When It Pays to Be Insincere: On the Benefits of Verbal Irony

Verbal irony is pervasive in social interaction, presumably because it can be used to achieve a number of communicative goals and effects. In general, verbal irony has a reputation for having negative effects, but in this article we present evidence for the cognitive, social, and emotional benefits of verbal irony and demonstrate the potential of this form of language to provide crucial psychological insights. The power of irony lies in its ability to create meaning that is in conflict with the literal meaning—thus altering our understanding of it and by doing so enhancing cognition, mediating emotions, or shaping social relationships.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The comprehension of irony in high and low emotional contexts

Verbal irony is when words intend the opposite of their literal meaning. We investigated the emotional function of irony by asking whether irony intensifies or mitigates negative feelings. Experiment 1 used ratings to assess the mental state of a speaker using irony or literal language following a negative event in either a high- or a low-emotional context. We found that regardless of context emotionality, speakers using irony were perceived as being in a less negative and less aroused mental state than speakers using literal language. In Experiment 2, we examined the time course of this process with ERPs. Initially, literal statements elicited a larger N100 than irony, regardless of context emotionality, suggesting that irony mitigates negative feelings overall. Later on, irony elicited a larger LPC than literal statements in high emotion contexts, but not in low emotion contexts. This suggests that irony required more mental state processing or/and more speaker emotion processing than literal language in emotionally loaded situations. These results indicate that whether irony intensifies or mitigates negative feelings depends on context and the point in time at which we assess its function. Public Significance Statement—Using brainwave and behavioral measures, we found that in a negative situation, people initially find literal statements more threatening and irony more difficult to process. After they have a second to integrate and re-analyze semantic, pragmatic, and emotional information, they think that the person using irony is less negatively impacted by the emotional situation. This study contributes to a broader understanding on the interaction between emotion and language.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Cursed Concepts: New insights on combinatorial processing from ERP correlates of swearing in context

Expressives (damn) convey speaker attitude and when used in context (Tom lost the damn dog) can be flexibly applied locally to the noun (dog) or globally to the whole sentence (the situation). We used ERPs to explore brain responses to expressives in sentences. Participants read expressive, descriptive, and pseudoword adjectives followed by nouns in sentences (The damn/black/flerg dog peed on the couch). At the adjective late-positivity-component (LPC), expressives and descriptives showed no difference, suggesting reduced social threat and that readers employ a ‘wait-and-see’ strategy to interpret expressives. Nouns preceded by expressives elicited a larger frontal P200, as well as reduced N400 and LPC than nouns preceded by descriptives. We associated the frontal P200 with emotional salience, the frontal N400 with mental imagery, and the LPC with cognitive load for combinatorics. We suggest that expressive adjectives are not bound to conceptual integration and conclude that parsers wait-and-see what is being damned.12 month embargo; available online: 13 January 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Do all facial emojis communicate emotion? The impact of facial emojis on perceived sender emotion and text processing

Facial emojis can express a variety of positive and negative emotions, and are commonly used in digital, written communication. However, little is known about how emojis impact text processing and how different emoji-text combinations give rise to a sender's mental state. In this study, we investigated how facial emojis with positive valence (= happy emojis) and facial emojis with negative valence (= upset emojis) embedded in emotionally ambiguous/neutral text affect the perceived mental state of the sender using ratings (Experiment 1) and the processing of the text messages using Event-Related Potentials (Experiment 2). We predicted that (1) the same text message with happy and upset emojis would convey different sender mental states, and (2) emoji valence would affect the processing of subsequent text in valence-specific ways. Our Experiment 1 results showed that while texts with upset emojis convey specific sender mental states, texts with happy emojis convey positive emotion more generally, with no further differentiation between emojis. In ERPs (Experiment 2), we found that emojis affect subsequent text processing at N400, and emoji valence affects processing downstream at the second word. We concluded that all facial-emojis impact text processing, but happy and upset emojis carry differential social-emotional salience and impact text processing differently when content becomes available.24 month embargo; available online: 8 September 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Narratives about Cancer: What Metaphors can tell us about Depressive Symptoms in Breast Cancer Patients

Metaphors are pervasive in cancer discourse. However, little is known about how metaphor use develops over time within the same patient, and how metaphor use and its content relate to the mental health of the patient. Here, we analyzed metaphor use in personal essays written by breast cancer patients shortly after the time of diagnosis and nine months later, in relation to their depressive symptoms at both time points. Results show that metaphor use can provide important insight into a patient’s current mental state. Specifically, patients who had no change in their depressive symptom levels used metaphors more densely after nine months. In addition, metaphor valence in the later essay was associated with depressive symptoms at study entry and nine months after. Lastly, we observed a shift in metaphor reference pattern for different symptom trajectories, such that those who recovered from initially elevated depressive symptoms used fewer self-referencing metaphors and more cancer-referencing metaphors in their later essay. Our work suggests that metaphor use reflects how a patient is coping with their diagnosis.18 month embargo; first published 09 August 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Can irony regulate negative emotion? Evidence from behaviour and ERPs

This study used ratings and event-related potentials (ERPs) to compare the mechanisms through which verbal irony and cognitive reappraisal mitigate negative emotion. Verbal irony is when the literal meaning of words contrasts with their intended meaning. Cognitive reappraisal is when we reconsider emotional stimuli to make them less intense. Our hypothesis was that cognitive reappraisal is a potential mechanism through which irony reduces negative emotion. Participants viewed mildly negative pictures first, then read an ironic or literal statement about it in one block, and used cognitive reappraisal of or attending to the picture in the other block. Participants then viewed the picture for a second time, before rating how negative they felt. Behaviourally, irony reduced negative feelings more than literal statements, and reappraisal reduced negative feelings more than attending, with a larger reduction from reappraisal than from irony. In ERPs, irony elicited a prolonged N400 compared to literal, indexing an initial contrast between picture and word affect and sustained processing of their combination. Cognitive reappraisal elicited a larger late positivity compared to attending at the instruction screen. No differences were found during second picture presentation. These findings suggest that irony and cognitive reappraisal can reduce negative affect in different ways.12 month embargo; first published 16 April 2024This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

How ready is speech-to-text for psychological language research? Evaluating the validity of AI-generated English transcripts for analyzing free-spoken responses in younger and older adults

For the longest time, the gold standard in preparing spoken language corpora for text analysis in psychology was using human transcription. However, such standard comes at extensive cost, and creates barriers to quantitative spoken language analysis that recent advances in speech-to-text technology could address. The current study quantifies the accuracy of AI-generated transcripts compared to human-corrected transcripts across younger (n = 100) and older (n = 92) adults and two spoken language tasks. Further, it evaluates the validity of Linguistic Inquiry and Word Count (LIWC)-features extracted from these two kinds of transcripts, as well as transcripts specifically prepared for LIWC analyses via tagging. We find that overall, AI-generated transcripts are highly accurate with a word error rate of 2.50% to 3.36%, albeit being slightly less accurate for younger compared to older adults. LIWC features extracted from either transcripts are highly correlated, while the tagging procedure significantly alters filler word categories. Based on these results, automatic speech-to-text appears to be ready for psychological language research when using spoken language tasks in relatively quiet environments, unless filler words are of interest to researchers.National Institutes of Health12 month embargo; first published 21 May 2024This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

Introduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. Methods: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. Results: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho = 0.529, p < 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho = -0.441, p < 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1 ± 1.2% and 76.3 ± 1.3%, respectively. Discussion: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.12 month embargo; first published 12 January 2024This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

PTEN opposes negative selection and enables oncogenic transformation of pre-B cells

PTEN is a negative regulator of PI3K-AKT signaling and a potent tumor suppressor in many types of cancer. To test a tumor suppressive role of PTEN in pre-B acute lymphoblastic leukemia (ALL), we induced Cre-mediated deletion of Pten in mouse models of pre-B ALL. In contrast to its role as a tumor suppressor in other cancers, loss of one or both alleles of Pten caused rapid cell death of pre-B ALL cells and was sufficient to clear transplant recipient mice of leukemia. Small molecule inhibition of PTEN in human pre-B ALL cells resulted in AKT hyperactivation, p53 checkpoint activation and cell death. Loss of PTEN function in pre-B ALL cells was functionally equivalent to acute activation of autoreactive pre-BCR signaling, which engaged a deletional checkpoint for removal of autoreactive B cells. We propose that targeted inhibition of PTEN and hyperactivation of AKT triggers a checkpoint for elimination of autoreactive B cells and represents a new strategy to overcome drug-resistance in human ALL