Prediction beyond the borders: ERP indices of boundary extension-related error

Boundary extension (BE) is a rapidly occurring memory error in which participants incorrectly remember having seen beyond the boundaries of a view. However, behavioral data has provided no insight into how quickly after the onset of a test picture the effect is detected. To determine the time course of BE from neural responses we conducted a BE experiment while recording EEG. We exploited a diagnostic response asymmetry to mismatched views (a closer and wider view of the same scene) in which the same pair of views is rated as more similar when the closer item is shown first than vice versa. On each trial, a closer or wider view was presented for 250 ms followed by a 250-ms mask and either the identical view or a mismatched view. Boundary ratings replicated the typical asymmetry. We found a similar asymmetry in ERP responses in the 265-285 ms interval where the second member of the close-then-wide pairs evoked less negative responses at left parieto-temporal sites compared to the wide-then-close condition. We also found diagnostic ERP effects in the 500-560 ms range, where ERPs to wide-then-close pairs were more positive at centro-parietal sites than in the other three conditions, which is thought to be related to participants’ confidence in their perceptual decision. The ERP effect in the 265-285 ms range suggests the falsely remembered region beyond the view-boundaries of S1 is rapidly available and impacts assessment of the test picture within the first 265 ms of viewing, suggesting that extrapolated scene structure may be computed rapidly enough to play a role in the integration of successive views during visual scanning

Brain-Based Indices for User System Symbiosis

The future generation user system interfaces need to be user-centric which goes beyond user-friendly and includes understanding and anticipating user intentions. We introduce the concept of operator models, their role in implementing user-system symbiosis, and the usefulness of brain-based indices on for instance effort, vigilance, workload and engagement to continuously update the operator model. Currently, the best understood parameters in the operator model are vigilance and workload. An overview of the currently employed brain-based indices showed that indices for the lower workload levels (often based on power in the alpha and theta band of the EEG) are quite reliable, but good indices for the higher workload spectrum are still missing. We argue that this is due to the complex situation when performance stays optimal despite increasing task demands because the operator invests more effort. We introduce a model based on perceptual control theory that provides insight into what happens in this situations and how this affects physiological and brain-based indices.We argue that a symbiotic system only needs to intervene directly in situations of under and overload, but not in a high workload situation. Here, the system must leave the option to adapt on a short notice exclusively to the operator. The system should lower task demands only in the long run to reduce the risk of fatigue or long recovery times. We end by indicating future operator model parameters that can be reflected by brain-based indices