演講訊息摘要如下：
第一場:
講題：Hacking into Sleep to Enhance Learning
講者：Dr. Ken A. Paller (Northwestern University/Department of Psychology; Director of the Cognitive Neuroscience Program)
時間：July10, 2017 星期一 10:00～11:00
地點：臺灣大學心理學系北館A教室
摘要: Most learning takes hold gradually and requires practice (memory reactivation), which need not be intentional. Recent findings suggest that memory reactivation during sleep supports memory consolidation and enduring long-term storage. In particular, Targeted Memory Reactivation (TMR) with subtle sensory stimulation can modify neural activity while avoiding arousal from sleep. Sounds associated with learning can be presented again during slow-wave sleep to promote memory reactivation. TMR can thus systematically and selectively enhance learning, including learning of object-location associations, verbal associations, skills, and habits. Recordings of brain oscillations during sleep can provide evidence about relevant neurophysiological mechanisms, and in this way elucidate critical contributions of sleep to memory consolidation. Furthermore, the TMR approach offers new opportunities for reinforcing learning offline during the many hours we spend sleeping, so we should investigate whether various clinical outcomes can be enhanced using TMR in conjunction with current therapies.
第二場:
講題：Evidence for early integration of visual and auditory speech signals
講者：Dr. Marcia Grabowecky (Northwestern University/Department of Psychology)
時間：July10, 2017 星期一 11:00～12:00
地點：臺灣大學心理學系北館A教室
摘要: Concurrent signals from different sensory modalities often provide redundant or complementary information. Speech perception is a prominent example of crossmodal integration, where viewing articulatory movements of the face and lips (i.e., speech movements) facilitates detection, identification, and comprehension of auditory speech. How does visual motion processing facilitate auditory speech processing? It is thought that preparatory speech movements provide anticipatory cues to indicate the timing of upcoming auditory speech signals, while concurrent visual speech movements provide content-based cues to optimize the processing of relevant auditory features. Although the mechanisms of the temporal-cueing effects have been elucidated, it is unclear how visual processing of speech movements provides content-based cues to auditory speech processing. Dynamic auditory cortical activity elicited by visual and auditory speech stimuli was recorded with electrodes placed within auditory cortex in epilepsy patients (using electrocorticography, ECoG). Speech-relevant content of the evoked activity was decoded using the recently developed pattern-classification method called deep Convolutional Neural Networks (CNNs) that are especially suited for decoding information from complex dynamic patterns. Understanding how visual speech movements crossmodally influence early auditory cortical activity is important because crossmodal influences at this early stage have cascading influences on higher-level speech processes.