Acquisition learning is stronger for aversive than appetitive events

Aversive conditioning is impaired in impulsive individuals: A study on learning asymmetries

BACKGROUND AND OBJECTIVES

Appetitive and aversive conditioning are thought to be involved in the development and maintenance of mental disorders including anxiety, mood, eating, and substance use disorders. However, few studies measure the relative strength of appetitive and aversive associations, and their relevance to the risk of mental disorders. This study aims to address this gap.

METHODS

We tested how readily healthy volunteers acquire appetitive vs. aversive associations. 150 participants associated complex 3D objects with either gain or loss and made decisions to gain or avoid losing points. We investigated the relationship of a learning asymmetry with neuroticism, impulsivity, and anhedonia, to test the hypothesis that a stronger learning asymmetry corresponds to more extreme scores on these traits.

RESULTS

Impulsivity was positively associated with the learning asymmetry (R2 = .10). This resulted from an inverse relation with the strength of aversive associations, indicating that impulsive individuals are worse at aversive learning. However, appetitive associations did not differ significantly. No correlations with neuroticism or anhedonia were found.

LIMITATIONS

Conditioning studies typically use primary reinforcers and a CS-. Lacking these may make these results less comparable to other studies.

CONCLUSIONS

We demonstrate that the learning asymmetry can measure individual differences linked to personality traits, and that impulsivity, normally linked with appetitive learning, also influences aversive learning. These results enable additional studies of learning asymmetry in relation to mental disorders, which could include measurements of mental health symptoms to provide further insight into how appetitive and aversive learning interacts with mental disorders.

Conditioning to true content and artificial intelligence in psychophysiological intention recognition

OBJECTIVE

The objective is to introduce a novel method for classical conditioning to true content (CtTC), and for the first time, apply this approach in the concealed information test (CIT) to effectively discern intentions. During CtTC, participants are trained to exhibit electrodermal responses whenever they recognize true content on a screen. Additionally, the objective is to evaluate a novel CIT-dataset preprocessing algorithm, employed to enhance machine learning (ML) classification performance.

METHODS

A total of 84 participants were evenly divided into four groups. Two groups of participants devised plans for stealing money from a supermarket, while the other two groups did not engage in any planning. One planning group and one non-planning group underwent CIT examination, while the remaining groups were subjected to CtTC.

RESULTS

The CIT accuracy initially stood at 52 % and increased to 71 % after Z-score and ML classification (McNemar test, p < 0.05). Conversely, the CtTC accuracy was 76 % and significantly improved to 93 % following Z-score and 95 % following ML classification (McNemar test, p < 0.05). In the best-performing classifiers, CtTC exhibited significantly superior metrics for guilty/innocent classification compared to CIT (Fisher's exact test, p < 0.05, power 1 - β > 0.90). In the CtTC group, reactivity and sensitivity significantly increased, indicated by higher EDR amplitudes (p < 0.05, two-tailed t-test, power 1 - β = 0.89) and the number of EDRs (p < 0.05, Fisher's exact test, power 1 - β = 0.90). There was no statistically significant difference between the Z-score and ML classification.

CONCLUSIONS

In the assessment of intentions, CtTC enhances both the sensitivity and accuracy of the CIT.

Prelimbic cortex ensembles promote appetitive learning-associated behavior

Memories of prior rewards bias our actions and future decisions. To determine the neural correlates of an appetitive associative learning task, we trained male mice to discriminate a reward-predicting cue over the course of 7 d. Encoding, recent recall, and remote recall were investigated to determine the areas of the brain recruited at each stage of learning. Using cFos as a proxy for neuronal activity, we found unique brain-wide patterns of activity across days that seem to correlate with distinct stages of learning. In particular, the prelimbic (PL) cortex was significantly recruited during the encoding of a novel association presentation, but its activity decreases as learning continues. To causally dissect the role of the PL in a reward memory across days, we chemogenetically inhibited first the PL entirely and then only tagged memory-bearing cells that were active during encoding in two stages of learning: early and late. Both nonspecific and specific PL inhibition experiments indicate that the PL drives behavior during late stages of learning to facilitate appropriate cue-driven behavior. Overall, our work underscores memory's role in discriminative reward seeking, and points to the PL as a target for modulating disorders in which impaired reward processing is a core component.

The influence of psychological traits and prior experience on treatment expectations

BACKGROUND

Placebo and nocebo responses are modulated by the treatment expectations of participants and patients. However, interindividual differences predicting treatment expectations and placebo responses are unclear. In this large-scale pooled analysis, we aim to investigate the influence of psychological traits and prior experiences on treatment expectations.

METHODS

This paper analyses data from six different placebo studies (total n = 748). In all studies, participants' sociodemographic information, treatment expectations and prior treatment experiences and traits relating to stress, somatization, depression and anxiety, the Big Five and behavioral inhibition and approach tendencies were assessed using the same established questionnaires. Correlation coefficients and structural equation models were calculated to investigate the relationship between trait variables and expectations.

RESULTS

We found small positive correlations between side effect expectations and improvement expectations (r = 0.187), perceived stress (r = 0.154), somatization (r = 0.115), agitation (r = 0.108), anhedonia (r = 0.118), and dysthymia (r = 0.118). In the structural equation model previous experiences emerged as the strongest predictors of improvement (β = 0.32, p = .005), worsening (β = -0.24, p = .005) and side effect expectations (β = 0.47, p = .005). Traits related to positive affect (β = - 0.09; p = .007) and negative affect (β = 0.04; p = .014) were associated with side effect expectations.

DISCUSSION

This study is the first large analysis to investigate the relationship between traits, prior experiences and treatment expectations. Exploratory analyses indicate that experiences of symptom improvement are associated with improvement and worsening expectations, while previous negative experiences are only related to side effect expectations. Additionally, a proneness to experience negative affect may be a predictor for side effect expectation and thus mediate the occurrence of nocebo responses.

Neural underpinnings of preferential pain learning and the modulatory role of fear

Due to its unique biological relevance, pain-related learning might differ from learning from other aversive experiences. This functional magnetic resonance imaging study compared neural mechanisms underlying the acquisition and extinction of different threats in healthy humans. We investigated whether cue-pain associations are acquired faster and extinguished slower than cue associations with an equally unpleasant tone. Additionally, we studied the modulatory role of stimulus-related fear. Therefore, we used a differential conditioning paradigm, in which somatic heat pain stimuli and unpleasantness-matched auditory stimuli served as US. Our results show stronger acquisition learning for pain- than tone-predicting cues, which was augmented in participants with relatively higher levels of fear of pain. These behavioral findings were paralleled by activation of brain regions implicated in threat processing (insula, amygdala) and personal significance (ventromedial prefrontal cortex). By contrast, extinction learning seemed to be less dependent on the threat value of the US, both on the behavioral and neural levels. Amygdala activity, however, scaled with pain-related fear during extinction learning. Our findings on faster and stronger (i.e. "preferential") pain learning and the role of fear of pain are consistent with the biological relevance of pain and may be relevant to the development or maintenance of chronic pain.