Probabilistic learning for selective dissemination of information

New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile

Is social decision making for close others consistent across domains and within individuals?

Humans make decisions across a variety of social contexts. Though social decision-making research has blossomed in recent decades, surprisingly little is known about whether social decision-making preferences are consistent across different domains. We conducted an exploratory study in which participants made choices about 2 types of close others: parents and friends. To elicit decision making preferences, we pit the interests in parents and friends against one another. To assess the consistency of preferences for close others, decision making was assessed in three domains-risk taking, probabilistic learning, and self-other similarity judgments. We reasoned that if social decision-making preferences are consistent across domains, participants ought to exhibit the same preference in all three domains (i.e., a parent preference, based on prior work), and individual differences in preference magnitude ought to be conserved across domains within individuals. A combination of computational modeling, random coefficient regression, and traditional statistical tests revealed a robust parent-over-friend preference in the risk taking and probabilistic learning domains but not the self-other similarity domain. Preferences for parent-over-friend in the risk-taking domain were strongly associated with similar preferences in the probabilistic learning domain but not the self-other similarity domain. These results suggest that distinct and dissociable value-based and social-cognitive computations underlie social decision making. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Modelling children's negation errors using probabilistic learning in MOSAIC.

Cognitive models of language development have often been used to simulate the pattern of errors in children’s speech. One relatively infrequent error in English involves placing inflection to the right of a negative, rather than to the left. The pattern of negation errors in English is explained by Harris & Wexler (1996) in terms of very early knowledge of inflection on the part of the child. We present data from three children which demonstrates that although negation errors are rare, error types predicted not to occur by Harris & Wexler do occur, as well as error types that are predicted to occur. Data from MOSAIC, a model of language acquisition, is also presented. MOSAIC is able to simulate the pattern of negation errors in children’s speech. The phenomenon is modelled more accurately when a probabilistic learning algorithm is used

Human substantia nigra neurons encode unexpected financial rewards

The brain's sensitivity to unexpected outcomes plays a fundamental role in an\ud organism's ability to adapt and learn new behaviors. Emerging research suggests that\ud midbrain dopaminergic neurons encode these unexpected outcomes. We used\ud microelectrode recordings during deep brain stimulation surgery to study neuronal activity in\ud the human substantia nigra (SN) while patients with Parkinson's disease engaged in a\ud probabilistic learning task motivated by virtual financial rewards. Based on a model of the ..

Gait learning for soft microrobots controlled by light fields

Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing conditions. Albeit, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semi-synthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot's locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on light-controlled soft microrobots and probabilistic learning control.Comment: 8 pages, 7 figures, to appear in the proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 201

Research on the reasoning, teaching and learning of probability and uncertainty

In this editorial, we set out the aims in the call to publish papers on informal statistical inference, randomness, modelling and risk. We discuss how the papers published in this issue have responded to those aims. In particular, we note how the nine papers contribute to some of the major debates in mathematics and statistics education, often taking contrasting positions. Such debates range across: (1) whether knowledge is fractured or takes the form of mental models; (2) heuristic or intuitive thinking versus operational thinking as for example in dual process theory; (3) the role of different epistemic resources, such as perceptions, modelling, imagery, in the development of probabilistic reasoning; (4) how design and situation impact upon probabilistic learning