diff --git a/External_URLs/Supporting_Literature/Markdown/Cognitive_Dissonance/CD-1.md b/External_URLs/Supporting_Literature/Markdown/Cognitive_Dissonance/CD-1.md index a6012ad..8153a0a 100644 --- a/External_URLs/Supporting_Literature/Markdown/Cognitive_Dissonance/CD-1.md +++ b/External_URLs/Supporting_Literature/Markdown/Cognitive_Dissonance/CD-1.md @@ -1,107 +1,158 @@ # Cognitive Dissonance Research Review ## Study Overview +**Neural Correlates of Cognitive Dissonance and Choice-Induced Preference Change** -Neural Correlates of Cognitive Dissonance and Choice-Induced Preference Change +--- ## Citation Information +- **Author(s):** Keise Izuma, Madoka Matsumoto, Kou Murayama, Kazuyuki Samejima, Norihiro Sadato, Kenji Matsumoto +- **Title:** *Neural Correlates of Cognitive Dissonance and Choice-Induced Preference Change* +- **Journal/Source:** *Proceedings of the National Academy of Sciences (PNAS)* +- **Publication Year:** 2010 +- **DOI/URL:** [10.1073/pnas.1011879108](https://doi.org/10.1073/pnas.1011879108) +- **Affiliation:** Tamagawa University, University of Munich, National Institute for Physiological Sciences -* **Author(s)**: Keise Izuma, Madoka Matsumoto, Kou Murayama, Kazuyuki Samejima, Norihiro Sadato, Kenji Matsumoto -* **Title**: Neural correlates of cognitive dissonance and choice-induced preference change -* **Journal/Source**: Proceedings of the National Academy of Sciences (PNAS) -* **Publication Year**: 2010 -* **DOI/URL**: 10.1073/pnas.1011879108 -* **Affiliation**: Tamagawa University, University of Munich, National Institute for Physiological Sciences +--- ## Audience +- **Target Audience:** + - Neuroscientists, psychologists, cognitive science researchers, and behavioral economists. +- **Application:** + - Investigates the neural mechanisms of decision-making to refine cognitive-behavioral therapies, enhance economic models of preference, and advance neuroeconomics research. +- **Outcome:** + - Provides insight into how choices reshape preferences at psychological and neural levels. -* **Target Audience**: Neuroscientists, psychologists, cognitive science researchers, and those in behavioral economics -* **Application**: Investigate neural mechanisms of decision-making, apply insights to improve cognitive-behavioral therapies, and refine economic models of preference -* **Outcome**: Enhanced understanding of how choices influence preferences at both psychological and neural levels +--- ## Relevance +- **Significance:** + - Challenges classical economic assumptions by exploring whether actions influence preferences, a foundational question in psychology and neuroscience. +- **Real-World Implications:** + - Applications in marketing, mental health therapies, and educational frameworks to leverage cognitive dissonance mechanisms for behavior change. -* **Significance**: Explores foundational questions about whether actions can reshape preferences, challenging classical economic assumptions -* **Real-world Implications**: Insights into cognitive dissonance mechanisms can impact marketing strategies, mental health therapies, and educational frameworks +--- ## Conclusions ### Key Takeaways +1. **Choice-Induced Preference Change:** Validated at both behavioral and neural levels. +2. **Neural Correlates:** + - The anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) track cognitive dissonance. + - The anterior ventral striatum encodes post-choice changes in preferences. +3. **Behavioral Impact:** Actions do not merely reflect preferences—they shape them. -1. Choice-induced preference change is validated at both behavioral and neural levels -2. Anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) track cognitive dissonance -3. The anterior ventral striatum encodes changes in neural representation of preferences post-choice +### Practical Implications +- Provides actionable strategies for analyzing decision-making in various fields, including marketing, education, and therapy. -* **Practical Implications**: The findings suggest actionable strategies in fields requiring decision-making analysis or interventions -* **Potential Impact**: May reshape economic theories, therapeutic models, and understanding of cognitive dissonance +### Potential Impact +- Offers a foundation to reshape economic theories, therapeutic practices, and models of cognitive dissonance. + +--- ## Abstract Summary ### In a Nutshell - -The study confirms that choices, even those inducing cognitive dissonance, can alter self-reported and neural preferences, validating the existence of choice-induced preference change using fMRI. +- This study confirms that choices, even those inducing cognitive dissonance, alter both self-reported preferences and neural representations, demonstrating choice-induced preference change through fMRI data. ### Keywords +- Cognitive Dissonance +- Neuroeconomics +- Anterior Cingulate Cortex (ACC) +- Preference Change +- Striatum -* Cognitive Dissonance -* Neuroeconomics -* Anterior Cingulate Cortex (ACC) -* Preference Change -* Striatum +--- -### Gap/Need +## Gap/Need +- **Problem Addressed:** + - Investigates whether preference changes are genuine phenomena or methodological artifacts. +- **Contribution:** + - Bridges behavioral and neural research, isolating the effects of cognitive dissonance from experimental noise. -Addresses whether preference changes are genuine or artifacts of methodology. +--- -### Innovation +## Innovation +- **Novel Perspective:** + - Integrates neural imaging and behavioral data to validate cognitive dissonance mechanisms. -Integrates behavioral data with neural imaging, isolating cognitive dissonance effects from methodological noise. +--- ## Key Quotes +1. *“Actions not only reflect, but indeed create, preferences.”* +2. *“The ACC tracks the degree of cognitive dissonance on a trial-by-trial basis.”* +3. *“Choice-induced preference changes were observed as changes in neural representation in the anterior striatum.”* -1. "Actions not only reflect, but indeed create, preferences." -2. "The ACC tracks the degree of cognitive dissonance on a trial-by-trial basis." -3. "Choice-induced preference changes were observed as changes in neural representation in the anterior striatum." +--- ## Questions and Answers -1. Can actions influence preferences? Yes, confirmed at behavioral and neural levels -2. Which brain regions correlate with cognitive dissonance? ACC and DLPFC -3. What is the role of the anterior striatum? Encodes preference changes +1. **Can actions influence preferences?** + - Yes, confirmed at both behavioral and neural levels. +2. **Which brain regions correlate with cognitive dissonance?** + - The anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC). +3. **What is the role of the anterior striatum?** + - Encodes changes in neural representation of preferences after choice. + +--- ## Paper Details ### Purpose/Objective +- **Goal:** + - Confirm the existence of choice-induced preference change and identify its neural correlates. -* **Goal**: Confirm the existence and neural correlates of choice-induced preference change -* **Research Questions**: - * Do choices change preferences? - * What neural mechanisms underlie this process? +#### Research Questions +1. Do choices actively alter preferences? +2. What neural mechanisms underlie this process? + +--- ### Methodology +- **Research Design:** + - Combination of behavioral rating tasks and fMRI imaging. +- **Participants:** + - 20 subjects aged 18–24 years. +- **Tools:** + - Functional MRI (fMRI), cognitive dissonance indices. +- **Data Analysis:** + - General linear models tested neural responses to preference and cognitive dissonance. -* **Research Design**: Combination of behavioral rating tasks and fMRI imaging -* **Participants**: 20 subjects aged 18-24 years -* **Tools**: fMRI, cognitive dissonance indices -* **Data Analysis**: General linear models tested neural responses to preference and cognitive dissonance +--- ### Main Results/Findings +- **Metrics:** + - **Behavioral:** Significant preference changes were observed post-choice. + - **Neural:** Anterior striatum activity correlated with preference changes. +- **Key Insight:** + - The ACC tracked cognitive dissonance on a trial-by-trial basis. +- **Data Availability:** + - Supplementary materials available online. -* **Metrics**: - * Behavioral: Significant preference changes post-choice - * Neural: Anterior striatum activity aligned with preference changes -* **Data Availability**: Supplementary materials available online +--- ### Limitations +1. Small sample size reduces generalizability. +2. Single experimental context limits applicability across broader populations. -* Small sample size and single experimental context limit generalizability +--- ### Proposed Future Work +1. Study cognitive dissonance in diverse contexts and populations. +2. Examine the temporal stability of neural changes linked to preference shifts. -* Explore cognitive dissonance in diverse contexts and populations +--- ## Expert Commentary -* **Critiques**: Limited participant diversity reduces broader applicability -* **Praise**: Excellent integration of behavioral and neural measures -* **Questions**: How stable are these neural changes over time? +### Critiques +- Limited participant diversity and sample size restrict the generalizability of the findings. +- The experimental context may not fully reflect real-world decision-making scenarios. + +### Praise +- Strong integration of behavioral and neural measures validates key theories of cognitive dissonance. +- Provides a clear link between neural mechanisms and behavioral outcomes. + +### Questions +1. How stable are neural changes associated with preference shifts over time? +2. What role might external factors (e.g., cultural or environmental) play in cognitive dissonance processes? \ No newline at end of file diff --git a/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-2.md b/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-2.md index 9d2f6c0..f274ef0 100644 --- a/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-2.md +++ b/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-2.md @@ -1,107 +1,144 @@ # Free Will Beliefs are Better Predicted by Dualism than Determinism Beliefs Across Different Cultures -Citation Information -• Author(s): David Wisniewski, Robert Deutschlander, John-Dylan Haynes -• Title: Free will beliefs are better predicted by dualism than determinism beliefs across different cultures -• Journal/Source: PLOS ONE -• Publication Year: September 11, 2019 -• DOI/URL: 10.1371/journal.pone.0221617 -• Affiliation: Charité-Universitätsmedizin Berlin, Ghent University, and Humboldt-Universität zu Berlin +## Citation Information +- **Author(s):** David Wisniewski, Robert Deutschlander, John-Dylan Haynes +- **Title:** *Free Will Beliefs are Better Predicted by Dualism than Determinism Beliefs Across Different Cultures* +- **Journal/Source:** *PLOS ONE* +- **Publication Year:** September 11, 2019 +- **DOI/URL:** [10.1371/journal.pone.0221617](https://doi.org/10.1371/journal.pone.0221617) +- **Affiliation:** Charité-Universitätsmedizin Berlin, Ghent University, and Humboldt-Universität zu Berlin + +--- ## Audience +- **Target Audience:** + - Philosophers, cognitive scientists, cultural psychologists, and researchers in consciousness and social behavior. +- **Application:** + - Examines the relationship between free will beliefs (FWB), dualism, and determinism across cultural contexts. +- **Outcome:** + - Enhances understanding of how cultural factors influence free will beliefs and their implications for social systems like justice. -• Target Audience: Philosophers, cognitive scientists, cultural psychologists, and researchers in consciousness and social behavior. -• Application: Exploring the relationship between free will beliefs (FWB), dualism, and determinism across cultural contexts. -• Outcome: Improved understanding of how lay beliefs about free will are influenced by cultural factors and their implications for social systems like justice. +--- ## Relevance +- **Significance:** + - Highlights that beliefs in a non-physical mind (dualism) are central to free will beliefs across diverse cultures, shifting focus from determinism. +- **Real-World Implications:** + - Reveals how dualism beliefs may impact attitudes toward responsibility, justice, and behavior in collectivist versus individualist societies. -• Significance: Shifts the focus from determinism’s influence on free will beliefs to dualism, revealing that beliefs in a non-physical mind are central to free will beliefs across cultures. -• Real-world Implications: Highlights the potential impact of dualism beliefs on attitudes toward responsibility, justice, and behavior in collectivist versus individualist societies. +--- ## Conclusions -- Takeaways: - -1. Dualism predicts free will beliefs more strongly than determinism across diverse cultures. -2. Lay beliefs about free will are logically inconsistent but culturally robust, showing significant reliance on dualistic notions. -3. Determinism beliefs vary between individualistic (e.g., the US) and collectivist cultures (e.g., Singapore), yet free will beliefs remain consistent. -4. Practical Implications: Calls for integrating dualistic beliefs into philosophical and empirical discussions on free will and responsibility. -5. Potential Impact: May influence the design of social interventions, including education, justice systems, and cross-cultural policies. +### Key Takeaways: +1. Dualism is a stronger predictor of free will beliefs than determinism across diverse cultures. +2. Lay beliefs about free will are logically inconsistent but culturally robust, heavily relying on dualistic notions. +3. Determinism beliefs vary significantly between individualistic (e.g., the US) and collectivist cultures (e.g., Singapore), but free will beliefs remain consistent. -## Contextual Insight +### Practical Implications: +- Encourages integrating dualistic beliefs into philosophical and empirical discussions on free will and responsibility. -### Abstract in a Nutshell +### Potential Impact: +- Could influence the design of social interventions, such as education systems, justice policies, and cross-cultural frameworks. -This cross-cultural study examines free will beliefs (FWB) in the United States and Singapore, using a large representative sample. It finds that dualism—not determinism—is the strongest predictor of FWB. The study highlights how FWB interact with cultural orientations like individualism and collectivism. +--- -#### Abstract Keywords +## Abstract and Contextual Insight --Free Will Beliefs +### Abstract in a Nutshell: +- A cross-cultural study conducted in the United States and Singapore shows that dualism—not determinism—is the strongest predictor of free will beliefs. It explores how free will beliefs interact with cultural orientations like individualism and collectivism. -- Dualism -- Determinism -- Cultural Psychology -- Responsibility +### Abstract Keywords: +- Free Will Beliefs +- Dualism +- Determinism +- Cultural Psychology +- Responsibility -### Gap/Need +--- -Investigates free will beliefs beyond academic debates, addressing their cultural variations and dualism’s role, which is often overlooked. +## Gap/Need +- **Critique:** + - Moves beyond academic debates to investigate cultural variations in free will beliefs and the overlooked role of dualism. -### Innovation +- **Call to Action:** + - Promotes a nuanced understanding of how cultural and philosophical factors shape beliefs about free will and responsibility. -Compares the contributions of dualism, determinism, and cultural context to free will beliefs using large, diverse samples and rigorous statistical modeling. +--- -### Key Quotes +## Innovation +- **Novel Perspective:** + - Demonstrates the cultural robustness of dualistic free will beliefs using large, diverse samples and advanced statistical methods. -1. “Believing in free will goes hand-in-hand with a belief in a non-physical mind.” -2. “The relation of free will to dualism is stronger and more consistent than to determinism.” -3. “Cross-cultural differences in determinism beliefs emerge without affecting general free will beliefs.” +--- -### Questions and Answers +## Key Quotes +1. *“Believing in free will goes hand-in-hand with a belief in a non-physical mind.”* +2. *“The relation of free will to dualism is stronger and more consistent than to determinism.”* +3. *“Cross-cultural differences in determinism beliefs emerge without affecting general free will beliefs.”* -1. What is the strongest predictor of free will beliefs? - - Dualism, or the belief in a non-physical mind +--- -2. How do cultures differ in free will beliefs? - - Belief in free will is consistent, but determinism beliefs differ, with higher acceptance in collectivist cultures like Singapore +## Questions and Answers -3. What does this imply about responsibility? +1. **What is the strongest predictor of free will beliefs?** + - Dualism, or the belief in a non-physical mind. +2. **How do cultures differ in free will beliefs?** + - Free will beliefs are consistent, but determinism beliefs vary, with higher acceptance in collectivist cultures like Singapore. +3. **What does this imply about responsibility?** + - Responsibility may differ in cultures emphasizing dualism versus determinism, affecting justice systems and social norms. -- Responsibility may be viewed differently in cultures emphasizing dualism versus determinism, affecting justice and social norms. +--- ## Paper Details -### Purpose/Objective +### Purpose/Objective: +- **Goal:** + - Identify whether dualism or determinism better predicts free will beliefs across individualist and collectivist cultures. + +#### Research Questions: +1. How do dualism and determinism beliefs correlate with free will beliefs? +2. Do these relationships differ across cultural contexts? + +--- + +### Methodology: +- **Participants:** + - 1,800 adults (900 each in the US and Singapore). +- **Tools:** + - Free Will Inventory (FWI) to measure general free will beliefs, determinism, and dualism. +- **Data Analysis:** + - Bayesian and frequentist statistical models comparing cultural and individual-level variables. + +--- + +### Main Results/Findings: +- **Metrics:** + - Dualism strongly correlated with free will beliefs (25% explained variance in Singapore). + - Determinism showed weaker and culturally variable correlations. +- **Cross-Cultural Consistency:** + - Free will beliefs remained stable across cultures despite differences in determinism beliefs. -- Goal: Identify whether dualism or determinism better predicts free will beliefs across individualist and collectivist cultures. +--- -#### Research Questions +### Limitations: +1. Limited to two countries, reducing generalizability to other cultural contexts. +2. Did not experimentally manipulate cultural or belief variables. - 1. How do dualism and determinism beliefs correlate with free will beliefs? - 2. Do these relationships differ across cultural contexts? +### Proposed Future Work: +1. Expand the study to additional cultural contexts and belief systems. +2. Investigate how dualism influences moral and legal judgments. -Methodology - • Participants: 1,800 adults (900 each in the US and Singapore). - • Tools: Free Will Inventory (FWI), measuring general free will beliefs, determinism, and dualism. - • Data Analysis: Bayesian and frequentist models comparing cultural and individual-level variables. +--- -Main Results/Findings - • Metrics: - • Strong correlation between free will beliefs and dualism (25% explained variance in Singapore). - • Determinism had weaker, culturally variable correlations. - • Cross-Cultural Consistency: - • Free will beliefs were stable across cultures despite differences in determinism. +## AutoExpert Insights and Commentary -Limitations - • Study focused only on two countries; findings may not generalize globally. - • Did not experimentally manipulate cultural or belief variables. +### Critiques: +- Future studies should integrate longitudinal designs to explore causal relationships between dualism and free will beliefs. -Proposed Future Work - • Expand to other cultures and belief systems. - • Explore how dualism influences moral and legal judgments. +### Praise: +- Cross-cultural representative sampling strengthens the study’s conclusions and relevance. -AutoExpert Insights and Commentary - • Critiques: Future studies should integrate longitudinal designs to explore causal relationships between dualism and free will beliefs. - • Praise: The cross-cultural, representative sampling strengthens the study’s conclusions and relevance. - • Questions: How do educational and religious influences shape the relationship between dualism and free will beliefs? Could these findings inform debates on consciousness in neuroscience? +### Questions: +1. How do educational and religious influences shape the relationship between dualism and free will beliefs? +2. Could these findings inform debates on consciousness and neuroscience? \ No newline at end of file diff --git a/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-3.md b/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-3.md index 21db5a8..bc65943 100644 --- a/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-3.md +++ b/External_URLs/Supporting_Literature/Markdown/Philosophy_Framework/PHIL-3.md @@ -1,100 +1,151 @@ # The Light Triad vs. Dark Triad of Personality -Citation Information - • Author(s): Scott Barry Kaufman - • Title: The Light Triad vs. Dark Triad of Personality - • Journal/Source: Scientific American Mind, Vol. 30, No. 4 (July/August 2019), pp. 20-23 - • DOI/URL: Stable URL - • Affiliation: Columbia University +## Citation Information +- **Author(s):** Scott Barry Kaufman +- **Title:** *The Light Triad vs. Dark Triad of Personality* +- **Journal/Source:** *Scientific American Mind*, Vol. 30, No. 4 (July/August 2019), pp. 20-23 +- **DOI/URL:** Stable URL +- **Affiliation:** Columbia University -Audience - • Target Audience: Psychologists, personality researchers, and educators in psychological assessment. - • Application: Provides a balanced understanding of personality traits for psychological studies, interventions, and educational programs. - • Outcome: A better grasp of both prosocial and antisocial traits in personality and their broader implications for behavior and relationships. +--- -Relevance - • Significance: Offers a new perspective on positive personality traits (the Light Triad) to counterbalance the extensive research focus on the negative traits (the Dark Triad). - • Real-world Implications: Insights into promoting prosocial behaviors, improving interpersonal relationships, and mitigating negative social impacts of the Dark Triad traits. +## Audience +- **Target Audience:** + - Psychologists, personality researchers, and educators in psychological assessment. +- **Application:** + - Provides a balanced understanding of personality traits for psychological studies, interventions, and educational programs. +- **Outcome:** + - Advances understanding of both prosocial and antisocial traits in personality and their broader implications for behavior and relationships. -Conclusions - • Takeaways: +--- - 1. The Light Triad consists of three positive traits: Kantianism, Humanism, and Faith in Humanity. - 2. The Dark Triad (narcissism, Machiavellianism, psychopathy) is linked to manipulation and self-serving behaviors, while the Light Triad emphasizes intrinsic goodness and positive social connections. - 3. While the traits oppose each other, their correlation is moderate, suggesting a mix of light and dark tendencies in most individuals. - • Practical Implications: Understanding these traits aids in psychological assessments, highlighting paths to foster kindness and diminish antisocial tendencies. - • Potential Impact: Supports initiatives for character education and therapeutic approaches that nurture prosocial traits. +## Relevance +- **Significance:** + - Introduces positive personality traits (the Light Triad) to balance the extensive focus on negative traits (the Dark Triad). +- **Real-World Implications:** + - Offers insights into promoting prosocial behaviors, improving interpersonal relationships, and mitigating the negative social impacts of Dark Triad traits. -Contextual Insight +--- -Abstract in a Nutshell +## Conclusions -This article compares the Light Triad (prosocial traits) and the Dark Triad (antisocial traits) of personality. It presents findings from a newly developed Light Triad Scale and explores how these traits influence behaviors, values, and interpersonal outcomes. +### Key Takeaways: +1. The Light Triad comprises three positive traits: **Kantianism**, **Humanism**, and **Faith in Humanity**. +2. The Dark Triad traits (narcissism, Machiavellianism, psychopathy) are linked to manipulation and self-serving behaviors, while the Light Triad emphasizes intrinsic goodness and positive social connections. +3. Though the traits oppose each other, their correlation is moderate, suggesting a spectrum of light and dark tendencies in individuals. -Abstract Keywords - • Light Triad - • Dark Triad - • Kantianism - • Humanism - • Faith in Humanity +### Practical Implications: +- Aids psychological assessments by highlighting pathways to foster kindness and reduce antisocial tendencies. -Gap/Need +### Potential Impact: +- Supports initiatives in character education and therapeutic practices aimed at nurturing prosocial traits. -This study fills the gap in personality research by focusing on positive prosocial traits, which have been relatively understudied compared to the Dark Triad. +--- -Innovation +## Abstract and Contextual Insight -Introduces the Light Triad Scale, a tool for measuring prosocial traits, and contrasts it against the well-established Dark Triad framework. +### Abstract in a Nutshell: +- The article contrasts the Light Triad (prosocial traits) with the Dark Triad (antisocial traits), introducing the Light Triad Scale as a tool for measuring traits like compassion, trust, and altruism. It explores how these traits influence behavior, values, and interpersonal outcomes. -Key Quotes +### Abstract Keywords: +- Light Triad +- Dark Triad +- Kantianism +- Humanism +- Faith in Humanity - 1. “The Light Triad emphasizes kindness and a belief in human goodness, contrasting with the manipulative and self-serving nature of the Dark Triad.” - 2. “The Light Triad traits—Kantianism, Humanism, and Faith in Humanity—are distinct, yet together reflect a fundamentally positive worldview.” - 3. “Our research suggests that while the Dark Triad is tied to power and self-enhancement, the Light Triad aligns with compassion, acceptance, and gratitude.” +--- -Questions and Answers +## Gap/Need +- **Critique:** + - Fills a gap in personality research by emphasizing prosocial traits, which have been relatively understudied compared to the Dark Triad. - 1. What are the components of the Light Triad? - • Kantianism (treating others as ends, not means), Humanism (valuing individual dignity), and Faith in Humanity (believing in human goodness). - 2. How are the Light and Dark Triads related? - • They are moderately negatively correlated, indicating a spectrum of traits within individuals. - 3. What practical uses does the Light Triad Scale have? - • It helps measure prosocial tendencies and can inform interventions to foster positive behaviors. +- **Call to Action:** + - Encourages a balanced understanding of personality traits to inform interventions and foster prosocial behavior. -Paper Details +--- -Purpose/Objective - • Goal: Introduce and validate the Light Triad Scale and compare it to the Dark Triad, providing a fuller picture of personality traits. - • Research Questions: +## Innovation +- **Novel Perspective:** + - Introduces the Light Triad Scale, a validated measure of prosocial traits, contrasting it with the well-established Dark Triad framework. - 1. What traits comprise the Light Triad? - 2. How does the Light Triad correlate with personality and behavioral outcomes? - 3. Can the Light Triad Scale reliably assess prosocial personality traits? +--- -Methodology - • Data Collection: Surveys with thousands of participants across diverse demographics. - • Measures: - • Light Triad Scale: Items related to forgiveness, trust, honesty, and altruism. - • Correlations with Dark Triad traits and other personality measures. - • Analysis: Statistical evaluations, including factor analysis, to validate the Light Triad Scale. +## Key Quotes +1. *“The Light Triad emphasizes kindness and a belief in human goodness, contrasting with the manipulative and self-serving nature of the Dark Triad.”* +2. *“The Light Triad traits—Kantianism, Humanism, and Faith in Humanity—are distinct, yet together reflect a fundamentally positive worldview.”* +3. *“Our research suggests that while the Dark Triad is tied to power and self-enhancement, the Light Triad aligns with compassion, acceptance, and gratitude.”* -Main Results/Findings - • Metrics: - • The Light Triad shows positive correlations with traits like compassion, openness, and life satisfaction. - • The Dark Triad aligns with power motives, narcissism, and utilitarian ethics. - • Balance Score: - • Most individuals lean towards the Light Triad, with extreme Dark Triad tendencies being rare. - • Applications: Findings inform psychological and educational practices aimed at cultivating prosocial behavior. +--- -Limitations - • The study is exploratory, requiring further validation of the Light Triad Scale. - • The sample may not represent extreme populations (e.g., clinical groups). +## Questions and Answers -Proposed Future Work - • Examine interactions between Light and Dark Triad traits in social dynamics. - • Investigate how these traits influence long-term life outcomes. +1. **What are the components of the Light Triad?** + - **Kantianism** (treating others as ends, not means), **Humanism** (valuing individual dignity), and **Faith in Humanity** (believing in human goodness). +2. **How are the Light and Dark Triads related?** + - They are moderately negatively correlated, indicating a spectrum of traits within individuals. +3. **What practical uses does the Light Triad Scale have?** + - Measures prosocial tendencies, informing interventions to foster positive behaviors. -AutoExpert Insights and Commentary - • Critiques: The Light Triad Scale, while promising, needs more extensive validation in diverse contexts. Further studies could explore the interaction between Light and Dark Triad traits in complex social settings. - • Praise: The paper provides a refreshing perspective by focusing on positive aspects of personality, offering valuable tools for fostering kindness and social harmony. - • Questions: Could Light Triad traits predict resilience in challenging environments? How might cultural factors influence the balance between Light and Dark Triad tendencies? +--- + +## Paper Details + +### Purpose/Objective: +- **Goal:** + - Introduce and validate the Light Triad Scale, comparing it with the Dark Triad to provide a fuller picture of personality traits. + +#### Research Questions: +1. What traits comprise the Light Triad? +2. How does the Light Triad correlate with personality and behavioral outcomes? +3. Can the Light Triad Scale reliably assess prosocial personality traits? + +--- + +### Methodology: +- **Data Collection:** + - Surveys conducted with thousands of participants from diverse demographics. +- **Measures:** + - **Light Triad Scale:** Items assessing traits like forgiveness, trust, honesty, and altruism. + - **Comparisons:** Correlations with Dark Triad traits and other personality measures. +- **Analysis:** + - Statistical evaluations, including factor analysis, to validate the Light Triad Scale. + +--- + +### Main Results/Findings: +- **Metrics:** + - The Light Triad is positively correlated with compassion, openness, and life satisfaction. + - The Dark Triad is aligned with power motives, narcissism, and utilitarian ethics. +- **Balance Score:** + - Most individuals lean towards the Light Triad, with extreme Dark Triad tendencies being rare. +- **Applications:** + - Informs psychological and educational practices to cultivate prosocial behavior. + +--- + +### Limitations: +1. The study is exploratory and requires further validation of the Light Triad Scale. +2. The sample may not represent extreme populations (e.g., clinical groups). + +--- + +### Proposed Future Work: +1. Examine how Light and Dark Triad traits interact in social dynamics. +2. Investigate the influence of these traits on long-term life outcomes. + +--- + +## AutoExpert Insights and Commentary + +### Critiques: +- The Light Triad Scale shows promise but needs further validation in diverse contexts. +- Future research should explore the interaction of Light and Dark Triad traits in complex social settings. + +### Praise: +- Provides a refreshing perspective by focusing on positive aspects of personality. +- Offers valuable tools for promoting kindness and social harmony. + +### Questions: +1. Could Light Triad traits predict resilience in challenging environments? +2. How might cultural factors influence the balance between Light and Dark Triad tendencies? \ No newline at end of file diff --git a/README.md b/README.md index 2ff44a6..e39030a 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ -
- # 🧠 The Neural Architecture of Truth & Deception Project -## Last Update - 01.07.25 +

+ +## Last Update - 01.11.25 [![Project Status](https://img.shields.io/badge/Status-Active-success?style=for-the-badge)](CHANGELOG.md) -[![Last Update](https://img.shields.io/badge/Updated-01.07.24-blue?style=for-the-badge)](CHANGELOG.md) +[![Last Update](https://img.shields.io/badge/Updated-01.11.25-blue?style=for-the-badge)](CHANGELOG.md) [![License](https://img.shields.io/badge/License-MIT-yellow?style=for-the-badge)](LICENSE) [![Python](https://img.shields.io/badge/Python-76.5%25-3776AB?style=flat-square&logo=python)](Scripts/) diff --git a/Scripts/CustomGPT_Scripts/Python/Dictionaries/dictionaries_v2.py b/Scripts/CustomGPT_Scripts/Python/Dictionaries/dictionaries_v2.py new file mode 100644 index 0000000..2e88c9f --- /dev/null +++ b/Scripts/CustomGPT_Scripts/Python/Dictionaries/dictionaries_v2.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Subject Classification Script for GPT Knowledge Base Integration + +This script classifies user prompts into subjects, leveraging keyword-based detection +across multiple knowledge domains (e.g., Astronomy & Physics, Neuroscience & Psychology, etc.). +It is designed to be integrated into a GPT-based system that references an external +subjects.csv file or any other knowledge base files for more nuanced handling. + +Features: +1. Robust keyword detection for each subject category. +2. Immediate subject matching upon the first occurrence of relevant keywords. +3. Extended commentary and docstrings for maintainability. +4. Example usage demonstrating basic input prompts and subject classification. + +Note: +- Modify and expand 'SUBJECT_DICTIONARIES' to align with your CSV or any external references. +- This script stops after matching the first subject with relevant keywords. + If you prefer multiple subject matches, adjust the logic accordingly. +""" + +import re + +# --------------------------------------------------------------------- +# 1. SUBJECT DICTIONARIES +# --------------------------------------------------------------------- +# In production, you may load these from a CSV or database. Here, we provide +# a static reference dictionary for demonstration. Adjust as needed. +SUBJECT_DICTIONARIES = { + "astronomy_physics": { + "keywords": [ + "planet", "star", "galaxy", "black hole", "quantum", "relativity", + "gravitational", "space", "astronomy", "physics", "cosmos", "orbit" + ], + "description": "Astronomy and Physics" + }, + "neuroscience_psychology": { + "keywords": [ + "brain", "neuron", "cognition", "behavior", "psychology", + "neuroscience", "memory", "emotion", "mental", "mind", "perception" + ], + "description": "Neuroscience and Psychology" + }, + "history_geography": { + "keywords": [ + "history", "geography", "war", "empire", "civilization", "dynasty", + "exploration", "continent", "culture", "archaeology", "migration", "map" + ], + "description": "History and Geography" + }, + "literature_grammar": { + "keywords": [ + "poetry", "prose", "grammar", "syntax", "novel", "author", + "rhetoric", "metaphor", "language", "literature", "verse", "essay" + ], + "description": "Literature and Grammar" + }, + "mathematics_geometry": { + "keywords": [ + "algebra", "geometry", "calculus", "equation", "theorem", + "proof", "trigonometry", "vector", "integral", "derivative", + "shape", "angle" + ], + "description": "Mathematics and Geometry" + } +} + + +# --------------------------------------------------------------------- +# 2. ASSESS INPUT +# --------------------------------------------------------------------- +def assess_input(prompt): + """ + Takes a user prompt (string) and returns a dictionary with the matched subject, + the subject's description, and any keywords found in that prompt. + + :param prompt: str, The user-provided question or statement. + :return: dict, Contains: + { + "subject": str or None, + "description": str, + "keywords": list of str + } + """ + normalized_prompt = prompt.lower() + + # By default, no subject is matched + matched_subject = None + matched_keywords = [] + + # Check each subject for keyword matches + for subject, data in SUBJECT_DICTIONARIES.items(): + # For each keyword, use a regex word boundary check + matches = [ + word + for word in data["keywords"] + # Example: re.search(r'\bgeometry\b', prompt) + if re.search(r'\b' + re.escape(word.lower()) + r'\b', normalized_prompt) + ] + + # If we find any matches, set the subject + if matches: + matched_subject = subject + matched_keywords = matches + break # Stop after the first subject match + + if matched_subject: + return { + "subject": matched_subject, + "description": SUBJECT_DICTIONARIES[matched_subject]["description"], + "keywords": matched_keywords + } + + # If no matches found at all + return { + "subject": None, + "description": "No relevant subject identified", + "keywords": [] + } + + +# --------------------------------------------------------------------- +# 3. EXAMPLE USAGE +# --------------------------------------------------------------------- +if __name__ == "__main__": + sample_prompts = [ + "What is the theory of relativity?", + "Can you explain the structure of a neuron?", + "Tell me about the Roman Empire's influence on modern culture.", + "What are some examples of metaphors in Shakespeare's plays?", + "How do you calculate the area of a triangle?", + "Which star is nearest to Earth?" + ] + + for prompt in sample_prompts: + result = assess_input(prompt) + print(f"Prompt: {prompt}") + print(f"Matched Subject: {result['subject']}") + print(f"Description: {result['description']}") + print(f"Keywords Found: {result['keywords']}") + print("-" * 70) \ No newline at end of file diff --git a/Scripts/CustomGPT_Scripts/Python/dictionaries_v1.py b/Scripts/CustomGPT_Scripts/Python/dictionaries_v1.py new file mode 100644 index 0000000..6426c21 --- /dev/null +++ b/Scripts/CustomGPT_Scripts/Python/dictionaries_v1.py @@ -0,0 +1,91 @@ +import re + +# Define keyword dictionaries for each subject +subject_dictionaries = { + "astronomy_physics": { + "keywords": [ + "planet", "star", "galaxy", "black hole", "quantum", "relativity", + "gravitational", "space", "astronomy", "physics", "cosmos", "orbit" + ], + "description": "Astronomy and Physics" + }, + "neuroscience_psychology": { + "keywords": [ + "brain", "neuron", "cognition", "behavior", "psychology", + "neuroscience", "memory", "emotion", "mental", "mind", "perception" + ], + "description": "Neuroscience and Psychology" + }, + "history_geography": { + "keywords": [ + "history", "geography", "war", "empire", "civilization", "dynasty", + "exploration", "continent", "culture", "archaeology", "migration", "map" + ], + "description": "History and Geography" + }, + "literature_grammar": { + "keywords": [ + "poetry", "prose", "grammar", "syntax", "novel", "author", + "rhetoric", "metaphor", "language", "literature", "verse", "essay" + ], + "description": "Literature and Grammar" + }, + "mathematics_geometry": { + "keywords": [ + "algebra", "geometry", "calculus", "equation", "theorem", + "proof", "trigonometry", "vector", "integral", "derivative", "shape", "angle" + ], + "description": "Mathematics and Geometry" + } +} + +# Function to assess input and select the appropriate subject +def assess_input(prompt): + # Normalize and tokenize the input + prompt = prompt.lower() + matched_subject = None + matched_keywords = [] + + # Iterate through each subject's dictionary and match keywords + for subject, data in subject_dictionaries.items(): + keywords = data["keywords"] + matches = [word for word in keywords if re.search(r'\b' + re.escape(word) + r'\b', prompt)] + + if matches: + matched_subject = subject + matched_keywords = matches + break + + # If no subject matched, return default response + if not matched_subject: + return { + "subject": None, + "description": "No relevant subject identified", + "keywords": [] + } + + # Return subject and matching details + return { + "subject": matched_subject, + "description": subject_dictionaries[matched_subject]["description"], + "keywords": matched_keywords + } + +# Example usage +if __name__ == "__main__": + # Sample prompts to test + sample_prompts = [ + "What is the theory of relativity?", + "Can you explain the structure of a neuron?", + "Tell me about the Roman Empire's influence on modern culture.", + "What are some examples of metaphors in Shakespeare's plays?", + "How do you calculate the area of a triangle?" + ] + + # Process each prompt and display results + for prompt in sample_prompts: + result = assess_input(prompt) + print(f"Prompt: {prompt}") + print(f"Subject: {result['description']}") + print(f"Keywords Found: {result['keywords']}") + print("-" * 50) \ No newline at end of file diff --git a/Scripts/CustomGPT_Scripts/tests/test_prompts.py b/Scripts/CustomGPT_Scripts/tests/test_prompts.py new file mode 100644 index 0000000..d490f34 --- /dev/null +++ b/Scripts/CustomGPT_Scripts/tests/test_prompts.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +test_prompts.py + +Simple tests for verifying prompt creation logic in PromptGenerator. +Run with pytest or another test framework. + +Usage: + pytest test_prompts.py +""" + +import pytest +from ..prompt_engineering.prompt_generator import PromptGenerator + +def test_generate_prompt(): + pg = PromptGenerator(model="gpt-4") + prompt = pg.generate_prompt("truth_scenario_1", "Additional user context.") + assert isinstance(prompt, str) + assert "Honest Investigator" in prompt \ No newline at end of file diff --git a/Scripts/CustomGPT_Scripts/utils/api_wrapper.py b/Scripts/CustomGPT_Scripts/utils/api_wrapper.py new file mode 100644 index 0000000..e21fe93 --- /dev/null +++ b/Scripts/CustomGPT_Scripts/utils/api_wrapper.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +api_wrapper.py + +Provides a higher-level interface around the OpenAI API calls. +Uses environment variables for API keys and model parameters. + +Usage: + from api_wrapper import ApiWrapper + + api = ApiWrapper() + response = api.send_prompt("Hello, GPT!") + print(response) +""" + +import os +import openai +from .config_manager import ConfigManager +from ..experimental.rate_limiter import RateLimiter +from ..experimental.token_manager import TokenManager + +class ApiWrapper: + def __init__(self): + self.config = ConfigManager() + self.rate_limiter = RateLimiter() + self.token_manager = TokenManager() + openai.api_key = self.config.get("OPENAI_API_KEY") + self.model = self.config.get("DEFAULT_MODEL", "gpt-4") + self.temperature = float(self.config.get("TEMPERATURE", "0.7")) + self.max_tokens = int(self.config.get("MAX_TOKENS", "2000")) + + def send_prompt(self, prompt, **kwargs): + """ + Sends a prompt to the OpenAI API using default or custom parameters. + """ + self.rate_limiter.wait_for_slot() + try: + response = openai.ChatCompletion.create( + model=kwargs.get("model", self.model), + messages=[{"role": "user", "content": prompt}], + max_tokens=kwargs.get("max_tokens", self.max_tokens), + temperature=kwargs.get("temperature", self.temperature), + n=1 + ) + usage = response["usage"]["total_tokens"] + self.token_manager.record_token_usage(usage) + + return response["choices"][0]["message"]["content"] + except Exception as e: + # Basic error handling; more robust logic can be delegated to error_handler + print(f"OpenAI API Error: {e}") + return None \ No newline at end of file diff --git a/Scripts/CustomGPT_Scripts/utils/config_manager.py b/Scripts/CustomGPT_Scripts/utils/config_manager.py new file mode 100644 index 0000000..8ac6f61 --- /dev/null +++ b/Scripts/CustomGPT_Scripts/utils/config_manager.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +config_manager.py + +Manages configuration settings, including environment variable loading +and fallback to defaults. +""" + +import os +from dotenv import load_dotenv + +class ConfigManager: + def __init__(self, env_file=".env"): + load_dotenv(env_file) + + def get(self, key, default=None): + """ + Retrieve configuration value by key from environment variables. + """ + return os.getenv(key, default) \ No newline at end of file