In our everyday lives, we must often weigh the different attributes of items in order to select the item that best fits our current goals, allowing us to make optimal decisions. Construal Level Theory proposes a psychological mechanism for re-weighting attributes, utilizing selective attention as the process by which we implement self-control. It has been hypothesized that switching attention between attributes is facilitated by the suppression of cortical oscillations over posterior brain regions within the alpha (8-12 Hz) frequency range. To test this idea, we re-examined previously collected whole-brain electroencephalography (EEG) data from a dietary choice experiment in which participants made decisions naturally or with a weight loss incentive. Prior analysis found that although hungry subjects primarily relied on taste properties while responding naturally, they increased their behavioral and neural weighting of health when motivated to lose weight. Reanalyzing this data using time-frequency analyses, we compared alpha oscillations related to healthy versus unhealthy foods under natural and self-control conditions. We predicted that when participants exercised self-control we would see suppression of alpha oscillations over occipital sensors starting around 400 ms post-stimulus onset, for trials presenting healthy relative to unhealthy foods; no such suppression should appear during natural responding when ignoring health information. Consistent with our hypothesis, we found a significant decrease in alpha oscillations over occipital sensors between 440 and 800 ms post-stimulus onset for healthy compared to unhealthy items in the self-control condition. No such effect was seen for health information in natural choice, or for taste. Our findings extend previous research by linking alpha band suppression to the neural re-weighting of multiple attributes, suggesting a neuro-cognitive mechanism for self-control that uses selective attention to choose between multiple attributes.

Bei einem Spaziergang im Mondlicht nehmen wir Ereignisse verschiedenster Intensität wahr. Vom blendenden Licht eines vorbeifahrenden Motorrads bis hin zu undeutlichen Schatten. Verarbeiten wir diese Ereignisse alle gleich? In dieser Arbeit untersuchte ich, wie das visuelle System auf Stimuli reagiert, die so niedrig in ihrer Intensität sind, dass wir sie nicht wahrnehmen. Frühere somatosensorische Studien zeigten eine kortikale Deaktivierung aufgrund subliminaler Stimulation. Diese wurde als Hemmungsmechanismus interpretiert zur Unterdrückung kortikalen Rauschens. Unterstützt wurde diese Aussage durch ein Verhaltensexperiment, in dem der Schwellwert für somatosensorische Stimuli bei subliminaler Stimulation erhöht war. Gibt es im visuellen System äquivalente Hemmungsmechanismen? In Studie I untersuchte ich die Wahrnehmung schwellnaher visueller Zielreize: die Wahrnehmung verschlechterte sich bei gleichzeitiger subliminaler Stimulation im selben Hemifield. Inhibitorische Interneurone könnten diesen Effekt hervorrufen. Gleichzeitig zeigten Nervenzell-Studien die Degeneration intrakortikaler Inhibition mit dem fortschreitendem Alter von Affen. In Studie II untersuchte ich daher Unterschiede inhibitorischer Mechanismen einer kleinen Gruppe von älteren Probanden und verglich diese mit den jüngeren Probanden aus Studie I. In Studie III, einer elektrophysiologischen Studie, führt subliminale Stimulation zu einer Verstärkung des Alpha-Rhythmus. Supraliminale Stimulation führt zu einer Verstärkung niedriger Frequenzen und einer Abschwächung des Alpha-Rhythmus. Die spezifische neuronale Signatur aufgrund subliminaler Stimulation deutet darauf hin, dass die neuronale Verarbeitung des Stimulus zu einer Verringerung der Aktivität in involvierten Arealen führt. Ein Rauschunterdrückungs-Mechanismus wurde im somatosensorischen System beschrieben und könnte für die verringerte Wahrnehmung der schwellnahen Zielreize bei subliminaler Stimulation verantwortlich sein. / Walking along a street on a moonlit night, we can perceive visual events at a wide range of intensities – from the blinding light of a passing motorcycle to faint shadows. Does the visual system react similarly to all of these events? Here, I investigated how the visual system reacts to stimuli that are so low in their intensity that they are not perceived. It has been shown that subliminal low-intensity somatosensory stimuli lead to a cortical deactivation or inhibition. This deactivation was interpreted as inhibition mechanism that usually protects the cortex against activation by noise. Also, a behavioural experiment showed an increased sensitivity threshold for peri-liminal stimuli during subliminal stimulation. Does a similar mechanism exist within the visual System? In Study I, I investigated the perception of visual peri-liminal target-stimuli under different conditions. The threshold for target-stimuli significantly increased when presented during subliminal stimulation on the same side as the target-stimulus. The underlying mechanism could be mediated through intracortical inhibition. Concurrently, studies in macaque senescent neurons suggest a degradation of intracortical inhibition with age. In Study II, I therefore investigated differences of inhibitory responses in a group of elderly subjects and compared the results to the young participants in Study I. In Study III, using electroencephalography, I show that subliminal stimulation leads to an alpha-band power increase, whereas supraliminal stimulation leads to a lower frequency increase and an alpha-band power decrease. Specific neural signature in response to subliminal stimulation indicate neural processing of the stimulus that lead to a down-regulation of areas involved in stimulus processing. This mechanism could serve a suppression of input noise that has been described in the somatosensory system and may lead to decreased detection of peri-liminal target-stimuli during subliminal stimulation.

Our brains handle vast amounts of information incoming through our senses. Continuously exposed to sensory input, the sense of touch, however, may miss tactile stimuli, no matter how much attention we pay to them. In four empirical studies, this thesis tested (1) the feasibility of investigating undetectable stimulation by electrical finger nerve pulses, (2) how its neural correlates dissociate from detectable stimulation and (3) whether and how selective somatosensory attention nevertheless affects the neural representation of undetectable stimuli. The first two studies showed that there is a natural range of electrical stimulation intensities that cannot be detected. A rigorous statistical evaluation with Bayes factor analysis indicated that the evidence of chance performance after undetectable stimulation reliably outweighed evidence of above-chance performance. A subsequent study applying electroencephalography (EEG) revealed qualitative differences between the processing of detectable and undetectable stimulation, which is evident in altered event-related potentials (ERP). Specifically, undetectable stimulation evokes a single component that is not predictive of stimulus detectability but lacks a subsequent component, which correlates with upcoming stimulus detection. The final study showed that attention nevertheless affects neural processing of undetectable stimuli in a top-down manner as it does for detectable stimuli and fosters the view of attention and awareness being two separate and mostly independent mechanisms. The influence of the pre-stimulus oscillatory (~10 Hz) alpha amplitude—a putative marker of attentional deployment—on the ERP depended on the current attentional state and indicates that both processes are interacting but not functionally matching.:1 Touch, Consciousness, And Attention – Theoretical Considerations ........ 1-11 1.1 A Neural Account To (Un-) Consciousness ............................................ 1-12 1.2 Controlling detectability of external stimulation ...................................... 1-14 1.3 Thresholds in the light of signal detection theory ................................... 1-17 1.4 Selective attention in touch .................................................................... 1-19 1.5 Research questions ............................................................................... 1-21 2 Empirical Evidence .................................................................................... 2-25 2.1 General methods .................................................................................... 2-25 2.1.1 Stimulation ........................................................................................... 2-25 2.1.2 Threshold assessment procedure ....................................................... 2-25 2.1.3 Behavioral analysis .............................................................................. 2-26 2.1.4 Electrophysiological measurement ...................................................... 2-28 2.1.5 Analysis of event-related potentials ..................................................... 2-30 2.1.6 Spectral Analysis resolved over time ................................................... 2-30 2.2 Psychophysical assessment of subthreshold stimulation ........................ 2-33 2.2.1 A method for assessing the individual absolute detection threshold (ADTH) ......................................................................................................... 2-33 2.2.2 Validation of absolute detection threshold assessment by signal detection theory measures and Bayesian Null-Hypothesis testing ................ 2-39 2.3 Non-invasive neural markers of unconscious perception ....................... 2-47 2.3.1 Neural Correlates of Undetectable Somatosensory Stimulation in EEG and fMRI ...................................................................................................... 2-47 2.3.2 Prediction of stimulus perception by features of the evoked potential for different stimulation intensities along the psychometric function ................. 2-51 2.4 The role of Rolandic Alpha Activity in Somatosensation and its Relation to Attention ................................................................................................. 2-75 3 General Discussion and Conclusions ...................................................... 3-101 3.1 Summary of empirical results ................................................................ 3-101 3.2 Neural processing of undetectable stimulation ..................................... 3-102 3.3 Attention, awareness and neural oscillatory activity ............................. 3-104 3.4 Limits of the current studies and future perspectives ........................... 3-109 References .................................................................................................... 113 Summary ....................................................................................................... 137 Zusammenfassung ........................................................................................ 143 Curriculum Vitae ............................................................................................ 151 Selbständigkeitserklärung ............................................................................. 155 Nachweis über die Anteile der Co-Autoren .................................................... 157

Vision identifies objects rapidly and efficiently. In contrast, object recognition by touch is much slower. Furthermore, haptics usually serially accumulates information from different parts of objects, whereas vision typically processes object information in parallel. Is haptic object identification slower simply due to sequential information acquisition and the resulting memory load or due to more fundamental processing differences between the senses? To compare the time course of visual and haptic object recognition, we slowed visual processing using a novel, restricted viewing technique. In an electroencephalographic (EEG) experiment, participants discriminated familiar, nameable from unfamiliar, unnamable objects both visually and haptically. Analyses focused on the evoked and total fronto-central theta-band (5–7 Hz; a marker of working memory) and the occipital upper alpha-band (10–12 Hz; a marker of perceptual processing) locked to the onset of classification. Decreases in total upper alpha-band activity for haptic identification of objects indicate a likely processing role of multisensory extrastriate areas. Long-latency modulations of alpha-band activity differentiated between familiar and unfamiliar objects in haptics but not in vision. In contrast, theta-band activity showed a general increase over time for the slowed-down visual recognition task only. We conclude that haptic object recognition relies on common representations with vision but also that there are fundamental differences between the senses that do not merely arise from differences in their speed of processing.

In everyday life we regularly update our expectations about the locations at which sensory events may occur, and about the motor responses that are appropriate in a given situation. The experiments in this thesis investigated the neural correlates of perceptual processes and motor preparation during human decision making, and the regions that causally contribute to decision making in the human brain. In Chapter 3, I used electroencephalography (EEG) to investigate whether alpha-band (~8-14 Hz) oscillations provide a graded index of participants’ preparatory attentional states. Time-frequency analysis revealed that manipulating spatial certainty regarding the location of an upcoming visual target led to parametric changes in the lateralization of preparatory occipito-parietal alpha oscillations, and to parametric modulation of parieto-central beta-band (~15-25 Hz) power typically associated with response preparation. In Chapter 4, I used EEG to investigate whether evolution of lateralization of sensorimotor alpha- and beta-band activity reflected participants’ evolving expectations about an upcoming motor response. Lateralization of activity in both frequency bands varied parametrically with the available evidence, suggesting such lateralized activity correlates with participants’ internal decision variables. Further analysis identified unique contributions to lateralized and non-lateralized oscillatory activity due to the prior evidence, evidence update, and surprise related to the observed information at each stage of the task. In Chapter 5, I extended the paradigm developed in Chapter 4 for use with online repetitive transcranial magnetic stimulation (TMS) and concurrent EEG recording. Delivery of TMS during decision making allowed investigation of the causal role played by a left hemisphere medial intraparietal region that is the putative human homologue of the macaque medial intraparietal cortex (MIP). MIP stimulation disrupted decision-making behaviour by biasing participants’ decisions against contralateral-to-stimulation (i.e., right-handed) responses. Comparison of the magnitude of TMS-induced changes in behaviour and beta-band activity demonstrated that the intraparietal cortex plays a causal role both in decision making and in the appearance of beta-band activity over the motor cortex. In Chapter 6, the broader consequences of the experimental work presented in this thesis are discussed, in addition to promising directions for future research.

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