Anna Thorwart

Expectancies as core features of mental disorders.

Purpose of review Expectancies are core features of mental disorders, and change in expectations is therefore one of the core mechanisms of treatment in psychiatry. We aim to improve our understanding of expectancies by summarizing factors that contribute to their development, persistence, and modification. We pay particular attention to the issue of persistence of expectancies despite experiences that contradict them. Recent findings Based on recent research findings, we propose a new model for expectation persistence and expectation change. When expectations are established, effects are evident in neural and other biological systems, for example, via anticipatory reactions, different biological reactions to expected versus unexpected stimuli, etc. Psychological ‘immunization’ and ‘assimilation’, implicit self-confirming processes, and stability of biological processes help us to better understand why expectancies persist even in the presence of expectation violations. Summary Learning theory, attentional processes, social influences, and biological determinants contribute to the development, persistence, and modification of expectancies. Psychological interventions should focus on optimizing expectation violation to achieve optimal treatment outcome and to avoid treatment failures.

ALTSim: A MATLAB simulator for current associative learning theories

ALTSim is a MATLAB-based simulator of several associative learning models, including Pearce’s configural model, the extended configural model, the Rescorla-Wagner model, the unique cue hypothesis, the modified unique cue hypothesis, the replaced elements model, and Harris’s elemental model. It allows for specifying all relevant parameters, as well as exact stimulus sequences by graphical user interfaces. It is an easy-to-use tool that facilitates evaluating and comparing the featured associative learning models. ALTSim is available free of charge from www.staff.uni-marburg.de/~lachnit/ALTSim/.We study the problem of learning a non-parametric mapping between two continuous spaces without having access to input-output pairs for training, but rather to groups of input-output pairs, where the correspondence structure within each group is unknown and where outliers may be present. This problem is solved by transforming each space using the channel representation, and finding a linear mapping on the transformed domain. The asymptotical behavior of the method for a large number of training samples is found to be very related to the case of known correspondences. The results are evaluated on simulated data

Symmetrical generalization decrements: Configural stimulus processing in human contingency learning

Models of associative learning differ in their predictions concerning the symmetry of generalization decrements. Whereas Pearce’s (1994) configural model predicts the same response decrement after adding elements to and after removing elements from a previously trained stimulus, elemental models, such as the replaced elements model and Harris’s (2006) model, anticipate more of a decrement for removing than for adding elements. In three contingency learning experiments, we manipulated the motion and the spatial arrangement of colored dots in order to induce configural or elemental processing by perceptual grouping. The results reliably showed symmetrical decrements for the added and removed groups. The manipulations of the stimuli had no effect on stimulus processing. This is in line with Pearce’s configural model, but it is at variance with the elemental models and previous studies.

Rapid-REM: a MATLAB simulator of the replaced-elements model.

A recent proposal for an elemental account of associative learning phenomena is the replaced-elements model (REM) put forward by Wagner (2003). Although the ideas underlying this model are comparatively simple, implementation of the model is rather complex. In this article, we present Rapid-REM, a MATLAB simulator of Wagner’s model. Rapid-REM features a graphical user interface for manipulating all essential parameter values and for control of the simulation process, graphical visualization of the simulation course and the results, and the alternative possibility of simulating the replaced-elements model as it was originally proposed (Wagner & Brandon, 2001). Rapid-REM is available free of charge from www.staff.uni-marburg.de/≈lachnit/Rapid-REM/. This simulator makes it easy to derive predictions for REM and evaluate them, and it will therefore facilitate insights into the mechanisms of associative learning.

Comparing learned predictiveness effects within and across compound discriminations

In four human learning experiments, we examined the extent to which learned predictiveness depends upon direct comparison between relatively good and poor predictors. Participants initially solved (a) linear compound discriminations in which one or both of the stimuli in each compound were predictive of the correct outcome, (b) biconditional discriminations where only the configurations of the stimuli were predictive of the correct outcome, or (c) pseudodiscriminations in which no stimulus features were predictive. In each experiment, subsequent learning and test stages were used to assay changes in the associability of each stimulus brought about by its role in the initial discriminations. Although learned predictiveness effects were observed in all experiments (i.e., previously predictive cues were more readily associated with a new outcome than previously nonpredictive cues), the same changes in associability were observed regardless of whether the stimulus was initially learned about in the presence of an equally predictive, more predictive, or less predictive stimulus. The results suggest that learned associability is not controlled by competitive allocation of attention, but rather by the absolute predictiveness of each individual cue.

Comparing positive and negative patterning in human learning

In human causal learning, positive patterning (PP) and negative patterning (NP) discriminations are often acquired at roughly the same rate, whereas PP is learned faster than NP in most experiments with nonhuman animals. One likely reason for this discrepancy is that most causal learning scenarios encourage participants to treat the presentation and omission of the relevant outcome as two events of comparable significance and likelihood. To investigate this, the current experiments compared PP and NP using a predictive learning paradigm based on a mock gambling task. In Experiment 1, one outcome (winning) was made more salient by being less frequent than the alternative outcome (losing). Under these circumstances, PP was learned faster than NP. In Experiment 2, subjects learned two PP and two NP discriminations, one involved win versus no change outcomes, the other involved lose versus no change outcomes. The subjects learned PP faster than NP, but only when discriminating win from no change. We argue that a difference in difficulty between PP and NP relies on a difference in the salience of the outcomes, consistent with the predictions of a relatively simple model of associative learning.

HMS: a MATLAB simulator of the Harris model of associative learning.

Harris (2006) recently proposed a new elemental model of the processes involved in associative learning. Although Harris explicated all relevant mathematical and conceptual details of the model in his article, implementing a computer simulation of his model requires considerable programming expertise and work. We therefore present the Harris model simulator (HMS), a MATLAB simulator of Harris’s model. HMS provides a graphical user interface for manipulating all essential parameter values and for controlling the simulation process, the graphical visualization of the simulation course, and the numerical results. HMS is available free of charge from www.staff.uni-marburg.de/≈lachnit/harris/. HMS allows researchers to easily derive and evaluate predictions for the Harris model, and it will therefore facilitate insights into the mechanisms of associative learning.

Why Harmless Sensations Might Hurt in Individuals with Chronic Pain: About Heightened Prediction and Perception of Pain in the Mind

In individuals with chronic pain harmless bodily sensations can elicit anticipatory fear of pain resulting in maladaptive responses such as taking pain medication. Here, we aim to broaden the perspective taking into account recent evidence that suggests that interoceptive perception is largely a construction of beliefs, which are based on past experience and that are kept in check by the actual state of the body. Taking a Bayesian perspective, we propose that individuals with chronic pain display a heightened prediction of pain [prior probability p(pain)], which results in heightened pain perception [posterior probability p(pain|sensation)] due to an assumed link between pain and a harmless bodily sensation [p(sensation|pain)]. This pain perception emerges because their mind infers pain as the most likely cause for the sensation. When confronted with a mismatch between predicted pain and a (harmless bodily) sensation, individuals with chronic pain try to minimize the mismatch most likely by active inference of pain or alternatively by an attentional shift away from the sensation. The active inference results in activities that produce a stronger sensation that will match with the prediction, allowing subsequent perceptual inference of pain. Here, we depict heightened pain perception in individuals with chronic pain by reformulating and extending the assumptions of the interoceptive predictive coding model from a Bayesian perspective. The review concludes with a research agenda and clinical considerations.

Normalization between stimulus elements in a model of Pavlovian conditioning: Showjumping on an elemental horse

Harris and Livesey. Learning & Behavior, 38, 1–26, (2010) described an elemental model of associative learning that implements a simple learning rule that produces results equivalent to those proposed by Rescorla and Wagner (1972), and additionally modifies in “real time” the strength of the associative connections between elements. The novel feature of this model is that stimulus elements interact by suppressively normalizing one another’s activation. Because of the normalization process, element activity is a nonlinear function of sensory input strength, and the shape of the function changes depending on the number and saliences of all stimuli that are present. The model can solve a range of complex discriminations and account for related empirical findings that have been taken as evidence for configural learning processes. Here we evaluate the model’s performance against the host of conditioning phenomena that are outlined in the companion article, and we present a freely available computer program for use by other researchers to simulate the model’s behavior in a variety of conditioning paradigms.

Generalization decrements: Further support for flexibility in stimulus processing

Thorwart and Lachnit (2009) found reliable symmetrical decrements in two generalization tasks: Removing an already trained component from a compound did not result in larger decrements than adding a new one did. In two contingency learning experiments, we investigated first whether time pressure during stimulus processing, as well as the degree of perceptual grouping, was effective in controlling the symmetry of the decrements (Experiment 1); and second, whether the symmetry was affected by the causal versus predictive nature of the relationship between the cue and the outcome (Experiment 2). The experiments generated unexpected results, since both revealed asymmetrical decrements independent of the manipulations introduced. They therefore demonstrate that more research is needed in order to understand the variables influencing stimulus representation in human associative learning.