Matching Items (2)
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- All Subjects: recognition
- Creators: Ferguson, Ryan
Description
Recognition memory was investigated for naturalistic dynamic scenes. Although visual recognition for static objects and scenes has been investigated previously and found to be extremely robust in terms of fidelity and retention, visual recognition for dynamic scenes has received much less attention. In four experiments, participants view a number of clips from novel films and are then tasked to complete a recognition test containing frames from the previously viewed films and difficult foil frames. Recognition performance is good when foils are taken from other parts of the same film (Experiment 1), but degrades greatly when foils are taken from unseen gaps from within the viewed footage (Experiments 3 and 4). Removing all non-target frames had a serious effect on recognition performance (Experiment 2). Across all experiments, presenting the films as a random series of clips seemed to have no effect on recognition performance. Patterns of accuracy and response latency in Experiments 3 and 4 appear to be a result of a serial-search process. It is concluded that visual representations of dynamic scenes may be stored as units of events, and participant's old
ew judgments of individual frames were better characterized by a cued-recall paradigm than traditional recognition judgments.
ew judgments of individual frames were better characterized by a cued-recall paradigm than traditional recognition judgments.
ContributorsFerguson, Ryan (Author) / Homa, Donald (Thesis advisor) / Goldinger, Stephen (Committee member) / Glenberg, Arthur (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2014
Description
Single molecule identification is one essential application area of nanotechnology. The application areas including DNA sequencing, peptide sequencing, early disease detection and other industrial applications such as quantitative and quantitative analysis of impurities, etc. The recognition tunneling technique we have developed shows that after functionalization of the probe and substrate of a conventional Scanning Tunneling Microscope with recognition molecules ("tethered molecule-pair" configuration), analyte molecules trapped in the gap that is formed by probe and substrate will bond with the reagent molecules. The stochastic bond formation/breakage fluctuations give insight into the nature of the intermolecular bonding at a single molecule-pair level. The distinct time domain and frequency domain features of tunneling signals were extracted from raw signals of analytes such as amino acids and their enantiomers. The Support Vector Machine (a machine-learning method) was used to do classification and predication based on the signal features generated by analytes, giving over 90% accuracy of separation of up to seven analytes. This opens up a new interface between chemistry and electronics with immediate implications for rapid Peptide/DNA sequencing and molecule identification at single molecule level.
ContributorsZhao, Yanan, 1986- (Author) / Lindsay, Stuart (Thesis advisor) / Nemanich, Robert (Committee member) / Qing, Quan (Committee member) / Ros, Robert (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2014