An investigation on humans’ sensitivity to environmental temperature

doi:10.21203/rs.3.rs-3101216/v1

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An investigation on humans’ sensitivity to environmental temperature

https://doi.org/10.21203/rs.3.rs-3101216/v1

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published 04 Dec, 2023

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Thermal perception research has focused on detecting temperature changes on specific body parts, while investigating thermal comfort aims to understand comfortable temperatures in an environment. However, understanding overall body thermal perception remains challenging. To address this, we used four climate chambers with temperatures between 24°C ± 1°C, where 26 participants transitioned between chambers and indicated if the temperature felt warmer or colder. We collected 3120 temperature comparisons and analysed the data using generalized linear mixed-effects models (GLMM). Results showed a Point of Subjective Equality (PSE) of − 0.13°C (± 0.1°C), a Just Noticeable Difference (JND) of 0.38°C (± 0.1°C), revealing the minimum detectable temperature change, a negative absolute threshold of − 0.91°C (± 0.28°C) and a positive absolute threshold of 0.8°C (± 0.34°C). These findings demonstrate participants’ accurate temperature discrimination abilities (JND) and limited variation between individuals, suggesting an intrinsic mechanism governing temperature sensitivity. The GLMM analysis demonstrated remarkable accuracy in explaining the observed data, with an R-squared value of 0.72. In conclusion, our experiment provides compelling evidence of human sensitivity to environmental temperatures, addressing a long-standing question in the field of thermal perception.

Biological sciences/Psychology

Earth and environmental sciences/Environmental social sciences/Psychology and behaviour

Biological sciences/Neuroscience/Cognitive neuroscience

Biological sciences/Neuroscience/Sensory processing

Thermal perception

environment

sensitivity

climate chambers

GLMM.

As humans, our perception and understanding of the world are shaped by the unique characteristics and stimuli present in the environment. Various psychological theories attempt to explain how our mind integrates this information to form a unified and comprehensive worldview. According to the grounded cognition theory^[1], the process of perception and cognition does not involve combining the different sensory modalities to form a final semantic representation that transcends individual senses. Instead, it emphasises the connection between our minds, bodies, and the environment. Even during abstract reasoning, our thoughts are grounded in our bodily experiences and activate sensorimotor memories. Therefore, it may be worthwhile to investigate how our environment affects our minds.

The environment encompasses various factors that can impact our cognition, including green spaces, humidity levels, natural light, and temperature^[2]. Temperature, in particular, plays a crucial role in our survival, as the maintenance of a constant body and brain temperature is necessary for proper physiological functioning^[3]. To perceive temperature, our bodies possess specialised sensory receptors called thermoreceptors. These thermoreceptors are free nerves endings located in the skin, which transmit signals to the spinal cord and thalamus via the thermosensory spinothalamic tract^[4]. Interestingly, different thermoreceptors are responsible for processing cold and warm stimuli, indicating partial segregation of these processes^[4]. Furthermore, thermoreceptors exhibit distinct responses to static and dynamic temperature stimuli. When entering a warm environment, warm thermoreceptors become more active compared to cold thermoreceptors. Conversely, when the environment starts to cool down, the firing rate of cold thermoreceptors suddenly increases. As the environment becomes cold, cold thermoreceptors continue to respond, while the activity of warm thermoreceptors decreases^[5]. This dynamic behaviour enables us to be aware of the current temperature we are exposed to and any potential temperature fluctuations in the environment.

The thermosensory spinothalamic tract subsequently reaches the posterior insular cortex, which serves as the primary thermosensory cortex involved in both discriminatory and affective processes associated with skin thermal sensations^[6]. This cerebral area is also responsible for the broader representation of our body, known as interoception^[4]. The signals originating from this brain region play a fundamental role in maintaining homeostasis and regulating the body’s internal state. Thermoregulation encompasses various mechanisms, including autonomic reflexes (such as, shivering or sweating)^[7] and conscious behaviours (i.e., change of clothing) that we have learned to employ in order to feel comfortable even when the environment is changing^[8]. To effectively perform these adaptive behaviours, we also need to perceive the thermal proprieties of one own’s body and the surrounding environment. Research suggests that the thermal sensations coming from the head play a crucial role in our perception of thermal comfort^[9], and changes in peripheral temperature have a more significant impact on our thermal regulation compared to changes in core temperature^[10].

Previous research has predominantly focused on quantifying human temperature sensitivity by applying thermal stimuli to specific areas of the body. These studies have identified temperature ranges perceived as extremely cold or hot (< 15°C / > 45°C) and comfortably cool or warm (15°C < T < 30°C / 30°C < T < 45°C). Furthermore, this research has revealed varying thermal sensitivities across different body parts, with the head being more sensitive to warm temperatures and the torso more responsive to cold stimuli^[11]. In a recent study, Crucianelli and colleagues (2021) found that the minimum difference participants could perceive (also known as the just noticeable difference, JND) in cold stimuli applied to the palm is 2.02°C, while is 1.48°C on the forearm. For warm stimuli, the JND on the palm is 2.65°C, and on the forearm, is 2.61°C^[12].

However, the existing literature lacks information on the JND when individuals are immersed in an environment with a specific temperature and the whole body is involved in the perception. Further research is needed to investigate this aspect of temperature perception. Therefore, we conducted a study to determine human sensitivity to a comfortable environmental temperature of 24°C ± 1°C. Participants were instructed to move between four distinct climate chambers and compare the temperatures in each chamber to determine the minimum difference they could perceive (i.e., the JND).

Table 1 summarises the demographical information of the 26 participants. On average, they have a normal emphatic quotient and normal awareness of their own internal bodily states, and they have a normal level of daily activity. Regarding their usual preferences for temperature, 13 of them declared to prefer a cold environment and to get easily warm during different activities; 4 participants reported the opposite, so they prefer warm environments and get easily cold; and the remaining 9 participants did not have clear preferences.

Table 1: summer of 26 participants’ demographical information

EQ = Emphatic Quotient (cut-off < 33); BPQ_BOA = Body Perception Questionnaire_Body Awareness (cut-off < 18); BPQ_ASR = Body Perception Questionnaire_Autonomous System Stress Response (cut-off > 16); GPAQ_m = Global Activity Questionnaire_moderate level of activity (cut-off < 150 minutes); GPAQ_i = Global Activity Questionnaire_intense level of activity (cut-off < 75 minutes).

26 participants	Age	Sex	BMI	Place of birth	Temperature preferences	EQ	BPQ_BOA	BPQ_ASR	GPAQ_m	GPAQ_i
characterisation	max 47; min 20	50% females	max 24.8; min 19	18 born in north Italy, 7 born in south Italy	4 prefer warm, 9 no preferences, 13 prefer cold	5 < cut-off	8 < cut-off	1 > cut-off	1 < cut-off	6 < cut-off
Average	30	/	22	/	/	41	21	15	600	382
St. deviation	6	/	2	/	/	8	6	2	468	460

As described in the analysis section, we first proceeded in computing the two generalised linear mixed-effects models (glmm0 and glmm1) and then we compared their goodness of fit by means of an ANOVA. The results are reported in Table 2. The Akaike information criterion (AIC) of the model considering also the difference in temperature as a random effect (glmm1) is smaller, indicating that it explains better our data with respect to the model considering only the participants as a random effect. The same information is extracted by looking at the Bayesian information criterion (BIC) and at the significant p-value.

Table 2: results of the ANOVA between the two generalised linear mixed-effects models

models	AIC	BIC	Pr(> Chisq)
glmm0	2389.2	2407.3
glmm1	2363.0	2393.3	2.85e-07 ***

Next, we calculated the R² of glmm1 to analyse the percentage of variance explained by the fixed effects alone (R2m) and by the fixed effects together with the random effects (R2c). Results are reported in Table 3. Our fixed effects alone explain 68% of our data, while the addition of the random effect added a 4% of explanation. Taken together, these results show both the adequacy of our model in explaining our data and the small variability that the single participants add to them.

The summary of the model (Table 4) shows that for each increment of ± 1°C, the probability to have a correct answer is almost double (Estimate = 1.79) and therefore the participants’ sensitivity drastically increases.

Table 3: results of the R² calculation for glmm1

Table 4: summary of the model glmm1

R2m	R2c	Fixed Effects	Estimate	Std. Error	z value	Pr(>\|z\|)
0.68	0.72	(Intercept)	0.23	0.04	5.30	1.16e-07 ***
		DT [ °C]	1.79	0.1	17.82	< 2e-16 ***

Then, we calculated the average PSE and the JND using the function MixDelta. Table 5 reports these results showing a PSE of − 0.13°C and JND of 0.38°C, while Fig. 4 shows the average psychometric curve obtained with these data.

Table 5: PSS and JND results

	Estimate [° C]	Std. Error [° C]	Inferior [° C]	Superior [° C]
PSE	-0.13	0.02	-0.17	-0.08
JND	0.38	0.02	0.33	0.42

The absolute thresholds were + 0.8°C (SD = ± 0.34°C) and − 0.91°C (SD = ± 0.28°C). The data of every single participant, obtained by using a Generalized Linear Models (GLM), is contained in Table 6. Interestingly, the results obtained using a GLM do not differ much from the ones obtained using a GLMM confirming once again the small variability added by the single participants.

Table 6: Summary of all participants’ data

Subject	PSE [ °C]	JND [ °C]	Abs TH + [ °C]	Abs TH - [ °C]
1	-0.18	0.33	0.6	-0.8
2	-0.15	0.35	0.56	-1.46
3	-0.2	0.5	0.78	-1.36
4	-0.35	0.38	0.44	-1.23
5	-0.22	0.41	1.39	-0.8
6	0.02	0.28	0.48	-0.69
7	-0.09	0.51	1.21	-1.4
8	0.07	0.29	0.46	-0.89
9	-0.08	0.37	1.01	-0.82
10	0.06	0.32	0.92	-0.42
11	-0.23	0.27	0.48	-0.72
12	-0.23	0.31	0.81	-0.7
13	0.1	0.41	0.96	-0.91
14	-0.14	0.21	0.36	-0.5
15	-0.15	0.52	0.77	-1.01
16	-0.28	0.42	0.82	-0.83
17	-0.16	0.29	0.79	-0.66
18	-0.18	0.41	0.88	-0.95
19	-0.1	0.24	0.58	-0.55
20	-0.34	0.35	0.31	-0.83
21	-0.21	0.93	1.85	-1.39
22	-0.09	0.28	0.47	-0.8
23	-0.2	0.56	1.05	-1.02
24	-0.07	0.52	0.84	-0.95
25	-0.05	0.54	1.13	-1.21
26	0.05	0.36	0.84	-0.74
MEAN	-0.13	0.4	0.8	-0.91
SD	0.12	0.14	0.34	0.28

Regarding the data about skin temperature, we did not identify any relevant variation of it during the experiment. On average, the temperature measured on the right upper arm was 30.01°C (SD = ± 0.13°C), on the left chest was 30.86°C (SD = ± 0.16°C), on the right anterior thigh was 30.26°C (SD = ± 0.17°C), and on the right calf was 30.89°C (SD = ± 0.16°C) (more data on Supplementary Table S1). At the same time, the core temperature measured on the forehead of the participants remained constant at an average temperature of 36.30°C (SD = ± 0.11°C) (more data in Supplementary Table S2).

In this study, we examined human sensitivity to environmental temperatures using a unique experimental paradigm. By having participants move between climate chambers with temperatures ranging from 23°C to 25°C, we aimed to assess their ability to perceive temperature differences. To mitigate potential confounding factors related to uncomfortable temperatures, we conducted experiments using temperatures defined as comfortable according to the ANSI/ASHRAE standards (2017). One potential confounding factor arises from Weber's rule, which suggests that the perceived sensitivity is influenced by the initial magnitude of the stimulus. In essence, this principle states that our ability to perceive a change in a stimulus is not absolute but rather influenced by the characteristics of the stimulus itself. For instance, when detecting a change in weight, we can perceive a difference if it shifts from 1 kg to 1.5 kg. However, if the weight initially stands at 50 kg and then changes to 50.5 kg, we would be unable to detect the difference. Notably, the absolute change in weight remains the same in both scenarios. This illustrates that our sensitivity is influenced by the initial magnitude of the stimulus^[13]. Therefore, we can anticipate a similar pattern in relation to thermal stimuli. The results for the tested temperature range indicated that participants demonstrated a high level of sensitivity, being able to detect temperature changes of at least ± 1°C. This ability was consistent across all participants, regardless of their individual temperature preferences, indicating that this mechanism could be automatic and intrinsic in the human body.

Interestingly, these findings contrast with a previous study by Crucianelli and colleagues (2021) that reported larger thresholds for perceiving temperature differences on specific body parts. In particular, they found a thermal threshold for cold stimuli applied to the palm of the hand and to the forearm of respectively 2.02°C and 1.48°C. At the same time, they reported the threshold for warm stimuli applied in the same body part of 2.65°C and 2.61°C. Crucianelli and colleagues employed an adaptation of the Martsock methods of limits^[14], which involves a continuous rate of temperature change (1°C/s). Our study, instead, involved discrete changes as participants moved between chambers. The greater ability to detect discrete change than continuous ones^[15] may address the difference between our results and previous ones.

Another possible explanation is that in Crucianelli and colleagues’ study, the thermal stimuli were directly applied to specific parts of the participants’ bodies. In contrast, in our experiment the entire body was exposed to the thermal stimuli (i.e., the air of the chambers), allowing for a more widespread and generalised contact with the temperature variations. It is plausible that when the whole body or specific body parts are exposed to thermal stimuli, such as ambient temperature, the brain integrates these sensations from various sources and combines them to form an overall perception of the temperature. This integration of thermal signals from multiple areas of the body may enhance the sensitivity to temperature changes, leading to a better overall perception of temperature differences. Considering the complexity of thermal perception and the involvement of various neural pathways, it is likely that both the direct stimulation of specific body parts and the overall exposure of the body to temperature variations contribute to our ability to perceive and discriminate different temperatures. Further investigations are needed to unravel the specific mechanisms involved in the integration of thermal signals and how different body parts contribute to the perception of environmental temperatures.

The unexpected negative point of subjective equality (PSE) of − 0.13°C, which can be approximated to 0°C considering the standard deviation (± 0.12°C) and sensor uncertainty (± 0.16°C), raises interesting questions. Given that the tested temperatures were within the comfortable range according to ANSI/ASHRAE standards (2017), it was anticipated that participants would not feel warm or cold while still in the air-lock. Additionally, half of our participants are individuals who typically experience rapid sensations of warmth and exhibit a preference for colder temperatures. Notably, only in this part of the population, which is more susceptible to feeling warm, we could have anticipated a negative PSE indicating a greater proportion of “warmer” answers. Nevertheless, on average, all our participants showed this behaviour, consistently perceiving the target chamber as warmer compared to the reference chamber. One possible explanation for this observation is that the act of moving between chambers and performing the task increased participants’ body temperature. The physical activity involved in walking could have resulted in a temporary increase in body heat, causing participants to subjectively perceive the target chamber as warmer. It would be beneficial to consider this potential confounding factor in future studies by monitoring participants’ body temperature or introducing a rest period before entering the target chamber to minimise the impact of physical activity.

Despite this unexpected finding, our study has important implications for real-life applications, particularly in indoor environments. By demonstrating that temperature differences of less than ± 1°C are imperceptible to participants, our results provide valuable insights for energy-saving strategies. Optimising indoor temperature control algorithms by adjusting the temperature by a degree in either direction during summer and winter within the imperceptible range, could lead to significant energy savings and cost reductions without compromising occupant comfort.

Our study employed a novel experimental paradigm to investigate human sensitivity to environmental temperatures. The findings revealed that participants exhibited remarkable accuracy in detecting temperature changes greater than ± 1°C. This threshold appears to be smaller than those identified for specific body parts in previous studies, indicating the need for further investigation into this discrepancy. Moreover, the limited variability in participants' behaviours suggests that the mechanism underlying thermal sensitivity could be automatic and intrinsic to every individual’s body. These findings contribute to our understanding of human temperature perception and may have practical implications for energy-efficient temperature control in indoor environments.

Participants

A total of 26 participants (13 males and 13 females) took part in our research. Table 1 summarises the characteristics of the participants. A priori power analysis and a review of the literature^[16] indicated that our sample size provided sufficient power (0.8) to detect the effects of interests. Inclusion criteria for participants comprised being between 18 and 65 years old, having a BMI in the range of 18.5 to 24.9, and being able to provide informed content. Exclusion criteria consisted of having a history of psychiatric or neurological conditions, a cardiac illness, health or sensory conditions that might result in skin alterations (e.g., psoriasis), and suffering from claustrophobia. The study was approved both by the ethical committee of the University of Trento and by the Azienda Sanitaria of the province of Bozen, and was conducted in accordance with the Declaration of Helsinki (Fortaleza, 2013). Moreover, all participants gave their informed consent prior to start the experiment.

We administered an online questionnaire to assess participants' eligibility and gather information about relevant psychological aspects. The questionnaire included the following measures:

Experienced Temperature Sensitivity and Regulation Survey (ETSRS^[17]): This survey was used to determine participants' usual temperature preferences and classify them into three groups: those who prefer cold temperatures, those who prefer warm temperatures, and those without specific preferences.

Empathy Quotient (EQ^[18]): The EQ was used to assess participants' level of empathy. Empathy has been shown to affect activity in the somatosensory cortex, which is relevant to the processing of thermal stimuli^[19].

Body Perception Questionnaire (BPQ^[20]): The BPQ was used to evaluate participants' level of awareness of their own body's internal states.

Global Physical Activity Questionnaire (GPAQ^[21]): We employed the GPAQ to gather information about participants' typical level of daily physical activity. This was relevant as our task involved periods of movement lasting 15 minutes.

Throughout the experiment, participants wore standardised clothing to ensure consistent skin coverage and equal thermal insulation between their bodies and the environment. Both male and female participants wore long jeans, short-sleeved t-shirts, and closed shoes. To monitor the participants’ skin temperature, we placed four thermal sensors (SHT31 Smart Gadget from Sensirion company) in specific locations on their bodies: the left part of the chest, the right upper arm, the right anterior thigh and the right anterior calf. We based this decision on a previous work of Liu and colleagues^[22], who have shown that using four sensors in these sites reliably assesses the mean skin temperature of the body. Additionally, we measured participants’ core temperature at the beginning, middle, and end of the experiment using an infrared laser thermometer aimed at their foreheads (results reported in Supplementary Table S2).

Apparatus

We used four different climate chambers connected through an air-lock (Fig. 1), used as a waiting room before the test and between the different experimental blocks. The temperature in the airlock was monitored throughout the entire test, with a mean value of 22.8°C (see Supplementary Figure S2). The four chambers (3 m x 3 m x 3 m each) were kept at a constant relative humidity equal to 45% while the temperatures were oscillating between 23°C and 25°C as shown in Fig. 2. The temperature range was chosen in the comfort range to prevent some possible confounds that could emerge from an uncomfortable condition. We defined these temperatures as comfortable by looking at the ASHRAE standards (ANSI/ASHRAE, 2017) and by conducting a pilot. In order to evaluate the thermal stratification during the experiment, three PT100 temperature sensors (uncertainty of ± 0.16°C, k = 2) were placed at three different heights corresponding to the head, the arm and the calf of the participant. The effect of the thermal stratification is shown in Figure S1. We chose as temperature reference the average temperature measured at the head and arm level given the exposed participant’s skin due to the clothing. During the whole experimental campaign, the chambers’ temperature pattern was kept the same (Fig. 2), while each participant’s shift pattern between the chambers was randomised. Our aim was to prevent participants from developing a cognitive schema for detecting the temperature pattern and relying on it to make judgments about temperature differences between the chambers. To achieve this, we designed the temperature fluctuations to follow a complex pattern that would be difficult for participants to discern, thereby forcing them to rely solely on their sensory experience to make temperature judgments. This approach minimised the risk of bias in participant responses and ensured that the data collected was an accurate reflection of their perceptual experiences.

Procedure

Throughout the experiment, participants transitioned between different climate chambers and compared the temperature of the target chamber (the second they moved in) to that of the reference chamber (the first one they entered). We maintained stable communication with the participants using walkie-talkies, and we monitored the entire process using five video cameras. Participants were allowed to spend 5 seconds in each chamber (both reference and target) to perceive and assess the temperature difference. Upon leaving the target chamber, participants provided their response in the airlock area. Each experimental block consisted of 24 temperature comparisons and lasted approximately 15 minutes. There were a total of 5 experimental blocks, with 5-minute breaks between them to allow participants to rest. Overall, the entire procedure took 100 minutes to complete, involving a total of 120 temperature comparisons.

Analysis

Figure 3 reports the example of one experimental block with the temperature variations inside the four climate chambers and the 24 comparisons the participant made together with the answers. For each comparison, we measured the temperature difference between the target chamber (red triangle), i.e. the second chamber participant entered, and the reference chamber (black triangle), i.e. the first chamber participant entered. Then, we compared each temperature difference with the participant’s answer, where 1 means “warmer” while 0 means “colder”. We grouped the data obtaining a final dataset of 3120 observations in 3 variables (subject, temperature difference and answer).

Next, we fitted two generalised linear mixed-effects models^[23] using the function glmer inside RStudio (version 2022.02.0 + 443). Given that we computed repetitively measures on single participants, we decided to analyse our data using generalised linear mixed-effects models that better take into account such source of variability^[24]. The first model we computed contained the number of colder and warmer answers and the differences in temperature as fixed effects and the subjects ID as random effect (glmm0). The second model added to the previous one the difference in temperature as random effect (glmm1) to see whether this aspect of our experiment brought variability in the data. Then we compared the two models through means of an ANOVA and looked at which model had the smallest AIC criterion. Finally, we computed the marginal and conditional R² of the best model to analyse the percentage of variance explained respectively by the fixed effects only and by the fixed effects plus the random ones.

Then, we extracted the average Point of Subjective Equality (PSE) and the Just Noticeable Difference (JND) using the MixedPsy R package^[25]. Besides these data, we were also interested in the absolute thresholds, i.e. the minimum value needed to correctly identify the difference in the temperature 100% of the time. To obtain this data, for each participant we looked at the positive difference in temperature above which all answers were “warmer” (positive absolute threshold) and the negative difference in temperature below which all answers were “colder” (negative absolute threshold). Finally, we obtained the general JND and absolute thresholds by averaging these data for all the participants.

Acknowledgements

The authors are grateful to all the participants involved in the study and to everyone who contributed with their help. In particular, the authors thank Claudio Zandonella Callegher and Alice Deruti for their contribution given to the data analysis.

Authors Contribution

B.L.: experiment conceptualisation, data analysis, writing; V.A.: experiment conceptualisation; Z.M.: experiment conceptualisation, writing; P.R.: experiment conceptualisation, writing.

Data availability statement

Anonymous data and analysis script can be shared on request by contacting the first author Laura Battistel at her personal email address: [email protected]

Additional Information

The authors declare no competing interests.

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No competing interests reported.

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