#electionprediction — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #electionprediction, aggregated by home.social.
-
DATE: June 22, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How a new forecasting model accurately predicted the outcome of the 2024 presidential election
A forecasting framework that measures how voters evaluate candidates’ future leadership abilities and policy expertise accurately anticipated the closely contested outcome of the 2024 United States presidential election. The method offers an alternative to conventional models that rely on past economic performance, providing campaign strategists with specific guidance on shaping public perception. The research was published in Research and Politics.
Election forecasting has a long history within political science, frequently relying on a concept known as retrospective voting. This theory assumes that citizens act as auditors of the incumbent party, punishing or rewarding candidates based on recent economic indicators and job approval ratings. These models calculate a candidate’s chances by looking backward at the outgoing administration’s track record.
However, retrospective forecasting becomes complicated during open-seat elections where the sitting president is not on the ballot. This was the exact scenario in the 2024 election following President Joe Biden’s withdrawal from the race. Without a direct performance record for the new candidate, voters tend to shift their focus away from judgments about the past.
Instead, many voters rely on prospective voting, a process of evaluating candidates based on anticipated future performance. Researchers have found that these forward-looking assessments become a primary driver of voter behavior in open-seat contests. Voters look ahead at what policies and leadership styles the new candidates might bring to the office.
Andreas Graefe, a researcher at the Macromedia University of Applied Sciences in Munich, Germany, developed a forecasting tool known as the Issues and Leaders model to capture these forward-looking dynamics. Graefe designed the framework to address common limitations in mainstream election forecasting. Popular contemporary models, such as standard poll aggregators, often update daily to provide real-time snapshots of a race.
While these aggregators help campaigns identify where to allocate financial resources, they offer little strategic advice regarding campaign messaging. They tell observers who is currently winning a standard preference poll, but they fail to explain which policy areas or personality traits are driving those voter preferences. Graefe sought to create a tool that tracks the specific considerations underlying voter choices.
The Issues and Leaders model focuses entirely on two variables: issue-handling competence and leadership perception. To calculate issue-handling scores, the model requires three conditions to be met. Voters must be aware of an issue, they must perceive it as important, and they must trust one candidate more than the other to manage it.
To determine the relative importance of various issues, the model utilizes responses from Gallup’s regular surveys asking Americans to name the country’s most pressing problem. These problems are categorized into economic, foreign policy, and other domestic concerns. The model then weighs how much importance the electorate assigns to each category.
Once the relative weight of the issues is established, the model analyzes survey data regarding which candidate voters trust to handle them. For the 2024 analysis, Graefe utilized polling data compiled by the election analysis website FiveThirtyEight. The dataset included 586 questions regarding issue competence drawn from 87 unique surveys conducted between October 2023 and November 2024.
The model calculates a daily average of voter trust for the incumbent party’s candidate across all these issues. It uses a mathematical technique called exponential smoothing, which gives more weight to recent polling while retaining some influence from older data. This prevents the model from overreacting to minor, short-term fluctuations in public opinion.
The second component of the model measures leadership perception. This metric relies on polls asking respondents a simple, direct question regarding which candidate they believe is a stronger leader. The 2024 tracking incorporated 22 unique surveys conducted between February and October that focused explicitly on this single dimension of leadership strength.
To turn these daily scores into an actual election forecast, Graefe relied on historical data spanning thirteen U.S. presidential elections from 1972 to 2020. By analyzing past voting patterns, a statistical model determines exactly how much weight to assign to partisanship, issue-handling, and leadership scores at different stages of a campaign. The model evaluates how these variables interact with the incumbent party’s final share of the two-party popular vote.
The historical analysis reveals a notable shift in voter behavior as Election Day approaches. One hundred days out, a large percentage of intention is tied simply to underlying partisan loyalty. By the final hours of a campaign, that baseline partisan influence drops by more than half. Instead, candidate-specific evaluations concerning policy expertise and leadership gain substantial predictive weight.
When applied to the 2024 race, the model tracked evolving voter perceptions in real time. Beginning exactly 100 days before the election, Vice President Kamala Harris maintained a very slight edge over former President Donald Trump regarding overall issue competence. By contrast, she started at a massive disadvantage regarding leadership perception, trailing Trump by 20 points in late July.
Over the subsequent months, Harris steadily narrowed the leadership perception gap. By Election Eve, she had reduced Trump’s advantage in this category to less than five points. Despite this late momentum, Trump maintained just enough of an edge in perceived leadership to offset Harris’s slight advantage on policy issues.
The final forecast generated by the model on Election Eve predicted a near tie, with Trump receiving 50.2 percent of the two-party popular vote and Harris receiving 49.8 percent. This cautious projection stood in contrast to many conventional polling averages, which generally showed Harris retaining a slight lead. Ultimately, Trump won the national popular vote by approximately 1.5 percentage points.
The model’s final forecast underestimated Trump’s eventual vote share by just half a percentage point. Across the entire 100-day tracking period, the model’s average error was only 0.65 percentage points. This level of accuracy is consistent with its performance in the 2012, 2016, and 2020 election cycles, demonstrating its reliability as an out-of-sample predictive tool.
Beyond providing an accurate forecast, Graefe notes that the model offers campaigns specific strategic directions. Recognizing that voter perceptions decide elections, candidates can actively attempt to improve their reputations regarding vital policies. They can also try to steer the media narrative toward topics where they already enjoy a reputational advantage.
Because perceptions of leadership are heavily influenced by relatively fixed traits like professional background and personal demeanor, parties could use these metrics to make more informed choices during primary elections. Identifying candidates who naturally exude the leadership qualities expected by the broader electorate might give a party an early, structural advantage.
A primary limitation of the current model is its restriction to the national popular vote. Due to a lack of detailed, state-level polling on specific issue-handling and leadership questions, the model cannot generate an Electoral College forecast. In the United States system, the popular vote does not dictate the winner of the presidency, making state-by-state predictions highly desirable.
Expanding this methodology to individual states is a promising direction for future research. A state-level approach could capture regional differences in what voters care about most. For example, immigration policy might hold more weight for voters in border states, while economic manufacturing issues might dominate in industrial regions.
Access to localized data would allow researchers to refine the model’s sensitivity to these geographic differences. Such an expansion would provide campaigns with localized intelligence for tailoring advertisements and stump speeches. Until such polling becomes widely available, the national model remains an effective tool for understanding the underlying expectations that shape voting behavior.
The study, “Prospective voting and the issues and leaders model: Forecasting the 2024 U.S. presidential election,” was authored by Andreas Graefe.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ForecastingTheElection #ProspectiveVoting #IssuesAndLeadersModel #ElectionPrediction #OpenSeatElection #VoterPerception #LeadershipTrust #PolicyCompetence #PollAnalytics #ElectionForecasting
-
DATE: June 22, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How a new forecasting model accurately predicted the outcome of the 2024 presidential election
A forecasting framework that measures how voters evaluate candidates’ future leadership abilities and policy expertise accurately anticipated the closely contested outcome of the 2024 United States presidential election. The method offers an alternative to conventional models that rely on past economic performance, providing campaign strategists with specific guidance on shaping public perception. The research was published in Research and Politics.
Election forecasting has a long history within political science, frequently relying on a concept known as retrospective voting. This theory assumes that citizens act as auditors of the incumbent party, punishing or rewarding candidates based on recent economic indicators and job approval ratings. These models calculate a candidate’s chances by looking backward at the outgoing administration’s track record.
However, retrospective forecasting becomes complicated during open-seat elections where the sitting president is not on the ballot. This was the exact scenario in the 2024 election following President Joe Biden’s withdrawal from the race. Without a direct performance record for the new candidate, voters tend to shift their focus away from judgments about the past.
Instead, many voters rely on prospective voting, a process of evaluating candidates based on anticipated future performance. Researchers have found that these forward-looking assessments become a primary driver of voter behavior in open-seat contests. Voters look ahead at what policies and leadership styles the new candidates might bring to the office.
Andreas Graefe, a researcher at the Macromedia University of Applied Sciences in Munich, Germany, developed a forecasting tool known as the Issues and Leaders model to capture these forward-looking dynamics. Graefe designed the framework to address common limitations in mainstream election forecasting. Popular contemporary models, such as standard poll aggregators, often update daily to provide real-time snapshots of a race.
While these aggregators help campaigns identify where to allocate financial resources, they offer little strategic advice regarding campaign messaging. They tell observers who is currently winning a standard preference poll, but they fail to explain which policy areas or personality traits are driving those voter preferences. Graefe sought to create a tool that tracks the specific considerations underlying voter choices.
The Issues and Leaders model focuses entirely on two variables: issue-handling competence and leadership perception. To calculate issue-handling scores, the model requires three conditions to be met. Voters must be aware of an issue, they must perceive it as important, and they must trust one candidate more than the other to manage it.
To determine the relative importance of various issues, the model utilizes responses from Gallup’s regular surveys asking Americans to name the country’s most pressing problem. These problems are categorized into economic, foreign policy, and other domestic concerns. The model then weighs how much importance the electorate assigns to each category.
Once the relative weight of the issues is established, the model analyzes survey data regarding which candidate voters trust to handle them. For the 2024 analysis, Graefe utilized polling data compiled by the election analysis website FiveThirtyEight. The dataset included 586 questions regarding issue competence drawn from 87 unique surveys conducted between October 2023 and November 2024.
The model calculates a daily average of voter trust for the incumbent party’s candidate across all these issues. It uses a mathematical technique called exponential smoothing, which gives more weight to recent polling while retaining some influence from older data. This prevents the model from overreacting to minor, short-term fluctuations in public opinion.
The second component of the model measures leadership perception. This metric relies on polls asking respondents a simple, direct question regarding which candidate they believe is a stronger leader. The 2024 tracking incorporated 22 unique surveys conducted between February and October that focused explicitly on this single dimension of leadership strength.
To turn these daily scores into an actual election forecast, Graefe relied on historical data spanning thirteen U.S. presidential elections from 1972 to 2020. By analyzing past voting patterns, a statistical model determines exactly how much weight to assign to partisanship, issue-handling, and leadership scores at different stages of a campaign. The model evaluates how these variables interact with the incumbent party’s final share of the two-party popular vote.
The historical analysis reveals a notable shift in voter behavior as Election Day approaches. One hundred days out, a large percentage of intention is tied simply to underlying partisan loyalty. By the final hours of a campaign, that baseline partisan influence drops by more than half. Instead, candidate-specific evaluations concerning policy expertise and leadership gain substantial predictive weight.
When applied to the 2024 race, the model tracked evolving voter perceptions in real time. Beginning exactly 100 days before the election, Vice President Kamala Harris maintained a very slight edge over former President Donald Trump regarding overall issue competence. By contrast, she started at a massive disadvantage regarding leadership perception, trailing Trump by 20 points in late July.
Over the subsequent months, Harris steadily narrowed the leadership perception gap. By Election Eve, she had reduced Trump’s advantage in this category to less than five points. Despite this late momentum, Trump maintained just enough of an edge in perceived leadership to offset Harris’s slight advantage on policy issues.
The final forecast generated by the model on Election Eve predicted a near tie, with Trump receiving 50.2 percent of the two-party popular vote and Harris receiving 49.8 percent. This cautious projection stood in contrast to many conventional polling averages, which generally showed Harris retaining a slight lead. Ultimately, Trump won the national popular vote by approximately 1.5 percentage points.
The model’s final forecast underestimated Trump’s eventual vote share by just half a percentage point. Across the entire 100-day tracking period, the model’s average error was only 0.65 percentage points. This level of accuracy is consistent with its performance in the 2012, 2016, and 2020 election cycles, demonstrating its reliability as an out-of-sample predictive tool.
Beyond providing an accurate forecast, Graefe notes that the model offers campaigns specific strategic directions. Recognizing that voter perceptions decide elections, candidates can actively attempt to improve their reputations regarding vital policies. They can also try to steer the media narrative toward topics where they already enjoy a reputational advantage.
Because perceptions of leadership are heavily influenced by relatively fixed traits like professional background and personal demeanor, parties could use these metrics to make more informed choices during primary elections. Identifying candidates who naturally exude the leadership qualities expected by the broader electorate might give a party an early, structural advantage.
A primary limitation of the current model is its restriction to the national popular vote. Due to a lack of detailed, state-level polling on specific issue-handling and leadership questions, the model cannot generate an Electoral College forecast. In the United States system, the popular vote does not dictate the winner of the presidency, making state-by-state predictions highly desirable.
Expanding this methodology to individual states is a promising direction for future research. A state-level approach could capture regional differences in what voters care about most. For example, immigration policy might hold more weight for voters in border states, while economic manufacturing issues might dominate in industrial regions.
Access to localized data would allow researchers to refine the model’s sensitivity to these geographic differences. Such an expansion would provide campaigns with localized intelligence for tailoring advertisements and stump speeches. Until such polling becomes widely available, the national model remains an effective tool for understanding the underlying expectations that shape voting behavior.
The study, “Prospective voting and the issues and leaders model: Forecasting the 2024 U.S. presidential election,” was authored by Andreas Graefe.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ForecastingTheElection #ProspectiveVoting #IssuesAndLeadersModel #ElectionPrediction #OpenSeatElection #VoterPerception #LeadershipTrust #PolicyCompetence #PollAnalytics #ElectionForecasting
-
DATE: June 21, 2026 at 06:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Voters rewrite past election predictions to protect their political identities
Following national elections, voters rewrite their memories of the political event and distort their initial expectations to align closely with the eventual outcome. This psychological phenomenon relies on self-serving cognitive biases to protect individual self-esteem and group identity, helping to maintain profound partisan divides. A recent study exploring these dynamics was published in the journal Communications Psychology.
People use similar mental resources to remember the past and imagine the future, a cognitive ability often described as mental time travel. By piecing together fragments of past experiences, individuals can simulate what might happen in the days or years ahead. Previous psychological evaluations indicated that future simulations are usually rated as more positive and important than past memories. Those same assessments indicated that past memories are generally experienced more vividly.
Yet, many of these earlier tests asked participants to generate completely different events for the past and the future, leaving room for a selection bias. Participants might naturally choose happier milestones when imagining the future while recalling grimmer experiences from the past. Marius Boeltzig, a researcher at the University of Münster in Germany, worked with colleagues to test how these mental processes operate when narrowed down to a single, shared public event. The researchers chose to focus on national political elections.
Democratic elections are highly anticipated, widely observed, and deeply tied to a voter’s personal identity. In highly polarized environments, political affiliations can become fused with an individual’s sense of self. By tracking specific elections, Boeltzig and his team aimed to observe how the reality of an event’s outcome influences the way people shift their psychological narratives over time. The team suspected that self-serving biases would warp how individuals remembered both the event and their own previous expectations.
These psychological tendencies are designed to protect a person’s self-esteem and group identity from threatening information. When an individual identifies strongly with a political party, a win for that party can feel like a personal victory, just as a loss can feel like a personal defeat. To capture these cognitive shifts, the researchers conducted three separate longitudinal studies surrounding three major 2024 elections. They recruited participants prior to the European Union parliamentary election in Germany, the general election in the United Kingdom, and the presidential election in the United States.
The EU vote in Germany was seen as a test for the country’s then-ruling coalition, while the American contest was exceptionally unpredictable. A few days before each respective vote, participants completed a survey about their expectations. The participants rated how vividly they could picture the upcoming election outcome, how important the result would be to them personally, and how positive or negative they expected to feel. About a week after the elections concluded, the participants answered the exact same questions.
This time, they answered based on their memories of the actual results rather than their original predictions. This design allowed the researchers to compare pre-election expectations against post-election retrospection. In the United States group, the researchers added another layer to the experiment to track specific cognitive distortions. They asked the American voters to recall the specific predictions they had made before the election regarding fairness, eventual winners, and emotional reactions.
Overall, the results revealed that the psychological differences between imagining the future and remembering the past depend heavily on the outcome of the event itself. Across all three countries, participants who supported the winning political parties experienced a notable memory shift. Election winners began to view the election as much more important after knowing they had won. They also recalled the event more vividly than they had originally predicted they would. Conversely, voters whose preferred candidates lost tended to reduce the importance they assigned to the election after the fact.
These findings suggest that people adjust their emotional appraisals of an event based upon how well it serves their personal identities. If a political result turns out better than expected, the voter mentally inflates the importance of the event. This adjustment makes the victory feel even more rewarding, boosting the individual’s self-appraisal. On the other hand, downplaying a loss helps ease the sting of defeat by making the event seem less consequential.
The American survey provided the deepest look into how people harmonize their past expectations with their current realities. After Donald Trump won the presidential election, his supporters misremembered their initial predictions regarding the election’s intrinsic fairness. Trump voters recalled predicting a much fairer election than they had actually assessed days earlier. According to the researchers, this revision justified their post-election belief that the system functioned fairly because their candidate won. They also underestimated how optimistic they had been before the vote, a shift that likely amplified their positive feelings about the victory.
In contrast, voters who supported Kamala Harris overestimated how optimistic they had been prior to the election. The sample generally leaned toward believing they had predicted a Harris win more strongly than they actually did. The researchers proposed a reasoning for this specific mental distortion in the wake of an electoral defeat. They suggested that overestimating their past optimism may have helped Harris voters rationalize the intense negative emotions they felt after the loss. Believing they had been highly optimistic made their current feelings of profound disappointment feel logical and justified.
These psychological adjustments push people toward a false illusion of consistency. Individuals subconsciously distort their past thoughts so that their old predictions match their current emotional states and political identities. By doing so, they maintain a coherent self-image, but they also strengthen their partisan beliefs. When voters rewrite their memories to fit party lines, they unintentionally reinforce a deeply polarized view of the world. As these biased memories are involuntarily retrieved during everyday life, they continue to shape a person’s cognitive landscape.
The researchers acknowledge a few caveats in their experimental design. The sampling methods and the political makeup of the participant groups varied across the three countries due to logistical constraints. The European and UK elections were also largely predictable, whereas the American contest was highly polarized and uncertain. That specific unpredictability might have influenced the magnitude of the mental shifts observed in the United States. A completely balanced replication across equally polarized elections could help verify the exact strength of these cognitive shifts in different global contexts.
Future investigations could explore whether these cognitive distortions apply to less politically charged group events, such as sports championships or economic market shifts. Researchers could also test whether these same biases alter the memories of private events like academic exams or job interviews. Addressing these mental adjustments on a broader scale might offer researchers a better understanding of how collective memory forms within societies. Unraveling the mechanisms of memory bias could also help global communities navigate shared realities despite experiencing deep partisan divides.
The study, “Self-serving biases shape the relationship between future thinking and remembering of elections,” was authored by Marius Boeltzig, Ricarda I. Schubotz, Scott Cole, and Clare J. Rathbone.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #VoterMemoryBias #ElectionPsychology #SelfServingBias #PartisanIdentity #MemoryDistortion #FutureVsPast #PoliticalPolarization #CognitiveBias #ElectionPrediction #MemoryResearch
-
DATE: June 21, 2026 at 06:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Voters rewrite past election predictions to protect their political identities
Following national elections, voters rewrite their memories of the political event and distort their initial expectations to align closely with the eventual outcome. This psychological phenomenon relies on self-serving cognitive biases to protect individual self-esteem and group identity, helping to maintain profound partisan divides. A recent study exploring these dynamics was published in the journal Communications Psychology.
People use similar mental resources to remember the past and imagine the future, a cognitive ability often described as mental time travel. By piecing together fragments of past experiences, individuals can simulate what might happen in the days or years ahead. Previous psychological evaluations indicated that future simulations are usually rated as more positive and important than past memories. Those same assessments indicated that past memories are generally experienced more vividly.
Yet, many of these earlier tests asked participants to generate completely different events for the past and the future, leaving room for a selection bias. Participants might naturally choose happier milestones when imagining the future while recalling grimmer experiences from the past. Marius Boeltzig, a researcher at the University of Münster in Germany, worked with colleagues to test how these mental processes operate when narrowed down to a single, shared public event. The researchers chose to focus on national political elections.
Democratic elections are highly anticipated, widely observed, and deeply tied to a voter’s personal identity. In highly polarized environments, political affiliations can become fused with an individual’s sense of self. By tracking specific elections, Boeltzig and his team aimed to observe how the reality of an event’s outcome influences the way people shift their psychological narratives over time. The team suspected that self-serving biases would warp how individuals remembered both the event and their own previous expectations.
These psychological tendencies are designed to protect a person’s self-esteem and group identity from threatening information. When an individual identifies strongly with a political party, a win for that party can feel like a personal victory, just as a loss can feel like a personal defeat. To capture these cognitive shifts, the researchers conducted three separate longitudinal studies surrounding three major 2024 elections. They recruited participants prior to the European Union parliamentary election in Germany, the general election in the United Kingdom, and the presidential election in the United States.
The EU vote in Germany was seen as a test for the country’s then-ruling coalition, while the American contest was exceptionally unpredictable. A few days before each respective vote, participants completed a survey about their expectations. The participants rated how vividly they could picture the upcoming election outcome, how important the result would be to them personally, and how positive or negative they expected to feel. About a week after the elections concluded, the participants answered the exact same questions.
This time, they answered based on their memories of the actual results rather than their original predictions. This design allowed the researchers to compare pre-election expectations against post-election retrospection. In the United States group, the researchers added another layer to the experiment to track specific cognitive distortions. They asked the American voters to recall the specific predictions they had made before the election regarding fairness, eventual winners, and emotional reactions.
Overall, the results revealed that the psychological differences between imagining the future and remembering the past depend heavily on the outcome of the event itself. Across all three countries, participants who supported the winning political parties experienced a notable memory shift. Election winners began to view the election as much more important after knowing they had won. They also recalled the event more vividly than they had originally predicted they would. Conversely, voters whose preferred candidates lost tended to reduce the importance they assigned to the election after the fact.
These findings suggest that people adjust their emotional appraisals of an event based upon how well it serves their personal identities. If a political result turns out better than expected, the voter mentally inflates the importance of the event. This adjustment makes the victory feel even more rewarding, boosting the individual’s self-appraisal. On the other hand, downplaying a loss helps ease the sting of defeat by making the event seem less consequential.
The American survey provided the deepest look into how people harmonize their past expectations with their current realities. After Donald Trump won the presidential election, his supporters misremembered their initial predictions regarding the election’s intrinsic fairness. Trump voters recalled predicting a much fairer election than they had actually assessed days earlier. According to the researchers, this revision justified their post-election belief that the system functioned fairly because their candidate won. They also underestimated how optimistic they had been before the vote, a shift that likely amplified their positive feelings about the victory.
In contrast, voters who supported Kamala Harris overestimated how optimistic they had been prior to the election. The sample generally leaned toward believing they had predicted a Harris win more strongly than they actually did. The researchers proposed a reasoning for this specific mental distortion in the wake of an electoral defeat. They suggested that overestimating their past optimism may have helped Harris voters rationalize the intense negative emotions they felt after the loss. Believing they had been highly optimistic made their current feelings of profound disappointment feel logical and justified.
These psychological adjustments push people toward a false illusion of consistency. Individuals subconsciously distort their past thoughts so that their old predictions match their current emotional states and political identities. By doing so, they maintain a coherent self-image, but they also strengthen their partisan beliefs. When voters rewrite their memories to fit party lines, they unintentionally reinforce a deeply polarized view of the world. As these biased memories are involuntarily retrieved during everyday life, they continue to shape a person’s cognitive landscape.
The researchers acknowledge a few caveats in their experimental design. The sampling methods and the political makeup of the participant groups varied across the three countries due to logistical constraints. The European and UK elections were also largely predictable, whereas the American contest was highly polarized and uncertain. That specific unpredictability might have influenced the magnitude of the mental shifts observed in the United States. A completely balanced replication across equally polarized elections could help verify the exact strength of these cognitive shifts in different global contexts.
Future investigations could explore whether these cognitive distortions apply to less politically charged group events, such as sports championships or economic market shifts. Researchers could also test whether these same biases alter the memories of private events like academic exams or job interviews. Addressing these mental adjustments on a broader scale might offer researchers a better understanding of how collective memory forms within societies. Unraveling the mechanisms of memory bias could also help global communities navigate shared realities despite experiencing deep partisan divides.
The study, “Self-serving biases shape the relationship between future thinking and remembering of elections,” was authored by Marius Boeltzig, Ricarda I. Schubotz, Scott Cole, and Clare J. Rathbone.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #VoterMemoryBias #ElectionPsychology #SelfServingBias #PartisanIdentity #MemoryDistortion #FutureVsPast #PoliticalPolarization #CognitiveBias #ElectionPrediction #MemoryResearch
-
DATE: June 21, 2026 at 06:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Voters rewrite past election predictions to protect their political identities
Following national elections, voters rewrite their memories of the political event and distort their initial expectations to align closely with the eventual outcome. This psychological phenomenon relies on self-serving cognitive biases to protect individual self-esteem and group identity, helping to maintain profound partisan divides. A recent study exploring these dynamics was published in the journal Communications Psychology.
People use similar mental resources to remember the past and imagine the future, a cognitive ability often described as mental time travel. By piecing together fragments of past experiences, individuals can simulate what might happen in the days or years ahead. Previous psychological evaluations indicated that future simulations are usually rated as more positive and important than past memories. Those same assessments indicated that past memories are generally experienced more vividly.
Yet, many of these earlier tests asked participants to generate completely different events for the past and the future, leaving room for a selection bias. Participants might naturally choose happier milestones when imagining the future while recalling grimmer experiences from the past. Marius Boeltzig, a researcher at the University of Münster in Germany, worked with colleagues to test how these mental processes operate when narrowed down to a single, shared public event. The researchers chose to focus on national political elections.
Democratic elections are highly anticipated, widely observed, and deeply tied to a voter’s personal identity. In highly polarized environments, political affiliations can become fused with an individual’s sense of self. By tracking specific elections, Boeltzig and his team aimed to observe how the reality of an event’s outcome influences the way people shift their psychological narratives over time. The team suspected that self-serving biases would warp how individuals remembered both the event and their own previous expectations.
These psychological tendencies are designed to protect a person’s self-esteem and group identity from threatening information. When an individual identifies strongly with a political party, a win for that party can feel like a personal victory, just as a loss can feel like a personal defeat. To capture these cognitive shifts, the researchers conducted three separate longitudinal studies surrounding three major 2024 elections. They recruited participants prior to the European Union parliamentary election in Germany, the general election in the United Kingdom, and the presidential election in the United States.
The EU vote in Germany was seen as a test for the country’s then-ruling coalition, while the American contest was exceptionally unpredictable. A few days before each respective vote, participants completed a survey about their expectations. The participants rated how vividly they could picture the upcoming election outcome, how important the result would be to them personally, and how positive or negative they expected to feel. About a week after the elections concluded, the participants answered the exact same questions.
This time, they answered based on their memories of the actual results rather than their original predictions. This design allowed the researchers to compare pre-election expectations against post-election retrospection. In the United States group, the researchers added another layer to the experiment to track specific cognitive distortions. They asked the American voters to recall the specific predictions they had made before the election regarding fairness, eventual winners, and emotional reactions.
Overall, the results revealed that the psychological differences between imagining the future and remembering the past depend heavily on the outcome of the event itself. Across all three countries, participants who supported the winning political parties experienced a notable memory shift. Election winners began to view the election as much more important after knowing they had won. They also recalled the event more vividly than they had originally predicted they would. Conversely, voters whose preferred candidates lost tended to reduce the importance they assigned to the election after the fact.
These findings suggest that people adjust their emotional appraisals of an event based upon how well it serves their personal identities. If a political result turns out better than expected, the voter mentally inflates the importance of the event. This adjustment makes the victory feel even more rewarding, boosting the individual’s self-appraisal. On the other hand, downplaying a loss helps ease the sting of defeat by making the event seem less consequential.
The American survey provided the deepest look into how people harmonize their past expectations with their current realities. After Donald Trump won the presidential election, his supporters misremembered their initial predictions regarding the election’s intrinsic fairness. Trump voters recalled predicting a much fairer election than they had actually assessed days earlier. According to the researchers, this revision justified their post-election belief that the system functioned fairly because their candidate won. They also underestimated how optimistic they had been before the vote, a shift that likely amplified their positive feelings about the victory.
In contrast, voters who supported Kamala Harris overestimated how optimistic they had been prior to the election. The sample generally leaned toward believing they had predicted a Harris win more strongly than they actually did. The researchers proposed a reasoning for this specific mental distortion in the wake of an electoral defeat. They suggested that overestimating their past optimism may have helped Harris voters rationalize the intense negative emotions they felt after the loss. Believing they had been highly optimistic made their current feelings of profound disappointment feel logical and justified.
These psychological adjustments push people toward a false illusion of consistency. Individuals subconsciously distort their past thoughts so that their old predictions match their current emotional states and political identities. By doing so, they maintain a coherent self-image, but they also strengthen their partisan beliefs. When voters rewrite their memories to fit party lines, they unintentionally reinforce a deeply polarized view of the world. As these biased memories are involuntarily retrieved during everyday life, they continue to shape a person’s cognitive landscape.
The researchers acknowledge a few caveats in their experimental design. The sampling methods and the political makeup of the participant groups varied across the three countries due to logistical constraints. The European and UK elections were also largely predictable, whereas the American contest was highly polarized and uncertain. That specific unpredictability might have influenced the magnitude of the mental shifts observed in the United States. A completely balanced replication across equally polarized elections could help verify the exact strength of these cognitive shifts in different global contexts.
Future investigations could explore whether these cognitive distortions apply to less politically charged group events, such as sports championships or economic market shifts. Researchers could also test whether these same biases alter the memories of private events like academic exams or job interviews. Addressing these mental adjustments on a broader scale might offer researchers a better understanding of how collective memory forms within societies. Unraveling the mechanisms of memory bias could also help global communities navigate shared realities despite experiencing deep partisan divides.
The study, “Self-serving biases shape the relationship between future thinking and remembering of elections,” was authored by Marius Boeltzig, Ricarda I. Schubotz, Scott Cole, and Clare J. Rathbone.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #VoterMemoryBias #ElectionPsychology #SelfServingBias #PartisanIdentity #MemoryDistortion #FutureVsPast #PoliticalPolarization #CognitiveBias #ElectionPrediction #MemoryResearch
-
The tea leaves are telling me #ItsAGirl 💙🔵💙🇺🇸 #election2024 #prediction #ElectionPrediction
-
Allan Lichtman is based. I ironically believe in the keys to the White House. Fuck most pollsters for having a polling bias for Republicans and Donald Trump.
Resource:
https://en.wikipedia.org/wiki/The_Keys_to_the_White_House -
Allan Lichtman is based. I ironically believe in the keys to the White House. Fuck most pollsters for having a polling bias for Republicans and Donald Trump.
Resource:
https://en.wikipedia.org/wiki/The_Keys_to_the_White_House -
Top election predictor: TRUMP LIKELY WINNER if Biden is replaced - https://youtu.be/X4VemewGU6o?si=PgIE9feoS38B57QE #USPolitics #DavidPakman #AllanLichtman #KeysToTheWhiteHouse #PresidentialElection #ElectionPrediction #VOTE #Election2024 #DumpTrump
-
Top election predictor: TRUMP LIKELY WINNER if Biden is replaced - https://youtu.be/X4VemewGU6o?si=PgIE9feoS38B57QE #USPolitics #DavidPakman #AllanLichtman #KeysToTheWhiteHouse #PresidentialElection #ElectionPrediction #VOTE #Election2024 #DumpTrump