Tyler Mari

Previous research has demonstrated that machine learning (ML) could not effectively decode passive observation of neutral versus pain photographs by using electroencephalogram (EEG) data. Consequently, the present study explored whether active viewing, i.e., requiring participant engagement in a task, of neutral and pain stimuli improves ML performance. Random forest (RF) models were trained on cortical event-related potentials (ERPs) during a two-alternative forced choice paradigm, whereby participants determined the presence or absence of pain in photographs of facial expressions and action scenes. Sixty-two participants were recruited for the model development sample. Moreover, a within-subject temporal validation sample was collected, consisting of 27 subjects. In line with our previous research, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The results demonstrated that the RF successfully classified discrete categories of visual stimuli (faces and scenes) with accuracies of 78% and 66% on cross-validation and external validation, respectively. However, despite promising cross-validation results of 61% and 67% for the classification of neutral and pain scenes and neutral and pain faces, respectively, the RF models failed to exceed chance performance on the external validation dataset on both empathy classification attempts. These results align with previous research, highlighting the challenges of classifying complex states, such as pain empathy using ERPs. Moreover, the results suggest that active observation fails to enhance ML performance beyond previous passive studies. Future research should prioritise improving model performance to obtain levels exceeding chance, which would demonstrate increased utility.

Previous research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.

Background: Statistics anxiety is common among social science students. Despite much evidence examining statistics anxiety and test performance, little research has explored the role of student self-prediction on test performance in a higher education setting. Objective: The purpose of this study was to investigate the relationship between statistics anxiety and both students’ self-prediction of their future exam performance and actual test performance on a formal statistics assessment at undergraduate level in psychology students in the UK. Method: Using a cross-sectional design, two hundred and two students were required to complete Statistics Anxiety Rating Scales, the Mathematical Prerequisites for Psychometrics Scale, and provided self-predicted test performance scores. Test performance data was obtained from a formal statistics assessment. Results: As predicted, we demonstrated statistics test anxiety to be negatively associated with self-predicted performance. Additionally, we found statistics anxiety was positively associated with test performance. Conclusion: The findings highlight the complex relationship between statistics anxiety and test performance, suggesting there may be an optimal level of anxiety for performance in statistics assessments. Implications: The results we report have implications for psychology research methods and statistics instructors who may wish to incorporate the findings into statistics instruction modules in order to assuage high levels of statistics anxiety and foster student well-being.

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time–frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML’s clinical potential for pain classification.