Depressed patients show cognitive deficits along with mood disturbances. Growing evidence suggests an impairment at the level of executive control, which might account in part for patients' difficulties in everyday activities and cognitive performance. Furthermore, there is evidence that depressive patients show information processing biases for emotional information which are thought to play a role in the etiology and maintenance of the disorder. Attentional bias occurs in an early stage of information processing, while memory bias occurs in a later stage of processing (strategic elaboration). The goal of this study was to investigate executive control (the Stroop test) and information processing biases for emotional information in an early stage of processing (the emotional Stroop test) and in a later stage of processing (memory recognition test) in healthy subjects and depressive patients. A further objective of this study was to compare the performance of melancholic and non-melancholic depressive patients in the Stroop test, in the emotional Stroop test and in the memory recognition test. Last, we wanted to investigate the relationship between the performance in an executive control task (the Stroop effect) and information processing bias measures for emotional information. This study is the first to investigate the Stroop test, the emotional Stroop test and the memory recognition test in the same healthy subjects and depressed patients. Furthermore, this is the first study investigating information processing biases for emotional information in the melancholic and non-melancholic patients.
Executive control was investigated using the Stroop task, which has been extensively used to study executive control. The emotional Stroop task has widely been used to investigate attentional biases in anxiety and depression and was therefore employed also in this study. Memory bias was examined with the memory recognition test since it allowed us to study both “pure” memory and response bias. Response accuracy d’ and response bias beta were calculated according to the signal-detection model. Twenty-three depressive patients and 27 healthy subjects performed computerized mixed trial
Stroop and emotional Stroop tests. Afterwards, the subjects performed the memory recognition task. Depressive patients were divided according to DSM-IV diagnosis into melancholic and non-melancholic subgroups. Furthermore the level of anxiety and depression was assessed in all subjects.
The ice streams in the West Antarctica Ice Sheet flow at several hundred metres per year. The lateral increase in speed from typical inland ice sheet speeds of a few metres per year to ice stream speeds of several hundred metres per year occurs over a short distance (~ 2 km) in the outer part of the ice stream known as the marginal shear zone (MSZ). The ice in this zone is highly crevassed and chaotically jumbled. This thesis is an effort to understand the dynamics of the MSZ and to find out whether the velocity of the ice stream is controlled primarily by the stresses in its MSZs or by stresses at the base.
There is a long-standing literature on seasonal patterns, say at the monthly, quarterly, or annual frequency, in stock returns (see Keim (1983), Ariel (1987), Lakonishok and Smidt (1988)). Some of this periodicity is consistent with patterns of trading by investors. For example, Keim (1989) finds the turn-of the-year trading patterns induce patterns in equity trades that occur at the ask price versus the bid price and that this trading pattern explains the size-related turn-of-the-year effect in stock prices. Intraday patterns in returns and volatility are found by Wood, McInish, and Ord (1985). Returns and volatility are higher, on average, at the beginning and end of the trading day. Harris (1986) and Andersen and Bollerslev (1997) find similar results.
While intraday patterns of volume and volatility found in Wood, McInish, and Ord (1985), Harris (1986), and Pagano, Peng, and Schwartz (2008) can be justified with models of discretionary liquidity trading (e.g., Admati and Pfleiderer (1998) and Hora (2006)), predictable patterns in returns are harder to explain. We study the nature of this intraday periodicity of returns. We divide the trading day into 13 half-hour trading intervals. A stock’s return over a trading interval is negatively related to its returns over recent intervals, which is consistent with the negative autocorrelation induced by "microstructure effects" such as bid-ask bounce. However, there is a statistically significant positive relation between a stock’s return over an interval and its past returns at daily frequencies (i.e., 13, 26, 39, ... interval lags). This relation is stronger over the first and last half-hour of the trading day, as one might expect, given the results of Wood, McInish, and Ord (1985) and Harris (1986), but remains statistically significant over the other periods of the day. Thus, the intraday return pattern is not merely due to uniformly high returns at the beginning and end of the trading day.
