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Cognitivism01:17

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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Cognitive psychologist Richard Lazarus proposed the cognitive-mediational theory of emotions, which emphasizes how individuals' assessments of stressors significantly affect their experience of stress. According to Lazarus, the stress response is determined by a two-step appraisal process: primary appraisal and secondary appraisal. These cognitive appraisals help individuals evaluate the potential impact of a stressor and determine the adequacy of their coping resources.
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in...
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Related Experiment Video

Updated: Oct 9, 2025

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
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Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

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Conservatism predicts aversion to consequential Artificial Intelligence.

Noah Castelo1, Adrian F Ward2

  • 1Department of Marketing, Business Economics, and Law, University of Alberta, Edmonton, Alberta, Canada.

Plos One
|December 20, 2021
PubMed
Summary
This summary is machine-generated.

Political conservatism is linked to lower trust and comfort with artificial intelligence (AI), a phenomenon termed AI aversion. This aversion stems from conservatives perceiving AI as riskier, impacting its societal adoption.

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Area of Science:

  • Social Psychology
  • Human-Computer Interaction
  • Political Science

Background:

  • Artificial intelligence (AI) offers transformative potential across various societal domains, including healthcare, transportation, and law.
  • Public trust and acceptance are crucial for realizing AI's benefits, yet lay perceptions significantly influence AI adoption.
  • Understanding factors shaping public perception, such as political ideology, is essential for navigating AI's societal integration.

Purpose of the Study:

  • To investigate the relationship between political conservatism and public perceptions of artificial intelligence (AI).
  • To determine if political conservatism influences comfort levels and trust in AI systems.
  • To explore the underlying mechanisms, specifically risk perception, connecting conservatism and AI aversion.

Main Methods:

  • Five online survey experiments were conducted to assess the impact of political conservatism on AI perceptions.
  • Participants' comfort with and trust in AI for various tasks were measured.
  • The role of risk perception as a mediator between conservatism and AI aversion was examined.

Main Results:

  • A significant association was found between higher levels of political conservatism and lower comfort with and trust in AI (AI aversion).
  • Conservatism's effect on AI aversion was mediated by the perception of AI as being inherently riskier.
  • An intervention involving moral reframing was tested as a potential strategy to mitigate AI aversion among conservatives.

Conclusions:

  • Political conservatism is a key demographic factor associated with resistance to AI adoption.
  • Perceived risk is a critical explanatory factor for AI aversion among conservative individuals.
  • Targeted interventions, such as moral reframing, may be effective in reducing AI aversion and promoting broader AI acceptance.