Does descriptive text change how people look at art? A novel analysis of eye-movements using data-driven Units of Interest

Does reading a description of an artwork affect how a person subsequently views it? In a controlled study, we show that in most cases, textual description does not influence how people subsequently view paintings, contrary to participants’ self-report that they believed it did. To examine whether the description affected transition behaviour, we devised a novel analysis method that systematically determines Units of Interest (UOIs), and calculates transitions between these, to quantify the effect of an external factor (a descriptive text) on the viewing pattern of a naturalistic stimulus (a painting). UOIs are defined using a grid-based system, where the cell-size is determined by a clustering algorithm (DBSCAN). The Hellinger distance is computed for the distance between two Markov chains using a permutation test, constructed from the transition matrices (visual shifts between UOIs) of the two groups for each painting. Results show that the description does not affect the way in which people transition between UOIs for all but one of the paintings -- an abstract work -- suggesting that description may play more of a role in determining transition behaviour when a lack of semantic cues means it is unclear how the painting should be interpreted. The contribution is twofold: to the domain of art/curation, we provide evidence that descriptive texts do not effect how people view paintings, with the possible exception of some abstract paintings; to the domain of eye-movement research, we provide a method with the potential to answer questions across multiple research areas, where the goal is to determine whether a particular factor or condition consistently affects viewing behaviour of naturalistic stimuli

Convincing or Odd: Anthropomorphic Design Cues in Chatbots

Chatbot anthropomorphic design is essential to the perception and behaviors of users who interact with chatbots. However, limited attention has been given to empirical investigations on whether certain anthropomorphic design successfully induce users’ perception of anthropomorphism. To fill in the gap, the study draws on dual-process theory to examine the impact of different design cues on users’ perception of chatbot’s anthropomorphism. By analyzing the data of a between-subject online experiment, we found significant impact of identity cues and communicative cues on users’ perception of anthropomorphism in user-chatbot interaction. Meanwhile, the frequently used visual cues are not so influential as expected in prior studies. Moreover, when multiple cues are present at the same time, a compensation effect is found between the impact of visual/identity cues and that of communicative cues

UK daily meteorology, air quality, and pollen measurements for 2016–2019, with estimates for missing data

In recent years, quantifying the impacts of detrimental air quality has become a global priority for researchers and policy makers. At present, the systems and methodologies supporting the collection and manipulation of this data are difficult to access. To support studies quantifying the interplay between common gaseous and particulate pollutants with meteorology and biological particles, this paper presents a comprehensive data-set containing daily air quality readings from the Automatic Urban and Rural Network, and pollen and weather data from Met Office monitoring stations, in the years 2016 to 2019 inclusive, for the United Kingdom. We describe (1) the sources from which the data were collected, (2) the methods used for the data cleaning process and (3) how issues related to missing values and sparse regional coverage were addressed. The resulting data-set is designed to be used ‘as is’ by those using air quality data for research; we also describe and provide open access to the methods used for curating the data to allow modification of or addition to the data-set