The risk and crisis communication process in public health emergencies (PHERCC, public health emergency risk and crisis communication) matrix, as proposed by Spitale, Germani, and Biller-Andorno (2...

Words sound slightly different each time they are said, both by the same talker and across talkers. Rather than hurting learning, lab studies suggest that talker variability helps infants learn similar sounding words. However, very little is known about how much variability infants hear within a single talker or across talkers in naturalistic input. Here, we quantified these types of talker variability for highly frequent words spoken to 44 infants, from naturalistic recordings sampled longitudinally over a year of life (from (...) 6 to 17 months). We used non‐contrastive acoustic measurements (e.g., mean pitch, duration, harmonics‐to‐noise ratio) and holistic measures of sound similarity (normalized acoustic distance) to quantify acoustic variability. We find three key results. First, pitch‐based variability was generally lower for infants' top talkers than across their other talkers, but overall acoustic distance is higher for tokens from the top talker versus the others. Second, the amount of acoustic variability infants heard could not be predicted from, and thus was not redundant with, other properties of the input such as the number of talkers or tokens, or proportion of speech from particular sources (e.g., women, children, electronics). Finally, we find that patterns of pitch‐based acoustic variability heard in naturalistic input were similar to those found with in‐lab stimuli that facilitated word learning. This large‐scale quantification of talker variability in infants' everyday input sets the stage for linking naturally occurring variability “in the wild” to early word learning. (shrink)

John Searle's Chinese room argument is perhaps the most influential andwidely cited argument against artificial intelligence (AI). Understood astargeting AI proper – claims that computers can think or do think– Searle's argument, despite its rhetorical flash, is logically andscientifically a dud. Advertised as effective against AI proper, theargument, in its main outlines, is an ignoratio elenchi. It musterspersuasive force fallaciously by indirection fostered by equivocaldeployment of the phrase "strong AI" and reinforced by equivocation on thephrase "causal powers" (at least) equal (...) to those of brains." On a morecarefully crafted understanding – understood just to targetmetaphysical identification of thought with computation ("Functionalism"or "Computationalism") and not AI proper the argument is still unsound,though more interestingly so. It's unsound in ways difficult for high church– "someday my prince of an AI program will come" – believersin AI to acknowledge without undermining their high church beliefs. The adhominem bite of Searle's argument against the high church persuasions of somany cognitive scientists, I suggest, largely explains the undeserved reputethis really quite disreputable argument enjoys among them. (shrink)