Upload VAD Paper 01 scripts, results, and computational artifacts (part 3)

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Dominance/Dominance/Dominance_metadata.json

@@ -0,0 +1,57 @@
+{
+    "dependent_variable": "IA_REGRESSION_PATH_DURATION",
+    "dependent_variable_label": "3-GPT: Go-Past Time",
+    "model": "Dominance",
+    "model_type": "main",
+    "effect": "Dominance",
+    "effect_type": "main_effect",
+    "predictor": "Dominance",
+    "predictor_label": "Dominance",
+    "moderator": null,
+    "moderator_label": null,
+    "formula": "IA_REGRESSION_PATH_DURATION ~ Dominance + Length + Predictability + Frequency",
+    "prediction_method": "Fixed effects only",
+    "confidence_interval": 0.95,
+    "confidence_interval_type": "Fixed-effect mean confidence interval",
+    "duration_transformation": "Natural log",
+    "back_transformation": "exp(log fitted value)",
+    "back_transformed_value_interpretation": "Back-transformed fitted duration; no lognormal bias correction applied",
+    "percent_change_reference": "All standardized lexical and affective predictors held at z = 0",
+    "y_axis": "percent",
+    "plot_settings": {
+        "figure_size": [
+            7.5,
+            5.5
+        ],
+        "line_width": 2.5,
+        "confidence_alpha": 0.25,
+        "prediction_points": 101,
+        "default_z_range": [
+            -2,
+            2
+        ],
+        "moderator_levels": [
+            -1,
+            1
+        ]
+    },
+    "axis_limits": {
+        "x": [
+            -2.2,
+            2.2
+        ],
+        "y": [
+            -2.312179937663543,
+            2.3571107155212974
+        ]
+    },
+    "software": {
+        "python": "3.11.14",
+        "numpy": "2.4.2",
+        "pandas": "3.0.1",
+        "matplotlib": "3.10.8",
+        "patsy": "1.0.2"
+    },
+    "figure_version": "1.0.0",
+    "generated_on": "2026-08-26T16:07:58"
+}

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Dominance/Dominance/Dominance_prediction_grid.csv

@@ -0,0 +1,102 @@
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05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Dominance/Dominance/README.txt

@@ -0,0 +1,66 @@
+Effect
+------
+Dominance
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Dominance
+
+Model Type
+----------
+main
+
+Predictor
+---------
+Dominance
+
+Moderator
+---------
+None
+
+Formula
+-------
+IA_REGRESSION_PATH_DURATION ~ Dominance + Length + Predictability + Frequency
+
+Prediction Method
+-----------------
+Fixed effects only
+
+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
+
+Conditioning
+------------
+All non-focal standardized lexical and affective predictors
+are held at z = 0.
+
+Duration Scale
+--------------
+The LME dependent variable was modeled on the natural-log scale.
+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
+but may depend on the Python/Matplotlib environment.
+
+Generated
+---------
+2026-08-26T16:07:58

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Arousal/Arousal.fig.pkl

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+size 142354

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Arousal/Arousal_metadata.json

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+{
+    "dependent_variable": "IA_REGRESSION_PATH_DURATION",
+    "dependent_variable_label": "3-GPT: Go-Past Time",
+    "model": "Full Additive",
+    "model_type": "full_additive",
+    "effect": "Arousal",
+    "effect_type": "main_effect",
+    "predictor": "Arousal",
+    "predictor_label": "Arousal",
+    "moderator": null,
+    "moderator_label": null,
+    "formula": "IA_REGRESSION_PATH_DURATION ~ Valence + Arousal + Dominance + Length + Predictability + Frequency",
+    "prediction_method": "Fixed effects only",
+    "confidence_interval": 0.95,
+    "confidence_interval_type": "Fixed-effect mean confidence interval",
+    "duration_transformation": "Natural log",
+    "back_transformation": "exp(log fitted value)",
+    "back_transformed_value_interpretation": "Back-transformed fitted duration; no lognormal bias correction applied",
+    "percent_change_reference": "All standardized lexical and affective predictors held at z = 0",
+    "y_axis": "percent",
+    "plot_settings": {
+        "figure_size": [
+            7.5,
+            5.5
+        ],
+        "line_width": 2.5,
+        "confidence_alpha": 0.25,
+        "prediction_points": 101,
+        "default_z_range": [
+            -2,
+            2
+        ],
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+    "software": {
+        "python": "3.11.14",
+        "numpy": "2.4.2",
+        "pandas": "3.0.1",
+        "matplotlib": "3.10.8",
+        "patsy": "1.0.2"
+    },
+    "figure_version": "1.0.0",
+    "generated_on": "2026-08-26T16:07:59"
+}

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Arousal/Arousal_prediction_grid.csv

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05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Arousal/README.txt

@@ -0,0 +1,66 @@
+Effect
+------
+Arousal
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Full Additive
+
+Model Type
+----------
+full_additive
+
+Predictor
+---------
+Arousal
+
+Moderator
+---------
+None
+
+Formula
+-------
+IA_REGRESSION_PATH_DURATION ~ Valence + Arousal + Dominance + Length + Predictability + Frequency
+
+Prediction Method
+-----------------
+Fixed effects only
+
+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
+
+Conditioning
+------------
+All non-focal standardized lexical and affective predictors
+are held at z = 0.
+
+Duration Scale
+--------------
+The LME dependent variable was modeled on the natural-log scale.
+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
+but may depend on the Python/Matplotlib environment.
+
+Generated
+---------
+2026-08-26T16:07:59

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Dominance/Dominance.fig.pkl

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+size 142356

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Dominance/Dominance_metadata.json

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+{
+    "dependent_variable": "IA_REGRESSION_PATH_DURATION",
+    "dependent_variable_label": "3-GPT: Go-Past Time",
+    "model": "Full Additive",
+    "model_type": "full_additive",
+    "effect": "Dominance",
+    "effect_type": "main_effect",
+    "predictor": "Dominance",
+    "predictor_label": "Dominance",
+    "moderator": null,
+    "moderator_label": null,
+    "formula": "IA_REGRESSION_PATH_DURATION ~ Valence + Arousal + Dominance + Length + Predictability + Frequency",
+    "prediction_method": "Fixed effects only",
+    "confidence_interval": 0.95,
+    "confidence_interval_type": "Fixed-effect mean confidence interval",
+    "duration_transformation": "Natural log",
+    "back_transformation": "exp(log fitted value)",
+    "back_transformed_value_interpretation": "Back-transformed fitted duration; no lognormal bias correction applied",
+    "percent_change_reference": "All standardized lexical and affective predictors held at z = 0",
+    "y_axis": "percent",
+    "plot_settings": {
+        "figure_size": [
+            7.5,
+            5.5
+        ],
+        "line_width": 2.5,
+        "confidence_alpha": 0.25,
+        "prediction_points": 101,
+        "default_z_range": [
+            -2,
+            2
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+    "software": {
+        "python": "3.11.14",
+        "numpy": "2.4.2",
+        "pandas": "3.0.1",
+        "matplotlib": "3.10.8",
+        "patsy": "1.0.2"
+    },
+    "figure_version": "1.0.0",
+    "generated_on": "2026-08-26T16:07:59"
+}

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Dominance/Dominance_prediction_grid.csv

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05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Dominance/README.txt

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+Effect
+------
+Dominance
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Full Additive
+
+Model Type
+----------
+full_additive
+
+Predictor
+---------
+Dominance
+
+Moderator
+---------
+None
+
+Formula
+-------
+IA_REGRESSION_PATH_DURATION ~ Valence + Arousal + Dominance + Length + Predictability + Frequency
+
+Prediction Method
+-----------------
+Fixed effects only
+
+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
+
+Conditioning
+------------
+All non-focal standardized lexical and affective predictors
+are held at z = 0.
+
+Duration Scale
+--------------
+The LME dependent variable was modeled on the natural-log scale.
+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
+but may depend on the Python/Matplotlib environment.
+
+Generated
+---------
+2026-08-26T16:07:59

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Valence/README.txt

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+Effect
+------
+Valence
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Full Additive
+
+Model Type
+----------
+full_additive
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+Predictor
+---------
+Valence
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+Moderator
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+None
+
+Formula
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+IA_REGRESSION_PATH_DURATION ~ Valence + Arousal + Dominance + Length + Predictability + Frequency
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+Prediction Method
+-----------------
+Fixed effects only
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+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
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+Conditioning
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+Duration Scale
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+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
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+
+Generated
+---------
+2026-08-26T16:07:58

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Valence/Valence.fig.pkl

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+}

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Additive/Valence/Valence_prediction_grid.csv

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05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Arousal/README.txt

@@ -0,0 +1,66 @@
+Effect
+------
+Arousal
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Full Factorial
+
+Model Type
+----------
+factorial
+
+Predictor
+---------
+Arousal
+
+Moderator
+---------
+None
+
+Formula
+-------
+IA_REGRESSION_PATH_DURATION ~ Valence * Arousal * Dominance + Length + Predictability + Frequency
+
+Prediction Method
+-----------------
+Fixed effects only
+
+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
+
+Conditioning
+------------
+All non-focal standardized lexical and affective predictors
+are held at z = 0.
+
+Duration Scale
+--------------
+The LME dependent variable was modeled on the natural-log scale.
+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
+but may depend on the Python/Matplotlib environment.
+
+Generated
+---------
+2026-08-26T16:08:00

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Dominance/Dominance.fig.pkl

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+size 126813

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Dominance/Dominance_metadata.json

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+{
+    "dependent_variable": "IA_REGRESSION_PATH_DURATION",
+    "dependent_variable_label": "3-GPT: Go-Past Time",
+    "model": "Full Factorial",
+    "model_type": "factorial",
+    "effect": "Dominance",
+    "effect_type": "main_effect",
+    "predictor": "Dominance",
+    "predictor_label": "Dominance",
+    "moderator": null,
+    "moderator_label": null,
+    "formula": "IA_REGRESSION_PATH_DURATION ~ Valence * Arousal * Dominance + Length + Predictability + Frequency",
+    "prediction_method": "Fixed effects only",
+    "confidence_interval": 0.95,
+    "confidence_interval_type": "Fixed-effect mean confidence interval",
+    "duration_transformation": "Natural log",
+    "back_transformation": "exp(log fitted value)",
+    "back_transformed_value_interpretation": "Back-transformed fitted duration; no lognormal bias correction applied",
+    "percent_change_reference": "All standardized lexical and affective predictors held at z = 0",
+    "y_axis": "percent",
+    "plot_settings": {
+        "figure_size": [
+            7.5,
+            5.5
+        ],
+        "line_width": 2.5,
+        "confidence_alpha": 0.25,
+        "prediction_points": 101,
+        "default_z_range": [
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+        "numpy": "2.4.2",
+        "pandas": "3.0.1",
+        "matplotlib": "3.10.8",
+        "patsy": "1.0.2"
+    },
+    "figure_version": "1.0.0",
+    "generated_on": "2026-08-26T16:08:00"
+}

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Dominance/Dominance_prediction_grid.csv

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05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Dominance/README.txt

@@ -0,0 +1,66 @@
+Effect
+------
+Dominance
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Full Factorial
+
+Model Type
+----------
+factorial
+
+Predictor
+---------
+Dominance
+
+Moderator
+---------
+None
+
+Formula
+-------
+IA_REGRESSION_PATH_DURATION ~ Valence * Arousal * Dominance + Length + Predictability + Frequency
+
+Prediction Method
+-----------------
+Fixed effects only
+
+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
+
+Conditioning
+------------
+All non-focal standardized lexical and affective predictors
+are held at z = 0.
+
+Duration Scale
+--------------
+The LME dependent variable was modeled on the natural-log scale.
+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
+but may depend on the Python/Matplotlib environment.
+
+Generated
+---------
+2026-08-26T16:08:00

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Valence/README.txt

@@ -0,0 +1,66 @@
+Effect
+------
+Valence
+
+Effect Type
+-----------
+main_effect
+
+Dependent Variable
+------------------
+3-GPT: Go-Past Time
+
+Model
+-----
+Full Factorial
+
+Model Type
+----------
+factorial
+
+Predictor
+---------
+Valence
+
+Moderator
+---------
+None
+
+Formula
+-------
+IA_REGRESSION_PATH_DURATION ~ Valence * Arousal * Dominance + Length + Predictability + Frequency
+
+Prediction Method
+-----------------
+Fixed effects only
+
+Confidence Interval
+-------------------
+95% confidence interval for the estimated fixed-effect mean
+
+Conditioning
+------------
+All non-focal standardized lexical and affective predictors
+are held at z = 0.
+
+Duration Scale
+--------------
+The LME dependent variable was modeled on the natural-log scale.
+Displayed millisecond values are exponentiated fitted values and
+are not lognormal bias-corrected arithmetic mean durations.
+
+Percent-Change Reference
+------------------------
+Model prediction with all standardized lexical and affective
+predictors held at z = 0.
+
+Reproducibility
+---------------
+The CSV prediction grid and JSON metadata are the primary
+machine-readable visualization records. PDF and SVG are durable
+vector outputs. The .fig.pkl file is provided for convenience
+but may depend on the Python/Matplotlib environment.
+
+Generated
+---------
+2026-08-26T16:07:59

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Valence/Valence.fig.pkl

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+size 126811

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Valence/Valence_metadata.json

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+{
+    "dependent_variable": "IA_REGRESSION_PATH_DURATION",
+    "dependent_variable_label": "3-GPT: Go-Past Time",
+    "model": "Full Factorial",
+    "model_type": "factorial",
+    "effect": "Valence",
+    "effect_type": "main_effect",
+    "predictor": "Valence",
+    "predictor_label": "Valence",
+    "moderator": null,
+    "moderator_label": null,
+    "formula": "IA_REGRESSION_PATH_DURATION ~ Valence * Arousal * Dominance + Length + Predictability + Frequency",
+    "prediction_method": "Fixed effects only",
+    "confidence_interval": 0.95,
+    "confidence_interval_type": "Fixed-effect mean confidence interval",
+    "duration_transformation": "Natural log",
+    "back_transformation": "exp(log fitted value)",
+    "back_transformed_value_interpretation": "Back-transformed fitted duration; no lognormal bias correction applied",
+    "percent_change_reference": "All standardized lexical and affective predictors held at z = 0",
+    "y_axis": "percent",
+    "plot_settings": {
+        "figure_size": [
+            7.5,
+            5.5
+        ],
+        "line_width": 2.5,
+        "confidence_alpha": 0.25,
+        "prediction_points": 101,
+        "default_z_range": [
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+            2
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+        "moderator_levels": [
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+    "axis_limits": {
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+        "y": [
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+    "software": {
+        "python": "3.11.14",
+        "numpy": "2.4.2",
+        "pandas": "3.0.1",
+        "matplotlib": "3.10.8",
+        "patsy": "1.0.2"
+    },
+    "figure_version": "1.0.0",
+    "generated_on": "2026-08-26T16:07:59"
+}

05-Research_Program-Affective_Provo/01-Fixation_Duration_Research/VAD-Paper_01-Interaction_Within_Affect/02-Results/05-LME-Effect-Plots/01-Conditional-Main-Effects/3-GPT/Full_Factorial/Valence/Valence_prediction_grid.csv

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