Dichotic listening was evaluated in free-recall and directed-recall (pre-cued, post-cued) response conditions using interleaved one-, two-, and three-pair dichotic digit materials. In the free-recall condition, the subjects recalled in any order the digits presented. In the directed-recall condition, a response task was examined where subjects recalled all digits presented to the cued ear (pre- or post-cued) followed by the digits presented to the opposite (non-cued) ear. Thirty 20- to 29-year-old adults with normal hearing and 30 60- to 79-year-old adults with mild-to-moderate sensorineural hearing loss were evaluated. In all conditions, performance by the younger listeners was better than performance by the elderly listeners. As the difficulty of the dichotic digit task increased, recognition performance decreased. The recognition performance of elderly listeners was more affected by increases in the difficulty of the stimulus materials as compared to the younger listeners. In the free-recall condition, there was a right-ear advantage for both age groups. When instructional bias was imposed, the results favored the ear of instructed bias. The differences in recognition performance between young and elderly listeners likely reflect differences in the difficulty of the dichotic digit test conditions and variations in the demand on auditory memory.

abstract: Temporal-order judgments can require integration of self-generated action-events and external sensory information. In a previous study, it was found that participants are biased to perceive one’s own action-events to occur prior to simultaneous external events. This phenomenon, named the “Egocentric Temporal Order Bias”, or ETO bias, was demonstrated as a 67% probability for participants to report self-generated events as occurring prior to simultaneous externally-determined events. These results were interpreted as supporting a feed-forward, constructive model of perception. However, the empirical data could support many potential mechanisms. The present study tests whether the ETO bias is driven by attentional differences, feed-forward predictability, or action. These findings support that participants exhibit a bias due to both feed-forward predictability and action, and a Bayesian analysis supports that these effects are quantitatively unique. Therefore, the results indicate that the ETO bias is largely driven by one’s own action, over and above feed-forward predictability. / Dissertation/Thesis / Masters Thesis Psychology 2020

In recent years, there has been a surge of research interest in the analysis of time series and spatial data. While on one hand more and more sophisticated models are being developed, on the other hand the resulting theory and estimation process has become more and more involved. This dissertation addresses the development of statistical inference procedures for data exhibiting dependencies of varied form and structure. In the first work, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of (possibly) nonlinear functions of finitely many future observations in a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy. Finite sample properties of the method are investigated through a simulation study. The next work considers nonparametric inference on spatial data. In this work the asymptotic distribution of the Discrete Fourier Transformation (DFT) of spatial data under pure and mixed increasing domain spatial asymptotic structures are studied under both deterministic and stochastic spatial sampling designs. The deterministic design is specified by a scaled version of the integer lattice in IRd while the data-sites under the stochastic spatial design are generated by a sequence of independent random vectors, with a possibly nonuniform density. A detailed account of the asymptotic joint distribution of the DFTs of the spatial data is given which, among other things, highlights the effects of the geometry of the sampling region and the spatial sampling density on the limit distribution. Further, it is shown that in both deterministic and stochastic design cases, for "asymptotically distant" frequencies, the DFTs are asymptotically independent, but this property may be destroyed if the frequencies are "asymptotically close". Some important implications of the main results are also given.