Ingroup biases and cognitive load (Experiment 2)
Bibliographic record
Abstract
NOTE: this registration was initially drafted prior to data collection. We have now collected data from 72 participants (prior to exclusion) but have not looked at the data yet in order to maintain the integrity of this registration. Given the results from our initial experiment, we have decided to proceed with a follow-up experiment. In this experiment we will explore whether cognitive load differently impacts the way individuals allocate chips when the ingroup is based on an external grouper (university) vs. an internal grouper (shared social preference). In the first experiment, we had a target N of ~70 but were left with a number of exclusions that led to a smaller number of useable participants. Thus, in the follow-up experiment, we aimed for an N of about 80, while also aiming to conclude collecting data by the end of the academic term due to time constraints of this honours thesis. The “internal” grouper will be whether the hypothetical other players share or don’t share the participant’s preference for intimate gatherings vs. large parties, which participants will indicate prior to completing the experimental task. The task they will complete will be almost identical to the initial casino chip allocation task in Experiment 1, but the names of the fellow players will be removed and that hypothetical player’s social preference (i.e., intimate gatherings vs large parties) will be added. This change entails that on any given trial, participants will see a “fellow player” who is considered to be a double-ingroup member (UNSW and social preference), partial-ingroup (UNSW and other preference), partial-outgroup (McGill and shared preference) or double outgroup member (McGill and other preference). Based on the results from Experiment 1, we anticipate that fewer chips will be allocated to McGill students than to UNSW students, but that this will primarily occur under cognitive load. This would replicate the results of Experiment 1, with the impact of an externally-based grouper emerging most robustly under load. The critical question for Experiment 2 is what happens when ingroup/outgroup is defined via something more internally based such as shared social preference. We predict that – in contrast to the impact of an external grouper – the impact of an internally-based grouper will be most robust under no load (we reason that it would take more cognitive resources to activate internally driven, more abstract group membership criteria). This may be reflected in a weaker main effect of internal-grouper under load, and it may also be reflected in a weaker degree (under load) to which the internal grouper modulates the impact of the external grouper (whereas the degree to which the external grouper modulates the impact of the internal grouper will be weaker under no load). Additionally, we will assess whether chip allocation is greatest for double-ingroup players (i.e., same university and movie preference as the participant), least for double-outgroup players, and at an intermediate level for half-ingroup/half-outgroup players. Consistent with Experiment 1, we intend to exclude participants who have less than a 75% accuracy rate on their cognitive load trials, i.e. if they get less than 15 out of 20 correct. This is because we cannot be sure that they engaged with the cognitive load task to the best of their ability.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".