A machine-level software program for the generation of aversive conditioning schedules using the KIM-1 microprocessor

A microprocessor system can be used both to control aversive conditioning (classical, signaled and Sidman avoidance, and conflict) experiments and to acquire behavioral data in multiple subjects at a fraction of the cost of solid state systems. The software described in this paper can be used to program tones and shocks and count responses, independently, in up to 12 animals simultaneously.     

Balancing the ledger: An application of Nagy's theories to the study of continuity and change among generations

This study uses an interpretive interview method to examine the micro-processes of generational continuity and change among a group of social activists. Boszormenyi-Nagy's theory of responsibility between generations, alternative models outside the family, and choice are used to explain the instances of change. Implications for research and practice are addressed.     

Suicidal Behaviors in Surviving Monozygotic and Dizygotic Co‐Twins: Is the Nature of the Co‐Twin's Cause of Death a Factor?

Genetically informative samples can address hereditary and experiential influences on suicide‐related behaviors. The frequency of suicide‐related behaviors was compared in twins from two survivor groups: (1) those whose co‐twins' deaths were suicides (monozygotic [MZ]: n = 47; dizygotic [DZ]: n = 31), and (2) those whose co‐twins' deaths were nonsuicides (MZ: n = 347; DZ: n = 170). The frequency of suicide attempts among suicide survivors was significantly higher in MZ than DZ twins, while the frequency of suicide attempts among nonsuicide twin survivors did not differ between MZ and DZ twins. Twin concordance for suicide attempts more likely reflects a genetic predisposition than a behavioral reaction to the loss.     

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden-path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden-path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word-by-word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self-paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden-path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability.