Explanatory preferences for complexity matching.
Ontology highlight
ABSTRACT:
SUBMITTER: Lim JB
PROVIDER: S-EPMC7173929 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recently, we demonstrated complexity as a major factor for explaining individual differences in visual preferences for abstract digital art. We have shown that participants could best be separated into two groups based on their liking ratings for abstract digital art comprising geometric patterns: one group with a preference for complex visual patterns and another group with a preference for simple visual patterns. In the present study, building up on these results, we extended our investigations for complexity preferences from highly controlled visual stimuli to ecologically valid stimuli in the auditory modality. Similar to visual preferences, we showed that music preferences are highly influenced by stimulus complexity. We demonstrated this by clustering a large number of participants based on their liking ratings for song excerpts from various musical genres. Our results show that, based on their liking ratings, participants can best be separated into two groups: one group with a preference for more complex songs and another group with a preference for simpler songs. Finally, we considered various demographic and personal characteristics to explore differences between the groups, and reported that at least for the current data set age and gender to be significant factors separating the two groups.
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Project description:BACKGROUND: Experiments under controlled laboratory conditions can produce decisive evidence for testing biological hypotheses, provided they are representative of the more complex natural conditions. However, whether this requirement is fulfilled is seldom tested explicitly. Here we provide a lab/field comparison to investigate the identity of an egg-marking signal of ant queens. Our study was based on ant workers resolving conflict over male production by destroying each other's eggs, but leaving queen eggs unharmed. For this, the workers need a proximate cue to discriminate between the two egg types. Earlier correlative evidence indicated that, in the ant Pachycondyla inversa, the hydrocarbon 3,11-dimethylheptacosane (3,11-diMeC(27)) is more abundant on the surface of queen-laid eggs. METHODOLOGY: We first tested the hypothesis that 3,11-diMeC(27) functions as a queen egg-marking pheromone using laboratory-maintained colonies. We treated worker-laid eggs with synthetic 3,11-diMeC(27) and found that they were significantly more accepted than sham-treated worker-laid eggs. However, we repeated the experiment with freshly collected field colonies and observed no effect of treating worker-laid eggs with 3,11-diMeC(27), showing that this compound by itself is not the natural queen egg-marking pheromone. We subsequently investigated the overall differences of entire chemical profiles of eggs, and found that queen-laid eggs in field colonies are more distinct from worker-laid eggs than in lab colonies, have more variation in profiles, and have an excess of longer-chain hydrocarbons. CONCLUSIONS: Our results suggest that queen egg-marking signals are significantly affected by transfer to the laboratory, and that this change is possibly connected to reduced queen fertility as predicted by honest signaling theory. This change is reflected in the worker egg policing response under field and laboratory conditions.
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Project description:When two people synchronize their rhythmic behaviors (e.g., finger tapping; walking) they match one another not only at a local scale of beat-to-beat intervals, but also at a global scale of the complex (fractal) patterns of variation in their interval series. This "complexity matching" had been demonstrated in a variety of timing behaviors, but the current study was designed to address two important gaps in previous research. First, very little was known about complexity matching outside of synchronization tasks. This was important because different modes are associated with differences in the strength of coordination and the fractal scaling of the task performance. Second, very little was known about the dynamics of the asynchrony series. This was important because asynchrony is a variable directly quantifying the coordination between the two timing behaviors and the task goal. So, the current study explored complexity matching in both synchronized and syncopated finger tapping tasks, and included analyses of the fractal scaling of the asynchrony series. Participants completed an interpersonal finger tapping task, in both synchronization and syncopation conditions. The magnitude of variation and the exact power law scaling of the tapping intervals were manipulated by having one participant tap in time with a metronome. Complexity matching was most stable when there was sufficient variation in the task behavior and when a persistent scaling dynamic was presented. There were, however, several interesting differences between the two coordination modes, in terms of the heterogeneity of the complexity matching effect and the scaling of the asynchronies. These findings raised a number of important points concerning how to approach and understand the interaction of inherently complex systems.
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Project description:There is a growing consensus that a fuller understanding of social cognition depends on more systematic studies of real-time social interaction. Such studies require methods that can deal with the complex dynamics taking place at multiple interdependent temporal and spatial scales, spanning sub-personal, personal, and dyadic levels of analysis. We demonstrate the value of adopting an extended multi-scale approach by re-analyzing movement time-series generated in a study of embodied dyadic interaction in a minimal virtual reality environment (a perceptual crossing experiment). Reduced movement variability revealed an interdependence between social awareness and social coordination that cannot be accounted for by either subjective or objective factors alone: it picks out interactions in which subjective and objective conditions are convergent (i.e., elevated coordination is perceived as clearly social, and impaired coordination is perceived as socially ambiguous). This finding is consistent with the claim that interpersonal interaction can be partially constitutive of direct social perception. Clustering statistics (Allan Factor) of salient events revealed fractal scaling. Complexity matching defined as the similarity between these scaling laws was significantly more pronounced in pairs of participants as compared to surrogate dyads. This further highlights the multi-scale and distributed character of social interaction and extends previous complexity matching results from dyadic conversation to non-verbal social interaction dynamics. Trials with successful joint interaction were also associated with an increase in local coordination. Consequently, a local coordination pattern emerges on the background of complex dyadic interactions in the PCE task and makes joint successful performance possible.
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Project description:Gestures and speech are clearly synchronized in many ways. However, previous studies have shown that the semantic similarity between gestures and speech breaks down as people approach transitions in understanding. Explanations for these gesture-speech mismatches, which focus on gestures and speech expressing different cognitive strategies, have been criticized for disregarding gestures' and speech's integration and synchronization. In the current study, we applied three different perspectives to investigate gesture-speech synchronization in an easy and a difficult task: temporal alignment, semantic similarity, and complexity matching. Participants engaged in a simple cognitive task and were assigned to either an easy or a difficult condition. We automatically measured pointing gestures, and we coded participant's speech, to determine the temporal alignment and semantic similarity between gestures and speech. Multifractal detrended fluctuation analysis was used to determine the extent of complexity matching between gestures and speech. We found that task difficulty indeed influenced gesture-speech synchronization in all three domains. We thereby extended the phenomenon of gesture-speech mismatches to difficult tasks in general. Furthermore, we investigated how temporal alignment, semantic similarity, and complexity matching were related in each condition, and how they predicted participants' task performance. Our study illustrates how combining multiple perspectives, originating from different research areas (i.e., coordination dynamics, complexity science, cognitive psychology), provides novel understanding about cognitive concepts in general and about gesture-speech synchronization and task difficulty in particular.
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Project description:BackgroundArtificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people's preferences for AI clinicians and traditional clinicians are worth exploring.ObjectiveWe aimed to quantify and compare people's preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people's preferences were affected by the pressure of pandemic.MethodsWe used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people's preferences for different diagnosis methods.ResultsIn total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis.ConclusionsIndividuals' preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.
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