Poster No:
623
Submission Type:
Late-Breaking Abstract Submission
Authors:
Mengfan Zhang1, Kathrin Kostorz1, Filip Melinscak1, Frank Scharnowski1
Institutions:
1University of Vienna, Vienna, Austria
First Author:
Co-Author(s):
Introduction:
Understanding the mental representations of anxiety-provoking spider stimuli is critical for advancing our understanding of arachnophobia. Previous research has primarily focused on predefined psychological dimensions (e.g., fear, valence, arousal), potentially overlooking other essential aspects of how these stimuli are cognitively structured. We extend this approach by employing a similarity-based framework to minimize predefined assumptions about fear processing, with similarity serving as a key representational principle. Using spider phobia as a use case, we integrate similarity metrics with neuroimaging to systematically investigate the neurocognitive representations underlying fear of spiders. We expect that the neural representations of spider-related stimuli will be structured by both low-level visual features and higher-order cognitive mechanisms.
Methods:
We conducted an online behavioral experiment to collect similarity judgments of spider-related images, as well as a functional magnetic resonance imaging (fMRI) experiment with spider-fearful individuals who passively viewed these images. Specifically, using an online spatial arrangement task (Hout et al., 2013), 355 participants provided similarity judgments for 314 spider images by rearranging images on a two-dimensional canvas to position similar images proportionally closer together. This method allows us to construct a data-driven behavioral model of perceived similarity. A subset of 225 images was then selected for an fMRI experiment, in which another 49 participants passively viewed these spider images, as well as 75 neutral images, in the scanner. At the neural level, we conducted a single-trial general linear model (GLM) analysis for each participant per spider image (Mumford, 2013). In this analysis, a regressor was assigned to the trial of interest, while additional regressors accounted for the remaining trials according to trial types and confounding variables. This generated 49 participant-level beta-maps for each image. We then performed a second-level GLM analysis on these 49 beta-maps to obtain a group-level t-map for each image. Next, we employed a searchlight-based representational similarity analysis (Kriegeskorte et al., 2008) using the 225 group-level t-maps to identify brain regions encoding the behavioral similarity structure.
Results:
Results revealed strong relations between similarity judgments and neural representations in the visual cortex and extensive parts of the ventral stream. The ventral stream is known for its role in object recognition, visual categorization, and processing complex visual features. The activation patterns indicate that spider-fearful individuals rely heavily on fine-grained perceptual details when structuring their mental representations of spider images. Additionally, the hippocampal areas showed representational alignment with the behavioral similarity structure, indicating that memory-related processes might contribute to spider image representations. One possible explanation is that participants retrieved past experiences or learned associations with spiders while viewing the images. Planned analyses will employ multidimensional scaling to investigate the underlying dimensions of neural representations.
Conclusions:
Our approach bridges behavioral and neural levels of analysis, offering a scalable framework for investigating spider-related neural representations in spider-fearful individuals. By moving beyond predefined psychological dimensions, this study provides new insights into how spider-relevant stimuli are encoded and structured, both cognitively and neurally. Our findings highlight the interplay between perception and memory in shaping the mental representation of feared stimuli, with the ventral stream potentially supporting detailed visual processing and the hippocampus contributing to memory-based associations.
Emotion, Motivation and Social Neuroscience:
Emotional Perception 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Multivariate Approaches 2
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
Emotions
FUNCTIONAL MRI
Modeling
Multivariate
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
fMRIPrep
Provide references using APA citation style.
Hout, M. C., Goldinger, S. D., & Ferguson, R. W. (2013). The versatility of SpAM: A fast, efficient, spatial method of data collection for multidimensional scaling. Journal of Experimental Psychology: General, 142(1), 256–281. https://doi.org/10.1037/a0028860
Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2. https://doi.org/10.3389/neuro.06.004.2008
Mumford, J. A. (2013). Considerations When Using Single-Trial Parameter Estimates in Representational Similarity Analyses.
No