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Ensuring the reliability and validity of data-driven quadrotor model predictions is essential for their accepted and practical use. This is especially true for grey- and black-box models wherein the mapping of inputs to predictions is not transparent and subsequent reliability notoriously difficult to ascertain. Nonetheless, such techniques are frequently and successfully used to identify quadrotor models. Prediction intervals (PIs) may be employed to provide insight into the consistency and accuracy of model predictions. This paper estimates such PIs for polynomial and Artificial Neural Network (ANN) quadrotor aerodynamic models. Two existing ANN PI estimation techniques - the bootstrap method and the quality driven method – are validated numerically for quadrotor aerodynamic models using an existing high-fidelity quadrotor simulation. Quadrotor aerodynamic models are then identified on real quadrotor flight data to demonstrate their utility and explore their sensitivity to model interpolation and extrapolation. It is found that the ANN-based PIs widen considerably when extrapolating and remain constant, or shrink, when interpolating. While this behaviour also occurs for the polynomial PIs, it is of lower magnitude. The estimated PIs establish probabilistic bounds within which the quadrotor model outputs will likely lie, subject to modelling and measurement uncertainties that are reflected through the PI widths.

Implicit (‘unconscious’) approach–avoidance tendencies towards stimuli can be measured using the Approach Avoidance Task (AAT). We recently expanded a toolbox for analyzing the raw data of a novel, mobile version of the AAT (mAAT), that asks participants to move their phone towards their face (pull) or away (push) in response to images presented on the phone. We here tested the mAAT reaction time and the mAAT distance in a study with 71 Dutch participants that were recruited online and performed an experiment without coming to the laboratory. The participants used both the mAAT and (explicit) rating scales to respond to photographic images of food. As hypothesized, the rated wanting, rated valence and mAAT reaction time indicated a preference for palatable over unpalatable food, and for Dutch over Asian food. Additionally, as expected, arousal was rated higher for unpalatable than for palatable food, and higher for Dutch than for Asian food. The mAAT distance indicated that the unpalatable food images were moved across larger distances, regardless of the movement direction (pull or push), compared to the palatable food images; and the Dutch food images were moved across larger distances than the Asian food images. We conclude that the mAAT can be used to implicitly probe approach–avoidance motivation for complex images in the food domain. The new measure of mAAT distance may be used as an implicit measure of arousal. The ratings and the mAAT measures do not reflect the exact same information and may complement each other. Implicit measures, such as mAAT variables, are particularly valuable when response biases that can occur when using explicit ratings are expected.

Implicit approach-avoidance tendencies can be measured by the approach-avoidance task (AAT). The emergence of mobile variants of the AAT enable its use for both in-the-lab and in-the-field experiments. Within the food domain, use of the AAT is concentrated in research on eating disorders or healthy eating and is seldom used as an implicit measure of food experience. Given the prevalence of explicit measures in this field, the AAT may provide additional valuable insights into food experience. To facilitate the use of the AAT as an implicit measure, a processing tool and accompanying graphical user interface (GUI) have been developed for a mobile smartphone variant of the AAT. This tool improves upon the existing processing framework of this mobile AAT by applying more robust filtering and introduces additional sensor correction algorithms to improve the quality of sensor data. Along with refined estimates of reaction time (RT) and reaction force (RF), these processing improvements introduce a new metric: the response distance (RD). The capabilities of the tool, along with the potential added value of calculating RF and RD, are explained in this paper through the processing of pilot data on molded and unmolded food. In particular, the RF and RD may be indicative of participants' arousal. The tool developed in this paper is open source and compatible with other experiments making use of the mobile AAT within, and beyond, the domain of food experience.