The effects of ambient temperature on aggressive cognitions
Dublin Core
Title
The effects of ambient temperature on aggressive cognitions
Creator
Melissa Barclay
Date
2015
Description
The world is getting warmer and it is of interest to researchers to explore how changes in temperature experience affect human behaviour. The heat hypothesis suggests that an increase in heat is associated with an increase in antisocial behaviour (e.g. violence, aggression). However social embodiment studies have also demonstrated hotter temperatures to be associated with less antisocial behaviour (e.g. greater gift giving). This study investigated whether higher ambient temperatures are associated with more or less antisocial responding using a controlled laboratory approach. Participants were placed into either a cold room or a hot room whilst they completed two tasks that implicitly measured the accessibility of aggressive cognitions. Using a combination of linear mixed effects analyses and regression analyses, the results demonstrated that there was no significant difference between the two temperature conditions concerning the accessibility of aggressive cognitions in a lexical decision go/no-go task and a word fragment completion task. Consequently the heat hypothesis and theories based upon a social embodiment framework were not supported in this case. Possible alternative explanations and limitations of the study are discussed regarding the inconsistent results to that proposed by particular theoretical frameworks and illustrated in previous research. Directions for future research are suggested in light of the present findings.
Subject
Ambient, temperature, aggression
Linear mixed effects modelling, regression, correlation
Linear mixed effects modelling, regression, correlation
Source
Participants
In total, 65 participants took part in this study. Unfortunately the preregistered sample size figure of 120 participants was unable to be reached due to recruitment limitations. Participants were recruited via Lancaster University’s SONA system, via adverts, were friends of the researcher or were recruited on an opportunistic basis around the Lancaster University campus. As a reward for participating, participants were entered into a prize draw to win one of 12 £10 Amazon vouchers. Participants were excluded if they met any of several a priori agreed upon rules for exclusion: (a) non-native English speaker, or (b) made a connection in the debrief section between the room temperature and aggression measurements. Three participants were excluded from the analyses on this basis. Therefore 62 participants data remained in the analyses. Demographic information was obtained using questions on the Qualtrics survey (Qualtrics, Provo, UT). The mean age of participants was 25.29 years (SD = 8.83; 43 female, 19 male). It was preregistered that participants must be between 18 and 55 years of age however due to the prospect of increasing the sample size, the age range was increased to 18 to 60 years of age. Participants were randomly assigned to the cold condition (n = 31) or the hot condition (n = 31).
Materials
Lexical decision go/no-go task. A lexical decision go/no-go task was used to gauge the accessibility of aggressive cognitions. The standard lexical decision task (LDT) is an indirect measure of semantic activation of specific constructs (e.g. aggression) and is an excellent method to assess the activation of such semantic networks (Marsh & Landau, 1995; see Parrott, Zeichner & Evces, 2005). Advantageously, as the task does not require conscious expression, it is not easily affected by demand characteristics (see Greitemeyer & Osswald, 2011). The LDT task was used in conjunction with a go/no-go response whereby participants are instructed to respond as quickly as possible to a word (alike to the LDT) but to withhold any response if the presented stimulus is a nonword. The lexical decision go/no-go task has been demonstrated to be an excellent alternative to the standard LDT but also measures performance in a similar manner (Perea, Rosa & Gomez, 2002). Essentially, network activation is measured by the response latency with which participants respond to particular stimulus words, with faster reaction times (RTs) demonstrating more accessibility of the target construct (i.e. aggression) (Forster & Davis, 1984; Johnson & Hasher, 1987; Schacter, 1987; Morton, 1970). Specifically, faster RTs to aggressive words by participants in the hot condition, compared to the cold condition, would suggest that the construct of aggression is more accessible in hotter conditions.
The lexical decision go/no-go task included the presentation of one hundred letter strings; 25 of which were aggressive-related words (e.g., gun), 25 of which were nonaggressive words (e.g., leaf) and 50 of which were nonword letter strings (e.g., breaff). The aggressive-related words were taken from Anderson, Carnagey & Eubanks (2003) and Johnson (2012). The non-aggressive items were extracted from Anderson et al. (2003) or chosen by the experimenter. Three independent raters who were blind to the study aims assessed the nonaggressive and aggressive words to determine if they were appropriately determined as nonaggressive or aggressive respectively. Fleiss Kappa demonstrated perfect agreement between the three individuals judgments, κ = 1, p < .0001, indicating that the raters agreed that all items coded as aggressive or nonaggressive were appropriately coded as such. Nonword letter strings took the form of pseudowords to prevent the possibility of participants classifying the words by a simple surface analysis of substrings. To illustrate this, a letter string consisting of “xx” can be quickly and easily recognised as a nonword without in-depth processing because no valid English words contain “xx” (see Bösche, 2010).
Furthermore, research has demonstrated that more frequent words (e.g. Perea et al., 2002) and shorter words are responded to quicker (e.g. Spieler & Balota, 2000). Given this, the word frequency of each real word (i.e., aggressive-related words and nonaggressive words) was obtained using the SUBLECT database (Van Heuven, Mandera, Keuleers, & Brysbaert, 2014) and the word type categories were matched on word length. According to Welch's t-test, there was no significant difference between the aggressive related words and nonaggressive words in terms of word frequency, (t (40) = 1.64, p = .12), and word length, (t (48) = 0, p = 1). Together this reduces the effect that word length and frequency might have on response latencies.
In the lexical decision go/no-go task, participants were instructed to respond by pressing the ‘spacebar’ key on the keyboard when presented with a valid English word (i.e. go response) however to withhold any response if presented with a nonword (i.e. no-go response). The experimental trials consisted of 50 real word letter strings and 50 nonword letter string trials. The onset of each trial was marked by a plus sign (+), which acted as a fixation point for the participant. After a 1000ms latency, the fixation point was replaced by a letter string. This stimulus item disappeared after a latency of 3000ms and was followed by the next fixation point and then the next letter string was presented automatically in the same aforementioned fashion. The presentation and randomization of letter strings, and the recording of response latencies were controlled by JavaScript code running on Qualtrics.
Word Fragment Completion (WFC) Task. To measure the activation of aggressive thoughts participants also completed a WFC task consisting of 50 word fragments (adapted from Anderson et al., 2003). Using Qualtrics, participants filled the blanks with letters to form a valid English word within a five-minute timeframe. Of the 50 word fragments, 25 could be completed to form either a nonaggressive word or aggressive word (e.g., “ki__” could be completed with “kill” or “kite”). The other 25 word fragments could be completed with only nonaggressive words. Only the word fragments with possible aggressive-completions were used in the analyses. The remaining 25 fragments were used as a decoy to ensure that participants would not guess that aggression was being measured. If a word could not be completed, participants were required to leave the answer box blank. This task is a valid measure of aggressive cognitions (Anderson et al., 2003). The outcome variable of aggressive cognitions was calculated by dividing the number of word fragments that were completed as aggressive words by the total number of word-fragment completions that could be completed as aggressive. Fragments were presented in a randomized fashion for each participant controlled by Qualtrics.
Baseline Temperature Comfort. A measure of baseline temperature comfort level was also included in the Qualtrics survey, which measured how cold or hot the participant generally feels. A rating scale of -50 to +50 measured this, where a higher score indicates a feeling of generally hotter. Many aspects can affect an individual’s thermal perception and comfort ranging from physical to cultural aspects (Laskari, et al., 2017; see for e.g. Djamila, 2017). For example, body temperature deviations can have their roots in physiology such as age (Castle, Norman, Yeh, Miller & Yoshikawa, 1991). These factors vary across individuals, raising the possibility that individuals have baseline temperatures or comfort levels that differ systematically from the average population (Obermeyer, Samra, & Mullainathan, 2017). In other words, the same temperature that is normal for one person might be cold for another. Given this, variations in an individuals subjective measure of baseline temperature comfort will be explored to see whether this moderates temperature effects on aggressive cognitions.
Outside Temperature. A measure of outside temperature was not originally planned and its inclusion was not preregistered. However data from the local weather station was used to calculate outside temperature during each testing session. Overall, the mean outside temperature was 18.6°C (SD = 2.91) and ranged from 12.6 to 22.9°.
Procedure and Design
Participants were welcomed into either the cold or hot room depending on their random allocation. The room temperature reading before each testing session began was recorded, which demonstrated that the range of temperatures for all sessions was at 15.5-16.9°C (M = 16.14, SD = 0.39) and 27.8–29.8°C (M = 28.56, SD = 0.60) for the cold and hot condition respectively. The heat-controlled room consisted of five workplaces equipped with conventional PCs allowing for simultaneous data collection from five participants at one time. Participants were separated from each other by partitions between the workstations. Whilst at a workstation, participants received the study information and gave their consent to participate. They then completed four decision making tasks using the Qualtrics survey software, two of which measured the accessibility of aggressive thoughts (i.e. lexical decision go/no-go task and WFC task) and two of which measured cognitive ability (as part of another students MSc project). All instructions concerning the tasks were given via the computer. The four tasks were presented in a randomised order between participants by Qualtrics to reduce order effects (e.g. participants may be tired for tasks at the end) and carryover effects (e.g. earlier tasks may influence behaviour on subsequent tasks) (see Shaughnessy, Zechmeister & Zechmeister, 2006).
In total, 65 participants took part in this study. Unfortunately the preregistered sample size figure of 120 participants was unable to be reached due to recruitment limitations. Participants were recruited via Lancaster University’s SONA system, via adverts, were friends of the researcher or were recruited on an opportunistic basis around the Lancaster University campus. As a reward for participating, participants were entered into a prize draw to win one of 12 £10 Amazon vouchers. Participants were excluded if they met any of several a priori agreed upon rules for exclusion: (a) non-native English speaker, or (b) made a connection in the debrief section between the room temperature and aggression measurements. Three participants were excluded from the analyses on this basis. Therefore 62 participants data remained in the analyses. Demographic information was obtained using questions on the Qualtrics survey (Qualtrics, Provo, UT). The mean age of participants was 25.29 years (SD = 8.83; 43 female, 19 male). It was preregistered that participants must be between 18 and 55 years of age however due to the prospect of increasing the sample size, the age range was increased to 18 to 60 years of age. Participants were randomly assigned to the cold condition (n = 31) or the hot condition (n = 31).
Materials
Lexical decision go/no-go task. A lexical decision go/no-go task was used to gauge the accessibility of aggressive cognitions. The standard lexical decision task (LDT) is an indirect measure of semantic activation of specific constructs (e.g. aggression) and is an excellent method to assess the activation of such semantic networks (Marsh & Landau, 1995; see Parrott, Zeichner & Evces, 2005). Advantageously, as the task does not require conscious expression, it is not easily affected by demand characteristics (see Greitemeyer & Osswald, 2011). The LDT task was used in conjunction with a go/no-go response whereby participants are instructed to respond as quickly as possible to a word (alike to the LDT) but to withhold any response if the presented stimulus is a nonword. The lexical decision go/no-go task has been demonstrated to be an excellent alternative to the standard LDT but also measures performance in a similar manner (Perea, Rosa & Gomez, 2002). Essentially, network activation is measured by the response latency with which participants respond to particular stimulus words, with faster reaction times (RTs) demonstrating more accessibility of the target construct (i.e. aggression) (Forster & Davis, 1984; Johnson & Hasher, 1987; Schacter, 1987; Morton, 1970). Specifically, faster RTs to aggressive words by participants in the hot condition, compared to the cold condition, would suggest that the construct of aggression is more accessible in hotter conditions.
The lexical decision go/no-go task included the presentation of one hundred letter strings; 25 of which were aggressive-related words (e.g., gun), 25 of which were nonaggressive words (e.g., leaf) and 50 of which were nonword letter strings (e.g., breaff). The aggressive-related words were taken from Anderson, Carnagey & Eubanks (2003) and Johnson (2012). The non-aggressive items were extracted from Anderson et al. (2003) or chosen by the experimenter. Three independent raters who were blind to the study aims assessed the nonaggressive and aggressive words to determine if they were appropriately determined as nonaggressive or aggressive respectively. Fleiss Kappa demonstrated perfect agreement between the three individuals judgments, κ = 1, p < .0001, indicating that the raters agreed that all items coded as aggressive or nonaggressive were appropriately coded as such. Nonword letter strings took the form of pseudowords to prevent the possibility of participants classifying the words by a simple surface analysis of substrings. To illustrate this, a letter string consisting of “xx” can be quickly and easily recognised as a nonword without in-depth processing because no valid English words contain “xx” (see Bösche, 2010).
Furthermore, research has demonstrated that more frequent words (e.g. Perea et al., 2002) and shorter words are responded to quicker (e.g. Spieler & Balota, 2000). Given this, the word frequency of each real word (i.e., aggressive-related words and nonaggressive words) was obtained using the SUBLECT database (Van Heuven, Mandera, Keuleers, & Brysbaert, 2014) and the word type categories were matched on word length. According to Welch's t-test, there was no significant difference between the aggressive related words and nonaggressive words in terms of word frequency, (t (40) = 1.64, p = .12), and word length, (t (48) = 0, p = 1). Together this reduces the effect that word length and frequency might have on response latencies.
In the lexical decision go/no-go task, participants were instructed to respond by pressing the ‘spacebar’ key on the keyboard when presented with a valid English word (i.e. go response) however to withhold any response if presented with a nonword (i.e. no-go response). The experimental trials consisted of 50 real word letter strings and 50 nonword letter string trials. The onset of each trial was marked by a plus sign (+), which acted as a fixation point for the participant. After a 1000ms latency, the fixation point was replaced by a letter string. This stimulus item disappeared after a latency of 3000ms and was followed by the next fixation point and then the next letter string was presented automatically in the same aforementioned fashion. The presentation and randomization of letter strings, and the recording of response latencies were controlled by JavaScript code running on Qualtrics.
Word Fragment Completion (WFC) Task. To measure the activation of aggressive thoughts participants also completed a WFC task consisting of 50 word fragments (adapted from Anderson et al., 2003). Using Qualtrics, participants filled the blanks with letters to form a valid English word within a five-minute timeframe. Of the 50 word fragments, 25 could be completed to form either a nonaggressive word or aggressive word (e.g., “ki__” could be completed with “kill” or “kite”). The other 25 word fragments could be completed with only nonaggressive words. Only the word fragments with possible aggressive-completions were used in the analyses. The remaining 25 fragments were used as a decoy to ensure that participants would not guess that aggression was being measured. If a word could not be completed, participants were required to leave the answer box blank. This task is a valid measure of aggressive cognitions (Anderson et al., 2003). The outcome variable of aggressive cognitions was calculated by dividing the number of word fragments that were completed as aggressive words by the total number of word-fragment completions that could be completed as aggressive. Fragments were presented in a randomized fashion for each participant controlled by Qualtrics.
Baseline Temperature Comfort. A measure of baseline temperature comfort level was also included in the Qualtrics survey, which measured how cold or hot the participant generally feels. A rating scale of -50 to +50 measured this, where a higher score indicates a feeling of generally hotter. Many aspects can affect an individual’s thermal perception and comfort ranging from physical to cultural aspects (Laskari, et al., 2017; see for e.g. Djamila, 2017). For example, body temperature deviations can have their roots in physiology such as age (Castle, Norman, Yeh, Miller & Yoshikawa, 1991). These factors vary across individuals, raising the possibility that individuals have baseline temperatures or comfort levels that differ systematically from the average population (Obermeyer, Samra, & Mullainathan, 2017). In other words, the same temperature that is normal for one person might be cold for another. Given this, variations in an individuals subjective measure of baseline temperature comfort will be explored to see whether this moderates temperature effects on aggressive cognitions.
Outside Temperature. A measure of outside temperature was not originally planned and its inclusion was not preregistered. However data from the local weather station was used to calculate outside temperature during each testing session. Overall, the mean outside temperature was 18.6°C (SD = 2.91) and ranged from 12.6 to 22.9°.
Procedure and Design
Participants were welcomed into either the cold or hot room depending on their random allocation. The room temperature reading before each testing session began was recorded, which demonstrated that the range of temperatures for all sessions was at 15.5-16.9°C (M = 16.14, SD = 0.39) and 27.8–29.8°C (M = 28.56, SD = 0.60) for the cold and hot condition respectively. The heat-controlled room consisted of five workplaces equipped with conventional PCs allowing for simultaneous data collection from five participants at one time. Participants were separated from each other by partitions between the workstations. Whilst at a workstation, participants received the study information and gave their consent to participate. They then completed four decision making tasks using the Qualtrics survey software, two of which measured the accessibility of aggressive thoughts (i.e. lexical decision go/no-go task and WFC task) and two of which measured cognitive ability (as part of another students MSc project). All instructions concerning the tasks were given via the computer. The four tasks were presented in a randomised order between participants by Qualtrics to reduce order effects (e.g. participants may be tired for tasks at the end) and carryover effects (e.g. earlier tasks may influence behaviour on subsequent tasks) (see Shaughnessy, Zechmeister & Zechmeister, 2006).
Publisher
Lancaster University
Format
Data/Excel.csv
Identifier
Barclay2015
Contributor
Ellie Ball
Rights
Open
Relation
None
Language
English
Type
Data
Coverage
LA1 4YF
LUSTRE
Supervisor
Dermot Lynott
Project Level
MSc
Topic
Cognitive Psychology
Social Psychology
Social Psychology
Sample Size
65 Participants
Statistical Analysis Type
Confirmatory Analysis
Exploratory Analysis
Regression Analysis
Exploratory Analysis
Regression Analysis
Files
Collection
Citation
Melissa Barclay, “The effects of ambient temperature on aggressive cognitions
,” LUSTRE, accessed May 17, 2024, https://www.johnntowse.com/LUSTRE/items/show/72.
,” LUSTRE, accessed May 17, 2024, https://www.johnntowse.com/LUSTRE/items/show/72.