Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16–26 trajectory features during a choice-reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, (...) or emotional pictures, and they indicated their emotions with questionnaires. Our 10-fold cross-validation analysis shows that statistical models formed from “known” participants could predict nearly 10%–20% of the variance of positive affect and attentiveness ratings of “unknown” participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users. (shrink)

Although several theories of online syntactic processing assume the parallel activation of multiple syntactic representations, evidence supporting simultaneous activation has been inconclusive. Here, the continuous and non‐ballistic properties of computer mouse movements are exploited, by recording their streaming x, y coordinates to procure evidence regarding parallel versus serial processing. Participants heard structurally ambiguous sentences while viewing scenes with properties either supporting or not supporting the difficult modifier interpretation. The curvatures of the elicited trajectories revealed both an effect of visual context (...) and graded competition between simultaneously active syntactic representations. The results are discussed in the context of 3 major groups of theories within the domain of sentence processing. (shrink)