Adolescence is a period of life in which peer relationships become increasingly important. Adolescents have a greater likelihood of taking risks when they are with peers rather than alone. In this study, we investigated the development of social influence on risk perception from late childhood through adulthood. Five hundred and sixty-three participants rated the riskiness of everyday situations and were then informed about the ratings of a social-influence group (teenagers or adults) before rating each situation again. All age groups showed a significant social-influence effect, changing their risk ratings in the direction of the provided ratings; this social-influence effect decreased with age. Most age groups adjusted their ratings more to conform to the ratings of the adult social-influence group than to the ratings of the teenager social-influence group. Only young adolescents were more strongly influenced by the teenager social-influence group than they were by the adult social-influence group, which suggests that to early adolescents, the opinions of other teenagers about risk matter more than the opinions of adults.

This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions.Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial aims to provide an accessible introduction to these techniques. We will introduce Gaussian processes which generate distributions over functions used for Bayesian non-parametric regression, and demonstrate their use in applications and didactic examples including simple regression problems, a demonstration of kernel-encoded prior assumptions and compositions, a pure exploration scenario within an optimal design framework, and a bandit-like exploration-exploitation scenario where the goal is to recommend movies. Beyond that, we describe a situation modelling risk-averse exploration in which an additional constraint (not to sample below a certain threshold) needs to be accounted for.Lastly, we summarize recent psychological experiments utilizing Gaussian processes. Software and literature pointers are also provided.

From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using a variety of bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, where the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across a variety of different probabilistic and heuristic models, we find evidence that Gaussian Process function learning-combined with an optimistic Upper Confidence Bound sampling strategy-provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable, and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.All rights reserved. No reuse allowed without permission.was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/171371 doi: bioRxiv preprint first posted online Aug. 1, 2017; previous work exploring inductive biases in pure function learning contexts 21,22 and human behaviour in univariate function optimization 23 , we present a comprehensive approach using a robust computational modelling framework to understand how humans generalize in an active search task.Across three studies using uni-and bivariate multi-armed bandits with up to 121 arms, we compare a diverse set of computational models in their ability to predict individual human behaviour. In all experiments, the majority of subjects are best captured by a model combining function learning using Gaussian Process (GP) regression, with an optimistic Upper Confidence Bound (UCB) sampling strategy that directly balances expectations of reward with the reduction of uncertainty. Importantly, we recover meaningful and robust estimates about the nature of human generalization, showing the limits of traditional models of associative learning 24 in tasks where the environmental structure supports learning and inference.The main contributions of this paper are threefold:1. We introduce the spatially correlated multi-armed bandit as a paradigm for studying how people use generalization to guide search in larger problems space than traditionally used for studying human behaviour.2. We find that a Gaussian Process model of function learning robustly captures how humans generalize and learn about the structure of the environment, where an observed tendency towards undergeneralization is shown to sometimes be beneficial.3. We show that participants solve the exploration-exploitation dilemma by optimistically inflating expectations of reward by the underlying uncertainty, with recoverable evidence for the separate phenome...

AbstractdepmixS4 implements a general framework for defining and estimating dependent mixture models in the R programming language. This includes standard Markov models, latent/hidden Markov models, and latent class and finite mixture distribution models. The models can be fitted on mixed multivariate data with distributions from the glm family, the (logistic) multinomial, or the multivariate normal distribution. Other distributions can be added easily, and an example is provided with the exgaus distribution. Parameters are estimated by the expectation-maximization (EM) algorithm or, when (linear) constraints are imposed on the parameters, by direct numerical optimization with the Rsolnp or Rdonlp2 routines.

Decision-making in noisy and changing environments requires a fine balance between exploiting knowledge about good courses of action and exploring the environment in order to improve upon this knowledge. We present an experiment on a restless bandit task in which participants made repeated choices between options for which the average rewards changed over time. Comparing a number of computational models of participants' behaviour in this task, we find evidence that a substantial number of them balanced exploration and exploitation by considering the probability that an option offers the maximum reward out of all the available options.

Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in which participants were incentivized to learn the structure of probabilistic causal systems through free selection of multiple interventions. We develop models of participants' intervention choices and online structure judgments, using expected utility gain, probability gain, and information gain and introducing plausible memory and processing constraints. We find that successful participants are best described by a model that acts to maximize information (rather than expected score or probability of being correct); that forgets much of the evidence received in earlier trials; but that mitigates this by being conservative, preferring structures consistent with earlier stated beliefs. We explore 2 heuristics that partly explain how participants might be approximating these models without explicitly representing or updating a hypothesis space.

We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely independent memory signals (such as explicit and implicit memory) drive recognition and priming; (c) a multiple-systems-2 (MS2) model, in which there are also 2 memory signals, but some degree of dependence is allowed between these 2 signals (and this model subsumes the SS and MS1 models as special cases); and (d) a dual-process signal detection (DPSD1) model, 1 possible extension of a dual-process theory of recognition (Yonelinas, 1994) to priming, in which a signal detection model is augmented by an independent recollection process. The predictions of the models are tested in a continuous-identification-with-recognition paradigm in both normal adults (Experiments 1-3) and amnesic individuals (using data from Conroy, Hopkins, & Squire, 2005). The SS model predicted numerous results in advance. These were not predicted by the MS1 model, though could be accommodated by the more flexible MS2 model. Importantly, measures of overall model fit favored the SS model over the others. These results illustrate a new, formal approach to testing theories of explicit and implicit memory.

Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants. Nothing endures but change Heraclitus (540 BC -480 BC).Predicting future events from past experience is a fundamental aspect of daily life. For instance, policy makers have to predict the outcome of interventions and sports coaches must