Probability learning in normal and parkinson subjects: the effect of reward, context, and uncertainty

Erdeniz, Burak
In this thesis, the learning of probabilistic relationships between stimulus-action pairs is investigated under the probability learning paradigm. The effect of reward is investigated in the first three experiments. Additionally, the effect of context and uncertainty is investigated in the second and third experiments, respectively. The fourth experiment is the replication of the second experiment with a group of Parkinson patients where the effect of dopamine medication on probability learning is studied. In Experiment 1, we replicate the classical probability learning task by comparing monetary and non-monetary reward feedback. Probability learning behavior is observed in both monetary and non-monetary rewarding feedback conditions. However, no significant difference between the monetary and non-monetary feedback conditions is observed. In Experiment 2, a variation of the probability learning task which includes irrelevant contextual information is applied. Probability learning behavior is observed, and a significant effect is found between monetary and non-monetary feedback conditions. In Experiment 3; a probability learning task similar to that in Experiment 2 is applied, however, in this experiment, stimulus included relevant contextual information. As expected, due to the utilization of the relevant contextual information from the start of the experiment, no significant effect is found for probability learning behavior. The effect of uncertainty observed in this experiment is a replication of the reports in literature. Experiment 4 is identical to Experiment 2; except that the subject population is a group of dopamine medicated Parkinson patients and a group of age matched controls. This experiment is introduced to test the suggestions in the literature regarding the enhancement effect of dopamine medication in probability learning based on positive feedback conditions. In Experiment 4, probability learning behavior is observed in both groups, but the difference in learning performance between Parkinson patients and controls was not significant, probably due to the low number of subject recruited in the experiment. In addition to these investigations, learning mechanisms are also examined in Experiments 1 and 4. Our results indicate that subjects initially search for patterns which lead to probability learning. At the end of Experiments 1 and 4, upon learning the winning frequencies, subjects change their behavior and demonstrate maximization behavior, which makes them prefer continuously one option over the other.


Estimation and hypothesis testing in multivariate linear regression models under non normality
İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01)
This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Joint and interactive effects of trust and (inter) dependence on relational behaviors in long-term channel dyads
Yilmaz, C; Sezen, B; Özdemir, Özlem (Elsevier BV, 2005-04-01)
The authors investigate the effects of trust on the relational behaviors of firms in long-term channel dyads across different interdependence structures. Based on the long-term nature of the empirical setting, trust is posited to exert a positive effect on the emergence of relational behaviors in all interdependence conditions. This positive effect of trust is hypothesized to be stronger in highly and symmetrically interdependent channel dyads than in low-interdependence-type symmetric dyads. In addition, f...
Analysis of stochastic and non-stochastic volatility models.
Özkan, Pelin; Ayhan, Hüseyin Öztaş; Department of Statistics (2004)
Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order...
Comparison of regression techniques via Monte Carlo simulation
Mutan, Oya Can; Ayhan, Hüseyin Öztaş; Department of Statistics (2004)
The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolut...
Similarity matrix framework for data from union of subspaces
Aldroubi, Akram; Sekmen, Ali; Koku, Ahmet Buğra; Cakmak, Ahmet Faruk (2018-09-01)
This paper presents a framework for finding similarity matrices for the segmentation of data W = [w(1)...w(N)] subset of R-D drawn from a union U = boolean OR(M)(i=1) S-i, of independent subspaces {S-i}(i=1)(M), of dimensions {d(i)}(i=1)(M). It is shown that any factorization of W = BP, where columns of B form a basis for data W and they also come from U, can be used to produce a similarity matrix Xi w. In other words, Xi w(i, j) not equal 0, when the columns w(i) and w(j) of W come from the same subspace, ...
Citation Formats
B. Erdeniz, “Probability learning in normal and parkinson subjects: the effect of reward, context, and uncertainty,” M.S. - Master of Science, Middle East Technical University, 2007.