Individual differences in performance on perceptual multiple cue probability learning tasks

Download
2013
Bayındır, Mustafa
What makes an individual, or as the initial motivation of this study, a pilot, perform above the average in a complex task where speed is the primary concern? The answer of this question is closely related to the task and the individual in the task environment. It is plausible to model such a task by mapping it to responding to several stimuli (cues) that are related to a environmental variable (criterion). This model actually corresponds to a judgment analysis paradigm, studied extensively in the literature, and known as Multi Cue Probability Learning (MCPL). Properties of task, such as cue presentation mode (e.g. analog or digital), or individual differences in cognitive abilities (such as attentional mechanism, verbal and visual working memory, executive control, etc.) may effect the learning performance. Furthermore, presentation mode which is compatible with the decision maker’s cognitive capability may create an advantage in learning performance. Using MCPL paradigm, and manipulating the presentation and response modes in a typical MCPL task, as graphical and numerical, four experiments were conducted, and learning performance of the participants was measured as the dependent variable. Individuals’ working memory capacity was assessed by means of several working memory capacity span tasks. Results suggest that learning performance in graphical mode of MCPL task, is suiperior as compared to numerical mode. The main e↵ect of verbal working memory capacity measure is significant so that it can explain the variance in the learning performance irrespective of the presentation style. In certain task settings, visuo-spatial working memory capacity interacted with the presentation mode so that it affects learning performance when mode of presentation is graphical rather than numerical. As opposed to initial predictions, verbal working memory capacity does not interact with MCPL task modality, pointing in the direction of a single dissociation between the learning performances in graphical and numerical modes of the MCPL task. As result of this study we have found that the numerical and graphical presentation and response modalities and working memory capacity of decision maker is effective in the performance of MCPL tasks. Furthermore these factors interacts with each other such that when the cognitive ability and the modality are congruent with each other, there occurs a significant performance increase in learning. This and follow-up studies may have implications on designing human computer interfaces and predicting performance based on measured cognitive abilities.

Suggestions

Reinforcement learning control for helicopter landing in autorotation
Kopsa, Kadircan; Kutay, Ali Türker (2018-01-01)
This study presents an application of an actor-critic reinforcement learning method to the nonlinear problem of helicopter guidance during autorotation in order to achieve safe landing following engine power loss. A point mass model of an OH-58A helicopter in autorotation was built to simulate autorotation dynamics. The point-mass model includes equations of motion In vertical plane. The states of the point-mass model are the horizontal and vertical velocities, the horizontal and vertical positions, the rot...
Noise minimal & green trajectory and flight profile optimization for helicopters
Yücekayalı, Ard; Kutay, Ali Türker; Department of Aerospace Engineering (2020)
The main aim of this study is to provide a multi-disciplinary optimization and track environment to generate acoustic optimal trajectories through waypoints that ensures the rotorcraft of interest can follow at practical effort, safety, fuel consumption and speed. Rotorcraft noise annoyance remains as a challenge to solve complex, three dimensional and coupled rotary wing aerodynamics, aeroacoustics and flight dynamics interactively. Two essential paths can be acknowledged in order to reduce annoyance. One ...
Comparison of Functional Size Based Estimation and Story Points, Based on Effort Estimation Effectiveness in SCRUM Projects
Ungan, Erdir; Cizmeli, Numan; Demirörs, Onur (2014-08-29)
In this study, we compared the effectiveness of two approaches to effort estimation for organizations utilizing SCRUM. We compared SCRUM's native effort estimation method Story Points and poker planning, with effort estimation models based on COSMIC Function Points (CFP) for a selection of projects. We utilized different regression models and ANN methodology to develop estimation model from the backlog stories. Results indicated that, estimation model built with COSMIC measurement results prove to be a bett...
Short term load forecasting using genetically optimized neural network cascaded with a Modified Kohonen clustering process
Erkmen, İsmet; Ozdogan, A (1997-07-18)
In this study, a new intelligent approach is developed for short term load forecasting (STLF). The technique consists of three basic modules. The first module employ the clustering of daily load curves using Modified Kohonen algorithm (MKA). Second module determine the most appropriate supervised neural network topology and associated initial weight values for each cluster, extracted from the historical data base, by using genetic algorithm (GA). At the third module, genetically optimized three layered back...
A systematic review of eye-tracking-based research on animated multimedia learning
Coskun, Atakan; Çağıltay, Kürşat (2021-12-01)
Background The most challenging task in eye-tracking-based multimedia research is to establish a relationship between eye-tracking metrics (or cognitive processes) and learners' performance scores. Additionally, there are current debates about the effectiveness of animations (or simulations) in promoting learning in multimedia settings. Objectives As a result, the current study aimed to review eye tracking-based research on learners' cognitive processes in the animated/simulated multimedia learning domain. ...
Citation Formats
M. Bayındır, “Individual differences in performance on perceptual multiple cue probability learning tasks,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.