Efectos del procesamiento emocional sobre la impulsividad en la toma de decisiones

Zusammenfassung

Chapter 1 is an introductory chapter that reviews the concepts of impulsivity and risk preference. The chapter reviews the scientific literature about the possible functions of the area of the brain that is believed to control the interactions between emotion and impulsivity, and the representation and integration of the hedonic value of expected consequences of actions. Chapter 2 studies the short-term effects of the processing of emotional images on the ability to inhibit motor responses, using a go/no-go task. The chapter reports the existence of a facilitation effect of emotional processing on response execution, an effect that had not been described before, and that can be interpreted as a transient increment in preparation impulsivity caused by emotion. Chapter 3 investigates the effects of processing emotional sounds on driving behavior using a motorcycle simulator, and the consequences of such processing on the speed chosen by the rider and on the probability of suffering an accident in a risk scenario are analyzed. Since typical lab tasks, described above, can be considered of low ecological validity, the added value of this work was the use of medium-fidelity simulator, that enable to approach the study of emotional processing in situations closer to real life ones. Chapter 4 is the outcome of several exploratory studies that started from the idea that the process responsible for the suppression or inhibition of a motor response must act, following causal order, before that response is canceled. Using three classical experimental tasks that require response cancellation (go/no-go task, Eriksen flankers task and stop-signal task), muscular activity was measured during incorrect response suppression (during partial responses), simultaneously with EEG activity. At the group level, as well as at the individual subject level, and at the trial-by-trial level, it was observed that the onset of the N2 component lags the beginning of the cancellation of the associated partial response. Moreover, the area under the EMG curve and before the N2 onset was significantly larger than 50% of the total area under the EMG curve for that response. Those results lead us to conclude that the N2 (and hence any other posterior cognitive component) cannot be considered an index of the process responsible for the suppression/inhibition of partial responses, although this doesn¿t rule out the possibility that it reflects the mental activity responsible for the adjustment of the level of cognitive control exerted in subsequent trials. Finally, chapter 5 introduces a possible solution to one of the most pressing problems for the interpretation of the meaning of components in ERP waveforms: the fact that the electrophysiological activities evoked by different mental processes (perceptive, motor, etc.) overlap in the recorded waveform. Our original aim was to obtain a cleaner measure of motor components ¿so as to evaluate the effect of inhibition on them¿, and of the N2 component. Our contribution is based on assumptions commonly accepted in the psychological literature, especially on the linearity of the brain milieu and of the electrical processes that give rise to the observed waveform. These same assumptions underlie other source separation techniques, such as independent component analysis (ICA). ICA is perhaps the most accepted source separation method these days. However, although very effective to isolate non-brain electrical sources (such as mains noise or eye-movement related noise), in our analyses ICA has repeatedly proved unable to separate components such as the N2 and the P3, because their amplitudes tend to covary, despite those components being anatomically and functionally dissociable. Besides, ICA, and all blind source separation methods generally, perform their analyses without making use of any previously known information about the processes that are likely involved in the generation of the EEG waveforms. Although the approach of blind source separation methods has the appeal of being theoretically neutral, it implies neglecting all previous psychological and neurological knowledge about the processes under study, a knowledge that could be used to make conjectures about the temporal course of activity of the brain sources that might take part in a certain task. Our method tries to leverage on that knowledge to make a guided separation of the components mixed in the EEG waveform, and in chapter 5 we prove its ability to isolate the brain activity associated with motor actions of each hand separated, as well as that of two cognitive components, namely the N2 and the P3. In sum, the present doctoral thesis is the result of a research trajectory that has extended along several years of exploration of interrelated fields, to which we believe sound contributions, both theoretical and methodological, have been made. Among these contributions we may highlight the study of the effect of emotional manipulations on classical experimental tasks (go/no-go, stop-signal task) and ecological tasks (driving a motorcycle simulator). The said emotional effect is basically an increase in the urgency to respond, that leads to facilitation in the go/no-go task, and to an increase of the rate of accidents in the simulator task. Moreover, we have been able to show that a purported index of suppression/inhibition processes ¿the N2 component¿ cannot be involved in the intra-trial cancellation of incorrect or inappropriate responses (ruling out at the same time the involvement of any other posterior component in the same function). Finally, our methodological contribution has been the introduction of a method for the guided separation of components in the ERP waveform that could contribute to improve the way in which data from EEG studies are analyzed.