Theeuwes, J., & van Moorselaar, D. (2025). Learning to suppress what I fear.Emotion, 25(3), 782–786. https://doi.org/10.1037/emo0001433
Key Takeaways
- Focus: This study investigates whether attentional biases toward fear-inducing stimuli can be reduced through statistical learning.
- Aims: To determine if attentional capture by threatening objects can be modulated through learned spatial suppression, specifically among individuals with heightened fear responses.
- Findings: Participants with high spider fear successfully suppressed attentional capture by spider images at locations where distractors commonly appeared, demonstrating the effectiveness of spatial suppression.
- Implications: The results suggest potential for novel attentional training interventions to mitigate attentional biases associated with fear and anxiety disorders.
Rationale:
Previous research established that threatening stimuli automatically attract attention, particularly among anxious individuals (Mathews & Mackintosh, 1998; Schmidt et al., 2015).
This automatic attentional capture, historically attributed to bottom-up processes that are evolutionarily hard-wired (Öhman & Mineka, 2001; LeDoux, 1998), contributes to pathological anxiety (Mogg & Bradley, 1998).
Recent evidence, however, shows attentional capture can be influenced by prior learning experiences (Awh et al., 2012; Wang & Theeuwes, 2018), challenging the assumption of immutability.
Therefore, this study explores whether attentional biases toward fear-inducing stimuli like spiders can also be modified through statistical learning.
Such understanding could inform therapeutic techniques aimed at reducing maladaptive attentional patterns.
Future research should examine if suppression learned spatially extends to object-specific suppression regardless of spatial context (Failing et al., 2019).
Method:
Participants completed an online attentional task using the OpenSesame platform, responding to shapes while ignoring occasional distractor images.
Distractor locations had varying probabilities to induce learned spatial suppression.
Procedure:
- Participants identified line orientations within unique shapes in a visual search task.
- Distractors (leaves initially, later including spiders and butterflies) appeared with higher frequency in one location.
- Training blocks involved high-probability distractors (leaves) to encourage suppression learning.
- Test blocks assessed whether suppression generalized to threatening stimuli (spiders).
Sample:
- Total sample: 119 participants (Mage=30, 35 females).
- High spider-fear group: 30 participants (Mage=29, 15 females).
Measures:
- Fear of Spiders Questionnaire (FSQ): 18-item measure assessing spider-related fear (Szymanski & O’Donohue, 1995).
Statistical measures:
- Repeated-measures ANOVA.
- Paired t-tests for planned comparisons.
- Reaction time (RT) trimming procedure for accuracy and outliers.
Results
- Hypothesis 1: Spider distractors capture more attention than neutral distractors among fearful individuals.
- Result: Confirmed; spider stimuli slowed target detection compared to neutral stimuli.
- Hypothesis 2: Learned suppression reduces attentional capture at high-probability distractor locations, including for feared stimuli.
- Result: Confirmed; both feared (spiders) and neutral distractors were effectively suppressed at these locations.
Insight
Fearful individuals can adaptively reduce attention toward threatening stimuli through spatial suppression, indicating attentional biases are not entirely automatic.
This extends prior research by demonstrating the malleability of attentional capture beyond artificial distractors to real-world fear-inducing objects.
Future research could investigate suppression effectiveness independent of spatial location or in more naturalistic settings.
Clinical Implications
The findings of this study have significant implications for both psychological theory and clinical practice.
Traditionally, it has been widely assumed that attentional biases toward threatening stimuli are automatic and resistant to change due to evolutionary adaptive mechanisms (LeDoux, 1998; Öhman & Mineka, 2001).
However, the current results challenge this assumption by demonstrating that these biases can be altered through statistical learning.
Specifically, the study suggests that individuals with heightened fear responses can proactively suppress attention to feared stimuli at locations previously associated with distractors, even without explicit awareness of these regularities.
For practitioners, these results highlight a promising avenue for developing cognitive interventions aimed at reducing maladaptive attentional patterns in anxiety and phobia treatment.
Interventions could incorporate elements of statistical learning paradigms to train individuals to implicitly suppress attention to feared stimuli.
This approach might complement or enhance traditional cognitive-behavioral therapies such as exposure therapy by providing an implicit training method to reduce vigilance and threat-oriented attentional biases.
Policymakers and health service providers could consider supporting research and application of attentional training programs that leverage implicit learning processes.
If validated through further research, this approach could become a cost-effective supplementary technique within mental health services, reducing reliance solely on explicit, conscious strategies, which may sometimes exacerbate anxiety in highly sensitive patients.
Despite these potential benefits, challenges in implementing these findings in real-world settings include establishing the long-term effectiveness and transferability of learned suppression beyond controlled laboratory conditions.
Further studies will be essential to evaluate the sustainability of these effects, the optimal intensity and duration of training, and whether suppression can generalize across different types of threats and contexts.
Thus, future research could examine:
- How best to integrate statistical learning-based attentional training into existing therapeutic modalities like cognitive-behavioral therapy or virtual reality exposure.
- The effectiveness of attentional suppression training across various anxiety disorders beyond specific phobias.
- Whether these learned suppression effects persist over time and in complex, real-world environments.
Strengths:
- Clear methodological design using well-established attentional paradigms.
- Robust data handling with systematic exclusion of outliers.
- Transparent and open data practices (materials available publicly).
Limitations:
- Limited ecological validity due to online, lab-based task.
- Possible underestimation of explicit learning due to self-report biases.
- Unclear generalizability to other fear types or clinical populations.
Socratic Questions:
- How could attentional suppression techniques integrate with existing anxiety disorder treatments, such as exposure therapy?
- Can implicit learning effectively generalize to real-world scenarios, or does it predominantly work under controlled laboratory conditions?
- How might awareness of distractor regularities influence the effectiveness of learned suppression?
- Could similar learned suppression occur for social threats (e.g., angry faces), or is it specific to biologically prepared threats like spiders?
- What additional factors could facilitate or impede generalization of learned suppression to everyday environments?
References
Theeuwes, J., & van Moorselaar, D. (2025). Learning to suppress what I fear.Emotion, 25(3), 782–786. https://doi.org/10.1037/emo0001433
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top–down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences, 16(8), 437–443. https://doi.org/10.1016/j.tics.2012.06.010
Failing, M., Feldmann-Wüstefeld, T., Wang, B., Olivers, C., & Theeuwes, J. (2019). Statistical regularities induce spatial as well as feature-specific suppression. Journal of Experimental Psychology: Human Perception and Performance, 45(10), 1291–1303. https://doi.org/10.1037/xhp0000660
LeDoux, J. E. (1998). The emotional brain: The mysterious underpinnings of emotional life. Simon and Schuster.
Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22(6), 539–560. https://doi.org/10.1023/A:1018738019346
Mogg, K., & Bradley, B. P. (1998). A cognitive-motivational analysis of anxiety. Behaviour Research and Therapy, 36(9), 809–848. https://doi.org/10.1016/S0005-7967(98)00063-1
Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108(3), 483–522. https://doi.org/10.1037/0033-295X.108.3.483
Schmidt, L. J., Belopolsky, A. V., & Theeuwes, J. (2015). Attentional capture by signals of threat. Cognition and Emotion, 29(4), 687–694. https://doi.org/10.1080/02699931.2014.924484
Szymanski, J., & O’Donohue, W. (1995). Fear of spiders questionnaire. Journal of Behavior Therapy and Experimental Psychiatry, 26(1), 31–34. https://doi.org/10.1016/0005-7916(94)00072-T
Theeuwes, J., Bogaerts, L., & van Moorselaar, D. (2022). What to expect where and when: How statistical learning drives visual selection. Trends in Cognitive Sciences, 26(10), 860–872. https://doi.org/10.1016/j.tics.2022.06.001
Wang, B., & Theeuwes, J. (2018). Statistical regularities modulate attentional capture. Journal of Experimental Psychology: Human Perception and Performance, 44(1), 13–17. https://doi.org/10.1037/xhp0000472
