Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation

Understanding how humans and animals learn about statistical regularities in stable and volatile environments, and utilize these regularities to make predictions and decisions, is an important problem in neuroscience and psychology. Using a Bayesian modeling framework, specifically the Dynamic Belief Model (DBM), it has previously been shown that humans tend to make the default assumption that environmental statistics undergo abrupt, unsignaled changes, even when environmental statistics are actually stable. Because exact Bayesian inference in this setting, an example of switching state space models, is computationally intensive, a number of approximately Bayesian and heuristic algorithms have been proposed to account for learning/prediction in the brain. Here, we examine a neurally plausible algorithm, a special case of leaky integration dynamics we denote as EXP (for exponential filtering), that is significantly simpler than all previously suggested algorithms except for the delta-learning rule, and which far outperforms the delta rule in approximating Bayesian prediction performance. We derive the theoretical relationship between DBM and EXP, and show that EXP gains computational efficiency by foregoing the representation of inferential uncertainty (as does the delta rule), but that it nevertheless achieves near-Bayesian performance due to its ability to incorporate a “persistent prior” influence unique to DBM and absent from the other algorithms. Furthermore, we show that EXP is comparable to DBM but better than all other models in reproducing human behavior in a visual search task, suggesting that human learning and prediction also incorporates an element of persistent prior. More broadly, our work demonstrates that when observations are information-poor, detecting changes or modulating the learning rate is both difficult and (thus) unnecessary for making Bayes-optimal predictions.

Portreti druge polovice 18. stoljeća na tlu Hrvatske – svjedoci odjevnih oblika i modnih utjecaja razdoblja rokokoa

his paper will attempt to analyze the clothing from the rococo period and underline fashion as an important segment in the reconstruction of a specific style era. Based on conserved portraits from the second half of the 18th century, as well as rare artifacts of clothing from the period found in Croatia, a description of primarily women’s types of clothing, accessories and the terms used to describe them will be given. Influences had, primarily through cultural and trade routes, come from the fashion capital of the period – France, and fashion innovations and the intensity of their changes were under the influence of the personal style first of Madame de Pompadour and afterwards Marie Antoinette. Croatia, which had at the time been part of the Habsburg monarchy and under the Republic of Venice, tended toward French influences in fashion, which represents a considerable move from the prior influence of Italian and German style.

Generalization of unconstrained reaching with hand-weight changes

Studies of motor generalization usually perturb hand reaches by distorting visual feedback with virtual reality or by applying forces with a robotic manipulandum. Whereas such perturbations are useful for studying how the central nervous system adapts and generalizes to novel dynamics, they are rarely encountered in daily life. The most common perturbations that we experience are changes in the weights of objects that we hold. Here, we use a center-out, free-reaching task, in which we can manipulate the weight of a participant's hand to examine adaptation and generalization following naturalistic perturbations. In both trial-by-trial paradigms and block-based paradigms, we find that learning converges rapidly (on a timescale of approximately two trials), and this learning generalizes mostly to movements in nearby directions with a unimodal pattern. However, contrary to studies using more artificial perturbations, we find that the generalization has a strong global component. Furthermore, the generalization is enhanced with repeated exposure of the same perturbation. These results suggest that the familiarity of a perturbation is a major factor in movement generalization and that several theories of the neural control of movement, based on perturbations applied by robots or in virtual reality, may need to be extended by incorporating prior influence that is characterized by the familiarity of the perturbation.