The development of spontaneous facial responses to others’ emotions in infancy. An EMG study

Viewing facial expressions often evokes facial responses in the observer. These spontaneous facial reactions (SFRs) are believed to play an important role for social interactions. However, their developmental trajectory and the underlying neurocognitive mechanisms are still little understood. In the current study, 4- and 7-month old infants were presented with facial expressions of happiness, anger, and fear. Electromyography (EMG) was used to measure activation in muscles relevant for forming these expressions: zygomaticus major (smiling), corrugator supercilii (frowning), and frontalis (forehead raising). The results indicated no selective activation of the facial muscles for the expressions in 4-month-old infants. For 7-month-old infants, evidence for selective facial reactions was found especially for happy faces (leading to increased zygomaticus major activation) and fearful faces (leading to increased frontalis activation), while angry faces did not show a clear differential response. This suggests that emotional SFRs may be the result of complex neurocognitive mechanisms which lead to partial mimicry but are also likely to be influenced by evaluative processes. Such mechanisms seem to undergo important developments at least until the second half of the first year of life

Measuring long term individual trajectories of offending using multiple methods

Abstract: Criminal career researchers and developmental criminologists have identified describing individual trajectories of offending over time as a key research question. In response, recently various statistical methods have been developed and used to describe individual offending patterns over the life-course. Two approaches that are prominent in the current literature are standard growth curve modeling (GCM) and group-based trajectory models (GTM). The goal of this paper is to explore ways in which these different models with different sets of assumptions, do in fact lead to different outcomes on individual trajectories. Using a particularly rich dataset, the criminal career and life-course study, we estimate a unique trajectory for each individual in the sample using the GCM and GTM. We also estimate separate trajectories for each individual directly using the long time series. We then compare these three separate trajectories for each individual. We find that the average trajectories obtained from the different approaches match each other. However, for any given individual, these approaches tell very different stories. For example, each method identifies a rather different set of individuals as desistors. This comparison highlights the strengths and weaknesses of each approach, and more broadly, it reveals the uncertainty involved with measuring long term patterns of change in latent propensity to commit crimes.