Dataset Information

How seasons, weather, and part of day influence baseline affective valence in laboratory research participants?

ABSTRACT: Many people believe that weather influences their emotional state. Along similar lines, some researchers in affective science are concerned whether testing individuals at a different time of year, a different part of the day, or in different weather conditions (e.g., in a cold and rainy morning vs. a hot evening) influences how research participants feel upon entering a study; thus inflating the measurement error. Few studies have investigated the link between baseline affective levels and the research context, such as seasonal and daily weather fluctuation in temperature, air pressure, and sunshine duration. We examined whether individuals felt more positive or negative upon entering a study by clustering data across seven laboratory experiments (total N = 1108), three seasons, and daily times ranging from 9 AM to 7 PM. We accounted for ambient temperature, air pressure, humidity, cloud cover, precipitation, wind speed, and sunshine duration. We found that only ambient temperature was a significant predictor of valence. Individuals felt more positive valence on days when it was cooler outside. However, the effect was psychologically negligible with differences between participants above c.a. 30 degrees Celsius in ambient temperature needed to generate a difference in affective valence surpassing one standard deviation. Our findings have methodological implications for studying emotions by suggesting that seasons and part of the day do not matter for baseline affective valence reported by participants, and the effects of ambient temperature are unlikely to influence most research.

SUBMITTER: Behnke M

PROVIDER: S-EPMC8376062 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Taste plays an essential role in the evaluation of food quality by detecting potential harm and benefit in what animals are about to eat and drink. While the affective valence of taste signals is supposed to be innately determined, taste preference can also be drastically modified by previous taste experiences of the animals. However, how the experience-dependent taste preference is developed and the neuronal mechanisms involved in this process are poorly understood. Here, we investigate the effects of prolonged exposure to umami and bitter tastants on taste preference using two-bottle tests in male mice. Prolonged umami exposure significantly enhanced umami preference with no changes in bitter preference, while prolonged bitter exposure significantly decreased bitter avoidance with no changes in umami preference. Because the central amygdala (CeA) is postulated as a critical node for the valence processing of sensory information including taste, we examined the responses of cells in the CeA to sweet, umami, and bitter tastants using in vivo calcium imaging. Interestingly, both protein kinase C delta (Prkcd)-positive and Somatostatin (Sst)-positive neurons in the CeA showed an umami response comparable to the bitter response, and no difference in cell type-specific activity patterns to different tastants was observed. Meanwhile, fluorescence in situ hybridization with c-Fos antisense probe revealed that a single umami experience significantly activates the CeA and several other gustatory-related nuclei, and especially CeA Sst-positive neurons were strongly activated. Intriguingly, after prolonged umami experience, umami tastant also significantly activates the CeA neurons, but the Prkcd-positive neurons instead of Sst-positive neurons were highly activated. These results suggest a relationship between amygdala activity and experience-dependent plasticity developed in taste preference and the involvement of the genetically defined neural populations in this process.

Dataset Information

How seasons, weather, and part of day influence baseline affective valence in laboratory research participants?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets