Enhancing decision-making with data quality metadata

Purpose Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM. Design/methodology/approach The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting. Findings The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM. Research limitations/implications A drawback of this study is the sample size. With a sample size of 51, the power of the model to draw conclusions is weakened. Practical implications In today’s decision environments, decision-makers deal with extraordinary volumes of data the quality of which is unknown or not determinable with any certainty. The interface and its evaluation offer insights into the design of decision support systems that reduce the complexity of the data and facilitate the integration of DQM into the decision tasks. Originality/value To the best of my knowledge, this is the only research to build and evaluate a decision-support prototype for structured decision-making with DQM.

Multiteam Competitive Optimization Algorithm and Its Application in Bearing Fault Diagnosis

A novel multiteam competitive optimization (MTCO) algorithm has been proposed to diagnose the fault patterns of bearings. This algorithm is inspired by competitive behaviors of multiple teams. It is a three-level organization structure; thus, more potential optimal areas can be searched. By imitating human thinking, such as the betrayal and replacement behavior along with the introduction of an acceptable vector, new strategies within the MTCO are designed to increase the diversity and guide jumping out of location suboptimal areas. In addition to this, a kernel function has been introduced to reduce the recognition errors caused by data which are nonlinearly distributed in original space. The obtained experimental results demonstrate that the proposed MTCO is globally stable and optimal decision performance. After that the MTCO is applied for the fault diagnosis of bearings, and it has also been compared with other commonly used methods. The comparison indicates that the proposed algorithm has higher recognition accuracy.

Optimal policy for attention-modulated decisions explains human fixation behavior

Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.

Studying human-AI collaboration protocols: the case of the Kasparov’s law in radiological double reading

Purpose The integration of Artificial Intelligence into medical practices has recently been advocated for the promise to bring increased efficiency and effectiveness to these practices. Nonetheless, little research has so far been aimed at understanding the best human-AI interaction protocols in collaborative tasks, even in currently more viable settings, like independent double-reading screening tasks. Methods To this aim, we report about a retrospective case–control study, involving 12 board-certified radiologists, in the detection of knee lesions by means of Magnetic Resonance Imaging, in which we simulated the serial combination of two Deep Learning models with humans in eight double-reading protocols. Inspired by the so-called Kasparov’s Laws, we investigate whether the combination of humans and AI models could achieve better performance than AI models alone, and whether weak reader, when supported by fit-for-use interaction protocols, could out-perform stronger readers. Results We discuss two main findings: groups of humans who perform significantly worse than a state-of-the-art AI can significantly outperform it if their judgements are aggregated by majority voting (in concordance with the first part of the Kasparov’s law); small ensembles of significantly weaker readers can significantly outperform teams of stronger readers, supported by the same computational tool, when the judgments of the former ones are combined within “fit-for-use” protocols (in concordance with the second part of the Kasparov’s law). Conclusion Our study shows that good interaction protocols can guarantee improved decision performance that easily surpasses the performance of individual agents, even of realistic super-human AI systems. This finding highlights the importance of focusing on how to guarantee better co-operation within human-AI teams, so to enable safer and more human sustainable care practices.

Performance Differences Between Native and Non-Native Speakers on a New Happy–Sad Executive Function Measure

Objective The happy–sad task adapts the classic day–night task by incorporating two early acquired emotional concepts (“happy” and “sad”) and demonstrates elevated inhibitory demands for native speakers. The task holds promise as a new executive function measure for assessing inhibitory control across the lifespan, but no studies have examined the influence of language of test administration on performance. Method Seventy adult native English speakers and 50 non-native speakers completed the computerized day–night and the new happy–sad tasks administered in English. In two conditions, participants were categorized pictorial stimuli either in a congruent manner (“happy” for a happy face) or in a more challenging, incongruent manner (“sad” for a happy face). Lexical decision performance was obtained to estimate levels of English language proficiency. Results Native speakers and non-native speakers performed comparably except for the critical incongruent condition of the happy–sad task, where native speakers responded more slowly. A greater congruency effect for the happy–sad task was found for native than for non-native speakers. Lexical decision performance was associated with performance on the challenging incongruent conditions. Conclusion This study reinforced the usefulness of the happy–sad task as a new measure in evaluating inhibitory control in adult native-speakers. However, the language of test administration needs to be considered in assessment because it may lead to performance differences between native and non-native speakers.

Dogmatism manifests in lowered information search under uncertainty

When knowledge is scarce, it is adaptive to seek further information to resolve uncertainty and obtain a more accurate worldview. Biases in such information-seeking behavior can contribute to the maintenance of inaccurate views. Here, we investigate whether predispositions for uncertainty-guided information seeking relate to individual differences in dogmatism, a phenomenon linked to entrenched beliefs in political, scientific, and religious discourse. We addressed this question in a perceptual decision-making task, allowing us to rule out motivational factors and isolate the role of uncertainty. In two independent general population samples (n= 370 andn= 364), we show that more dogmatic participants are less likely to seek out new information to refine an initial perceptual decision, leading to a reduction in overall belief accuracy despite similar initial decision performance. Trial-by-trial modeling revealed that dogmatic participants placed less reliance on internal signals of uncertainty (confidence) to guide information search, rendering them less likely to seek additional information to update beliefs derived from weak or uncertain initial evidence. Together, our results highlight a cognitive mechanism that may contribute to the formation of dogmatic worldviews.