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Sure enough: efficient Bayesian learning and choice.

ABSTRACT: Probabilistic decision-making is a general phenomenon in animal behavior, and has often been interpreted to reflect the relative certainty of animals' beliefs. Extensive neurological and behavioral results increasingly suggest that animal beliefs may be represented as probability distributions, with explicit accounting of uncertainty. Accordingly, we develop a model that describes decision-making in a manner consistent with this understanding of neuronal function in learning and conditioning. This first-order Markov, recursive Bayesian algorithm is as parsimonious as its minimalist point-estimate, Rescorla-Wagner analogue. We show that the Bayesian algorithm can reproduce naturalistic patterns of probabilistic foraging, in simulations of an experiment in bumblebees. We go on to show that the Bayesian algorithm can efficiently describe the behavior of several heuristic models of decision-making, and is consistent with the ubiquitous variation in choice that we observe within and between individuals in implementing heuristic decision-making. By describing learning and decision-making in a single Bayesian framework, we believe we can realistically unify descriptions of behavior across contexts and organisms. A unified cognitive model of this kind may facilitate descriptions of behavioral evolution.

SUBMITTER: Foley BR

PROVIDER: S-EPMC5709194 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Sure enough: efficient Bayesian learning and choice.

Foley Brad R BR Marjoram Paul P

Animal cognition 20170701 5

Probabilistic decision-making is a general phenomenon in animal behavior, and has often been interpreted to reflect the relative certainty of animals' beliefs. Extensive neurological and behavioral results increasingly suggest that animal beliefs may be represented as probability distributions, with explicit accounting of uncertainty. Accordingly, we develop a model that describes decision-making in a manner consistent with this understanding of neuronal function in learning and conditioning. Th ...[more]

PMID: 28669114

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Project description:Aims: In addition to medical facts, medical students participating in simulation-based training are supposed to acquire general knowledge, e.g. heuristics to cope with critical incidents. While active participation is considered a major benefit of this kind of training, a large portion of students' time is often spent observing peers acting in the simulator. Thus, we instructionally supported learners with a collaboration script (i.e., a set of scaffolds that distribute roles and activities among learners in group learning situations) during observational phases of a simulation-based training. Our script was designed to help learners focus on heuristics and to facilitate more (inter-)active participation. We hypothesised that scripted learners would benefit from the instructional support with respect to individual and collaborative learning processes as well as individual learning outcomes. Methods: Thirty-four medical students in their 7th to 12th semester participated in this field study with control group design. The independent variable was the collaboration script (with/without). Four voluntary emergency courses with a full-scale simulator were examined. The acquisition of skills related to Crisis Resource Management (CRM) heuristics was one of the learning goals of these courses. The collaboration script induced learners to perform specific activities during and after each observational phase of the training. Further, the script sequenced the order of activities and assigned roles to the learners. Learning processes were measured on an individual level (by means of notes taken by learners during observational phases) and on a collaborative level (by means of learners' comments). Learning outcomes were measured with pre- and post-self-assessment of CRM skills and a brief video-based CRM skills test at the end of the course. Results: The collaboration script had the expected positive effect on individual and collaborative learning processes, leading to an increased focus on heuristic strategies and increased collaborative activity of scripted learners. There was no evidence that the experimental conditions differed regarding the objective measure of individual learning outcomes. However, self-assessment data revealed that students in the control condition perceived a higher improvement of CRM skills throughout the course. We suggest that our script might have helped learners adjust an illusion of their own competency - such an illusion may have appeared in the control group as a result of processing fluency. Conclusions: Findings suggest that simulation-based training in medical education can be enhanced with additional instructional support in the form of collaboration scripts designed to turn observational course phases into more active and better focused learning experiences.

Project description:Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model.We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called ? performed best. This score performed better than other BN scoring criteria and MDR at recall using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set.We conclude that representing epistatic interactions using BN models and scoring them using a BN scoring criterion holds promise for identifying epistatic genetic variants in data. In particular, the Bayesian scoring criterion with large values of a hyperparameter ? appears more promising than a number of alternatives.

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Sure enough: efficient Bayesian learning and choice.

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Sure enough: efficient Bayesian learning and choice.

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