with more uncertainty., Comment: 39 pages, 11 figures, 8 tables. v2: discussion improved, figures added, some parts shortened, conclusions unchanged, matches published version

The time-changing nature of our world demands processing of huge amounts of information in fast and reliable way to generate successful behaviors. Therefore, significant brain resources are devoted to process spatiotemporal information. Neural basis of spatial processing and their cognitive correlates are well established mostly for static environments. Nonetheless, in time-changing situations the brain exploits specific processing mechanisms for temporal information based on prediction and anticipation, as time compression during visual perception and mental navigation. Alternative hypothesis of time compaction integrates both views, postulating that dynamic situations are internally represented as static spatial maps where temporal information is extracted by predicting and structuring the relevant interactions. Nevertheless, empirical approaches tackling the biological soundness of time compaction are still lacking. Here we show that performance in a discrimination learning task involving dynamic situations can be either favored or hampered via previous exposition to interfering static scenes. In this sense, men were effectively conditioned in contrast to a control group, in coherence with the hypothesis. Meanwhile, women performed on par with control men, regardless of the previous conditioning. This suggests time compaction is a salient cognitive strategy in men when dealing with dynamic situations, while women seem to rely on a broader range of information processing strategies. Finally, we further corroborated the time compaction mechanism involved in these experimental findings through a mathematical model of the experimental process. Our results point to some form of static internal representation mechanism at cognitive level involved in decision-making and strategy planning in dynamic situations [...]

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 3960 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 nonlinear 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., Comment: 29 pages, 8 figures, 7 tables; v2-v3: theoretical motivation clarified, data of Harris et al. (2015) included in improved analysis, conclusions strengthened, typos corrected, references added, technicalities and data analysis moved into Supplementary Material (46 pages, 22 figures, 7 tables; available at https://www.frontiersin.org/articles/10.3389/fpsyg.2020.00612/full#supplementary-material)