AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and “collaborate” with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI “partners” with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying ‘psychological’ mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.

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Human decision-making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. To substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility, (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions, and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena.

Age-related differences in dual-task performance may be affected by factors such as skill integration and perceptual competition. Therefore these factors were examined in a dual-task experiment with young and older adults involving two one-dimensional compensatory tracking tasks. Single-task difficulty was individually adjusted for each subject. It was found that differences in pure dual-task performance between young and older subjects increase when the subtasks are coherent such that skills can be integrated. In addition, the degree to which integration reduces the effects of visual competition was larger for the young than for the older subjects. It is concluded that in dual tasks with coherent subtasks, older adults may show an impaired ability to perform the subtasks in an integrated manner.

The magnitude of age effects in single- and dual-tasks may be affected by the degree to which performance depends on well-learned skills that were previously developed. In addition, age-effects may be affected by the requirement to modify these skills and by attentional requirements emerging from the mutual relation of subtasks. Effects of skill modification and emergent attentional processes were examined in an experiment in which experienced subjects performed two perceptual-motor tasks, a vehicle steering task and car-following task in a driving simulator. Car-following was performed under two conditions of familiarity, determining whether or not a normal psychomotor routine had to be modified. In dual-task performance, the demand of subtasks was constant or alternating in counterphase. In general, the older subjects' performance did not differ from that of their younger counterparts, except when the single- or dual-task involved routine modification in car-following. Dual-task costs were basically manifested in the car-following task. Post hoc interpretations of the data indicated that the results were not completely consistent with the complexity hypothesis.