Reactive Inhibitory Control Precedes Overt Stuttering Events.

Research points to neurofunctional differences underlying fluent speech between stutterers and non-stutterers. Considerably less work has focused on processes that underlie stuttered vs. fluent speech. Additionally, most of this research has focused on speech motor processes despite contributions from cognitive processes prior to the onset of stuttered speech. We used MEG to test the hypothesis that reactive inhibitory control is triggered prior to stuttered speech. Twenty-nine stutterers completed a delayed-response task that featured a cue (prior to a go cue) signaling the imminent requirement to produce a word that was either stuttered or fluent. Consistent with our hypothesis, we observed increased beta power likely emanating from the right pre-supplementary motor area (R-preSMA)-an area implicated in reactive inhibitory control-in response to the cue preceding stuttered vs. fluent productions. Beta power differences between stuttered and fluent trials correlated with stuttering severity and participants' percentage of trials stuttered increased exponentially with beta power in the R-preSMA. Trial-by-trial beta power modulations in the R-preSMA following the cue predicted whether a trial would be stuttered or fluent. Stuttered trials were also associated with delayed speech onset suggesting an overall slowing or freezing of the speech motor system that may be a consequence of inhibitory control. Post-hoc analyses revealed that independently generated anticipated words were associated with greater beta power and more stuttering than researcher-assisted anticipated words, pointing to a relationship between self-perceived likelihood of stuttering (i.e., anticipation) and inhibitory control. This work offers a neurocognitive account of stuttering by characterizing cognitive processes that precede overt stuttering events.

Linguistic features of stuttering during spontaneous speech.

PURPOSE: Previous work shows that linguistic features (e.g., word length, word frequency) impact the predictability of stuttering events. Most of this work has been conducted using reading tasks. Our study examined how linguistic features impact the predictability of stuttering events during spontaneous speech. METHODS: The data were sourced from the FluencyBank database and consisted of interviews with 35 adult stutterers (27,009 words). Three logistic regression mixed models were fit as the primary analyses: one model with four features (i.e., initial phoneme, grammatical function, word length, and word position within a sentence), a second model with six features (i.e., the features from the previous model plus word frequency and neighborhood density), and a third model with nine features (i.e., the features from the previous model plus bigram frequency, word concreteness, and typical age of word acquisition). We compared our models using the Area Under the Curve statistic. RESULTS: The four-feature model revealed that initial phoneme, grammatical function, and word length were predictive of stuttering events. The six-feature model revealed that initial phoneme, word length, word frequency, and neighborhood density were predictive of stuttering events. The nine-feature model was not more predictive than the six-feature model. CONCLUSION: Linguistic features that were previously found to be predictive of stuttering during reading were predictive of stuttering during spontaneous speech. The results indicate the influence of linguistic processes on the predictability of stuttering events such that words associated with increased planning demands (e.g., longer words, low frequency words) were more likely to be stuttered.

A large-scale analysis of racial disparities in police stops across the United States.

We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a 'veil of darkness' masks one's race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers-but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities.

Predictive Analytics for City Agencies: Lessons from Children's Services.

Many municipal agencies maintain detailed and comprehensive electronic records of their interactions with citizens. These data, in combination with machine learning and statistical techniques, offer the promise of better decision making, and more efficient and equitable service delivery. However, a data scientist employed by an agency to implement these techniques faces numerous and varied choices that cumulatively can have significant real-world consequences. The data scientist, who may be the only person at an agency equipped to understand the technical complexity of a predictive algorithm, therefore, bears a good deal of responsibility in making judgments. In this perspective, I use a concrete example from my experience of working with New York City's Administration for Children's Services to illustrate the social and technical tradeoffs that can result from choices made in each step of data analysis. Three themes underlie these tradeoffs: the importance of frequent communication between the data scientist, agency leadership, and domain experts; the agency's resources and organizational constraints; and the necessity of an ethical framework to evaluate salient costs and benefits. These themes inform specific recommendations that I provide to guide agencies that employ data scientists and rely on their work in designing, testing, and implementing predictive algorithms.