A Computational Model Reveals Learning Dynamics During Interpretation Bias Training With Clinical Applications

Abstract

Background: Some psychopathologies, including anxiety and irritability, are associated with biases when judging ambiguous social stimuli. Interventions targeting these biases, or interpretation bias training (IBT), are amenable to computational modeling to describe their associative learning mechanisms. Here, we translated ALCOVE (attention learning covering map), a model of category learning, to describe learning in youths with affective psychopathology when training on more positive judgments of ambiguous face emotions.

Methods: A predominantly clinical sample comprised 71 youths (age range, 8-22 years) representing broad distributions of irritability and anxiety symptoms. Of these, 63 youths were included in the test sample by completing an IBT task with acceptable performance for computational modeling. We used a separate sample of 28 youths to translate ALCOVE for individual estimates of learning rate and generalization. In the test sample, we assessed associations between model learning estimates and irritability, anxiety, their shared variance (negative affectivity), and age.

Results: Age and affective symptoms were associated with category learning during IBT. Lower learning rates were associated with higher negative affectivity common in anxiety and irritability. Lower generalization, or improved discrimination between face emotions, was associated with increasing age.

Conclusions: This work demonstrates a functional consequence of age- and symptom-related learning during interpretation bias. Learning measured by ALCOVE also revealed learning types not accounted for in the prior literature on IBT. This work more broadly demonstrates the utility of measurement models for understanding trial-by-trial processes and identifying individual learning styles.

Keywords: Anxiety; Category learning; Cognitive bias modification; Interpretation bias training; Irritability.

Figure 1.

An interpretation bias training session. First an indifference point is assessed, where a person’s judgments of morphs switches from predominantly happy to angry. Training sessions provide feedback on judgments that encourage happy judgments of two ambiguous faces previously judged as angry. Training effects are measured by another indifference point assessment.

Figure 2.

Model architecture. A person’s judgment of the emotional valence of a face depends on their expectation of the face’s emotional valence (v). In turn, this depends on the perception of emotional features in the face, (a). This is shared with similar face stimuli, the degree to which it is shared is given by σ. The expected valence also depends on prior valence associations to perceived emotion in faces (w). Association weights update according to feedback encoded in prediction error (d) proportional to the learning rate (e) and to emotion activation (a). Red: Free parameters of interest, estimated from behavioral data. Blue: Internal variables, with learning dynamics predicted by the model.

Figure 3.

Learning typology is evident in the model parameters. Individuals clearly differ in the degree to which they are affected by feedback on the prior trial (generalization). The arbitrary, empirical classification suggests lower, less variable learning rates (top) and higher indifference points (bottom) in the group characterized by high generalization.

Figure 4.

A model free illustration of high generalization by prior trial feedback. Generalization refers to the influence of neighboring stimuli, or morphs, on learning. Its effects are clearly represented without the need for a model in a single ambiguous morph. This morph is highly vulnerable to uncertainty because it is the morph just on the happy side of the feedback threshold during training (see Figure 1). Recall, the position of the training threshold on the morph continuum differs for each participant by their initial balance point so, the morph just on the happy side of this threshold is denoted as morph 0 (the gray line) for reference. To represent the distance on the morph continuum of prior trials, the judgments of morph 0 are divided into categories that are defined by its distance to the morph in the prior trial. Relative to morph 0, prior trial morphs may be distant (4 to 7 morphs to the left of the feedback threshold) or may be close (within 3 morphs of either side of the threshold). Feedback during the prior trial encourages happy judgments on the left side of the gray line, and it encourages angry judgments to the right. High generalizers are clearly influenced by prior trial feedback, even if the feedback was to a morph distant to morph 0 (p<.01). Note, this represents 1/15^th of the data, i.e. judgments of morph 0, and ‘n’ refers to the number of judgments of morph 0 across participants.