Interpretation Bias Research Articles

Social media and youth mental health: Simple narratives produce biased interpretations.

Many academics and pundits contend that social media use is the primary cause of an international youth mental health crisis. However, these claims often rely on correlational evidence, ignoring the confounding effects of developmental, environmental, social, and psychological factors that influence mental health. This oversimplifies the complex etiology of mental health problems. We call for a more nuanced understanding of the role of social media in youth mental health that avoids oversimplification. Additionally, we urge researchers to move beyond vague, narrative-driven verbal theories and encode them into precise, testable causal models. Using simulation techniques and specification curve analyses, we show how misspecified models that ignore these confounding factors can lead to biased conclusions about social media's adverse effects. This simplistic focus on social media use diverts attention from the broader factors contributing to youth mental health problems, hindering the development of effective interventions and support. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Legacy of Hermann Rorschach and His Inkblots: Portrait of a Man or Mental Disease?

Hermann Rorschach was a Swiss psychiatrist and psychoanalyst who experimented with inkblots for the purpose of diagnosing mental illness and personality temperaments. This paper highlights the legacy of Rorschach through discussion of the events that led to the inkblots' creation during Rorschach's life and after his death. The foundational elements of the inkblots were conceived in Rorschach's 1911 dissertation regarding reflex hallucinations, a form of synesthesia, that ultimately served as Rorschach's experimental focus and development of his iconic inkblots. After Rorschach's death in 1922, the inkblots were disseminated in America by Samuel Beck and Bruno Klopfer during the 1930s and 1940s while expanding on intelligence and personality metrics. Further research regarding the concept of a "group Rorschach'' was expanded by Molly Harrower and ultimately applied by Douglas Kelly and Gustav Gilbert to the Nazi Defendants at Nuremberg with the aim of conceptualizing the "Nazi personality." However, due to interpreter bias and conflicting interpretation, the results surrounding the Nazi Rorschachs remain controversial. Further controversy ensued after John Exner's attemptto standardize the Rorschach methodology while introducing new diagnostic metrics. Today, the Rorschach inkblots are criticized for their lack of validity and clinical relevance.

Effect of post thrombolytic intracerebral hemorrhage volume on 90-day outcomes in acute ischemic stroke patients

Background and PurposePost thrombolytic intracerebral hemorrhage (ICH) is associated with higher rate of death or disability in acute ischemic stroke patients. We investigated the relationship between post thrombolytic ICH volume and change in volume and death or disability at 90 days in acute ischemic stroke patients. MethodsWe analyzed 110 patents recruited in the Safety Evaluation of 3K3A-APC in Ischemic Stroke (RHAPSODY) trial who received intravenous tissue plasminogen activator (tPA) followed by mechanical thrombectomy (if indicated) and 3K3A-APC or placebo. ICH volume was measured at Day 2 and Day 7 using susceptibility weighted sequence (SWI) on magnetic resonance imaging (MRI). We also calculated the post thrombolytic ICH volume change between Day 2 and Day 7. Outcomes were determined by using utility weighted modified Rankin scale (UW-mRs) at 90-days, Outcomes were determined by using utility weighted modified Rankin scale (UW-mRS) at 90 days. To minimize interpretation bias, outcome assessors were blinded to the treatment allocation and clinical data.We adjusted for age, gender, National Institutes of Health Stroke Scale (NIHSS) score (<10,10-19 and ≥20), location of hemorrhage (single basal ganglia hemorrhage, single lobar, single cerebellum, and multiple sites) in multivariate regression analysis. ResultsA total of 88 (80%) of 110 patients had post thrombolytic ICH (mean volume 28.3 ml ± SD 62 ml). The strata of ICH volume were not associated with UW-mRs at 90 days: <20 cc (regression coefficient (RC)-0.05, p= 0.58), 20-39 cc (RC-0.22, p=0.17), or ≥40 cc (RC-0.34, p= 0.083) compared with no ICH after adjusting for potential confounders. Change in ICH mean volume 26.78 ml ±59.68, 52 had increase in volume) between Day 2 and day 7 was not associated with UW-mRS at 90 days (RC -67.71, p= 0.06). ConclusionsWe did not observe any independent effect of post thrombolytic ICH volume on death or disability in acute ischemic stroke patients. Although further studies must be done, our data suggest that strategies to prevent ICH expansion such as antifibrinolytic medications and reduction in ICH volume such as surgical evacuation may not reduce death or disability in acute ischemic stroke patients with post thrombolytic ICH.

Mapping cognitive biases in multidisciplinary team (MDT) decision-making for cancer care in Scotland: a cognitive ethnography study protocol

IntroductionThe efficiency of multidisciplinary teams (MDTs) in cancer care hinges on facilitating clinicians’ cognitive processes as they navigate complex and uncertain judgements during treatment planning. When systems and workflows are...

Adaptation and validation of a suicide-focused Word Sentence Association Paradigm to assess suicide-specific interpretation biases

The cognitive model of suicide proposes that biased cognitive processes contribute to suicidal thoughts and behaviors, and initial evidence suggests that attentional bias to suicide-congruent cues may predict suicidal behavior beyond established clinical risk factors. To date, much less work has explored the potential role of interpretation biases in the development and prediction of suicide risk. The present study assessed the psychometric properties, including reliability, concurrent validity, and predictive validity, of a novel adaptation of the Word Sentence Association Paradigm (WSAP) to assess suicide-suicide interpretation biases. In a sample of 239 psychiatric inpatients, including patients with no recent suicidal ideation or lifetime attempts (n = 35), those with recent suicide attempts (n = 29), and those with recent suicidal ideation (n = 174), participants completed the modified WSAP as well as self-reported suicidal ideation and attempts for the subsequent 6 months. Although the WSAP demonstrated good reliability, evidence of concurrent and prospective validity (in terms of self-reported suicidal ideation and attempts) was limited. Specifically, the clinical groups did not differ from one another on any measure of interpretation bias, nor did suicide-specific interpretation endorsements predict concurrent or future suicidal ideation when controlling for dysphoric interpretation bias. However, suicide-specific interpretation biases were uniquely associated with a history of a lifetime suicide attempt. Future work should further clarify the extent and specificity of the relationship between suicide-specific interpretation biases and clinical outcomes.

An intervention study on college students’ employment anxiety based on interpretation bias modification: A randomized controlled experiment

External environmental factors and internal cognitive bias affect college students' anxiety while job hunting. The current study is an intervention study on alleviating employment anxiety among college students through an Interpretation Bias Modification (IBM) computer-based intervention. A total of 79 valid participants were recruited. The Interpretation Bias Modification (IBM) group participants were required to complete employment-related IBM Internet training twice a week for three weeks. The placebo control group participants were required to complete neutral Internet training at the same frequency. The waiting list control group did not undergo any training. The groups were tested at three time points: prior to the intervention (pre-test), immediately after (post-test), and one month after the intervention (one-month follow-up). The IBM intervention group [F(2, 72) = 31.68, p < 0.001] showed greater significance in reducing employment anxiety than participants in the placebo control group [F(2, 72) = 9.83, p < 0.001] from the pre-test to one-month follow-up. There was no significant difference in employment anxiety among the waiting-list control group over time. The IBM intervention for college students can effectively decrease employment anxiety and reduce interpretation bias, which can be maintained at the one-month follow-up.

Exploring the association between neuroticism and negativity bias in evaluative counterconditioning

People high on neuroticism express a negativity bias in attention, memory, and interpretation, giving primacy to negative rather than positive information. Investigating additional forms of negativity bias in neuroticism could provide further insights into the situations where people who score high on neuroticism tend to remain anchored on negative aspects. We examined whether neuroticism is related to negativity bias in evaluative learning. Instruction-based and experience-based counterconditioning are two evaluative learning procedures in which a conditioned stimulus (CS) is related to a positive or a negative unconditioned stimulus (US) in Phase 1 and with a US of the opposite valence in Phase 2. Using these procedures, we obtained partial support for a negativity bias in counterconditioning among people who scored high on neuroticism. Specifically, participants scoring high on negative emotionality altered the CSs ratings more easily from positive to negative than from negative to positive, a pattern observed in instruction-based counterconditioning (Experiment 1) but not in experience-based counterconditioning (Experiment 2). In line with previous findings, we could conclude that people who score high on neuroticism tend to give primacy to emotionally negative information at the expense of positive information, particularly in ambiguous (learning) contexts.

Leveraging Zero-Effort Technology for Quantification of Sleep Quality Metrics.

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual's sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions.

Evaluating the Reliability of the Word-Sentence Association Paradigm (WSAP) as an Interpretation bias Assessment across Ethnoracial Groups

Evaluating the Reliability of the Word-Sentence Association Paradigm (WSAP) as an Interpretation bias Assessment across Ethnoracial Groups

Negative interpretation bias towards ambiguous facial expressions in individuals with high empathy

Negative interpretation bias towards ambiguous facial expressions in individuals with high empathy

Predictive roles of cognitive biases in health anxiety: A machine learning approach.

Prior work suggests that cognitive biases may contribute to health anxiety. Yet there is little research investigating how biased attention, interpretation, and memory for health threats are collectively associated with health anxiety, as well as the relative importance of these cognitive processes in predicting health anxiety. This study aimed to build a prediction model for health anxiety with multiple cognitive biases as potential predictors and to identify the biased cognitive processes that best predict individual differences in health anxiety. A machine learning algorithm (elastic net) was performed to recognise the predictors of health anxiety, using various tasks of attention, interpretation, and memory measured across behavioural, self-reported, and computational modelling approaches. Participants were 196 university students with a range of health anxiety severity from mild to severe. The results showed that only the interpretation bias for illness and the attention bias towards symptoms significantly contributed to the prediction model of health anxiety, with both biases having positive weights and the former being the most important predictor. These findings underscore the central role of illness-related interpretation bias and suggest that combined cognitive bias modification may be a promising method for alleviating health anxiety.

Rodrigo Duterte as “the Trump of Asia”? The Limits and Pitfalls of Thin Comparison

The former Philippine president Rodrigo Duterte often gets lumped together with the former U.S. president Donald Trump on the basis of their autocratic practices. However, there are crucial differences in their respective situations and the reception of their autocratic practices on the ground. Consequently, if we understand Duterte only through Trump or with reference to the category of “emerging autocrat” then we end up misunderstanding the situation in the Philippines. This article is about the limits and pitfalls of thin comparison and the necessity of thicker ones. It advocates understanding practices embedded in the social structures and processes that make them meaningful. I proceed as follows: I present the case of Rodrigo Duterte and review the grounds for lumping him along with Trump as an emerging autocrat. I then pursue a thicker comparison of Duterte and Trump, putting their support in economic, social, political, and historical context and highlighting substantial differences in their respective situations. Finally, I argue that thin comparisons are especially susceptible to interpretive biases in general and highlight three such biases—toward contemporaneity, continuity, and centrality—using the case of Duterte to illustrate.

Assessing the extent and determinants of socioeconomic inequalities in epilepsy in the UK: a systematic review and meta-analysis of evidence

Socioeconomic inequalities in epilepsy incidence and its adverse outcomes are documented internationally, yet the extent of inequalities and factors influencing the association can differ between countries. A UK public health response to epilepsy, which prevents epilepsy without widening inequalities, is required. However, the data on UK epilepsy inequalities have not been synthesised in a review and the underlying determinants are unknown. In this systematic review and meta-analysis, we searched six bibliographic databases (MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus) and grey literature published between Jan 1, 1980, and Feb 21, 2024, to identify UK studies reporting epilepsy incidence or epilepsy-related adverse outcomes by socioeconomic factors (individual level or area level). We included longitudinal cohort studies, studies using routinely collected health-care data, cross-sectional studies, and matched cohort studies and excluded conference abstracts and studies not reporting empirical results in the review and meta-analysis. Multiple reviewers (KJB, EC, SER, WOP, and RHT) independently screened studies, KJB extracted data from included studies and a second reviewer (SM or EC) checked data extraction. We used Critical Appraisal Skills Programme checklists to assess quality. We used random-effects meta-analysis to pool incident rate ratios (IRRs) and synthesised results on adverse outcomes narratively. This study was registered on PROPSPERO (CRD42023394143). We identified 2471 unique studies from database searches. We included 26 studies, ten of which reported epilepsy incidence and 16 reported epilepsy-related adverse outcomes according to socioeconomic factors. Misclassification, participation, and interpretive biases were identified as study quality limitations. Meta-analyses showed an association between socioeconomic deprivation and epilepsy incidence, with greater risks of epilepsy incidence in groups of high-deprivation (IRR 1·34 [95% CI 1·16-1·56]; I2=85%) and medium-deprivation (IRR 1·23 [95% CI 1·08-1·39]; I2=63%) compared with low-deprivation groups. This association persisted in the studies that only included children (high vs low: IRR 1·36 [95% CI 1·19-1·57]; I2=0%). Only two studies examined factors influencing epilepsy incidence. There is limited evidence regarding UK inequalities in adverse outcomes. Socioeconomic inequalities in epilepsy incidence are evident in the UK. To develop an evidence-based public health response to epilepsy, further research is needed to understand the populations affected, factors determining the association, and the extent of inequalities in adverse outcomes. Epilepsy Research Institute UK.

Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models

ObjectiveThis study aimed to leverage real-world electronic medical record data to develop interpretable machine learning models for diagnosis of Kawasaki disease while also exploring and prioritizing the significant risk factors. MethodsA comprehensive study was conducted on 4087 pediatric patients at the Children’s Hospital of Chongqing, China. The study collected demographic data, physical examination results, and laboratory findings. Statistical analyses were performed using IBM SPSS Statistics, Version 26.0. The optimal feature subset was used to develop intelligent diagnostic prediction models based on the Light Gradient Boosting Machine, Explainable Boosting Machine (EBM), Gradient Boosting Classifier (GBC), Fast Interpretable Greedy-Tree Sums, Decision Tree, AdaBoost Classifier, and Logistic Regression. Model performance was evaluated in three dimensions: discriminative ability via receiver operating characteristic curves, calibration accuracy using calibration curves, and interpretability through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations). ResultsIn this study, Kawasaki disease was diagnosed in 2971 participants. Analysis was conducted on 31 indicators, including red blood cell distribution width and erythrocyte sedimentation rate. The EBM model demonstrated superior performance relative to other models, with an area under the curve of 0.97, second only to the GBC model. Furthermore, the EBM model exhibited the highest calibration accuracy and maintained its interpretability without relying on external analytical tools such as SHAP and LIME, thus reducing interpretation biases. Platelet distribution width, total protein, and erythrocyte sedimentation rate were identified by the model as significant predictors for the diagnosis of Kawasaki disease. ConclusionThis study used diverse machine learning models for early diagnosis of Kawasaki disease. The findings demonstrated that interpretable models such as EBM outperformed traditional machine learning models in terms of both interpretability and performance. Ensuring consistency between predictive models and clinical evidence is crucial for the successful integration of artificial intelligence into real-world clinical practice.

A Breakdown of the Black Hole–Bulge Mass Relation in Local Active Galaxies

We investigate the relation between black hole (BH) mass and bulge stellar mass for a sample of 117 local (z ∼ 0) galaxies hosting low-luminosity, broad-line active galactic nuclei (AGNs). Our sample comes from Reines & Volonteri, who found that, for a given total stellar mass, these AGNs have BH masses more than an order of magnitude smaller than those in early-type galaxies with quiescent BHs. Here, we aim to determine whether or not this AGN sample falls on the canonical BH-to-bulge mass relation by utilizing bulge–disk decompositions and determining bulge stellar masses using color-dependent mass-to-light ratios. We find that our AGN sample remains offset by more than an order of magnitude from the M BH–M bulge relation defined by early-type galaxies with dynamically detected BHs. We caution that using canonical BH-to-bulge mass relations for galaxies other than ellipticals and bulge-dominated systems may lead to highly biased interpretations. This work bears directly on predictions for gravitational-wave detections and cosmological simulations that are tied to the local BH-to-bulge mass relations.

Acceptability and Engagement of a Smartphone-Delivered Interpretation Bias Intervention in a Sample of Black and Latinx Adults: Open Trial.

Access to evidence-based interventions is urgently required, especially for individuals of minoritized identities who experience unique barriers to mental health care. Digital mental health interventions have the potential to increase accessibility. Previous pilot studies testing HabitWorks, a smartphone app providing an interpretation bias intervention, have found strong engagement and adherence for HabitWorks; however, previous trials' samples consisted of predominantly non-Hispanic, White individuals. This study conducted an open trial of HabitWorks in a community sample of adults who identified as Black, Hispanic or Latinx, or both. This study aims to test safety, acceptability, and engagement with the HabitWorks app for Black and Latinx adults. Black, Hispanic or Latinx adults (mean age 32.83, SD 11.06 y; 22/31, 71% women) who endorsed symptoms of anxiety or depression were asked to complete interpretation modification exercises via HabitWorks 3 times per week for 1 month. Interpretation bias and anxiety and depression symptoms were assessed at baseline and posttreatment assessments. Participants completed qualitative interviews to assess overall perceptions of HabitWorks. Of the 31 participants that downloaded the app, 27 (87%) used HabitWorks all 4 weeks. On average, participants completed 15.74 (SD 7.43) exercises out of the 12 prescribed, demonstrating high engagement. Acceptability ratings met all a priori benchmarks except for relevancy. Qualitative interviews also demonstrated high acceptability and few negative experiences. Significant improvements were found in interpretation style (t30=2.29; P<.001), with a large effect size (Cohen d=1.53); anxiety symptoms (t30=2.29; P=.03), with a small effect size (Cohen d=0.41); and depression symptoms (t30=3.065; P=.005), with a medium effect size (Cohen d=0.55). This study adds to the literature evaluating digital mental health interventions in Black and Latinx adults. Preliminary results further support a future controlled trial testing the effectiveness of HabitWorks as an intervention.

When and how is depression associated with ostracism among college students? The mediating role of interpretation bias and the moderating role of awareness rather than acceptance.

Depression is closely related to individual social functions. The current study aimed to examine whether depression is associated with ostracism, whether interpretation bias mediates this relationship, and whether trait mindfulness moderates direct and indirect relationships between depression and ostracism. Overall, 389 Chinese college students completed the Center for Epidemiological Survey, Depression Scale, Interpretation Bias Questionnaire, Philadelphia mindfulness scale, and perceived ostracism scale at two-time points. Latent Profile analysis and moderated mediation analysis were performed. After controlling for sex and age, depression (t1) was positively correlated to perceived ostracism, with this relationship being partially mediated by negative interpretation bias (IBN, t2). The effect of IBN on perceived ostracism was weak when awareness was high at time 2. Acceptance had a non-significant moderating role in the relationship between IBN and perceived ostracism at time 2. LPA delineated three profiles: high awareness, high acceptance, and medium mindfulness. The moderating role of the different profiles in the relationship between IBN (t2) and perceived ostracism (t2) was significant. Depressed individuals appear to experience more ostracism because of IBN. Awareness might alleviate the effect of IBN on perceived ostracism.

The Role of Pain and Interpretation Bias in Fear of Disease Progression in People With Diabetes

Research indicates that fear of progression (FoP) may be a transdiagnostic construct underlying anxiety in people with chronic health conditions. Theories propose that the interpretation of illness-related symptoms (such as pain) might be an important mechanism driving the development of FoP. However, FoP has rarely been studied in diabetes. In this study, we assessed FoP, pain, health threat-related interpretation bias, emotional states, and treatment adherence in 198 participants with type 1 or type 2 diabetes and an age- and gender-matched control group. We hypothesized that 1) people with diabetes will be more likely to interpret ambiguous stimuli as health threat-related than people without diabetes; 2) among those with diabetes, pain severity and interpretation bias will be associated with more severe levels of FoP; and 3) interpretation bias will moderate the relationship between pain severity and FoP in people with diabetes, such that the positive association between pain and FoP will become stronger when people have greater interpretation bias. Our results confirmed that people with diabetes were more likely to interpret ambiguous information as health threat-related compared with people without diabetes, with a large effect (d = .84). Moreover, people with diabetes who reported persistent pain were more likely to interpret ambiguous stimuli as health threat-related (d = .49). Among people with diabetes, pain severity and interpretation bias were significantly associated with FoP. However, we did not find evidence that interpretation bias moderated the relationship between pain and FoP and these relationships could not be accounted for by general psychopathology. PerspectivePeople with diabetes had greater health threat-related interpretation bias than people without diabetes, especially for those with persistent pain and more severe FoP. Both pain severity and interpretation bias were associated with greater FoP, but interpretation bias did not moderate the relationship between pain and FoP.

A randomized controlled trial modifying insomnia-consistent interpretation bias in students

This study aimed to determine the causal role of insomnia-consistent interpretation bias within the cognitive model of insomnia, by modifying this bias in students experiencing subclinical levels of insomnia and assessing subsequent effects on sleep parameters. A sample of 128 students underwent randomization to receive either a single session of online Cognitive Bias Modification-Interpretation (CBM-I) or a sham training. Participants then tracked their pre-sleep worry and sleep parameters for seven consecutive days. Interpretation bias was assessed using an encoding-recognition task specifically designed for insomnia-related interpretation bias. The CBM-I manipulation utilized ambiguous scenarios to redirect participants away from making insomnia-related interpretations. Results revealed that CBM-I effectively decreased insomnia-consistent interpretation bias compared to the sham treatment, with interpretation bias being absent post-training in the CBM-I group. This reduction did not lead to improvements in pre-sleep worry or any sleep parameters. This study has been the first to investigate the causal role of interpretation bias on symptoms of insomnia. Although results indicated this bias to be modifiable, its causality within the cognitive model proves to be more complicated. Future research focusing on optimization of cognitive bias modifications could shed more light on the effects of biased cognitions on insomnia symptoms.

Pronoun interpretation in intermediate-advanced L2 English speakers: L2 to L1 cross-linguistic effects

Aims and objectives: L2 learners who speak a null subject (L1 Spanish) and a non-null subject language (L2 English) may experience cross-linguistic interference from the L2 on L1 pronoun interpretation. In this study, we test pronoun interpretation in the L1 and L2 of adult learners, in comparison with two groups of monolingual speakers, to assess if L1 pronoun interpretation can change as a result of L2 acquisition at intermediate-advanced levels of L2 proficiency and in the absence of long L2 immersion. Methodology: A group of L2 English speakers (L1 Spanish) participated in two offline sentence comprehension tasks where they interpreted pronouns in the L1 and L2. The results are compared with English and Spanish monolingual speakers. Data and analysis: We find that adult English learners comprehend pronouns in their L1 (Spanish) differently than Spanish monolingual speakers, demonstrating a strong subject bias for interpreting null and overt pronouns. In addition, we show that pronoun interpretation patterns acquired in the L2 explain the changes to L1 interpretation biases. Conclusions: The results of this study significantly advance the understanding of the factors that contribute to bilingual language comprehension, showing the permeability of the L1 comprehension system at the discourse level as soon as the L2 sets in.