A friend sends you a message late at night. We need to talk. No smiley face, no explanation, no clue whether this is good news, bad news or something completely ordinary. For people experiencing anxiety, those guesses may lean more heavily towards threat. Within seconds, the mind starts whirring. Have I said something wrong? Are they annoyed with me?
Cognitive theories of anxiety propose that when situations are unclear, people become more likely to interpret information in ways that emphasise danger, rejection or threat (Beck & Clark, 1997). The unanswered message becomes evidence that someone is upset. A racing heart becomes a sign that something is physically wrong.
Life is full of situations where information is incomplete, and our brains need to fill in the gaps. Research has linked these interpretation biases to anxiety for many years, identifying this as an important factor in maintaining emotional difficulties (Hirsch & Mathews, 2012). Individual studies, however, often tell slightly different stories because they vary in who they include, how they measure interpretation bias and the types of anxiety they examine.
Würtz et al. (2026) therefore brought together the existing evidence to ask whether anxiety is associated with a greater tendency to interpret ambiguous information negatively, and what might influence the strength of this relationship.
Methods
The authors searched three major databases, alongside reference lists from previous reviews and unpublished datasets identified through professional networks. Studies were eligible if they examined the relationship between interpretation bias and anxiety, including diagnosed anxiety disorders, subclinical or non-specific anxiety symptoms, and at-risk samples. Studies were excluded if interpretation processes had already been experimentally altered before measurement or if other conditions could substantially influence interpretation processes.
Two independent raters screened articles and extracted data. They assessed risk of bias using an adapted version of the Downs and Black checklist (1998), examining reporting quality, validity and statistical power. The risk-of-bias assessment identified several methodological limitations, particularly around representativeness, the validity and reliability of interpretation-bias measures, and control of potential confounding variables.
The authors used a three-level meta-analysis, which allowed multiple effect sizes from the same study to be analysed together without treating them as independent findings. This reduces the chance that studies reporting many outcomes contribute disproportionately to the overall result.
Results
The review combined evidence from 265 studies, drawing on 295 independent samples, more than 50,000 participants and 1,450 comparisons.
The overall effect size was medium and statistically significant (g = 0.48, 95% CI [0.43 to 0.52], p < .001), indicating that higher anxiety is associated with more negative interpretation biases. However, the prediction interval was wide (95% PI [-0.43 to 1.38]), suggesting that the strength of this relationship could vary considerably across different studies and contexts. This association held after removing statistical outliers.
The picture becomes more nuanced when examining moderators.
Measurement matters considerably
Studies using direct measures, where participants explicitly rate how they would interpret scenarios, showed substantially larger effects than indirect measures based on reaction times (difference of g = 0.40, 95% CI [0.28 to 0.52]). Both findings were significant, but this gap raises important questions about what each approach captures.
Content specificity was important
Stronger effects emerged when scenarios matched a person’s specific concerns. When stimuli matched the specific concerns of a given disorder (e.g., bodily sensations for panic, social situations for social anxiety), effect sizes were notably larger than for generic threat stimuli (difference of g = 0.17, 95% CI [0.10 to 0.25]).
The review also found that verbal stimuli produced stronger effects than nonverbal stimuli (difference g = 0.24, 95% CI [0.10 to 0.39]). One explanation is that written scenarios provide greater experimental control, while visual information can contain many competing cues.
Negative biases dominated
The relationship with anxiety was stronger for actively making negative interpretations than for simply failing to generate positive alternatives. Studies assessing negative interpretations produced the largest effect (g = 0.60), followed by measures comparing negative and positive interpretations (g = 0.51). Measures assessing positive interpretations produced a smaller effect (g = 0.22).
This suggests that the association with anxiety is stronger when interpretation bias is measured through negative interpretations than when it is measured through positive interpretations alone.
What didn’t appear to make a difference
The authors did not find clear evidence that several other factors moderated the association, including differences between children and adults, clinical and non-clinical samples, different anxiety disorders, excluding depression, or comorbid anxiety disorders.
Conclusions
The authors concluded that their findings support the transdiagnostic relevance of interpretation biases:
Overall, results are consistent with the view that anxiety is associated with [interpretation biases] across different disorders and severity levels. These findings have implications for cognitive theories of anxiety and clinical interventions.
They emphasise that whilst methodological factors significantly influence effect sizes, there was no clear evidence that the sample characteristics examined did so. This suggests interpretation biases appear across different anxiety presentations, age groups and levels of symptom severity, although considerable unexplained variation between studies remained.
Strengths and limitations
Würtz and colleagues provide support for cognitive theories that place interpretation biases at the centre of anxiety (Beck & Clark, 1997; Hirsch & Mathews, 2012). The size of the evidence base also allowed them to examine factors that may shape the strength of this association, including how interpretation bias is measured and whether the content reflects a person’s specific concerns.
The review followed a careful process. It was preregistered, followed PRISMA guidance, and studies were screened and coded independently by multiple reviewers. Risk of bias was assessed formally, and the authors made their data and analysis code openly available. They also conducted several checks for publication and small-study bias. These found no clear sign of bias, although a worst-case analysis using only studies without positive results cut the effect to close to zero (g = 0.05), which was still statistically significant.
The findings varied according to how interpretation bias was measured, with stronger effects for direct measures than indirect tasks. Direct and indirect measures may capture different aspects of interpretation bias, making it difficult to know whether these differences reflect genuine differences in interpretation bias or differences in what each task assesses (Hirsch et al., 2016).
Considerable variation across studies remained even after accounting for factors including age, anxiety presentation and measurement type. This unexplained variation limits how precisely the overall effect can be interpreted. It should be understood as an average across a diverse evidence base, rather than as an estimate that will apply equally across every study or context.
The review also leaves an important question unresolved. The evidence could not determine the direction of the relationship between interpretation bias and anxiety. It remains unclear whether anxiety increases the tendency to interpret ambiguity as threatening, whether threat-focused interpretations contribute to anxiety, or whether these processes influence one another over time. Establishing direction matters clinically, since interpretation bias would represent a stronger target for prevention or treatment if it contributes to anxiety rather than primarily accompanying it.
Finally, potentially important factors, including ethnicity and socioeconomic status, could not be examined due to insufficient data, leaving uncertainty around how broadly these findings apply.
Implications for practice
The findings from this review do not suggest an immediate change in clinical practice. Cognitive approaches to treatment already encourage people to explore the meanings attached to situations, thoughts and physical sensations. This review therefore provides support for existing approaches rather than introducing a new target for intervention. Previous reviews examining cognitive bias modification have suggested that changing interpretation processes may influence emotional outcomes, though findings remain mixed (Hallion & Ruscio, 2011; Jones & Sharpe, 2017).
The most interesting implications from this research may sit outside traditional treatment settings. The consistency of the findings raises questions around whether interpretation processes could become useful targets for earlier and more scalable forms of support. Mental health services continue to face increasing demand and long waiting lists, creating growing interest in approaches that can be delivered before difficulties become more severe. Digital interventions, school-based programmes and lower-intensity services specifically designed to address interpretation biases may provide additional opportunities for this work. However, this review cannot establish whether changing interpretation biases would prevent anxiety or improve outcomes, so these remain possibilities for future intervention research rather than implications for current practice.
There are also important questions for future research. The review shows that interpretation bias and anxiety are closely linked, though it remains unclear whether these thinking patterns contribute to emotional difficulties or simply accompany them. Longitudinal and experimental studies would help clarify whether changing interpretation patterns leads to meaningful changes in outcomes. This would help establish whether interpretation bias represents a modifiable mechanism of anxiety, and strengthen the case for targeting it directly in prevention or treatment.
The findings also raise questions around who may benefit most from intervention. Interpretation biases appear strongest for the concerns that matter most to an individual, although they were also clear for more general threat. This supports assessment and intervention that focus on the personally meaningful situations, sensations or social contexts linked to a person’s anxiety, consistent with previous work in social anxiety and panic research (Chen et al., 2020; Ohst & Tuschen- Caffier, 2018).
Similarly, the review was unable to examine factors including socioeconomic status and ethnicity in detail, despite wider evidence showing inequalities in mental health experiences and access to care. Understanding whether these interpretation processes operate similarly across different groups may become important for developing interventions that are useful beyond controlled research settings.
Returning to the message from the beginning of this blog, perhaps the most useful question is not whether people expect the worst, but what happens once one explanation begins to take hold. By morning, we need to talk has transformed into a difficult conversation that never actually existed. The challenge may sit less in uncertainty itself and more in how quickly uncertainty can become reduced to a single, negative meaning.
Statement of interests
Helen England has no conflicts of interest to declare. AI tools were used during early planning, developing an outline and initial drafting. The critical appraisal, interpretation of the evidence and final writing were conducted by Helen.
Edited by
Dr Nina Higson-Sweeney.
Links
Primary paper
Felix Würtz, Marius Kunna, Simon E. Blackwell, Chiara Lindgraf, Elinor Abado, Yagmur Amanvermez, Jürgen Margraf, Jonas Everaert, & Marcella L. Woud (2026). Interpretation biases in anxiety: A three-level meta-analysis. Clinical Psychological Science. https://doi.org/10.1177/21677026251392855
Other references
Beck, A. T., & Clark, D. A. (1997). An information processing model of anxiety. Behaviour Research and Therapy, 35(1), 49–58. https://doi.org/10.1016/S0005-7967(96)00069-1
Chen, J., Short, M., & Kemps, E. (2020). Interpretation bias in social anxiety. A systematic review and meta-analysis. Journal of Affective Disorders, 276, 1119–1130. https://doi.org/10.1016/j.jad.2020.07.121
Downs, S. H., & Black, N. (1998). The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. Journal of Epidemiology & Community Health, 52(6), 377–384. https://doi.org/10.1136/jech.52.6.377
Hallion, L. S., & Ruscio, A. M. (2011). A meta-analysis of the effect of cognitive bias modification on anxiety and depression. Psychological Bulletin, 137(6), 940–958. https://doi.org/10.1037/a0024355
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