Personality cannot be predicted from the power of resting state EEG.

In the present study we asked whether it is possible to decode personality traits from resting state EEG data. EEG was recorded from a large sample of subjects (n = 289) who had answered questionnaires measuring personality trait scores of the five dimensions as well as the 10 subordinate aspects of the Big Five. Machine learning algorithms were used to build a classifier to predict each personality trait from power spectra of the resting state EEG data. The results indicate that the five dimensions as well as their subordinate aspects could not be predicted from the resting state EEG data. Finally, to demonstrate that this result is not due to systematic algorithmic or implementation mistakes the same methods were used to successfully classify whether the subject had eyes open or closed. These results indicate that the extraction of personality traits from the power spectra of resting state EEG is extremely noisy, if possible at all.

Visual evoked potentials to change in coloration of a moving bar.

In our previous study we found that it takes less time to detect coloration change in a moving object compared to coloration change in a stationary one (Kreegipuu etal., 2006). Here, we replicated the experiment, but in addition to reaction times (RTs) we measured visual evoked potentials (VEPs), to see whether this effect of motion is revealed at the cortical level of information processing. We asked our subjects to detect changes in coloration of stationary (0(°)/s) and moving bars (4.4 and 17.6(°)/s). Psychophysical results replicate the findings from the previous study showing decreased RTs to coloration changes with increase of velocity of the color changing stimulus. The effect of velocity on VEPs was opposite to the one found on RTs. Except for component N1, the amplitudes of VEPs elicited by the coloration change of faster moving objects were reduced than those elicited by the coloration change of slower moving or stationary objects. The only significant effect of velocity on latency of peaks was found for P2 in frontal region. The results are discussed in the light of change-to-change interval and the two methods reflecting different processing mechanisms.

vMMN for schematic faces: automatic detection of change in emotional expression.

Our brain is able to automatically detect changes in sensory stimulation, including in vision. A large variety of changes of features in stimulation elicit a deviance-reflecting event-related potential (ERP) component known as the mismatch negativity (MMN). The present study has three main goals: (1) to register vMMN using a rapidly presented stream of schematic faces (neutral, happy, and angry; adapted from Öhman etal., 2001); (2) to compare elicited vMMNs to angry and happy schematic faces in two different paradigms, in a traditional oddball design with frequent standard and rare target and deviant stimuli (12.5% each) and in an version of an optimal multi-feature paradigm with several deviant stimuli (altogether 37.5%) in the stimulus block; (3) to compare vMMNs to subjective ratings of valence, arousal and attention capture for happy and angry schematic faces, i.e., to estimate the effect of affective value of stimuli on their automatic detection. Eleven observers (19-32 years, six women) took part in both experiments, an oddball and optimum paradigm. Stimuli were rapidly presented schematic faces and an object with face-features that served as the target stimulus to be detected by a button-press. Results show that a vMMN-type response at posterior sites was equally elicited in both experiments. Post-experimental reports confirmed that the angry face attracted more automatic attention than the happy face but the difference did not emerge directly at the ERP level. Thus, when interested in studying change detection in facial expressions we encourage the use of the optimum (multi-feature) design in order to save time and other experimental resources.

Unattended and attended visual change detection of motion as indexed by event-related potentials and its behavioral correlates.

Visual mismatch negativity (vMMN) is a negative-going component amongst cognitive event-related potentials. It reflects an automatic change-detection process that occurs when an infrequent stimulus is presented that is incongruent with the representation of a frequent (standard) event. In our research we use visual motion (more specifically motion direction changes) to study vMMN. Since movement in the visual field is quite irresistible to our brain, the question in hand is, if the detection of motion direction changes is dependent on attention directed to the stimulus. We present a new continuous whole-display stimulus configuration, where the attention capturing primary task of motion onset detection is in the central part of the visual display and visual oddball sequence on the background. The visual oddball paradigm consisted of 85% standard and 15% deviant events, motion direction change being the deviant. We show that even though the unattended visual oddball sequence does not affect the performance in the demanding behavioral primary task, the differences appearing in that sequence are noticed by our brain and reflected in two distinguishable vMMN components in occipital and parietal scalp locations. When attention is directed toward the visual oddball sequence, we only see different processing of standards and deviants in later time-windows and task-related activity in frontal scalp location. Our results are obtained under strict attention manipulation conditions.

Reaction time to motion onset and magnitude estimation of velocity in the presence of background motion.

Reaction times (RT) to motion onset of a target grating moving at 0.4, 0.6, 0.8, 1.0 or 1.6 °/s and magnitude estimation of the same velocities were studied in the presence of the surrounding background motion which was either in the same or opposite direction. Surprisingly, we found no relative motion effect: if the background motion, irrespective of its direction, affected the target, then it delayed the RTs and decreased velocity ratings. The background motion was effective on RTs to motion onset only when the target was relatively small and immediately surrounded by a moving background. Increases in RTs were mostly explained by an apparent slowdown of the target stimulus velocity which was caused by the interference from the moving background. The background motion also affected velocity ratings by decreasing them without systematic effect of the background motion direction.