Circumstances and Outcomes of a Firearm Seizure Law: Marion County,Indiana, 2006–2013

Indiana statute allows police to seize firearms without a warrant if the officer believes a person meets the law's definition of “dangerous.” Review of the use of this law in Marion County (Indianapolis), Indiana, showed that prosecutors filed petitions in court to retain weapons seized by police under this law 404 times between 2006 and 2013. Police removed weapons from people due to identification of a risk of suicide (68%) or violence (21%), or the presence of psychosis (16%). The firearm seizures occurred in the context of domestic disputes in 28% of cases and intoxication was noted in 26% of cases. There were significant demographic differences in the circumstances of firearm seizures and the firearms seized. The seized firearms were retained by the court at the initial hearing in 63% of cases; this retention was closely linked to the defendant's failure to appear at the hearing. The court dismissed 29% of cases at the initial hearing, closely linked to the defendant's presence at the hearing. In subsequent hearings of cases not dismissed, the court ordered the destruction of the firearms in 72% of cases, all when the individual did not appear in court, and dismissed 24% of the cases, all when the individual was present at the hearing. Overall, the Indiana law removed weapons from a small number of people, most of whom did not seek return of their weapons. The firearm seizure law thus functioned as a months-long cooling-off period for those who did seek the return of their guns. Copyright © 2015 John Wiley & Sons, Ltd.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.