Ingroup biases and cognitive load

Rationale The minimal group paradigm refers to the task that reveals the tendency to favour one’s ingroup and exhibit bias, or even hostility, to one’s outgroup (Diehl, 1990). This trait can be seen in both human and non-human animals and it facilitates survival, social and cultural acceptance as well as positive social identity and self-esteem for the individual (Diehl, 1990; Gagnon & Bourhis, 1996). Social Identity Theory contends that an individual’s positive self-identity is fuelled by their affiliations with social groups (Diehl, 1990; Tajfel, Turner, Austin, & Worchel, 1979). Hence, they will try to achieve positive social identity by comparing their chosen ingroup to various outgroups and determining which is more desirable (Diehl, 1990; Tajfel et al., 1979). If the individual hence determines an outgroup to be favourable, they will defect to said out group or strive to make their original ingroup more positive and desirable (Diehl, 1990; Tajfel et al., 1979). The individual’s selected ingroup can be based on meaningful features such as culture, worldview, or political standing and, as meaningful factors, they provide some justification for why individuals would seek out and exhibit preference for such like-minded people. This ingroup bias is not only reserved for meaningful features of group membership; rather, it is robust across even the most arbitrary of group memberships (Diehl, 1990; Gagnon & Bourhis, 1996; Locksley, Ortiz & Hepburn, 1980). Various studies have examined the robust ingroup bias effect, via the minimal group paradigm. Said studies grouped people using membership features such as university affiliation, race, artwork preference, random allocation to one group over another, finding that individuals exhibit the bias reliably (Diehl, 1990; Gagnon & Bourhis, 1996; Hehman, Mania, & Gaertner, 2010; Hehman, Stanley, Gaertner & Simons, 2011; Locksley et al., 1980). One such study incorporated the use of a ‘random lottery ticket’ sorting method, by which participants were all allocated to the same group (the Phis), under the pretence that they were equally as likely to be allocated to another group (the Gammas) (Locksley et al., 1980). Further, the participants were instructed to allocate five other participants (three Gammas, two Phis) up to 100 chips each, emphasising that their allocations would not have an influence on the amount they themselves were allocated (Locksley et al., 1980). Hence, they were able to determine a robust ingroup and outgroup effect, when considering an arbitrary group allocation, p < .01 (Locksley et al., 1980). Locksley and colleages (1980) rejected the traditional resource allocation method, known as the Tajfel matrices, which incorporate (usually) three categories of allocation (Tajfel, 1970). These categories are as follows; a choice that yields maximum in group profit (MIP), a choice that yield maximum joint profit of both the ingroup and outgroup (MJP) and the maximum difference in resources in favor of the ingroup member (MD) (Tajfel, 1970). Locksley and colleagues (1980) argue that the Tajfel forced-choice matrix of alternatives opens the experiment up to ingroup bias more readily (MIP choice available for selection) than unconstrained resource allocation. Thus, the current study seeks to emulate their experimental method in order to counteract this readily available option that lends itself to ingroup bias (Locksley et al., 1980). It is here that the question,’ how can the robust ingroup bias effect be altered?”, becomes particularly relevant in the current study. Cognitive load refers to the mental burden a certain cognitive task may place on an individual’s ability to utilise their mental resources and faculties (Paas, Tuovinen, Tabbers, & Van Gerven, 2003). As such, it is often the case that tasks that induce cognitive load have adverse effects on judgements and decisions that need to be made concurrently with said cognitive task (Allen et al., 2014; Van Knippenberg, Dijksterhuis, & Vermeulen, 1999). One such adverse effect is the tendency for one to depend on stereotypes and biases to a greater extent, especially when the induced cognitive load is extensive (Allen et al., 2014; Knippenberg et al., 1999). This is due to the fact that heuristics and biases, especially stereotypical expectancy bias, are automatic, easy routes of thinking and reasoning (Allen et al., 2014; Knippenberg et al., 1999). Paying more attention to information that confirms any pre-existing stereotypes one has formed is much less taxing than slow, deliberative thinking and hence, once an individual is placed under cognitive load, this more purposeful thinking becomes less likely (Allen et al., 2014; Knippenberg et al., 1999). In the context of the current study, it seems logical that placing an individual under cognitive load during a task measuring ingroup and outgroup effects would have an effect on the outcome of the task. I aim to incorporate a simple task that will induce cognitive load by occupying the individual’s working memory capacity. This task emulates Allen and colleagues’ 2014 task, in which, participants were presented with an eight-digit number to remember while answering some questions, and then were asked to recall a number in the sequence. The current study aims to incorporate numbers and provide a response format that includes memory prompts. In combining the minimal group paradigm with the concept of cognitive load, I aim to determine if this robust ingroup effect can be manipulated, in order to gain a better understanding of the contributing factors of this phenomenon. Aims and hypotheses The aim of my proposed study is to investigate what happens to the robust ingroup bias effect when participants are placed under cognitive load. My proposed hypotheses include: Participants, during a resource allocation task, will allocate more resources to their identified ingroup. Participants under cognitive load will allocate a greater amount of resources to their ingroup, compared to those not under cognitive load group, due to reduced executive decision-making capacity and, thus dependence on heuristics and biases (Van Knippenberg et al., 1999). Method Design: The purpose of this experiment is to determine if cognitive load can moderate a robust ingroup effect. The experiment will incorporate a within-subjects design where participants will complete a resource allocation task that cycles between four no-cognitive load trials and four high-cognitive load trials, until they have completed 40 trials (ten blocks). The dependent variable of interest will be the amount of “chips” allocated to the group members. I will assess differences both in the number of chips allocated to the ingroup and outgroup and the variability of this allocation (e.g., there may be less variability in the number of chips allocated to the ingroup than the outgroup.) In addition, a memory test will be administered, where the dependent variable will be accuracy of memory for ingroup and outgroup individuals’ names. This memory test will examine how many ingroup vs outgroup names are recalled, adding another ingroup bias measure to our study. Participants: I will select 70 students from the pool of students enrolled in Psychology 1B, recruited via SONA. This ensures the participants will identify with the UNSW community. Procedure: Upon following the experimental link, participants will be redirected to psytoolkit, a web-based platform for running online questionnaires experiments. Once there, they will be provided with a virtual consent form, which they ‘sign’ by ticking a box that says, “Please confirm that you want to participate in this survey. Your information (including computer IP) will be stored and might be used for research. If you do not want to participate, please close the browser window” next to it. They will be told that the experiment is a product of collaboration between UNSW and McGill University. The participants will complete a brief demographics questionnaire (age, gender) and then they will be told they must allocate the students they will see with casino chips. They will be asked to pay close attention to the names and universities of the fellow students as there will be a “memory test” after they complete each block of four trials. This emphasis on the memory aspect of the game is to minimised demand characteristics or social desirability effects that might stem from an emphasis being placed on the resource allocation part of the experiment. They will read the following instructions: “You will see 4 consecutive images of students from UNSW or McGill University. It is your job to allocate them some casino chips. Pay special attention to their name and university, you will be tested on this at the end of the 4 trials. Please use the slider to allocate between 1-100 chips.” In the cognitive load set of trials, the instructions will have additional instructions indicating the following: “THIS BLOCK OF TRIALS WILL BE DIFFERENT FROM THE LAST BECAUSE YOU WILL HAVE AN ADDITIONAL MEMORY TASK BEFORE THE CHIP ALLOCATION TASK.” There will be ten blocks of four trials each, beginning with a no-cognitive load block, followed by a high-cognitive load block, back to a no-load block and so on. The first four trials will include a practice round. Chip ‘allocation’ will be done by moving a slider to a desired amount of chips and pressing a “continue button” to allocate. Moreover, individuals in the cognitive load condition will have to remember a six-digit series before being presented with the student/university stimulus. The digit series are preceded by fixation crosses. They must then allocate chips, and then be required to answer a prompt about the series on the following page. In order to administer the “memory test”, participants will be shown their fellow player’s faces again, sans university, in a random order. Participants will

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.