Previous studies have suggested that visual short-term memory (VSTM) has a storage limit of approximately four items. However, the type of high-threshold (HT) model used to derive this estimate is based on a number of assumptions that have been criticized in other experimental paradigms (e.g., visual search). Here we report findings from nine experiments in which VSTM for color, spatial frequency, and orientation was modeled using a signal detection theory (SDT) approach. In Experiments 1-6, two arrays composed of multiple stimulus elements were presented for 100 ms with a 1500 ms ISI. Observers were asked to report in a yes/no fashion whether there was any difference between the first and second arrays, and to rate their confidence in their response on a 1-4 scale. In Experiments 1-3, only one stimulus element difference could occur (T = 1) while set size was varied. In Experiments 4-6, set size was fixed while the number of stimuli that might change was varied (T = 1, 2, 3, and 4). Three general models were tested against the receiver operating characteristics generated by the six experiments. In addition to the HT model, two SDT models were tried: one assuming summation of signals prior to a decision, the other using a max rule. In Experiments 7-9, observers were asked to directly report the relevant feature attribute of a stimulus presented 1500 ms previously, from an array of varying set size. Overall, the results suggest that observers encode stimuli independently and in parallel, and that performance is limited by internal noise, which is a function of set size.

The attentional cost associated with the visual discrimination of the gender of a face was investigated. Participants performed a face-gender discrimination task either alone (single-task) or concurrently (dual-task) with a known attentional demanding task (5-letter T/L discrimination). Overall performance on face-gender discrimination suffered remarkably little under the dual-task condition compared to the single-task condition. Similar results were obtained in experiments that controlled for potential training effects or the use of low-level cues in this discrimination task. Our results provide further evidence against the notion that only low-level representations can be accessed outside the focus of attention.

Observers are often unaware of changes in their visual environment when attention is not focused at the location of the change . Because of its rather intriguing nature, this phenomenon, known as change blindness, has been extensively studied with psychophysics as well as with fMRI . However, whether change blindness can be tracked in the activity of single cells is not clear. To explore the neural correlates of change detection and change blindness, we recorded from single neurons in the human medial temporal lobe (MTL) during a change-detection paradigm. The preferred pictures of the visually responsive units elicited significantly higher firing rates on the attended trials when subjects correctly identified a change (change detection) compared to the unattended trials when they missed it (change blindness). On correct trials, the firing activity of individual units allowed us to predict the occurrence of a change, on a trial-by-trial basis, with 67% accuracy. In contrast, this prediction was at chance for incorrect, unattended trials. The firing rates of visually selective MTL cells thus constitute a neural correlate of change detection.

AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to careful data selection and weighting. In the cascaded approach, we combine high-quality hybrid automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our endto-end direct speech translation systems benefit from pretraining of adapted encoder and decoder components, as well as synthetic data and fine-tuning and thus are able to compete with cascaded systems in terms of MT quality. For simultaneous translation, we utilize a novel architecture that makes dynamic decisions, learned from parallel data, to determine when to continue feeding on input or generate output words. Experiments with speech and text input show that even at low latency this architecture leads to superior translation results.

In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment. The neural translation model was adapted to the subtitling content and style and extended by a simple, yet effective technique for utilizing intersentence context for short sentences such as dialog turns. The main contribution of the paper is a novel subtitle segmentation algorithm that predicts the end of a subtitle line given the previous word-level context using a recurrent neural network learned from human segmentation decisions. This model is combined with subtitle length and duration constraints established in the subtitling industry. We conducted a thorough human evaluation with two post-editors (English-to-Spanish translation of a documentary and a sitcom). It showed a notable productivity increase of up to 37% as compared to translating from scratch and significant reductions in human translation edit rate in comparison with the post-editing of the baseline non-adapted system without a learned segmentation model.

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In 1990, 13 regionally based Behaviour Intervention Support Teams (BISTs) were established in Victoria to assist agencies providing support to persons with an intellectual disability who exhibited challenging behaviour. A primary function of the BISTs was to conduct intensive interventions for clients with severe challenging behaviours. The outcomes of the interventions conducted by eight of these teams were monitored over the period from 1991 to 1993. During this time, a total of 134 such interventions were completed by the teams. Many outcome measures were used for each intervention. These included direct observational measures of the challenging behaviours, measures of skill acquisition by the clients, and satisfaction with the results by caregivers. It was concluded that the interventions resulted in a high rate of success (approximately 75%). Additionally, it was concluded that the use of regionally based specialist teams was an effective way of treating severe challenging behaviours that had previously proven difficult to manage.

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