Advances in hearing instrument technology have permitted the development of non-linear prescriptive methods to prescribe amplification characteristics for the hearing- impaired individual. The dispenser’s task in selecting the most appropriate prescriptive procedure for the young child is of utmost importance to ensure optimum hearing aid benefit for communication development. It was the aim of this study to compare and describe the effect of the two most widely used methods, DSL (i/o) and NAL-NL1, on speech recognition and loudness perception. An exploratory, descriptive research design was selected to realise this goal. Ten participants were selected using a convenient non-probability method of sampling. Articulation index calculations and a closed set speech recognition test were utilised in the evaluation of speech recognition, whereas functional gain results and loudness rating measurements provided an opportunity to describe loudness perception. The obtained results were analysed using the SAS (Statistical Analysis System). The study concluded that, although significant statistical differences existed in loudness perception, no statistical difference was observed in actual speech recognition measures. This effect may contribute to the individual amplification approaches of the two methods, which seem to reflect the uncertainties expressed by researchers as to the contribution of high frequency amplification to speech recognition in young children. / Dissertation (M (Communication Pathology))--University of Pretoria, 2006. / Speech-Language Pathology and Audiology / Unrestricted

Background: Johnson and Dillon (2011) provided a model-based comparison of current generic hearing aid prescriptive methods for adults with hearing loss based on the attributes of speech intelligibility, loudness, and bandwidth. Purpose: This study compared the National Acoustic Laboratories-Non-linear 2 (NAL-NL2) and Cambridge Method for Loudness Equalization 2-High-Frequency (CAM2) prescriptive methods using adult participants with less high-frequency hearing loss than Johnson and Dillon (2011). Of study interest was quantification of prescribed audibility, speech intelligibility, and loudness. The preferences of participants for either NAL-NL2 or CAM2 and preferred deviations from prescribed settings are also reported. Research Design: Using a single-blind, counter-balanced, randomized design, preference judgments for the prescriptive methods with regard to sound quality of speech and music stimuli were obtained. Preferred gain adjustments from the prescription within the 4-10 kHz frequency range were also obtained from each participant. Speech intelligibility and loudness model calculations were completed on the prescribed and adjusted amplification. Study Sample: Fourteen male Veterans, whose average age was 65 yr and whose hearing sensitivity averaged normal to borderline normal through 1000 Hz sloping to a moderately severe sensorineural loss, served as participants. Data Collection and Analysis: Following a brief listening time (∼10 min), typical of an initial fitting visit, the participants made paired comparison of sound quality between the NAL-NL2 and CAM2 prescriptive settings. Participants were also asked to modify each prescription in the range of 4-10 kHz using an overall gain control and make subsequent comparisons of sound quality preference between prescriptive and adjusted settings. Participant preferences were examined with respect to quantitative analysis of loudness modeling, speech intelligibility modeling, and measured high-frequency bandwidth audibility. Results: Consistent with the lack of difference in predicted speech intelligibility between the two prescriptions, sound quality preferences on the basis of clarity were split across participants while some participants did not have a discernable preference. Considering sound quality judgments of pleasantness, the majority of participants preferred the sound quality of the NAL-NL2 (8 of 14) prescription instead of the CAM2 prescription (2 of 14). Four of the 14 participants showed no preference on the basis of pleasantness for either prescription. Individual subject preferences were supported by loudness modeling that indicated NAL-NL2 was the softer of the two prescriptions and CAM2 was the louder. CAM2 did provide more audibility to the higher frequencies (5-8 kHz) than NAL-NL2. Participants turned the 4-10 kHz gain recommendation of CAM2 lower, on average, by a significant amount of 4 dB when making adjustments while no significant adjustment was made to the initial NAL-NL2 recommendation. Conclusions: NAL-NL2 prescribed gains were more often preferred at the initial fitting by the majority of participating veterans. For those patients with preference for a louder fitting than NAL-NL2, CAM2 is a good alternative. When the participant adjustment from the prescription between 4 and 10 kHz exceeded 4 dB from either NAL-NL2 (2 of 14) or CAM2 (11 of 14), the participants demonstrated a later preference for that adjustment 69% of the time. These findings are viewed as limited evidence that some individuals may have a preference for high-frequency gain that differs from the starting prescription.

Decision‐makers in organisations and businesses make numerous decisions every day, and these decisions are expected to be based on facts and carried out in a rational manner. However, most decisions are not based on precise information or careful analysis due to several reasons. People are, e.g., unable to behave rationally as a result of their experiences, socialisation, and additionally, because humans possess fairly limited capacities for processing information in an objective manner. In order to circumvent this human incapacity to handle decision situations in a rational manner, especially those involving risk and uncertainty, a widespread suggestion, at least in managerial decision making, is to take advantage of support in the form of decision support systems. One possibility involves decision analytical tools, but they are, almost without exception, not efficiently employed in organisations and businesses. It appears that one reason for this is the high demands the tools place on the decision‐maker in a variety of ways, e.g., by presupposing that reliable input data is obtainable by an exogenous process. Even though the reliability of current decision analytic tools is highly dependent on the quality of the input data, they rarely contain methods for eliciting data from the users. The problem focused on in this thesis is the unavailability and inefficiency of methods for eliciting decision information from the users. The aim is to identify problem areas regarding the elicitation of decision data in real decision making processes, and to propose elicitation methods that take people’s natural choice strategies and natural behaviour into account. In this effort, we have identified a conceptual gap between the decision‐makers, the decision models, and the decision analytical tools, consisting of seven gap components. The gap components are of three main categories (of which elicitation is one). In order to study elicitation problems, a number of empirical studies, involving more than 400 subjects in total, have been carried out in Sweden and Brazil. An iterative research approach has been adopted and a combination of quantitative and qualitative methods has been used. Findings made in this thesis include the fact that decision‐makers have serious problems in many decision situations due to not having access to accurate and relevant data in the first place, and secondly, not having the means for retrieving such data in a proper manner, i.e. lacking elicitation methods for this purpose. Employing traditional elicitation methods in this realm yield results that reveal an inertia gap, i.e. an intrinsic inertia in people’s natural behaviour to shift between differently framed prospects, and different groups of decisionmakers displaying different choice patterns. Since existing elicitation methods are unable to deal with the inertia, we propose a class of methods to take advantage of this natural behaviour, and also suggest a representation for the elicited information. An important element in the proposed class of methods is also that we must be able to fine‐tune methods and measuring instruments in order to fit into different types of decision situations, user groups, and choice behaviours.