1. Head before heart: cognitive empathy emerges before affective empathy in the developing brain

    This article has 5 authors:
    1. Chiara Bulgarelli
    2. Paola Pinti
    3. Tessel Bazelmans
    4. Antonia Hamilton
    5. Emily J Jones
    This article has been curated by 1 group:
    • Curated by eLife

      eLife Assessment

      The authors have presented a study which addresses a recognised gap in the literature, the emergence of the neural correlates of cognitive and affective empathy in children; they introduce a task for measuring both positive and negative empathy in a relatively large group of children aged 3-5. The task was combined with functional near-infrared spectroscopy to examine brain regions involved in the task. The findings are interpreted as providing evidence for the earlier emergence of cognitive than affective empathy. The study represents a valuable contribution to understanding the development of cognitive function, but in its current form, the strength of support for the conclusion is incomplete due to limited support for the comparison to the adult literature and a need to more clearly justify the pre-selected brain regions, their links to empathy and the justification of the hypotheses.

    Reviewed by eLife

    This article has 3 evaluationsAppears in 1 listLatest version Latest activity
  2. Bayesian causal inference unifies perceptual and neuronal processing of center-surround motion in area MT

    This article has 4 authors:
    1. Gabor Lengyel
    2. Sabyasachi Shivkumar
    3. Gregory C DeAngelis
    4. Ralf M Haefner
    This article has been curated by 1 group:
    • Curated by eLife

      eLife Assessment

      This manuscript represents a valuable contribution to understanding motion processing in the visual cortex. Based on a heterogeneous collection of previous empirical findings, the authors show that the diversity of tuning curves in the middle temporal (MT) area, in response to moving center-surround images, can be explained by Bayesian inference combined with neural sampling. The model rests on strong and solid assumptions about the prior and likelihood; independent evidence that neither of these factors is misspecified would strengthen the work.

    Reviewed by eLife

    This article has 3 evaluationsAppears in 1 listLatest version Latest activity
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