Obtaining quantitative survey responses that are both accurate and informative is crucial to a wide range of fields. Traditional and ubiquitous response formats such as Likert and visual analogue scales require condensation of responses into discrete or point values—but sometimes a range of options may better represent the correct answer. In this paper, we propose an efficient interval-valued response mode, whereby responses are made by marking an ellipse along a continuous scale. We discuss its potential to capture and quantify valuable information that would be lost using conventional approaches, while preserving a high degree of response efficiency. The information captured by the response interval may represent a possible response range—i.e., a conjunctive set, such as the real numbers between 3 and 6. Alternatively, it may reflect uncertainty in respect to a distinct response—i.e., a disjunctive set, such as a confidence interval. We then report a validation study, utilizing our recently introduced open-source software (DECSYS), to explore how interval-valued survey responses reflect experimental manipulations of several factors hypothesised to influence interval width, across multiple contexts. Results consistently indicate that respondents used interval widths effectively, and subjective participant feedback was also positive. We present this as initial empirical evidence for the efficacy and value of interval-valued response capture. Interestingly, our results also provide insight into respondents’ reasoning about the different aforementioned types of intervals—we replicate a tendency towards overconfidence for those representing epistemic uncertainty (i.e., disjunctive sets), but find intervals representing inherent range (i.e., conjunctive sets) to be well-calibrated.

Most of statistics and AI draw insights through modelling discord or variance between sources (i.e., inter-source) of information. Increasingly however, research is focusing on uncertainty arising at the level of individual measurements (i.e., within- or intra-source), such as for a given sensor output or human response. Here, adopting intervals rather than numbers as the fundamental data-type provides an efficient, powerful, yet challenging way forward—offering systematic capture of uncertainty-at-source, increasing informational capacity, and ultimately potential for additional insight. Following progress in the capture of interval-valued data in particular from human participants, conducting machine learning directly upon intervals is a crucial next step. This paper focuses on linear regression for interval-valued data as a recent growth area, providing an essential foundation for broader use of intervals in AI. We conduct an in-depth analysis of state-of-the-art methods, elucidating their behaviour, advantages, and pitfalls when applied to synthetic and real-world data sets with different properties. Specific emphasis is given to the challenge of preserving mathematical coherence, i.e., models maintain fundamental mathematical properties of intervals. In support of real-world applicability of the regression methods, we introduce and demonstrate a novel visualization approach, the interval regression graph, or IRG , which effectively communicates the impact of both position and range of variables within the regression models—offering a leap in their interpretability. Finally, the paper provides practical recommendations concerning regression-method choice for interval data and highlights remaining challenges and important next steps for developing AI with the capacity to handle uncertainty-at-source.

Comparing fuzzy sets by computing their similarity is common, with a large set of measures of similarity available. However, while commonplace in the computational intelligence community, the application and results of similarity measures are less common in the wider scientific context, where statistical approaches are the standard for comparing distributions. This is challenging, as it means that developments around similarity measures arising from the fuzzy community are inaccessible to the wider scientific community; and that the fuzzy community fails to take advantage of a strong statistical understanding which may be applicable to comparing (fuzzy membership) functions. In this paper, we commence a body of work on systematically relating the outputs of similarity measures to the notion of statistically significant difference; that is, how (dis)similar do two fuzzy sets need to be for them to be statistically different? We explain that in this context it is useful to initially focus on dis-similarity, rather than similarity, as the former aligns directly with the widely used concept of statistical difference. We propose two methods of applying statistical tests to the outputs of fuzzy dissimilarity measures to determine significant difference. We show how the proposed work provides deeper insight into the behaviour and possible interpretation of degrees of dis-similarity and, consequently, similarity, and how the interpretation differs in respect to context (e.g., the complexity of the fuzzy sets).

Over a series of decisions between two or more probabilistically rewarded options, humans have a tendency to diversify their choices, even when this will lead to diminished overall reward. In the extreme case of probability matching, this tendency is expressed through allocation of choices in proportion to their likelihood of reward. Research suggests that this behaviour is an instinctive response, driven by heuristics, and that it may be overruled through the application of sufficient deliberation and self-control. However, if this is the case, then how and why did this response become established? The present study explores the hypothesis that diversification of choices, and potentially probability matching, represents an overextension of a historically normative foraging strategy.This is done through examining choice behaviour on a simple simulated foraging task, designed to model the natural process of accumulation of unharvested resources over time. Behaviour was then directly compared with that observed on a standard fixed probability task (cf. Ellerby & Tunney, 2017). Results indicated a convergence of choice patterns on the simulated foraging task, between participants who acted intuitively and those who took a more strategic approach. These findings are also compared with those of another similarly motivated study (Schulze, van Ravenzwaaij, & Newell, 2017).

Domesticated cats have evolved from the obligate carnivore Felis silvestris. The instinct to eat small frequent meals and a crepuscular feeding rhythm was conserved during domestication. Declining senses, cognitive impairment, and poor oral health decrease food intake in aging cats. Combined with metabolic and physiological changes, they are at risk of sarcopenia and general body condition impairment. Aging cats may, therefore, benefit from modified nutritional strategies to achieve their metabolic energy requirements; however, little is known about feeding patterns in aging cats and influence of food format. This in-home study of 134 aging cats (≥7 years) assessed ad libitum dry, wet, and wet/dry (‘mixed’) feeding regimens to compare 24 h feeding frequency and caloric intakes. Feeding frequency was significantly different between wet and dry (p = 0.02) and mixed and dry diets (p < 0.001), with cats consuming 6.0 (dry), 6.9 (wet), or 7.2 meals (mixed) per day. Mean caloric intakes were significantly higher for dry (262.6 kcal/d) and mixed (222.6 kcal/d) regimens (both p < 0.001) versus wet (138.1 kcal/d). In conclusion, healthy aging cats eat small frequent meals, with feeding frequency and caloric intake influenced by dietary format. These findings support development of evidence-based feeding guidelines for aging cats.