Chronic pain is linked to changes in cognitive function. However, little is known about its influence on number sense, despite the fact that intact numerical-spatial processing is a prerequisite for valid scale-based pain assessments. This study aimed to elucidate whether number sense is changed in chronic pain (CP), to determine if changes have an impact on pain assessments using pain rating scales and what patient factors might contribute.
N=42 chronic pain patients and n=42 matched controls were analyzed (age range: 33-68 years). Numerical-spatial abilities were investigated by using number line tasks, where participants either estimated the position of a given number (position marking) or the value of a predefined mark (number naming). Pain intensity was assessed using numerical rating (NRS), verbal rating (VRS) and visual analogue scales (VAS). Additional measures included attention and working memory, verbal intelligence, medication and depression. Results revealed that in number naming, patients deviated more from expected responses than controls, and that VAS scores were significantly higher than both NRS and VRS and correlated with deviations in position making. Changes in number naming were predicted by pain intensity, sex and IQ but not by attention, memory or opioid medication.
This article presents new insight on which cognitive mechanisms are influenced by chronic pain with the focus on numerical spatial abilities. It could therefore provide useful knowledge in developing new pain assessment tools specifically for patients suffering from chronic pain.
In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we've been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We've been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.
