Nonsuicidal Self-injury,Suicide Planning,and Suicide Attempts Among High-risk Adolescents Prior to Psychiatric Hospitalization

Research on Child and Adolescent Psychopathology - The purpose of this study was to understand the trajectories of nonsuicidal self-injury (NSSI) and suicide plans (SP) in the 90 days...     

Understanding fish cognition: a review and appraisal of current practices

Animal Cognition - With over 30,000 recognized species, fishes exhibit an extraordinary variety of morphological, behavioural, and life-history traits. The field of fish cognition has grown...     

Timing the “magical number seven”: Presentation rate and regularity affect verbal working memory performance

The informative value of time and temporal structure often remains neglected in cognitive assessments. However, next to information about stimulus identity we can exploit temporal ordering principles, such as regularity, periodicity, or grouping to generate predictions about the timing of future events. Such predictions may improve cognitive performance by optimising adaptation to dynamic stimuli. Here, we investigated the influence of temporal structure on verbal working memory by assessing immediate recall performance for aurally presented digit sequences (forward digit span) as a function of standard (1000 ms stimulus-onset-asynchronies, SOAs), short (700 ms), long (1300 ms) and mixed (700–1300 ms) stimulus timing during the presentation phase. Participant's digit spans were lower for short and mixed SOA presentation relative to standard SOAs. This confirms an impact of temporal structure on the classic “magical number seven,” suggesting that working memory performance can in part be regulated through the systematic application of temporal ordering principles.     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.     

Talking Points: A Modulating Circle Increases Listening Effort Without Improving Speech Recognition in Young Adults

Psychonomic Bulletin & Review - Speech recognition is improved when the acoustic input is accompanied by visual cues provided by a talking face (Erber in Journal of Speech and Hearing Research,...