Many psychological studies have shown that human-generated sequences are hardly ever random in the strict mathematical sense. However, what remains an open question is the degree to which this (in)ability varies between people and is affected by contextual factors. Herein, we investigated this problem. In two studies, we used a modern, robust measure of randomness based on algorithmic information theory to assess human-generated series. In Study 1 (N = 183), in a factorial design with task description as a between-subjects variable, we tested the effects of context and mental fatigue on human-generated randomness. In Study 2 (N = 266), in online research, in experimental design, we further investigated the effect of mental fatigue on the randomness of human-generated series and the relationship between the need for cognition (NFC) and the ability to produce random-like series. Results of Study 1 show that the activation of the ability to produce random-like series depends on the relevance of the contextual cues (χ 2 (2) = 7.9828, p = .0192), whether they activate known representations of a random series generator and consequently help to avoid the production of trivial sequences. Our findings from both studies on the effect of mental fatigue (Study 1t (47, 529.5568) = −18.62, p < .001; Study 2 -F (edf = 3.587, Re f .df = 3.587) = 11.863, p < .0001) and cognitive motivation (t (180) = 2.66, p = .009) demonstrate that regardless of the context or task's novelty people quickly lose interest in the random series generation. Therefore, their performance decreases over time. However, people high in the NFC can maintain the cognitive motivation for a longer period and consequently on average generate more random series. In general, our results suggest that when Correspondence should be sent to Mikołaj Biesaga,
Many psychological studies have shown that human-generated sequences deviate from the mathematical notion of randomness. Therefore, the inability to generate perfectly random data is currently considered a well-established fact. What remains an open problem is the degree to which this (in)ability varies between different people and can be affected by contextual factors. In this paper we investigate this problem. We focus on between-subjects variability concerning the level of randomness of generated sequences under different task descriptions. In two studies, we used a modern, robust measure of randomness based on algorithmic information theory to assess human-generated series. We tested hypotheses regarding human generated randomness vis-a-vis effects of context, mathematical experience, fatigue, and the tendency to engage in challenging tasks. Our results show that the activation of the ability to produce random-like series depends on the relevance of contextual cues which rather help to avoid production of trivially non-random sequences than increase the rate of production of highly complex ones. We also show that people tend to get tired very quickly and after first few attempts to generate highly random-like series their performance decrease significantly and they start to produce more markedly patterned, sequences. Based on our results we propose a model that considers two main areas (intellectual and cognitive) of possible psychological factors related to the variability of ability to produce random-like series.
No abstract
This article presents an analysis of European smart city narratives and how they evolved under the pressure of the COVID-19 pandemic. We start with Joss et al.’s observation that the smart-city discourse is presently in flux, engaged in intensive boundary-work and struggling to gain wider support. We approach this process from the critical perspective of surveillance capitalism, as proposed by Zuboff, to highlight the growing privacy concerns related to technological development. Our results are based on analysing 184 articles regarding smart-city solutions, published on social media by five European journals between 2017 and 2021. We adopted both human and machine coding processes for qualitative and quantitative analysis of our data. As a result, we identified the main actors and four dominant narratives: regulation of artificial intelligence and facial recognition, technological fight with the climate emergency, contact tracing apps and the potential of 5G technology to boost the digitalisation processes. Our analysis shows the growing number of positive narratives underlining the importance of technology in fighting the pandemic and mitigating the climate emergency, but the latter is often mentioned in a tokenistic fashion. Right to privacy considerations are central for two out of four discovered topics. We found that the main rationale for the development of surveillance technologies relates to the competitiveness of the EU in the global technological rivalry, while ambitions like increasing societal well-being or safeguarding the transparency of new policies are nearly non-existent.
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