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Nurturing behavior may be critically influenced by the interplay of different hormones. The neuropeptide oxytocin is known to promote maternal behavior and its reduction has been associated with postpartum depression risk and child neglect. Contrariwise, the observed decrease in testosterone level during early parenthood may benefit caretaking behavior, whereas increased testosterone may reduce attention to infants. Here we used functional magnetic resonance imaging to investigate the interactive influence of testosterone and oxytocin on selective attention to and neural processing of the baby schema (BS). 57 nulliparous women performed a target detection task with human faces with varying degree of BS following double-blinded placebo-controlled oxytocin administration in a between-subjects design. Our results support the idea that oxytocin enhances attention to the BS. Oxytocin had a positive effect on activation of the inferior frontal junction during identification of infant targets with a high degree of BS that were presented among adult distractors. Further, activation of the putamen was positively correlated with selective attention to the BS, but only in women with high endogenous testosterone who received oxytocin. These findings provide initial evidence for the neural mechanism by which oxytocin may counteract the negative effects of testosterone in the modulation of nurturing behavior.
We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.