Bayesian theories of conditioning in a changing world

The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.     

Robust decision making in a nonlinear world

The authors propose a general modeling framework called the general monotone model (GeMM), which allows one to model psychological phenomena that manifest as nonlinear relations in behavior data without the need for making (overly) precise assumptions about functional form. Using both simulated and real data, the authors illustrate that GeMM performs as well as or better than standard statistical approaches (including ordinary least squares, robust, and Bayesian regression) in terms of power and predictive accuracy when the functional relations are strictly linear but outperforms these approaches under conditions in which the functional relations are monotone but nonlinear. Finally, the authors recast their framework within the context of contemporary models of behavioral decision making, including the lens model and the take-the-best heuristic, and use GeMM to highlight several important issues within the judgment and decision-making literature.     

A Bayesian formulation of behavioral control

Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or psychiatry, and the formal treatments in engineering appear to capture only limited aspects of the intuitive concepts. Here, we formalize controllability in terms of characteristics of prior distributions over affectively charged environments. We explore the relevance of this notion of control to reinforcement learning methods of optimising behavior in such environments and consider how apparently maladaptive beliefs can result from normative inference processes. These results are discussed with reference to depression and animal models thereof.     

Rich analysis and rational models: inferring individual behavior from infant looking data

Studies of infant looking times over the past 50 years have provided profound insights about cognitive development, but their dependent measures and analytic techniques are quite limited. In the context of infants' attention to discrete sequential events, we show how a Bayesian data analysis approach can be combined with a rational cognitive model to create a rich data analysis framework for infant looking times. We formalize (i) a statistical learning model, (ii) a parametric linking between the learning model's beliefs and infants' looking behavior, and (iii) a data analysis approach and model that infers parameters of the cognitive model and linking function for groups and individuals. Using this approach, we show that recent findings from Kidd, Piantadosi and Aslin ( 2012 ) of a U‐shaped relationship between look‐away probability and stimulus complexity even holds within infants and is not due to averaging subjects with different types of behavior. Our results indicate that individual infants prefer stimuli of intermediate complexity, reserving attention for events that are moderately predictable given their probabilistic expectations about the world.     

Experienced and Expected Similarity to Same-Gender Peers: Moving Toward a Comprehensive Model of Gender Segregation

Gender segregation is often explained by children being interested in interacting with other children who behave similarly to themselves. Children??s beliefs about girls and boys (i.e., their gender cognitions) may also play a role in gender segregation, but this idea has received little attention. In this study, we proposed a model of gender segregation that included similarity on gender-typed behavioral qualities (e.g., rough and tumble play) and gender cognitions concerning perceived similarity to same-gender others, and we assessed whether this more comprehensive heuristic model predicted observed peer interactions in young U.S. children (n?=?74; M age = 51 m; middle-class families). A multi-method design was employed including observations of behavior and child reports of gender cognitions. Support was found for the linkages proposed in this comprehensive model for boys; partial support was found for girls. Specifically, the inclusion of gender cognitions was supported for both genders: gender cognitions about perceived similarity related to interactional partner choices for both girls and boys, and accounted for variance in observed partner choices even after behavioral similarity was included in the model. The traditional link concerning behavioral similarity on rough-and-tumble play predicted boys?? but not girls?? interactions. The findings extend knowledge about the role of social cognitions in social behavior, and are consistent with ideas proposed by gender schema theory and other constructivist theories.     

Parenting cognitions and treatment beliefs as predictors of experience using behavioral parenting strategies in families of children with attention-deficit/hyperactivity disorder

We tested a model of mothers' parenting efficacy and attributions for child ADHD behaviors as predictors of experiences with behavioral treatment. The model proposed that mothers' beliefs regarding the acceptability and effectiveness of behavioral strategies would intervene between mothers' cognitions about parenting and child behavior and their treatment experiences. Participants were 101 mothers of 5- to 10-year-old children (82% male) with ADHD. Mothers reported their parenting efficacy and attributions for child behavior, and then received a single session of treatment teaching 2 behavior management strategies. Then, mothers reported their beliefs regarding the acceptability and effectiveness of these strategies. A follow-up phone interview 1 week later assessed mothers' experiences in using the behavioral strategies. The overall model fit the data. Attributions of child ADHD behavior as more pervasive, enduring, and within the child's control were related to seeing behavioral treatment as more acceptable, but neither attributions nor treatment acceptability predicted treatment experience. However, mothers with higher parenting efficacy viewed the behavioral strategies as more likely to be effective, and this pathway significantly predicted positive treatment experience. Implications for understanding the variables that contribute to parental decision-making and treatment participation for childhood ADHD are considered.     

WHAT LIARS CAN TELL US ABOUT THE KNOWLEDGE NORM OF PRACTICAL REASONING

If knowledge is the norm of practical reasoning, then we should be able to alter people's behavior by affecting their knowledge as well as by affecting their beliefs. Thus, as Roy Sorensen (2010 ) suggests, we should expect to find people telling lies that target knowledge rather than just lies that target beliefs. In this paper, however, I argue that Sorensen's discovery of “knowledge‐lies” does not support the claim that knowledge is the norm of practical reasoning. First, I use a Bayesian framework to show that in each of Sorensen's examples, knowledge‐lies alter people's behavior by affecting their beliefs. Second, I show that while we can imagine lies that target knowledge without targeting beliefs, they cannot alter people's behavior. In other words, knowledge‐lies actually work (i.e., manipulate behavior) by targeting beliefs or they do not work at all.     

Decision making and learning while taking sequential risks

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies the roles of learning and decision making during sequential risky choice. Results show that respondents adapt their learning processes and associated mental representations of the task to the stochastic environment. Furthermore, their Bayesian learning processes are shown to interfere with the paradigm's identification of risky drug use, whereas the decision-making process facilitates its diagnosticity. Theoretical implications of the results in terms of both understanding risk-taking behavior and improving risk-taking assessment methods are discussed.     

Beyond the purely cognitive: Belief systems,social cognitions,and metacognitions as driving forces in intellectual performance

This study explores the way that belief systems, interactions with social or experimental environments, and skills at the “control” level in decision-making shape people's behavior as they solve problems. It is argued that problem-solvers' beliefs (not necessarily consciously held) about what is useful in mathematics may determine the set of “cognitive resources” at their disposal as they do mathematics. Such beliefs may, for example, render inaccessible to them large bodies of information that are stored in long-term memory and that are easily retrieved in other circumstances. In other cases, individuals' reactions to an experimental setting (fear of failure, or the desire to “look mathematical” while being videotaped) may induce behavior that is almost pathological—and at the same time, so consistent that it can be modeled. In general, such “environmental” factors establish the context within which individuals access and utilize the information potentially at their disposal. Protocols illustrating these points are presented and discussed. A model based on an axiomatization of students' beliefs about plane geometry is outlined, and is shown to correspond closely to their problem-solving performance. A framework is offered for analyzing problem-solving performance at three qualitatively different levels: access to cognitive resources stored in LTM, executive or control decision-making, and belief systems.     

A Biased Inferential Naivety learning model for a network of agents

We propose a Biased Inferential Naivety social learning model. In this model, a group of agents tries to determine the true state of the world and make the best possible decisions. The agents have limited computational abilities. They receive noisy private signals about the true state and observe the history of their neighbors' decisions. The proposed model is rooted in the Bayesian method but avoids the complexity of fully Bayesian inference. In our model, the role of knowledge obtained from social observations is separated from the knowledge obtained from private observations. Therefore, the Bayesian inferences on social observations are approximated using inferential naivety assumption, while purely Bayesian inferences are made on private observations. The reduction of herd behavior is another innovation of the proposed model. This advantage is achieved by reducing the effect of social observations on agents' beliefs over time. Therefore, all the agents learn the truth, and the correct consensus is achieved effectively. In this model, using two cognitive biases, there is heterogeneity in agents' behaviors. Therefore, the growth of beliefs and the learning speed can be improved in different situations. Several Monte Carlo simulations confirm the features of the proposed model. The conditions under which the proposed model leads to asymptotic learning are proved.     

When do primes prime? The moderating role of the self-concept in individuals’ susceptibility to priming effects on social behavior

Using cooperative behavior in economic decision-making settings, we predicted and found that people’s susceptibility to priming influences is moderated by two factors: people’s chronic accessibility to a behavioral repertoire and people’s self-concept activation. In Experiment 1, we show that individuals highly consistent in their social value orientation (SVO) assimilate their behavior to their dispositions rather than to the primes, whereas the opposite effect is obtained among individuals with a low consistent SVO. In Experiment 2, we show that low consistent SVO individuals become less susceptible to priming influences when their self-concept is activated. These studies shed new light on individuals’ susceptibility to priming influences on social behavior.     

Locally Bayesian learning with applications to retrospective revaluation and highlighting

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component's target is the input to the next component that maximizes the probability of the next component's target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.     

Bayesian data analysis as a tool for behavior analysts

Bayesian approaches to data analysis are considered within the context of behavior analysis. The paper distinguishes between Bayesian inference, the use of Bayes Factors, and Bayesian data analysis using specialized tools. Given the importance of prior beliefs to these approaches, the review addresses those situations in which priors have a big effect on the outcome (Bayes Factors) versus a smaller effect (parameter estimation). Although there are many advantages to Bayesian data analysis from a philosophical perspective, in many cases a behavior analyst can be reasonably well‐served by the adoption of traditional statistical tools as long as the focus is on parameter estimation and model comparison, not null hypothesis significance testing. A strong case for Bayesian analysis exists under specific conditions: When prior beliefs can help narrow parameter estimates (an especially important issue given the small sample sizes common in behavior analysis) and when an analysis cannot easily be conducted using traditional approaches (e.g., repeated measures censored regression).