Disentangling rodent behaviors to improve automated behavior recognition

Automated observation and analysis of behavior is important to facilitate progress in many fields of science. Recent developments in deep learning have enabled progress in object detection and tracking, but rodent behavior recognition struggles to exceed 75–80% accuracy for ethologically relevant behaviors. We investigate the main reasons why and distinguish three aspects of behavior dynamics that are difficult to automate. We isolate these aspects in an artificial dataset and reproduce effects with the state-of-the-art behavior recognition models. Having an endless amount of labeled training data with minimal input noise and representative dynamics will enable research to optimize behavior recognition architectures and get closer to human-like recognition performance for behaviors with challenging dynamics

Measuring Behavior in the Home Cage : Study Design, Applications, Challenges, and Perspectives

FUNDING This research was supported by NIH grants R00AG056662, P20GM125528, AG057424 to WS and SLPeer reviewedPublisher PD

Measuring cooking experience implicitly and explicitly: Physiology, facial expression and subjective ratings

Understanding consumers’ emotional experience during the process of cooking is important to enable the development of food products. In addition to verbal (‘explicit’) reports, physiological variables and facial expression may be helpful measures since they do not interfere with the experience itself and are of a continuous nature. This study investigated the potential of a range of implicit and explicit measures (1) to differentiate between subtle differences in pleasantness of ingredients, and (2) to identify emotionally salient phases during the process of cooking. 74 participants cooked and tasted a curry dish following standardized timed auditory instructions, either with ‘basic’ or ‘premium’ versions of ingredients. Heart rate, skin conductance, EEG and facial expression were recorded continuously during cooking and tasting. Subjective ratings of valence and arousal were taken directly after. Before and after cooking, participants performed ‘dry cooking’ sessions without ingredients to acquire changes in the physiological variables caused by physical activity only. We found no differences between the ‘basic’ and ‘premium’ groups, neither in implicit, nor in explicit measures. However, there were several robust physiological effects reflecting different cooking phases. Most notably, heart rate was relatively high for two specific phases: adding curry paste from a sachet during cooking, and tasting the prepared dish. The verbal reports of valence and arousal showed similar patterns over phases. Thus, our method suggests that physiological variables can be used as continuous, implicit measures to identify phases of affective relevance during cooking and may be a valuable addition to explicit measures of emotion

Measuring cooking experience implicitly and explicitly : Physiology, facial expression and subjective ratings

Understanding consumers’ emotional experience during the process of cooking is important to enable the development of food products. In addition to verbal (‘explicit’) reports, physiological variables and facial expression may be helpful measures since they do not interfere with the experience itself and are of a continuous nature. This study investigated the potential of a range of implicit and explicit measures (1) to differentiate between subtle differences in pleasantness of ingredients, and (2) to identify emotionally salient phases during the process of cooking. 74 participants cooked and tasted a curry dish following standardized timed auditory instructions, either with ‘basic’ or ‘premium’ versions of ingredients. Heart rate, skin conductance, EEG and facial expression were recorded continuously during cooking and tasting. Subjective ratings of valence and arousal were taken directly after. Before and after cooking, participants performed ‘dry cooking’ sessions without ingredients to acquire changes in the physiological variables caused by physical activity only. We found no differences between the ‘basic’ and ‘premium’ groups, neither in implicit, nor in explicit measures. However, there were several robust physiological effects reflecting different cooking phases. Most notably, heart rate was relatively high for two specific phases: adding curry paste from a sachet during cooking, and tasting the prepared dish. The verbal reports of valence and arousal showed similar patterns over phases. Thus, our method suggests that physiological variables can be used as continuous, implicit measures to identify phases of affective relevance during cooking and may be a valuable addition to explicit measures of emotion.</p

Improving real-life, heart rate based estimates of emotion by taking metabolic heart rate into account : a perspective and an example in cooking

Physiological variables such as heart rate carry information about mental state (cognitive and emotional state - here we simply refer to ‘emotion’). An important advantage over verbal methods as a way to probe emotion, is that heart rate is an implicit measure that can be measured continuously. Extracting information about emotion from heart rate in real life is challenged by the concurrent effect of physical activity on heart rate caused by metabolic need. “Non-metabolic heart rate”, which refers to the heart rate that is caused by factors other than physical activity, would be a more sensitive and more universally applicable correlate of emotion than heart rate itself. To precisely determine non-metabolic heart rate is a challenge. We explored evidence from the literature that non-metabolic heart rate, as it has been determined up until now, indeed reflects emotion. We focused on methods using accelerometry since these sensors are readily available in devices suitable for daily life usage. We found no convincing evidence that these existing methods lead to estimates of non-metabolic heart rate that reflect emotion. This is probably caused by the fact that intensity of motion signals as recorded from accelerometers does not correspond one-on-one with muscle activity in cases that forces are exerted without much movement (isometric muscle activity). In addition, the placement of the sensors do not always match the currently moving body parts, leading to invalid estimates. Studies in the field of energy expenditure as measured through variables extracted from respiratory gas exchange, show that energy expenditure can be well estimated using accelerometry by first identifying the type of action that is performed, rather than (only) using the intensity of motion signals. We therefore suggest that for real-life cases, estimating the type and intensity of activities based on accelerometry (and other information), and in turn use those to determine the non-metabolic heart rate, is the most promising route to determining non-metabolic heart rate precisely enough in order for it to be useful in estimating emotional state. In a study on estimating emotion during real-life cooking, we determined non-metabolic heart rate by correcting heart rate in an activity specific way. Rather than using a model that estimates specific activities based on accelerometry, we made an effort to estimate a baseline metabolic heart rate from participants performing movements that occur during cooking, without the emotion that occurs during cooking. The aim was to investigate the potential of non-metabolic heart rate to reflect emotionally salient phases during the process of cooking a dish

Pre-donation cognitions of potential living organ donors: the development of the Donation Cognition Instrument in potential kidney donors

Background. Cognitions surrounding living organ donation, including the motivation to donate, expectations of donation and worries about donation, are relevant themes during living donor evaluation. However, there is no reliable psychometric instrument assessing all these different cognitions. This study developed and validated a questionnaire to assess pre-donation motivations, expectations and worries regarding donation, entitled the Donation Cognition Instrument (DCI). Methods. Psychometric properties of the DCI were examined using exploratory factor analysis for scale structure and associations with validated questionnaires for construct validity assessment. Results. From seven Dutch transplantation centres, 719 potential living kidney donors were included. The DCI distinguishes cognitions about donor benefits, recipient benefits, idealistic incentives, gratitude and worries about donation (Cronbach's alpha 0.76-0.81). Scores on pre-donation cognitions differed with regard to gender, age, marital status, religion and donation type. With regard to construct validity, the DCI was moderately correlated with expectations regarding donor's personal well-being and slightly to moderately to health-related quality of life. Conclusions. The DCI is found to be a reliable instrument assessing cognitions surrounding living organ donation, which might add to pre-donation quality of life measures in facilitating psychosocial donor evaluation by healthcare professional