Interpretation biases and visual attention in the processing of ambiguous information in chronic pain

Understanding Human Cognition Through Computational Modeling

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Does movement preparation enhance attending to bodily sensations in the back in people with persistent low back pain?

Attention has been proposed to play an important role in persisting pain, with excessive attentional processes towards pain information leading to worse pain outcomes and maladaptive behaviors. Nevertheless, research on somatosensory attending during the anticipation of pain-related movements is still scarce. This study investigated if individuals with chronic and recurrent lower back pain compared to pain-free controls, show enhanced attending to somatosensory information in the back while anticipating back-recruiting movements. 43 healthy control, 33 recurrent (RLBP) and 33 chronic low back (CLBP) pain sufferers were asked to perform back-recruiting movements. Before the movement initiation cue, a task-irrelevant tactile stimulus was administered to participants' lower back to elicit somatosensory evoked potentials (SEPs), used as an index of somatosensory attending. In contrast to our hypothesis, most identified SEP components did not differ across groups. The only exception was the P175 amplitude which was larger for the CLBP group compared to individuals with RLBP and healthy controls. The current study did not find robust evidence of enhanced somatosensory attending to the back in people with persisting lower back pain. The finding that CLBP, but not RLBP individuals, had larger amplitudes to the P175 component, is discussed as possibly reflecting a higher state of emotional arousal in these patients when having to prepare the back-recruiting movements.

Interpretation bias in endometriosis-related pain

ABSTRACT

Endometriosis-related pain has been predominantly medically managed, which has hindered understanding of psychological factors involved in these pain experiences. Models of chronic pain highlight the biased interpretation of ambiguous information as health threat related (interpretation bias) as an important process in the development and maintenance of chronic pain. Whether interpretation bias may also be similarly implicated in endometriosis-related pain is unclear. The current study aimed to address this gap in the literature by (1) comparing interpretation biases between a sample of participants with endometriosis and a control sample of participants without medical conditions and pain, (2) exploring relationships between interpretation bias and endometriosis-related pain outcomes, and (3) exploring whether interpretation bias moderated the relationship between endometriosis-related pain severity and pain interference. The endometriosis and healthy control samples comprised 873 and 197 participants, respectively. Participants completed online surveys assessing demographics, interpretation bias, and pain-related outcomes. Analyses revealed that interpretation bias was significantly stronger among individuals with endometriosis relative to controls, with a large effect size. Within the endometriosis sample, interpretation bias was significantly associated with increases in pain-related interference, however, interpretation bias was not associated with any other pain outcomes and did not moderate the relationship between pain severity and pain interference. This study is the first to evidence biased interpretation styles among individuals with endometriosis and to show this bias is associated with pain interference. Whether interpretation bias varies over time and whether this bias can be modified through scalable and accessible interventions to alleviate pain-related interference are avenues for future research.

Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM

The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation.

Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models

Greater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the hypothesis that higher eye movement consistency across trials, instead of a greater eyes-focused pattern, predicts better performance in children since it reflects capacity in developing visual routines. We first simulated visual routine development through combining deep neural network and hidden Markov model that jointly learn perceptual representations and eye movement strategies for face recognition. The model accounted for the advantage of eyes-focused pattern in adults, and predicted that in children (partially trained models) consistency but not pattern of eye movements predicted recognition performance. This result was then verified with data from typically developing children. In addition, lower eye movement consistency in children was associated with autism diagnosis, particularly autistic traits in social skills. Thus, children's face recognition involves visual routine development through social exposure, indexed by eye movement consistency.

Music reading experience modulates eye movement pattern in English reading but not in Chinese reading

Here we tested the hypothesis that in Chinese-English bilinguals, music reading experience may modulate eye movement planning in reading English but not Chinese sentences due to the similarity in perceptual demands on processing sequential symbol strings separated by spaces between music notation and English sentence reading. Chinese–English bilingual musicians and non-musicians read legal, semantically incorrect, and syntactically (and semantically) incorrect sentences in both English and Chinese. In English reading, musicians showed more dispersed eye movement patterns in reading syntactically incorrect sentences than legal sentences, whereas non-musicians did not. This effect was not observed in Chinese reading. Musicians also had shorter saccade lengths when viewing syntactically incorrect than correct musical notations and sentences in an unfamiliar alphabetic language (Tibetan), whereas non-musicians did not. Thus, musicians’ eye movement planning was disturbed by syntactic violations in both music and English reading but not in Chinese reading, and this effect was generalized to an unfamiliar alphabetic language. These results suggested that music reading experience may modulate perceptual processes in reading differentially in bilinguals’ two languages, depending on their processing similarities.

Impact of mask use on face recognition: an eye-tracking study

We examined how mask use affects performance and eye movements in face recognition and whether strategy change reflected in eye movements is associated with performance change. Eighty-eight participants performed face recognition with masked faces either during learning only, during recognition only, or during both learning and recognition. As compared with the baseline condition where faces were unmasked during both learning and recognition, participants had impaired performance in all three scenarios, with larger impairment when mask conditions during learning and recognition did not match. When recognizing unmasked faces, whether the faces were learned with or without a mask on did not change eye movement behavior. Nevertheless, when recognizing unmasked faces that were learned with a mask on, participants who adopted more eyes-focused patterns had less performance impairment as compared with the baseline condition. When recognizing masked faces, participants had more eyes-focused patterns and more consistent gaze transition behavior than recognizing unmasked faces regardless of whether the faces were learned with or without a mask on. Nevertheless, when recognizing masked faces that were learned without a mask, participants whose gaze transition behavior was more consistent had less performance impairment as compared with the baseline condition. Thus, although eye movements during recognition were mainly driven by the mask condition during recognition but not that during learning, those who adjusted their strategy according to the mask condition difference between learning and recognition had better performance. This finding has important implications for identifying populations vulnerable to the impact of mask use and potential remedial strategies.

Understanding the collinear masking effect in visual search through eye tracking

Recent research has reported that, while both orientation contrast and collinearity increase target salience in visual search, a combination of the two counterintuitively masks a local target. Through eye-tracking and eye-movement analysis with hidden Markov models (EMHMM), here we showed that this collinear masking effect was associated with reduced eye-fixation consistency (as measured in entropy) at the central fixation cross prior to the search display presentation. As a decreased precision of saccade landing position is shown to be related to attention shift away from the saccadic target, our result suggested that the collinear masking effect may be related to attention shift to a non-saccadic-goal location in expectation of the search display before saccading to the central fixation cross. This attention shift may consequently interfere with attention capture by the collinear distractor containing the target, resulting in the masking effect. In contrast, although older adults had longer response times, more dispersed eye-movement pattern, and lower eye-movement consistency than young adults during visual search, the two age groups did not differ in the masking effect, suggesting limited contribution from ageing-related cognitive decline. Thus, participants' pre-saccadic attention shift prior to search may be an important factor influencing their search behavior.

Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

The eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.