The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.
The Posner cueing paradigm is one of the most widely used paradigms in attention research. Importantly, when employing it, it is critical to understand which type of orienting a cue triggers. It has been suggested that large effects elicited by predictive arrow cues reflect an interaction of involuntary and voluntary orienting. This conclusion is based on comparisons of cueing effects of predictive arrows, nonpredictive arrows (involuntary orienting), and predictive numbers (voluntary orienting). Experiment 1 investigated whether this conclusion is restricted to comparisons with number cues and showed similar results to those of previous studies, but now for comparisons to predictive colour cues, indicating that the earlier conclusion can be generalized. Experiment 2 assessed whether the size of a cueing effect is related to the ease of deriving direction information from a cue, based on the rationale that effects for arrows may be larger, because it may be easier to process direction information given by symbols such as arrows than that given by other cues. Indeed, direction information is derived faster and more accurately from arrows than from colour and number cues in a direction judgement task, and cueing effects are larger for arrows than for the other cues. Importantly though, performance in the two tasks is not correlated. Hence, the large cueing effects of arrows are not a result of the ease of information processing, but of the types of orienting that the arrows elicit.
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