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One of the goals of augmented cognition is creation of adaptive human-machine collaboration that continually optimizes performance of the human-machine system. Augmented Cognition aims to compensate for temporal limitations in human information processing, for instance in the case of overload, cognitive lockup, and underload. Adaptive behavior, however, may also have undesirable side effects. The dynamics of adaptive support may be unpredictable and may lead to human factors problems such as mode errors, 'out-of-the-loop' problems, and trust related issues. One of the most critical challenges in developing adaptive human-machine collaboration concerns system mitigations. A combination of performance, effort and task information should be taken into account for mitigation strategies. This paper concludes with the presentation of an iterative cognitive engineering framework, which addresses the adaptation strategy of the human and machine in an appropriate manner carefully weighing the costs and benefits.

It is often assumed that two heads are better than one, but reliance on decision aids is often inappropriate. Decisions to rely on an aid are thought to be based on a comparison between the perceived reliability of own performance and that of the decision aid. Unfortunately, perceived reliabilities are unlikely to be perfectly calibrated. This may result in inappropriate decisions to rely on advice. In a laboratory experiment with 40 participants, we studied whether calibration improves after practice, whether calibration of own reliability differs from calibration of the aid's reliability and whether unreliability of the aid is attributed differently. Under-trust in own reliability disappears after practice but under-trust in the aid's reliability persists. Unreliability of the decision aid is less likely to be attributed to temporary, external and uncontrollable factors. This asymmetry in attribution and calibration may explain under-reliance on decision aids.

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