Contemporary pain models highlight cognitive-processing biases (ie, attention bias [AB], interpretation bias [IB], and memory bias [MB]) as key processes that contribute to poor pain outcomes. However, existing research has yielded inconsistent findings regarding the presence and impact of these biases on pain outcomes. Recognizing the need to explore these biases simultaneously, contemporary pain models suggest that cognitive biases (CBs) are interrelated, and may have a combined impact upon pain problems. The current study aims to investigate the interrelationships between CBs using the PainAIM paradigm, a novel approach enabling simultaneous evaluation of pain-related AB, IB, and MB using cues signaling actual pain rather than symbolic information. We hypothesized the presence and positive associations of biases for pain-related cues and the predictive value of combined AB and IB for poor pain outcomes. Eighty-four healthy participants completed the PainAIM paradigm, followed by a cold pressor task probing pain experience and pain-related task interference. The results indicated an inverse relationship between AB and IB for ambiguous pain cues. In addition, there was a positive association between participants’ AB for ambiguous pain and their MB for the same cues. Contrary to our hypotheses, CB indices did not predict experimental pain outcomes. These findings provide support for the interrelationships between pain-related CBs. However, future research on the temporal order of CBs and their combined impact on pain outcomes is needed. By overcoming the limitations associated with traditional paradigms, the PainAIM paradigm offers a promising research tool for the further study of combined CBs in the context of pain.

The current study provides insight into the associations between pain-related CBs (AB, IB, and MB) using ecologically valid (ambiguous) pain cues. The results indicated an inverse association between pain-related AB and IB, while a positive association was found between AB and MB. CBs did however not predict experimental pain outcomes.