The personal in the political: The influence of candidate attributes on information processing and decision-making in voting choices
Bibliographic record
Abstract
Objectives: Voting choices are fundamental to modern democracy. Political science largely studies such decisions in the aggregate, at the population level. Psychology and neuroscience study how individual brains make individual choices but have mainly focused on decision-making in economic paradigms. This thesis takes a neuroscience-informed approach to multi-attribute value-based decision-making in simulated voting choices. The guiding idea is that the value of multiple attributes may be integrated either configurally (holistically) or elementally (summed attribute by attribute), and that the nature of the information provided influences how attribute-values are combined to support choice. Inspired by the neuroscience of complex object recognition, we predicted that ‘policy and personal’ compared to ‘policy-only’ attributes would bias individuals to prioritize configural or elemental processing, respectively. Two experiments are reported here. The first drew on existing work showing distinct memory processes for configural or elemental information. We aimed to provide evidence that policy and personal information is remembered differently than policy-only information. The second experiment was grounded in the information processing decision-making literature. We used eye-tracking to assess whether information was acquired differently in policy-only compared to policy and personal conditions. Methods: Healthy eligible voters were recruited from the Montreal community. They made voting choices in the laboratory between pairs of candidates in simulated party leadership races under two conditions: a personal condition where candidates were characterized by both a policy and personal attribute and a policy condition where candidate profiles only included policy attributes. In Study 1 (N = 26), voting choices were followed by old/new and source memory tasks. In Study 2 (N = 38), information acquisition patterns were assessed with eye-tracking during pairwise voting choices. Participants subsequently rated the subjective value of each attribute. Attribute rating differences and rating interactions were used to predict choice. Generalized estimating equations and mixed effect regression models were used for the analysis. Results: The combination of policy and personal information was associated with different behavioural and eye-gaze patterns compared to policy attributes alone. Study 1 found better old/new discrimination accuracy for memory when personal characteristics were included, with significantly fewer false alarms compared to the policy-only condition. There was no difference in source memory accuracy across conditions. In Study 2, decisions between candidates had longer reaction times and the information acquisition pattern was more option-based in the policy-only condition. Subjective value differences between attributes predicted choice in both conditions. Conclusions: These findings provide evidence that memory and information processing in voting decisions are influenced by inclusion of personal attributes about candidates, compared to policy information alone. Across both studies, including personal information about candidates led to behavioral patterns more in keeping with configural processing. This work provides preliminary support for a novel framework for understanding how multiple attributes are combined in voting choices, influenced by the neuroscience of how complex information is represented and remembered in the brain
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".