Perception of sounds sequences: predictions for behavioural measurements generated with a computational model of auditory cortex
Cartesian state space difference calculations
Behavioural and neuroscientific research into sound perception shows that our auditory system is able to represent the temporal structure of sounds over a wide range of time windows – a process labelled as temporal binding. Recent computational modelling work suggests that synaptic depression in auditory cortex, responsible for adaptation of neural responses to repeated stimuli, is also the memory mechanism which allows for temporal structure of sounds to be represented. This project aimed to generate behavioural predictions of this explanation of temporal binding. Simulations examined how the cortex is able to discriminate between sound sequences differing from each other in terms of the timing, amplitude, and frequency of the sequence elements. Along with the temporal length of the sequences, the lifetime of neural adaptation was manipulated. The results predict that the thresholds for discriminating sound sequences should be tuned to a given sequence duration. These findings are discussed in light of the previous research on how the dynamics and anatomical structures within the auditory cortex may facilitate neural adaptation.
The current project investigated the processing of sound sequences by applying a computational model of the auditory cortex described in more detail in the study by May and Tiitinen (2013). The coding of the experiment, including stimulus design and experimental design, along with data analysis all took place in MATLAB. Model structure and dynamics Structure. The model is made up of 14 brain areas, with 13 cortical areas that include three core, eight belt and two parabelt areas of the auditory cortex, and one sub-cortical area simulating the thalamus. Each area is made up of 16 computational units representing cortical columns, with each column comprising one excitatory (pyramidal) and one inhibitory population of neurons. Altogether, this meant that there are 224 columns within the model. There are three levels of structural connections, namely interaction within columns, interaction between columns and interactions between the different cortical areas. Structural connections are expressed through connection matrices that describe synaptic connections between the excitatory populations (Wee), from inhibitory to excitatory populations (Wei), and from excitatory to inhibitory populations (Wie). Intra-column connections are assumed to be the strongest, with the synaptic weight for the within-column excitatory feedback (i.e., the diagonal values of Wee ) being set to 6, and the within-column weight values of Wei and Wie (inter-neuron connections targeting excitatory and inhibitory cells respectively) being set to 3.5. The excitatory population of each column made lateral connections to the excitatory populations of neighboring columns within the area. These connections extended to a distance of two columns on either side. Similarly, the excitatory population connected to neighboring inhibitory populations across a distance of five columns, with these connections accounting for lateral inhibition (see Figure 1). In both cases, there was a Gaussian drop off of the weight strength. Also, there was a stochastic element to the weights, with a 10% random jitter added to them. These procedures represent modifications in relation to the original model of May & Tiitinen (2013) and are described in Hajizadeh, Matysiak, May, König (in preparation). Connections made by the inhibitory population were assumed to be local, and so targeted only the excitatory population in the same column (see Figure 1). Inter-area connections were modelled from anatomical research in primates (Kaas and Hackett, 2000) and were contained entirely in Wee. The tonotopically organized afferent input Iaff targeted the thalamus, where each column functioned as a frequency channel to spectrally organize the input. The thalamus was connected to the three core areas. These were interconnected with the eight belt areas, and the eight belt areas were subsequently interconnected with two parabelt areas. The model had a serial structure, with no direct connections between the core and the parabelt (see Figure 2). Core and belt connections only occurred between neighboring areas, resulting in multiple core-belt-parabelt streams that had roughly a rostral and caudal subdivision (De la Moethe, Blumell, Kajikawa & Hackett, 2006). Connections between the areas were topographic, with each inter-area sub-division of Wee being characterized by most connections occurring near the diagonal, with a Gaussian drop-off in weight strength (as explained in Hajizadeh et al., in preparation). Dynamics. The dynamical unit of the model was the cortical microcolumn, which was made up of a population of excitatory and inhibitory cells, characterized by a single state variable u and v, respectively, expressing the mean activity of the population. For each excitatory population, its mean firing rate g depended on the state variable u through a non-linear monotonically increasing function g(u) = tanh (2/3) (u - ) for u > , g(u) = 0 otherwise, where  = 0.1 was a threshold constant. The mean firing rate of the inhibitory population was similarly determined as g(v). Collecting the states of the excitatory and inhibitory cell populations into vectors u = [u1….uN] and v = [v1…vN], the dynamic equation of the neural interactions were where m = 30ms is the membrane time constant and Iaff describes the afferent input targeting the thalamus. Adaptation. The underlying mechanism for neural adaptation operating on the time scale of seconds is short-term synaptic depression (Wehr & Zador, 2005). To simulate this, all excitatory connections in cortex (i.e., the elements ij of Wee and Wie) were modulated by a time dependent depression term aij(t), where i and j are the index of the post- and presynaptic population, respectively. This term depended on the pre-synaptic spiking rate through the equation. Here, on = 100ms is the onset time constant and rec is the time constant for the adaptation recovery from depression and thus expresses the lifetime of adaptation. In the current experiments, rec was varied in the 800-2000ms range in seven steps of 200ms. This range reflects electrophysiological findings whereby the adaptation of the N1m response (the MEG equivalent of the N1) can be encapsulated in a time constant that varies across participants in the range of 1-4 seconds (Lu, Williamson & Kaufman, 1992). Stimuli and Procedures Stimuli sets comprised sequences of three consecutively presented tones (50ms duration, 5ms linear onset & offset ramps), with the sequence being characterized by its total duration, measured as the onset from first tone to onset of third tone. For each measurement, two sequences of the same duration were presented to the model. While the third tone in each sequence was always the same (amplitude = 1; input via thalamic frequency channel 7, middle of tonotopic map), the two sequences differed in terms of the first two tones, that is, in terms of the stimulation history of the final tone (see Figure 3). Simulations were carried out in three experiments where the difference across the sequences was either in the timing, amplitude, or frequency of the first tones. In each experiment, all other aspects of the sequences were kept constant. This eliminated any counter-effects, with distinctions between sequences depending solely on the manipulation made (independent variable). In each experiment, the total duration of the sequence was varied in the range of 500-4000ms in steps of 200ms, creating a total of 18 different sequence durations. As explained above, the lifetime of adaptation was also varied (from 800-2000ms) to simulate a population of participants. For a diagram of the Stimuli sets, see Figure 3. Experiment 1: variations in timing. This looked at the model’s ability to discriminate temporal patterns represented by two sequences of three identical tones (amplitude = 1; frequency channel 7). These sequences were identical, except for the presentation time of the middle tone. In the first sequence, the SOI of the middle tone was jittered away from regular presentation by an amount representing 5% of the total duration of the sequence away (see Figure 3). The second sequence was a reversed version of the first. Experiment 2: variations in amplitude. Here, the two sequences varied in terms of the amplitude of the first two tones. In the first sequence, the amplitude of the first and second tone was 1.05 and 0.95, respectively. In the second sequence, these values were reversed. The final third tone had a fixed amplitude of 1. The three tones were presented at regular intervals, and their frequency was 7 on the tonotopic map of the thalamus. Experiment 3: variations in frequency. Here, the frequency history preceding the third tone was varied. In the first sequence, the first tone had a frequency of 6 and the second tone had frequency 8. Reversed frequencies were used in the second sequence. The three tones were presented at regular SOIs. Analysis The third tone was kept constant both within the two-sequence stimuli sets and across the experiments to ensure that the variations in the response to this final tone reflected changes in the stimulation history only. Thus, the ability of the model to discriminate between the temporal structure of two sequences could be analyzed by examining the activity elicited by the third tone of each sequence. As such, the firing rates of the excitatory populations in the cortical areas were treated as coding the previous stimulation history. The response to the third tone was quantified by averaging the firing rate of each excitatory population in a 200-ms time window following the onset of the third tone (see Figure 3). This resulted in a 208-dimensional vector, that is, a point in 208-dimensional state space where each axis represents the activity of one cortical column. The difference in the responses to two sequences was then quantified as the Cartesian distance (using the norm.m function in MATLAB) between the two respective points in state space. This distance measure, denoted by Dstate, was taken to represent the ability of cortex to discriminate between tone sequences. For each experiment, the analysis determined how Dstate changed as a function of the total duration of the sequence. Also, this dependence of Dstate on duration was examined in the case of different adaptation lifetimes
Running Memory Span Development: The Input Mechanism and Hebb effect
It is unclear whether active strategy or passive strategy is used and whether the Hebb effect is elicited in the running memory task. The aim of this study was to explore the input mechanism and the Hebb effect in the running memory task via a developmental study. Children were asked to perform four working memory tasks: counting span task, free recall task, Hebb digit task, and running memory task. In order to explore the Hebb effect in the running memory task, the last three digits of every third list were repeated. The results suggested that running memory was a recency-based phenomenon and the Hebb effect is elicited in children.
Participants Fifty-seven Chinese primary school students (23 female, 34 male), aged between 7 and 13 years (Mean = 9 years 6 months; SD = 1.754) took part in the present study. The children were recruited from Grade one to Grade six at Tianyi School in Xuancheng City. Chinese was the first language of all children. All the children completed a 45-minute testing session, which involved four memory tasks. At the end of the test, children received a notebook as a small gift of appreciation for taking part in the present study. Materials The experiment was presented using SuperLab 4.0 on a Sony Laptop with a 14-inch colour screen. The responses of participants were recorded by the tester on answer sheets. Every child completed a counting span task, a free recall task, a Hebb digit task, and a running memory task. Counting span task. The counting span arrays were developed from Towse and Hitch (1995) and consisted of equal number of target triangles and non-target squares. The target triangles were red, approximately 30 mm in length, and the non-target squares were blue, approximately 28 mm in length. The number of both target triangles and non-target squares varied from 3 to 9 (mean = 6). The counting span arrays were presented on the centre of the computer screen with a white background. The triangles and squares were randomly displayed at different positions in every display. Free recall task. For this task, 144 Chinese high-frequent two-syllable nouns (see Appendix A) were recorded by in a male’s voice at rate of 1 word per second. The words were recorded using Adobe Audition 3.0. Two practice lists and ten test lists were presented, and every list included 12 words at the rate of 1 word per second. The words were played by a computer. Hebb digit task. All digit lists were created had the digits 1 to 9 in random order, avoiding any repetition of digits (see Appendix B). The voice of digits was recorded by Adobe Audition 3.0 at the rate of 1 digit per second. There were 2 practice lists and 24 test lists, and each list contained nine digits. Among the test lists, 16 lists were different, and the other 8 were the same – termed as Hebb list – presented on every third trial beginning on Trial 3. The 24 test lists were divided into 8 blocks, which involved 2 different lists and a Hebb list. Running memory task. The lists included 12, 14, 16, 18, or 20 random digits from 1 to 9 (see Appendix C), which were recorded by voice. Two presentation rates were used in this task: 0.5 s per digit as the fast rate and 2.5 s per digit as the slow rate. In both conditions, there were 2 practice lists and 24 test lists. In order to test the Hebb effect in running memory task, the 24 test trials comprised 16 completely different lists, and 8 lists with the same last 3 digits which were the same and presented on every third trial. Procedure The experiment lasted 45 min, and every child completed 4 tasks. Each participant was seated on a chair in front of the computer screen, at a distance of 65 cm. All tasks included two practice trials for helping children be familiar with the procedure. Once children completed the practice trials and understood the procedure, they could proceed to the test trials. When children were performing the tasks, the experimenter gave no feedback about the accuracy of the words or digits. The order effect was counterbalanced as shown in the Table 1, which is a Latin Square design. Because there were two conditions in the running memory task, the fast speed and slow speed running, the tasks were counterbalanced. Therefore, in all, there were eight orders in the present study, and all children were equally divided into eight groups based on the eight orders. When participants completed each task, they were given sufficient time to rest. Counting span task. The children were informed to the counting and recall tasks. Before every trial, a fixation symbol was displayed on the centre of screen for 0.5 s. When the target triangles and non-target squares were presented, participants were required to count the red triangles aloud, and repeat the final number. Once the children repeated the last number, the experimenter pressed the keyboard to show the next display, and the counting speeds were recorded by the computer automatically. There were three trials in every level and every trial included the n + 1 displays in level n. For example, participants counted 2 displays in level 1 and 3 displays in level 2. The final level was level 4, which contained 5 displays. After 2 to 5 displays, children were asked to report all the final numbers of red target triangles in the previous displays. If a child failed to recall correctly for at least two of the three trials, the counting span task was ended at that level; otherwise, they could progress to the next level. Free recall task. Children were required to listen to some words, and repeat them as many as possible in any order, after the 12th word. The experimenter wrote down the responses of participants on answer sheets. If the children could not report a new word within 30 s, the experimenter would proceed to the next trial. Hebb digit task. The procedure for the Hebb digit task was developed by Hebb (1961). Children were asked to listen to every list, and report all digits in the right order. Children reported the digits orally, and the experimenter recorded the response on an answer sheet. Because the running memory task also involved Hebb lists, 48 children were asked whether they were aware of any regular pattern in the digit tasks after they completed both Hebb digit task and running memory task. Only 5 participants noticed the repetition in the running memory and Hebb digit tasks. Running memory task. Children were made to listen to some digits, different from those in the Hebb digit task; they were required to repeat the last three digits rather than all digits in the list. Two conditions were set to counterbalance the order effect: half of the children were administered the fast rate condition first and the other half were administered the slow rate condition first. Scoring Counting span task. Counting errors and counting speed were recorded and the scoring method used is the partial-credit unit scoring prescribed by Conway et al. (2005). Firstly, the correct items in each sequence were counted. If all items were correct in a sequence, this sequence was given one point. Otherwise, the score of a sequence was based on the proportion of correct items. Finally, the counting span of a participant was calculated as the sum the scores for all sequences. Free recall task. The scoring method used was the one prescribed by Tulving and Colotla (1970), which involved the calculation of intratrial retention interval (ITRI). The ITRI value was the number of items between the presentation and the reported items. For instance, if the sequence is A, B, C, D, E, F, and G, and a participant reported G, F, and A. The ITRISs for the items were 0, 2, and 8, respectively. Before calculating the ITRI, the digit span of the Hebb non-repeating lists was calculated for every child. If the digit span of a child was 5, the item would be classified as a word from primary memory when the ITRI was 5 or less, whereas the item would be classified as a word from the secondary memory when the ITRI was 6 or more. Hebb digit task. Every digit recalled correctly at the correct position was scored one point. The score of the non-repeating lists was the mean score of each non-repeating list, and the score of the repeating lists was the mean score of each repeating list. Running memory task. The score for the running memory span was calculated using the mean number of digits in the right positions. If 3 digits were recalled in correct sequence, the score was 3; if the sequence of 2 digits (for example the first and second digit, the second and the third digit, or the first and third digit) was in the correct serial order the score was 2; if there was a single digit in the correct position, the score was 1. Similar to the Hebb digit task, the scores for non-repeating and repeating lists were separated.
running memory span development input mechanism Hebb effect
The experiment was presented using SuperLab 4.0 on a Sony Laptop with a 14-inch colour screen. The responses of participants were recorded by the tester on answer sheets. Every child completed a counting span task, a free recall task, a Hebb digit task, and a running memory task. Counting span task. The counting span arrays were developed from Towse and Hitch (1995) and consisted of equal number of target triangles and non-target squares. The target triangles were red, approximately 30 mm in length, and the non-target squares were blue, approximately 28 mm in length. The number of both target triangles and non-target squares varied from 3 to 9 (mean = 6). The counting span arrays were presented on the centre of the computer screen with a white background. The triangles and squares were randomly displayed at different positions in every display. Free recall task. For this task, 144 Chinese high-frequent two-syllable nouns (see Appendix A) were recorded by in a male’s voice at rate of 1 word per second. The words were recorded using Adobe Audition 3.0. Two practice lists and ten test lists were presented, and every list included 12 words at the rate of 1 word per second. The words were played by a computer. Hebb digit task. All digit lists were created had the digits 1 to 9 in random order, avoiding any repetition of digits (see Appendix B). The voice of digits was recorded by Adobe Audition 3.0 at the rate of 1 digit per second. There were 2 practice lists and 24 test lists, and each list contained nine digits. Among the test lists, 16 lists were different, and the other 8 were the same – termed as Hebb list – presented on every third trial beginning on Trial 3. The 24 test lists were divided into 8 blocks, which involved 2 different lists and a Hebb list. Running memory task. The lists included 12, 14, 16, 18, or 20 random digits from 1 to 9 (see Appendix C), which were recorded by voice. Two presentation rates were used in this task: 0.5 s per digit as the fast rate and 2.5 s per digit as the slow rate. In both conditions, there were 2 practice lists and 24 test lists. In order to test the Hebb effect in running memory task, the 24 test trials comprised 16 completely different lists, and 8 lists with the same last 3 digits which were the same and presented on every third trial. Procedure The experiment lasted 45 min, and every child completed 4 tasks. Each participant was seated on a chair in front of the computer screen, at a distance of 65 cm. All tasks included two practice trials for helping children be familiar with the procedure. Once children completed the practice trials and understood the procedure, they could proceed to the test trials. When children were performing the tasks, the experimenter gave no feedback about the accuracy of the words or digits. The order effect was counterbalanced as shown in the Table 1, which is a Latin Square design. Because there were two conditions in the running memory task, the fast speed and slow speed running, the tasks were counterbalanced. Therefore, in all, there were eight orders in the present study, and all children were equally divided into eight groups based on the eight orders. When participants completed each task, they were given sufficient time to rest. Table 1 Task Orders for Four Tasks Task Orders a b c d e f g h Counting span task 1 2 3 4 1 2 3 4 Free recall task 2 1 4 3 2 1 4 3 Hebb digit task 3 4 1 2 3 4 1 2 Running memory task 4(FS) 3(FS) 2(FS) 1(FS) 4(SF) 3(SF) 2(SF) 1(SF) Note. F = Fast-running memory task, S = Slow-running memory task. Counting span task. The children were informed to the counting and recall tasks. Before every trial, a fixation symbol was displayed on the centre of screen for 0.5 s. When the target triangles and non-target squares were presented, participants were required to count the red triangles aloud, and repeat the final number. Once the children repeated the last number, the experimenter pressed the keyboard to show the next display, and the counting speeds were recorded by the computer automatically. There were three trials in every level and every trial included the n + 1 displays in level n. For example, participants counted 2 displays in level 1 and 3 displays in level 2. The final level was level 4, which contained 5 displays. After 2 to 5 displays, children were asked to report all the final numbers of red target triangles in the previous displays. If a child failed to recall correctly for at least two of the three trials, the counting span task was ended at that level; otherwise, they could progress to the next level. Free recall task. Children were required to listen to some words, and repeat them as many as possible in any order, after the 12th word. The experimenter wrote down the responses of participants on answer sheets. If the children could not report a new word within 30 s, the experimenter would proceed to the next trial. Hebb digit task. The procedure for the Hebb digit task was developed by Hebb (1961). Children were asked to listen to every list, and report all digits in the right order. Children reported the digits orally, and the experimenter recorded the response on an answer sheet. Because the running memory task also involved Hebb lists, 48 children were asked whether they were aware of any regular pattern in the digit tasks after they completed both Hebb digit task and running memory task. Only 5 participants noticed the repetition in the running memory and Hebb digit tasks. Running memory task. Children were made to listen to some digits, different from those in the Hebb digit task; they were required to repeat the last three digits rather than all digits in the list. Two conditions were set to counterbalance the order effect: half of the children were administered the fast rate condition first and the other half were administered the slow rate condition first. Scoring Counting span task. Counting errors and counting speed were recorded and the scoring method used is the partial-credit unit scoring prescribed by Conway et al. (2005). Firstly, the correct items in each sequence were counted. If all items were correct in a sequence, this sequence was given one point. Otherwise, the score of a sequence was based on the proportion of correct items. Finally, the counting span of a participant was calculated as the sum the scores for all sequences. Free recall task. The scoring method used was the one prescribed by Tulving and Colotla (1970), which involved the calculation of intratrial retention interval (ITRI). The ITRI value was the number of items between the presentation and the reported items. For instance, if the sequence is A, B, C, D, E, F, and G, and a participant reported G, F, and A. The ITRISs for the items were 0, 2, and 8, respectively. Before calculating the ITRI, the digit span of the Hebb non-repeating lists was calculated for every child. If the digit span of a child was 5, the item would be classified as a word from primary memory when the ITRI was 5 or less, whereas the item would be classified as a word from the secondary memory when the ITRI was 6 or more. Hebb digit task. Every digit recalled correctly at the correct position was scored one point. The score of the non-repeating lists was the mean score of each non-repeating list, and the score of the repeating lists was the mean score of each repeating list. Running memory task. The score for the running memory span was calculated using the mean number of digits in the right positions. If 3 digits were recalled in correct sequence, the score was 3; if the sequence of 2 digits (for example the first and second digit, the second and the third digit, or the first and third digit) was in the correct serial order the score was 2; if there was a single digit in the correct position, the score was 1. Similar to the Hebb digit task, the scores for non-repeating and repeating lists were separated.
National Identity of chinese overseas student
Since 21st century, with the development of global and China’s economy, an increasing number of Chinese students preferred study abroad. Chinese oversea students are very special group. They were influenced by the traditional Chinese culture and education. They also accept Western education and thought. This research aims to investigate whether the study-abroad experience will affect Chinese oversea students’ national identity. 35 Chinese students who are studying in UK and 35 Chinese students who are studying in China completed the questionnaire which assessed national identity. All questionnaires sent to the participants by email. According to the results of this study, Chinese students who are studying in UK did not show significant differences in national identity by compared with the Chinese students who are studying in China. Results of current study also indicated that Chinese students’ ethnocentrism scores were marginally higher than Chinese oversea students.
The national identity was assessed using the National Identity Scale. The National Identity Scale has five factors: membership, private, public, Identity and comparison. On a 7-point Liker Scale, participants reported their agreement (1 = strongly disagree, 7 = strongly agree) with items. Ethnocentrism was measured with the Revised Ethnocentrism Scale, on a 5-point Liker scale, participants indicated their agreement (1 = strongly disagree, 5 = strongly agree) with statements.
The Relationship Between Perspective-taking, Lie Detection and Self-construal Among Taiwanese
“Theory of Mind” (ToM) refers to an ability, which allows us to understand what other people may believe, think, know, feel. Also, ToM is considered to play an essential role in social interaction. Evidence suggests that improved ability to understand others’ mental states through training can also improve our ability to generate lies and understand what kind of situations people may lie. In addition, previous studies point that there are differences in lie-telling and perspective-taking between individualistic and collectivist cultures. Therefore, the current study aimed to investigate whether there is a relationship between the perspective-taking, lie detection and self-construal (individualism and collectivism). Data were collected from 40 typically developed adults in Taiwan (M = 23.98, SD = 2.99). Each participant was asked to complete three computer-based tasks, namely a perspective-taking task; a lie detection task, and a questionnaire of Auckland Individualism and Collectivism scale (AICS). The result showed that there is no relationship between the ability of perspective-taking and lie detection. Also, the people scored higher individualism will show better performance on pointing out truths, but worse on detecting lies. It might relate to the “truth bias”, which means that people will typically assume or believe others are telling truths rather than lies, especially distinguishing on individualists. However, because cultural effects such as language differences and self-construal might affect individuals’ performances on instances of ToM use, the current study suggests that people might need to use different cues to detect lies in a truth-versus-lie judgment between different cultures.
Participants Data were collected from 40 typically developing adults from an opportunity sample in Taiwan. The entire group was composed of 20 males and 20 females between the ages of 18 and 30 (M = 23.98, SD = 2.99). All participants stated that they were Taiwanese, speaking Chinese/Taiwanese Mandarin as their first language, with normal vision or vision which had been corrected to normal. All of the participants had not been diagnosed with any neurological or developmental disorders. The data of participant No.40 was excluded from analyses because the participant only responded with the same positive answer to each question in the lie detection task. The original minimum required sample size was 44, which was determined a priori by using G*Power software. This number was calculated based on assuming a medium effect size of 0.4 and a reasonable power of 0.8. However, 40 was considered as a more suitable sample size, as the experiment consisted of two orders for the perspective-taking task, four sub-sets of the lie detection task and a questionnaire. In order to counterbalance stimuli presentation, the target sample was set to 40, as it is a multiple of eight (two times four times one); it is also the closest number to 44. All participants had been given the consent form and the information sheet to understand the contents of the project before the tests began. Furthermore, this project was approved by the ethics process from the Department of Psychology at Lancaster University. Design and Procedure Each participant was asked to complete three computer-based tasks, namely, the perspective taking task, the lie detection task, and a questionnaire of the Auckland Individualism and Collectivism Scale (AICS). All of the tasks were translated or designed in the participants’ first language, in this case, Chinese/Taiwanese Mandarin. All of the tasks were presented on a laptop and participants responded by using the computer mouse. The full session took around an hour in total. Perspective-taking Task The original perspective-taking task was called the “director task”, which can be traced back to the studies from Keysar et al. (2000) and Apperly et al. (2010). The present study employed a similar version of the director task which was presented in the study from Wang et al. (2016). In the instruction of the perspective-taking task, participants were given a demonstration of how to use a computer mouse to select and move the object. Later, the experimenter explained to participants that the speaker/director was standing behind the shelf and would not be able to see the objects in the blocked slots. It was impossible for the speaker/director to ask participants to move the object which was in the blocked slot (see Figure 1). Participants were asked to consider the speaker/director’s perspective and respond as quickly and accurately as possible. Participants had a chance to practice (6 trials) and ask questions before the start of the task. The task was divided into four blocks, and participants were allowed to take breaks between each block. There were a total 128 trials in the task, 16 of which corresponded to the experimental trials; the other 16 corresponded to the control trials and the rest were fillers. The fillers were regarded as a baseline measure for the non-perspective taking aspect of the task, such as understanding and identifying the speaker/director’s instructions. In 16 of the experimental trials, there were differences between the speaker/director’s description and the participants’ point of view. In contrast, the control trials provided a close match in terms of visual and audio stimuli, but the control trials did not impose the demand to perspective-taking. For example, as can be seen in the right-hand picture in Figure 1, if the speaker/director ask participants to move the “bigger” balloon to take the speaker/director’s perspective, participants should move the yellow balloon rather than the pink one, which was the bigger balloon from the participants’ own perspective. By the end of the task, only the number of egocentric errors committed on experimental trials were counted; the errors reflect failure to account for the director’s perspective. Figure 1. Left: An example of the control trials. Right: An example of the experimental trials. Lie Detection Task Participants were asked to watch 16 videos (each video lasting around 15~45 seconds). The videos were recorded by four volunteer models from Lancaster University. All models were Taiwanese and spoke Chinese/Taiwanese Mandarin as their first language. Each model recorded 16 videos in total which comprised four stories. Each story contained to two truths and two lies, from a first person and a third person perspective for each story (there are four versions of each story). For the story contents, there were several elements that storytellers were required to include in their stories (see Appendix A). In addition, the storytellers were given two designated elements to lie about in the lie stories. There was a total of 64 videos. The videos were evenly and pseudo-randomly divided into four lists. For example, participants never watched two videos of the same storylines containing lies and told from a same perspective by different storytellers in one list. Therefore, each list contained 16 unique videos. Participants watched videos from one of the lists, and at the end of each video, they were asked to identify whether they thought that the storytellers were telling a truth or a lie. To make sure the participants would concentrate on watching videos rather than just guess the answers, participants were asked a question about an aspect of each video. The questions were used as inclusion criteria, whereby only correct responses of the aspects were included in the data analysis. Auckland Individualism and Collectivism Scale (AICS) The third test used was the Auckland Individualism and Collectivism scale (AICS), which was developed by Shulruf, Hattie and Dixon (2007) and was used to measure individuals’ self-construal, namely, individualism and collectivism. The questionnaire consists of 30 questions (see Appendix B and C), which includes three dimensions of individualism and two dimensions of collectivism. For individualism, the scale consists of 12 items and is divided into three dimensions, namely, responsibility, uniqueness, and competitiveness. For collectivism, the scale consists of 8 items, and two dimensions are referred to: advice and harmony. Each of the dimensions was composed of four items. The questionnaire was presented in an online form, and participants were asked to complete it after they had finished the lie detection task. The response to each question was scored using a six-point likert scale from 0 (Never) to 5 (Always). The maximum score in the individualism trial was 60, and the maximum score in the collectivism trial was 40. A higher score on each of the trials indicated that an individual was more inclined to individualism or collectivism. The AICS questionnaire has been shown to work in different cultures such as the United Kingdom, China, Romania and Italy (Bradford et al., 2018; Ewerlin, 2013). This means that this questionnaire can be used as a feasible measure in both individualistic and collectivistic cultures. Previous studies have mentioned that an individual can simultaneously show tendencies towards both individualism and collectivism; in other words, an individual may be able to achieve a high or low score on both subscales (Bradford et al., 2018; Shulruf et al., 2011). With this in mind, the analysis of the current study did not divide participants into two groups for individualism and collectivism. Instead, this study used the AICS questionnaire to obtain participants’ scores in individualism and collectivism, and to observe the relationship between individuals' self-construal and their ability to detect lies.
Is selfie-related behaviour motivated by sexual orientation and gender conformity
Selfie, Self-expression, Gender conformity, Sexual orientation
In the digital age, selfie culture has become an integral part of social media platforms. This globally widespread phenomenon created a distinctive form of self-expression, allowing selfie- makers to convey their identities, shape online personas, and build connections with others. Selfies are more than photos presented but also refer to a series of backstage to finally lead to creation and sharing. As research into selfies, gender differences in selfie-related behaviours have enabled further comprehension of selfies in terms of self-expression. Social Role Theory (SRT) explained the existence of gender differences in selfie culture that gender role norms and social expectations shape individuals' identity and behaviours. This study explored the concept of gender conformity among heterosexuals and non-heterosexuals and the impact on selfie-related behaviours. A total of 120 participants, categorized into heterosexual men, heterosexual women, non-heterosexual men, and non-heterosexual women, engaged in an online questionnaire, and contributed a total of 150 selfies. Data analysis involved one-way variance (ANOVA) to test the differences between the four groups, and multiple regression analysis to assess the influence of gender, the Traditional Masculinity-Femininity (TMF) scale and sexual attraction to me score. The results revealed no differences across the four groups in terms of the nine domains of selfie motives, as well as preoccupation. However, retention of moments and entertainment as the most prominent motives for selfies. For selfie behaviours, time spent on taking, editing, and selecting selfies, as well as taking amount and edit frequency differed significantly among the four groups. Specifically, both heterosexual women and non-heterosexual women tended to allocate more time on taking, editing, and selecting selfies for posting. Meanwhile, heterosexual women and non-heterosexual men displayed a higher trend for taking a greater number of selfies and editing selfies more frequently. These findings support the current studies indicating that women engage in selfie-related behaviours more actively than men, but more deeply that sexual orientation, especially the sexual attraction to men, also encourage some of selfie-related behaviours. While the results provide evidence for SRT as gender roles shape behaviours through socialisation, but also draw criticisms as TMF scale failed to predict the impacts on selfie-related behaviours and sexual orientation can break the traditional gender role expectations. Future research should keep exploring these relationships, offering deeper insights into gender conformity and gender non- conformity in the realm of self-presentation across diverse identity roles, thereby contributing to a more inclusive and diverse self-image narrative.
Participants This study recruited 120 normal adults as participants through a Qualtrics online questionnaire voluntarily and anonymously, of which 67 completed the questionnaire, including 22 men, 42 women and 3 self-identified as non-binary gender. However, since only binary gender was considered for the analysis of gender conformity, the three non-binary gender responses were removed. Additionally, in terms of sexual orientation, there were 48 heterosexuals, 6 homosexuals, 8 bisexuals and 2 others. The participants were divided into four sample groups, that is, 19 heterosexual men, 29 heterosexual women, three non-heterosexual men and 13 non-heterosexual women. Materials Sexual orientation and gender conformity were the two domains of prediction, and selfie- related behaviours were regarded as the outcomes. Sexual orientation was measured by self-rated sexual attraction to men and women, while gender conformity was measured by self-ascribed the Traditional masculinity/femininity (TMF) scale. As for selfie-related behaviour, it involves several aspects of selfie taking, editing, and posting. In addition, selfie motives and attributes of uploaded selfies were also be taken into account. Sexual attraction score Sexual orientation was self-identified by participants themselves as heterosexual, homosexual, bisexual, and others. The participants were asked to self-rated two statements about their sexual attraction to men and women. The latter, with some adjustment to Lippa's (2002) methodology, included two questions: "I am sexually attracted to men" and "I am sexually attracted to women". In both cases, participants were asked to self-rated on a scale of 0-10, where a higher score indicated greater sexual attraction to men or women. This measure transformed sexual orientation from a categorical variable to a continuous variable for further analyses of differences and relationships. In particular, the separate assessment of sexual attraction to men and sexual attraction to women could help to better detect whether sexual attraction to men is more influential in selfie-related behaviours. Traditional Masculinity-Femininity scale Gender conformity was measured as a continuous variable by using the Traditional Masculinity-Femininity (TMF) scale from Kachel et al. (2016). This scale comprises of six questions which are self-rated on a scale of 1-7, with 1 represented very feminine and 7 represented very masculine. Example items include "I consider myself as...", "Ideally, I would like to be..." regarded their preferred gender role. The remaining four questions concerned identified gender roles in terms of interests, attitudes and beliefs, behaviours, and appearance from a traditional perspective, being asked respectively as "Traditionally, my... would be considered as...". Then, the mean score of these six items would eventually be used as the individual’s masculinity/femininity score. Kachel et al.(2016) pointed out that the TMF scale had been proven to be a reliable one- dimensional construct tool to assess masculinity because it correlates well with another gender- related instrument, the Bem Sex Role Inventory (BSRI) and successfully distinguishes between groups, such as females vs. males, lesbians/gays vs. heterosexual females and males,that are expected to be different (See Figure 2). Figure 2 Mediation of the relation between BSRI and sexual orientation by the TMF (Kachel et al., 2016). Mean TMF scores separately for gender and sexual orientation (Kachel et al., 2016). Selfie Coding Participants were asked to upload 1-3 selfies that they would post on social media. All selfies were coded based on four aspects. First, participants were asked to whether their uploaded selfies had been edited or retouched. Then, the experimenter coded the number of people in the selfie (alone or in a group), the angle of the selfie (upward, horizontal, or downward), and the amount of body shown (face only, upper body, body without face, or whole body with face). Meanwhile, a total of 150 portrait pictures were collected from the participants, although 17 of them looked like taken by others rather than selfies. Selfie-related Behaviours Selfie-related behaviours and motivations measures were taken from Bij de Vaate et al. (2018). For motivations, 33 items were used to reflect nine domains of motives, and each item was an agreement extent scale (1 = totally disagree to 5 = totally agree). The nine domains of motives included "Retention of moments" (e.g., "I make selfies to memorise a moment"), "Entertainment" (e.g., "Making selfies is enjoyable"), "Expressive information sharing" (e.g., "I tell others something about myself by using selfies"), "Social interaction" (e.g., "I make selfies to keep in touch with friends and family"), "Social use" (e.g., "I make selfies to show who I am and what I do"), "Habitual passing of time" (e.g., "Making selfies is a habit"), "Relaxation" (e.g., "Making selfies enables me to relax"), "Imaginary audience" (e.g., "I post selfies with a specific audience in mind"), and "Social pressure and identity" (e.g., "I make selfies because everybody does it"). Preoccupation (e.g., "I often share selfies") implied the degree to take part in selfie behaviours, was measured with six items by an agreement extent scale (1 = totally disagree to 5 = totally agree). See Appendix A for specific questions of items in the questionnaire. Selfie-taking behaviour was measured by taking frequency, time spent and amount in the last three months, each of them was designed as an ordinal variable depending on an increasing degree. For instance, frequency referred to how often taking selfies, time spent referred to how long it taken within a selfie session, whereas amount referred to how many photos taken within a selfie session. Selfie-editing behaviour only accounted for two aspects, editing frequency and time spent, and selfie-posting behaviour used to select spend time instead of posting spend time. Additionally, four items were designed to collect feedback on related concerns and feelings about selfies with a Likert scale that ranged from 0 (totally unconcerned) to 10 (totally concerned), including the attractiveness of their online image, the attention and comments of others on their selfies post on the social platform, the comparison with other people's selfies. Finally, 3 questions were designed to reflect participants’ satisfaction degree on their appearance in real life, before retouching and after retouching by the same 11-point Likert scale (0 = extremely uncomfortable to 10 extremely comfortable). Procedure This study had been reviewed and approved by a member of the Psychology department from the Lancaster University Board of Ethics. At the beginning of the survey, participants were provided with a participant information sheet, informing them that the study is about selfie- related behaviours in terms of sexual orientation and gender conformity. Anonymity and confidentiality were ensured because of sensitive information such as selfies and sexual orientation. After the confirmation of the consent, all participants complete the same questionnaire which is conducted on Qualtrics (www.qualtrics.com). The survey questionnaire (See Appendix A) was designed to collect information through six key sections. In the first section, some demographic information was asked, such as age, relationship status, gender identity, sexual orientation, and sexual attraction. Then, it was a six- item self-ascribed Traditional Masculinity/Femininity (TMF) scale. For the third section, participants were expected to upload three different selfies that would be posted on social media and state whether these selfies have been retouched. This section was optional and if a selfie were uploaded, a specific selfie consent would be required to confirm. The last three sections involved a series of questions on selfie-motives and preoccupation, selfie-related behaviours and feelings. At the end of the survey, participants were given a debrief sheet upon completion and were allowed the chance to ask any questions after the survey was undertaken. Meanwhile, information consent would be confirmed to get the final approval about all responses before submitting the questionnaire. Analysis Pre-Tests Firstly, two pre-tests were conducted by one-way analyses of variance (ANOVA) to analyse differences in Traditional Masculinity/Femininity (TMF) score and sexual attraction to men score for each of the four sample groups, with corresponding post-hoc multiple comparison tests, to examine expected differences in TMF scale and sexual attraction to men score by the four sample groups. Main Tests Subsequently, selfie motives in nine domains, and preoccupation were examined for each of their differences across the four sample groups by ANOVAs with corresponding post-hoc multiple comparison tests. In the context of selfie behaviours, we tested three relevant stages: selfie-taking, editing and posting. For each of these behaviours, we conducted ANOVAs with corresponding post-hoc multiple comparison tests to assess differences across the four sample groups, and Multiple Linear Regression analyses to investigate the influences by self-identified gender, TMF score and sexual attraction to men score. In particular, we delved into three aspects of taking selfies, involving frequency, time spent, and amount. Similarly, we analysed two aspects of selfie editing, frequency and time spent, as well as three aspects of selfie posting , which included frequency, time spent on selection, and amount. In addition, in order to examine whether selfie content itself was affected by gender conformity and sexual orientation, a total of 150 uploaded selfies were coded according to four attributes: editing usage, selfie format, the shown part in the selfie, and the taking angle. Each of the attributes was firstly tested by a chi-square test to examine the association between attribute and the four sample groups because both were categorical variables. Furthermore, we conducted two separate ANOVAs, each followed by post-hoc multiple comparison tests. One used the TMF scores and the other used sexual attraction to men scores, with each of the four attribute of these selfies as independent variables to test for differences. In these analyses, the TMF scores and sexual attraction to men scores, as interval data, were regarded as dependent variables.
Exploring the impact of rewards on contextual cueing effect
contextual cueing effect, reward, selective attention
There is a huge amount of complex information about visual stimuli in the environment and the individual's visual processing system has a limited capacity to process this information, so selective attentional mechanisms prioritise the most valuable information. Fixed contextual cues in the environment help us to allocate attentional resources efficiently. In their study of context, Chun and Jiang proposed a contextual cueing effect (CC effect). This effect is likely to be an implicit learning resulting from selective attention. Specifically, subjects searched for the target faster in the repeated configuration than in the random configuration, as fixed contextual cues would help locate the target. It was found that this effect could be moderated by manipulating external motivation, i.e., reward. However, there is so far considerable debate as to whether high rewards can contribute to the cc effect, and whether rewards act on the cc effect or on the positional probability learning effect. The present experiment used a classical situational cueing task and a mixed between-*within group experimental design to explore the effect of reward on the contextual cueing effect. The experimental results show that high rewards did not contribute more significantly to the cc effect than low rewards, but high rewards did facilitate the target probability learning effect.
Participants Fifty-two Lancaster University students (20 identified as male and 32 as female; age M=23.9, SD=2.55 years, range: 19-33 years) participated in the experiment. Two participants were excluded from the final analysis (see below for details). All participants had normal or corrected normal vision. Participants were informed that the three participants with the highest scores in the experiment would receive a £20 Amazon voucher as a reward. At the end of the experiment, the three participants with the highest scores had received their £20 Amazon vouchers by e-mail. The experiment passed ethical review by the Department of Psychology at Lancaster University. All participants were shown a participant information sheet and signed a consent form to participate in this study prior to the start of the experiment. The Participant Debrief Sheet was presented to participants at the end of the experiment. Materials The materials were created and presented with the Psychophysics Toolbox Version 3 (Brainard, 1997) MATLAB (MathWorks, Sherborn, MA) toolbox. The stimuli were displayed on an MS-Windows machine on a screen with 1920 × 1080 pixels resolution and 60 Hz refreshing rate. Each display consists of 11 L-shaped and one T-shaped black 1.25° x 1.25° items, presented on a white background. The only T-shaped item in each display is the target, which has a 90° rotation clockwise (called left) or counterclockwise (called right). There were an equal number of times that the target was rotated to the left as it was rotated to the right across the experiment. The L-shaped distractors were randomly rotated by 0°, 90°, 180° or 270°. To increase the difficulty of the task (Jiang & Chun, 2001), the L-shaped items had a 4-pixel offset at the junction of the lines to make them similar to the T-shaped targets. In each display, all items were balanced within the quadrant of the display. This randomisation was carried out for each subject individually. Experiment design This experiment was conducted in a quiet testing room, with each subject alone in the room to complete the experiment. The experiment consisted of 20 training blocks. Each block consisted of 16 trials. Each trial began with a 0.5 second fixation cross, followed by a search display until the subject's manual response or reached the maximum response time limit of 6 seconds. Participants were asked to respond as quickly and accurately as possible, reporting the direction of the target by pressing C or N on the standard keyboard numeric keypad, respectively ("T" stems pointing left or right). Each of the 5 training blocks was divided into one epoch, for a total of four epoch, with subjects having a fixed 30 second rest period between epochs. The whole experiment will last about 40 minutes. Participants will be given a score (points) after each test based on their reaction time (correct response within 2 seconds), i. e. the 'reward' for the experiment. Each subject is informed before the experiment that they will have a final score at the end of the experiment and that the top three participants with the highest scores will receive a £20 voucher. The experiment will use two reward conditions, a high (score*10) and a low (score*1) reward. In the high reward condition, the correct answer will be scored as (2000 - reaction time) *10. In the low reward condition, the correct answer will be scored as (2000 - reaction time) *1. For each subject, eight positions in the imaginary ring were randomly selected as target positions. Each quadrant had an equal number of target positions. In each block, each target location was presented once in a repeated display and once in a new display in the same reward condition (twice in total). In the repeated display, the position and orientation of the distractor remained constant along with the target position, while in the new display both were changed randomly. In both the new and repeated displays, the target orientation was changed randomly so that no link could be made between the repeated configurations, target locations or reward values and specific responses. The eight target positions were divided into two different categories: (1) four target positions were always combined with a high reward (score*10) in both repeated and new displays; (2) the other four target positions were always combined with a low reward (score*1) in both repeated and new displays. Therefore, the configurations in the repeated trials were also only ever paired with high or low rewards. A mixed experiment design was used in this study, with the within-subjects factor being the feedback received after the subjects' responses. During the feedback phase of each trial, the score obtained for this experiment is displayed on the screen if the correct response is received within a time window of 2 seconds from the start of the display. The screen will also display whether this trial is a "10x bonus" one or a "normal trial". For trials with a correct response time of more than 2 seconds, no score is awarded, and the feedback is "too slow, 0 points" displayed in the centre of the screen. For trials with a reaction time of more than 6 seconds, 10,000 points will be deducted, and the feedback will be "Time out! Too slow, -10,000 points". For incorrect responses, 10,000 points will be deducted, and the feedback will be "Error! -10,000 points". The total number of points accumulated so far will be displayed below the feedback 1 second after feedback is presented. This experiment also had a between-subjects design in which subjects were randomly divided into two groups, with the odd-numbered participants being the “instructed group”, and those in the instructed group will see a prompt in the centre of the screen before the start of each trial, informing them that the trial is a high or low reward condition. For the high reward condition, "10x BONUS trial!" will be displayed in the centre of the screen in green. For the low reward condition, "Normal trial" will be displayed in the centre of the screen in white. Participants in the even numbered group are in the "not instructed group". Subjects in the “not-instructed group” will not see a prompt in the centre of the screen before the start of each trial and will only see if they have received 10x the reward for their score during the feedback phase.
The Decoy Effect on Choosing Branded and Non-Branded Alcohol-related Products
The decoy effect describes a phenomenon that the introduction of a third choice, usually an asymmetrically dominated one, would change the distribution of people’s preferences for the original two options. Monk et al. (2016) found a basic decoy effect on alcohol purchasing decisions. Extending this, the current study examined the impact of the decoy effect on alcohol-related purchasing decisions and whether the addition of brand names would further impact this. A total of 106 participants were asked to make decisions amongst four types of branded and unbranded drinks by completing an online questionnaire. They also completed the AUDIT, assessing problem drinking patterns, and a measure of trait effortful control. Results showed that the decoy appeared to affect alcoholic relative to non-alcoholic drinks, and affected branded products more than non-branded products. The results suggest that the decoy effect might affect alcohol-related purchasing decisions differently to non-alcoholic purchasing decisions, which might have managerial implications for marketers and health implications for hazardous alcohol consumptions.
All the stimuli were pictures of bottles presented with text descriptions including the number of bottles and the total prices. Participants viewed a total of 80 deals, with 40 alcoholic products and 40 non-alcoholic products. Half of the products were branded and half unbranded. With regards to branded stimuli, pictures of bottles similar to those presented in supermarkets were shown (see Figure 2), whilst unbranded ones used similar unbranded bottles in terms of colour and shape. This allowed the products to correspond with both the diversity of goods in real supermarket but also to avoid unnecessary brand association (e.g. red glass bottles always remind consumers of Coca-Cola; Underwood, 2003). In the control condition, participants were shown products with two options, one with less bottles but cheaper, and the other with a greater quantity of bottles but more expensive. As such, option 1 represented the competitor option, which was cost-effective, and option 2 represented the target option, which was moderately cost- effective. The order of cost-effective and moderate-cost effective products was randomised throughout the experiment. In comparison, the experimental conditions also included a decoy option, which presented a product that was the least cost-effective. Although the decoy option itself was unlikely to be selected, it was expected to result in a different distribution of selections from the output of the control group. The sequences of the choice A (cost-effective), B (moderate cost-effective), and the C (decoy) were set randomly. Alcohol Use Disorders Identification Test (AUDIT). This questionnaire was made up of 10-items concerning personal drinking habits, drinking frequency and amount (Saunders et al., 1993). On a scale of one to over 30, articipants responsed to the questions such as “How many units of alcohol do you drink on a typical day when you are drinking?” and a total AUDIT score was computed. The scores of each question were accumulated and coded. The output of the AUDIT test showed great reliability of this study, M = 15.09, SD = 4.60, Cronbach’s α = .79. It should be noted that although the figure for the AUDIT test was way above eight which was the hazardous cut-off, indicating a possible harmful alcohol use (Babor et al., 2001), this was in line with student’s drinking cultures in UK. Adult Temperament Questionnaire (ATQ). This questionnaire assessed self-reported effortful control (c.f., Evans & Rothbart, 2007) and comprised of 34-items, such as “When interrupted or distracted, I usually can easily shift my attention back to whatever I was doing before”. Participants were asked to answer the questions by selecting a 7-point Likert Scale (1 = extremely untrue, 7 = extremely true). Their responses were recorded and coded. A small amount of missing data (caused by unexpected errors on the web) was replaced by the mean of the sample on that specific item. The result of the ATQ test revealed internal consistency as well, M = 146.34, SD = 22.82, Cronbach’s α = .85. Design and Procedure This study conducted a 4 Stimuli (Beer, Cider, Orange Juice and Water) x 2 Brading (Branded vs unbranded) x 2 Selection (Cost-effectiveness vs. Moderate Cost-effectiveness) within-subjects research design, to examine the possible shifts of selections with the addition of the decoy. Participants were instructed to look at online supermarket choice sets and asked to make a choice out of two (control condition) of three product options (the experimental condition, with the decoy product added). At the beginning, they were asked to imagine that they were in a real supermarket, and they were told that their selections would be dependent on their own preferences. No other information was provided either in oral or on the screen in order to prevent demand characteristics. The main questionnaire had 80 questions, consisting of 80 trials of stimuli (i.e., 20 trials for beer, water, orange juice, and cider). Also 40 groups of bottles were branded and the other 40 were non-branded. The main questionnaire comprised four web pages, with 20 questions in each page and took approximately 30 minutes to complete. Participants were allowed to take short breaks when they finished one page of questions. There was no time limit for each of the questions as the pressure caused by time constraints has been found to affect one’s decision-making process (Dhar & Nowlis, 1999). Subsequently, participants completed a self-report measure of hazardous drinking behaviour (AUDIT) and the effortful control scale (ATQ). These questionnaires were completed at the end of the experiment to make sure the alcohol-related behaviours were not primed (Monk et al., 2016). At the end of the experiment, participants completed a manipulation check to ensure that they were able to accurately distinguish the cost-effectiveness and the quantity of the products set and that they fully understood the requirements of this study. They were then asked to report if they had consumed alcohol on the day of testing, as alcohol consumption has been shown to affect decision-making and may therefore affect the findings of the experiment (Steele & Josephs, 1990). Therefore, participants who had consumed alcohol before participating in the test were excluded when analysing the decoy effect (n = 8). Finally, participants were fully debriefed after they had finished the whole experiment, and were informed about the true aims and hypotheses of the study.