I am P sy AM : Modeling Happiness with Cognitive Appraisal Dimensions (2023)

Abstract: This paper proposes and evaluates PsyAM ( https://anonymous.4open.science/r/BERT-PsyAM-10B9 ), a framework that incorporates adaptor modules in a sequential multi-task learning setup to generate high-dimensional feature representations of hedonic well-being (momentary happiness) in terms of its psychological underpinnings. PsyAM models emotion in text through its cognitive antecedents through auxiliary models that achieve multi-task learning through novel feature fusion methods. We show that BERT-PsyAM has cross-task validity and cross-domain generalizability through experiments with emotion-related tasks – on new emotion tasks and new datasets, as well as against traditional methods and BERT baselines. We further probe the robustness of BERT-PsyAM through feature ablation studies, as well as discuss the qualitative inferences we can draw regarding the effectiveness of the framework for representing emotional states. We close with a discussion of a future agenda of psychology-inspired neural network architectures.

## introduction governments are increasingly investing money into surveying and reporting nationwide psychological well-being as an indicator of success and wellness @xcite , and some scholars have recommended monitoring social media for the unobtrusive measurement of regional trends in well-being and mental health. people are increasingly willing to post messages on social media to express their feelings. words relate to emotions because they reflect how humans perceive their surroundings and ongoing events @xcite ; therefore, it is not surprising that language models trained on social media posts offer predictive insights into emotions. emotions are an indicator of psychological states, such as happiness -the feelings of well-being related to momentary 1 https://github.com/stephenlaw30/bert-psyam happiness or pleasure @xcite . cognitive appraisal theory (cat) posits that emotions result from how individuals appraise their situation and its impact on their wellbeing @xcite . cat can model individual differences in emotional expressions; for instance, individuals may express happiness both, during a solitary walk as well as when they spend time with close friends if their core needs, drives, or motivations are suitably fulfilled. therefore, we propose that relying on the stable cognitive antecedents of emotions could help us to train models that improve the state-of-the-art predictive accuracy for emotions detection tasks, and generalize more readily to other problems that infer psychological states from language, such as hedonic well-being. in this work, we test a broad proposition that pretraining on cognitive auxiliary tasks improves emotion detection from text. this approach can bridge prior research in emotions classification with the increasing understanding of the link between selfexpression and psychology. we offer the following contributions: our work also offers a reality check to consider how leaderboard scores in emotion analysis translate to the real-world application of psycholinguistic models for unobtrusive mental health aggregates of communities. recent studies have raised concerns about pre-trained psycholinguistic models' cross-domain and cross-task validity beyond simple emotion detection. many word-trait relationships, such as that of self-referential language use and depression @xcite , or the of social words and extraversion @xcite , inexplicably break down in different communicative contexts. we believe this is because of overfitting models to training corpora and the lack of psychologically motivated machine learning architectures. prior work also often does not theorize why words relate to emotions or traits in the first place. consequently, it has been challenging to build research depth in the representational modeling of the psychology of emotions and develop sophisticated neural network models. herein lies an opportunity for social impact, as appropriately modeling and spatially aggregating emotions in social media make it possible to monitor mental health on a large scale @xcite . ## background previous studies exploring the psycholinguistics of text have evaluated text classification approaches that can best correspond to the psychological measurements of different psychological traits and states @xcite @xcite . for instance, @xcite elaborates on the psychological complexity of human reactions such as empathy and distress by annotating text data with the empathy assessments of their authors via multi-item scales and considering psychological tenets to be co-existent rather than correlated @xcite . in this study, we additionally offer two new tasks: predicting the duration of happiness and its fluctuation from linguistic expression. both these concepts are grounded in psychology literature, where they are known to provide into emotional stability and well-being. unlike a simple emotion detection task, the duration of happiness relates to distinguishing transient moods from persisting states of emotional well-being @xcite . therefore, it may require a more sophisticated representation of text semantics than one driven by affective words alone. given that our primary datasets comprise textual descriptions of happy moments, the focus on hedonia is only appropriate. secondly, emotional fluctuation has garnered a lot of recent interest as a more stable and tractable predictor of mental health as compared to mean-based emotion-based predictors, which are less sensitive to deviations and therefore to indicators of mental health issues. emotional fluctuations expressed in verbal or written expressions has been evaluated for its relationship with within-person fluctuations in other personality states, including affective states @xcite , emotional experience @xcite and wellbeing @xcite . in more recent work, emotional variance in social media posts has been also found to predict emotionally straining situations @xcite , and daily emotional well-being @xcite . we suggest that reorienting the understanding of emotions in text in its cognitive antecedents may offer a fruitful approach to its modeling, detection, and real-world application. we choose two cognitive constructs which, according to prior research, are often key to emotional appraisal @xcite @xcite . first, agency reflects the role and accountability of an individual in a situation. second, social interaction reflects the relationship of an individual with others in the situation (family, friends, or peers). among many other appraisal dimensions, we find that agency and social interaction have also been explored in other perspectives on understanding how humans appraise happiness @xcite . these concepts were also applied to enrich the claff-happydb happiness dataset in @xcite , but our work offers the first instance of applying them for the computational modeling of happiness. furthermore, we extend the central premise of the authors to explore and model the duration of happiness. our choice of modeling the cognitive antecedents of emotions found no match in prior work. 3 firstly, representing emotion at different semantic levels of abstraction, such as word embeddings @xcite @xcite , phrase-and sentencelevel representations @xcite , or even paragraph- @xcite and documentlevel @xcite representations involve evoked emotion rather than grounded emotion @xcite . secondly, while finetuned llms like bert offer state-of-the-art performance in emotion prediction, they are harder to adapt to multi-task learning problems. a finetuned bert is also not ideal for knowledge transfer between multiple tasks. for instance, if we finetune bert for n emotions, then we will need n times parameters inside bert(n*110m), which would incur a huge memory overhead. furthermore, our goal is to let different psychological tasks interact with the emotion task in the latent semantic space, which would necessitate re-engineering the end-toend finetuning pipeline. these considerations made us opt for interactive adaptor modules with fewer parameters. ## methods psyam is a broad proposition which can generalize to other frameworks. we chose to design our experiments centered on psyam coupled with bert (bert-psyam), which offers the best and most challenging opportunity to evaluate our claim in the high-dimensional space. this section defines and discusses our multi-task learning architecture (figure 1 ), learning strategy, and the latent representations of appraisal dimensions. 4 in the rest of the paper, we have referred to the appraisal dimensions more generally as psychological constructs, and the adapter modules as psychological adaptor modules to suggest that the framework could generalize to include other kinds of cognitive and psychological antecedents of emotions. ## experiments our experiments address the question, how well do the psychological constructs of agency and social interaction aid an understanding of emotions and well-being? we evaluate the bert-psyam framework on 5 different settings based on happiness, emotions, and well-being datasets. through extensive experiments, we show how different variants of the bert-psyam framework compares favorably to other approaches. with an ablation study and qualitative exploration, we reiterate the critical role of cognitive appraisal in well-being and emotion prediction tasks. ## discussion our findings offer the following insights towards a future agenda of neural networks inspired by human psychology: ## conclusion the main advantage of the bert-psyam framework is a modeling paradigm that transfers to new domains and tasks for detecting emotions and, by extension, well-being. psychologists can use psyam to build and test new hypotheses about selfexpression, cognitive appraisal, and behavior. it could also be helpful in interventionist scenarios requiring live monitoring and reporting problematic social media posts. psyam achieved substantive improvements over the state-of-the-art bert alternatives, and we show that this is because of the contribution of the high-dimensional feature representation inside the adaptor modules. different feature fusion methods achieved different degrees of improvement in different settings, and different latent variables may add value in different problem contexts.