sweetbean.stimulus.DefaultCategoryLearning

DefaultCategoryLearning

Bases: HtmlKeyboardResponse

Category-learning stimulus based on a binary feature vector.

Feature order

0 shape (0 circle, 1 triangle) 1 color (0 orange #E69F00, 1 blue #0072B2) 2 size (0 small, 1 large) 3 border (0 none, 1 pink #CC79A7) 4 pattern (0 filled, 1 striped) 5 inner_mark (0 none, 1 star)

Source code in sweetbean/stimulus/DefaultCategoryLearning.py

class DefaultCategoryLearning(HtmlKeyboardResponse):
    """
    Category-learning stimulus based on a binary feature vector.

    Feature order:
      0 shape      (0 circle, 1 triangle)
      1 color      (0 orange #E69F00, 1 blue #0072B2)
      2 size       (0 small, 1 large)
      3 border     (0 none, 1 pink #CC79A7)
      4 pattern    (0 filled, 1 striped)
      5 inner_mark (0 none, 1 star)
    """

    l_template = "You see a {{ feature_description }}."

    def __init__(
        self,
        duration=None,
        feature_vector=None,
        choices=None,
        correct_key="",
        prompt_template=None,
        response_template=None,
        side_effects=None,
    ):
        if feature_vector is None:
            raise ValueError("feature_vector is required for DefaultCategoryLearning.")
        # TimelineVariable / other Variables are resolved at runtime; validate then.
        if isinstance(feature_vector, list):
            _validate_feature_vector(feature_vector)
        elif not isinstance(feature_vector, Variable):
            raise ValueError(
                "feature_vector must be a list of 0/1 values or a SweetBean Variable "
                "(e.g. TimelineVariable('feature_vector'))."
            )
        stimulus = FunctionVariable(
            "default_category_learning_stimulus",
            _feature_vector_to_html,
            [feature_vector],
        )
        feature_description = FunctionVariable(
            "default_category_learning_description",
            _feature_vector_to_description,
            [feature_vector],
        )
        super().__init__(
            duration=duration,
            stimulus=stimulus,
            choices=choices,
            correct_key=correct_key,
            side_effects=side_effects,
        )
        if prompt_template is not None:
            self.l_template = prompt_template
        if response_template is not None:
            self.response_template = response_template
        self.arg.update(
            {
                "feature_vector": feature_vector,
                "feature_description": feature_description,
            }
        )