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Many studies have investigated the effects of background noise on cognitive functions, in particular memory and learning. But few studies have examined the effect of acute noise on the specific parts of the memory process. The purpose of the current study was to fill this gap in the research. Twenty-three students from Stockholm University were tested with two different semantic programming tasks during different white noise conditions. Working memory capacity and subjective sensitivity to noise was also tested. No significant effects were found on the participants’ recognition scores, but a significant main effect for noise during recognition, as well as a significant main effect of experimental group, was found on response times. The noise effect was positive, which puts the study in conflict with most previous ones. The results could perhaps be explained by the theory of Stochastic Resonance or the Yerkes-Dodson Effect. Other reaction-time related tasks are suggested as future topics of study.

Memory is a complex and multifaceted entity. Cognitive psychology has adopted terminology to help simplify the study of memory. For example, one can consider the cognitive process the brain is engaged in, such as encoding versus retrieval. Similarly, one can consider the content of information, such as words, faces, or scenes. Content and process can also interact such as with instructions to view a face that happens to be situated next to a house (item memory) versus instructions to evaluate whether the face 'belongs' in the house (relational memory). Although neuropsychology, animal lesion studies, and cognitive neuroscience have identified brain structures that are consistently associated with memory performance, such as the medial temporal lobes (MTL) and prefrontal cortex (PFC), the specifics of when and why such regions participate in memory is still largely unexplored. Theoretical standpoints are often at odds about whether regions such as the MTL operate as a functional unit, supporting memory in general, or whether subregions within the MTL support specific types of memory (e.g. item versus relational memory). To investigate how memory processes might recruit unique and common brain regions, three functional magnetic resonance imaging (fMRI) studies were conducted. Each study involved comparisons of successful encoding (trials later remembered versus forgotten) and successful retrieval (hits versus misses). Experiment 1, using semantic and perceptual word pairs, found unique contributions for subregions in the MTL and PFC, dependent on memory phase and stimulus class. One region in the left hippocampus was associated with memory success, regardless of either memory phase or stimulus class. Experiment 2, using faces and scenes, found unique contributions for 'stimulus sensitive' subregions of the fusiform gyrus and parahippocampal gyrus, as well as for the PFC, and MTL that were dependent on content-process interactions, or independent of content and process. Experiment 3, using faces, scenes, and face-scene pairings, found unique contributions for subregions of the MTL and PFC based on item versus relational processing and memory phase. Together, the results of the three experiments provide support for dichotomies in brain structures based on specific processes, specific content, or process-content interactions. / Dissertation

Debugging in the System-on-a-Chip (SoC) environment is a challenge since it was hard to observe their signals on a chip. How to obtain the chip internal signals to help chip designers effective to verify and debug has become an important issue.It is impractical to observe their signals on output pins due to pin number limitation. The conventional solution is to embed a monitor within the hardware for capturing the signals in real time and storing them in a on-chip trace memory. This thesis shows how the embedded multi-resolution AXI Bus Tracer can enable users to achieve the SoC debugging and performance evaluation efficiently, and it can trace the AXI Signals on the AMBA 3.0 AXI environment. Users can dynamically adjust the tracking resolution during the program execution, and we also provide an effective encoding algorithm for compressing the trace data. With our trace analysis software, we provide the detail information ranging from detail signal waveforms to transaction level waveforms, and transfer the trace signals into Value Change Dump (VCD) file. We also show several pie charts to analyze the portion of transfer types. In our work, we provide a synthesizable hardware to embed SOC for capturing signals. Then traced information through decompress and analysis can make users analyze system debugging and performance evaluation.