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Interactive Task Gallery

Try each bead task interface below. Every demo is a live jsPsych experiment running in your browser. Examples use stimuli from psycholinguistics research on acceptability, veridicality, semantic proto-roles, event typicality, and telicity.

Judgment Tasks

Likert Rating Scale

Rate a sentence on a discrete scale with labeled endpoints. This example asks about the naturalness of the verb hope in an NP-to-VP raising frame.

from bead.items.ordinal_scale import create_ordinal_scale_item

item = create_ordinal_scale_item(
    text="Someone hoped someone to leave.",
    prompt="How natural is this sentence?",
    scale_bounds=(1, 7),
    scale_labels={
        1: "Extremely unnatural",
        7: "Totally natural",
    },
)

{
  "type": "bead-rating",
  "prompt": "How natural is this sentence?",
  "stimulus": "Someone hoped someone to leave.",
  "scale_min": 1,
  "scale_max": 7,
  "scale_labels": {
    "1": "Extremely unnatural",
    "7": "Totally natural"
  },
  "metadata": {"verb": "hope", "frame": "NP_to_VP"}
}

Slider Rating

Continuous rating on a slider scale. This example asks how prototypical an event is.

from bead.items.ordinal_scale import create_ordinal_scale_item

item = create_ordinal_scale_item(
    text="The chef cooked the meal.",
    prompt="How prototypical is this event?",
    scale_bounds=(0, 100),
    scale_labels={
        0: "Very atypical",
        100: "Very prototypical",
    },
)

{
  "type": "bead-slider-rating",
  "prompt": "How prototypical is this event?",
  "stimulus": "The chef cooked the meal.",
  "slider_min": 0,
  "slider_max": 100,
  "slider_start": 50,
  "labels": ["Very atypical", "Very prototypical"],
  "metadata": {"verb": "cook"}
}

Forced Choice

Choose between two alternatives. This example uses a classic syntactic ambiguity to demonstrate comparative judgment.

from bead.items.forced_choice import create_forced_choice_item

item = create_forced_choice_item(
    alternatives=[
        "The turkey",
        "Something else",
    ],
    prompt="The turkey was ready to eat. What planned to eat?",
)

{
  "type": "bead-forced-choice",
  "prompt": "The turkey was ready to eat. What planned to eat?",
  "alternatives": [
    "The turkey",
    "Something else"
  ],
  "layout": "vertical",
  "metadata": {"sentence": "The turkey was ready to eat."}
}

Binary Judgment

Yes/No acceptability judgment. This example tests the verb persuade in an NP-to-VP object-control frame.

from bead.items.binary import create_binary_item

item = create_binary_item(
    text="Someone persuaded someone to leave.",
    prompt="Is this sentence acceptable?",
    options=["Acceptable", "Unacceptable"],
)

{
  "type": "bead-binary-choice",
  "prompt": "Is this sentence acceptable?",
  "stimulus": "Someone persuaded someone to leave.",
  "choices": ["Acceptable", "Unacceptable"],
  "metadata": {"verb": "persuade", "frame": "NP_to_VP"}
}

Categorical Classification

Select one category from an unordered set. This example tests factivity using a natural inference task.

from bead.items.categorical import create_categorical_item

item = create_categorical_item(
    text=(
        "Sentence 1: The doctor managed to treat the patient.\n"
        "Sentence 2: The patient was treated."
    ),
    prompt="If the first sentence is true, is the second sentence true?",
    categories=["Definitely not", "Maybe", "Definitely"],
)

{
  "type": "bead-categorical",
  "prompt": "If the first sentence is true, is the second sentence true?",
  "categories": ["Definitely not", "Maybe", "Definitely"],
  "metadata": {"recast_type": "factivity"}
}

Magnitude Estimation

Rate a target stimulus relative to a reference using an exponential slider. The slider maps linear position to exponential values via exp(x/100) - 1, placing the reference value at 1/3 from the left. Arrow keys give proportional ~3% changes at any scale; the right end approaches infinity.

from bead.items.magnitude import create_magnitude_item

item = create_magnitude_item(
    text="The alien cooked the pencil.",
    prompt="How typical is the target relative to the reference?",
    reference_text="The chef cooked the meal.",
    reference_value=100,
)

{
  "type": "bead-magnitude",
  "prompt": "How typical is the target relative to the reference?",
  "prompt_position": "below",
  "reference_stimulus": "The chef cooked the meal.",
  "reference_value": 100,
  "stimulus": "The alien cooked the pencil.",
  "input_mode": "exp-slider",
  "metadata": {"verb": "cook"}
}

Free Text Response

Open-ended text response, single-line or multiline. This example asks for an event summarization of a historical passage.

from bead.items.free_text import create_free_text_item

item = create_free_text_item(
    text="The 1846 US occupation of Monterey put an end to any Mexican "
         "military presence at the Presidio. The fort was abandoned in 1866.",
    prompt="Summarize the key event described in this passage.",
    multiline=True,
    min_length=5,
    max_length=200,
)

{
  "type": "bead-free-text",
  "prompt": "Summarize the key event described in this passage.",
  "multiline": true,
  "rows": 3,
  "min_length": 5,
  "max_length": 200,
  "metadata": {"event_type": "Abandoning"}
}

Selection Tasks

Cloze (Fill-in-the-Blank)

Dropdown selection for fill-in-the-blank gaps. This example tests temporal interpretation using a telicity cloze task with preposition, numeral, and duration unit fields.

from bead.items.cloze import create_cloze_item

item = create_cloze_item(
    text="The hurricane hit the coastline {{prep}} {{numeral}} {{unit}}.",
    constraints={
        "prep": ["in", "for"],
        "numeral": None,  # free text
        "unit": ["seconds", "minutes", "hours", "days", "weeks"],
    },
)

{
  "type": "bead-cloze-multi",
  "text": "The hurricane hit the coastline %% %% %%.",
  "fields": [
    {"type": "dropdown", "options": ["in", "for"]},
    {"type": "text", "placeholder": "#"},
    {"type": "dropdown", "options": ["seconds", "minutes", "hours", "days", "weeks"]}
  ],
  "require_all": true
}

Multi-Select

Select one or more options from a set using checkboxes. This example tests pronoun resolution in a discourse with multiple potential referents.

from bead.items.multi_select import create_multi_select_item

item = create_multi_select_item(
    text="Whenever anyone laughed, the magician scowled and their "
         "assistant smirked. They were secretly pleased.",
    prompt="Who was secretly pleased?",
    options=[
        "The magician",
        "The assistant",
        "Neither",
    ],
    min_selections=1,
)

{
  "type": "bead-multi-select",
  "prompt": "Who was secretly pleased?",
  "stimulus": "Whenever anyone laughed, the magician scowled and their assistant smirked. They were secretly pleased.",
  "options": [
    "The magician",
    "The assistant",
    "Neither"
  ],
  "metadata": {"phenomenon": "pronoun_resolution"}
}

Span Annotation

Interactive Span Labeling (Fixed Labels)

Select token ranges and assign labels from a searchable fixed set. Type to filter labels or use keyboard shortcuts 1-9.

from bead.items.span_labeling import create_interactive_span_item

item = create_interactive_span_item(
    text="The committee unanimously approved the new budget proposal "
         "after reviewing the evidence.",
    prompt="Select and label semantic roles.",
    label_set=[
        "Agent", "Patient", "Theme", "Experiencer",
        "Instrument", "Beneficiary", "Location", "Time",
        "Manner", "Cause", "Purpose", "Source",
        "Goal", "Stimulus", "Result", "Predicate",
    ],
)

{
  "type": "bead-span-label",
  "tokens": {
    "text": ["The", "committee", "unanimously", "approved", "the",
             "new", "budget", "proposal", "after", "reviewing",
             "the", "evidence", "."]
  },
  "span_spec": {
    "interaction_mode": "interactive",
    "label_source": "fixed",
    "labels": ["Agent", "Patient", "Theme", "Experiencer",
               "Instrument", "Beneficiary", "Location", "Time",
               "Manner", "Cause", "Purpose", "Source",
               "Goal", "Stimulus", "Result", "Predicate"]
  }
}

Wikidata Entity Labeling

Interactive span labeling with Wikidata autocomplete search for labels. Select entities and search Wikidata to link them.

from bead.items.span_labeling import create_interactive_span_item

item = create_interactive_span_item(
    text="Albert Einstein developed the theory of relativity "
         "at the Institute for Advanced Study in Princeton.",
    prompt="Select entities and search Wikidata to assign labels.",
    label_source="wikidata",
)

{
  "type": "bead-span-label",
  "tokens": {
    "text": ["Albert", "Einstein", "developed", "the", "theory",
             "of", "relativity", "at", "the", "Institute",
             "for", "Advanced", "Study", "in", "Princeton", "."]
  },
  "span_spec": {
    "interaction_mode": "interactive",
    "label_source": "wikidata",
    "wikidata_language": "en"
  }
}

Composite Tasks

Span highlights work as an orthogonal overlay on any existing task type. The same item can have both span annotations and a rating scale, forced choice, or binary judgment.

Prompt span references: prompts use [[label]] syntax to reference span labels. [[label]] auto-fills with the span's token text; [[label:custom text]] uses a custom surface form. At deployment, references are replaced with color-highlighted HTML matching the span colors in the stimulus. See the Items API guide for details.

Span + Likert Rating

Proto-role property rating with highlighted arguments using thematic role labels. Question text uses colored highlighting that matches the span colors.

from bead.items.ordinal_scale import create_ordinal_scale_item
from bead.items.span_labeling import add_spans_to_item
from bead.items.spans import Span, SpanLabel, SpanSegment

item = create_ordinal_scale_item(
    text="The boy broke the vase.",
    prompt="How likely is it that [[breaker]] existed after [[event:the breaking]]?",
    scale_bounds=(1, 5),
    scale_labels={1: "Very unlikely", 5: "Very likely"},
)

item = add_spans_to_item(
    item,
    spans=[
        Span(
            span_id="span_0",
            segments=[SpanSegment(element_name="text", indices=[0, 1])],
            label=SpanLabel(label="breaker"),
        ),
        Span(
            span_id="span_1",
            segments=[SpanSegment(element_name="text", indices=[2])],
            label=SpanLabel(label="event"),
        ),
        Span(
            span_id="span_2",
            segments=[SpanSegment(element_name="text", indices=[3, 4])],
            label=SpanLabel(label="breakee"),
        ),
    ],
)

{
  "type": "bead-rating",
  "prompt": "<div class=\"stimulus-container\">...(span-highlighted tokens)...</div><p class=\"bead-task-prompt\">How likely is it that <span class=\"bead-q-highlight\" style=\"background:#BBDEFB\">The boy<span class=\"bead-q-chip\" style=\"background:#1565C0\">breaker</span></span> existed after <span class=\"bead-q-highlight\" style=\"background:#C8E6C9\">the breaking<span class=\"bead-q-chip\" style=\"background:#2E7D32\">event</span></span>?</p>",
  "scale_min": 1,
  "scale_max": 5,
  "scale_labels": {"1": "Very unlikely", "5": "Very likely"},
  "metadata": {"trial_type": "likert_rating"}
}

Span + Slider Rating

Veridicality inference with highlighted spans but no labels (null labels). The highlighted regions draw attention to the predicate and embedded clause without adding subscript badges.

from bead.items.ordinal_scale import create_ordinal_scale_item
from bead.items.span_labeling import add_spans_to_item
from bead.items.spans import Span, SpanLabel, SpanSegment

item = create_ordinal_scale_item(
    text="Jo confirmed that Bo left.",
    prompt="How likely is it that someone left?",
    scale_bounds=(0, 100),
)

item = add_spans_to_item(
    item,
    spans=[
        Span(
            span_id="span_0",
            segments=[SpanSegment(element_name="text", indices=[1])],
            label=None,
        ),
        Span(
            span_id="span_1",
            segments=[SpanSegment(element_name="text", indices=[3, 4])],
            label=None,
        ),
    ],
)

{
  "type": "bead-slider-rating",
  "prompt": "<div class=\"stimulus-container\">...(span-highlighted tokens, no subscript badges)...</div><p class=\"bead-task-prompt\">How likely is it that someone left?</p>",
  "labels": ["Not at all", "Very much"],
  "slider_min": 0,
  "slider_max": 100,
  "slider_start": 50,
  "metadata": {"trial_type": "slider_rating"}
}

Span + Forced Choice

Compare change-of-state across predicates with thematic role labels. Question text uses colored highlighting matching the span annotation colors.

from bead.items.forced_choice import create_forced_choice_item

item = create_forced_choice_item(
    alternatives=[
        "The boy tapped the vase.",
        "The boy hit the vase.",
    ],
    prompt="In which event is it more likely that the vase broke?",
)

{
  "type": "bead-forced-choice",
  "prompt": "In which event is it more likely that the vase broke?",
  "alternatives": [
    "The boy tapped the vase.",
    "The boy hit the vase."
  ],
  "layout": "horizontal",
  "metadata": {"trial_type": "forced_choice"}
}

Span + Binary Judgment

Change-of-location property with four thematic role arguments. Question text uses colored highlighting matching the span colors.

from bead.items.binary import create_binary_item
from bead.items.span_labeling import add_spans_to_item
from bead.items.spans import Span, SpanLabel, SpanSegment

item = create_binary_item(
    text="The merchant traded the silk for the spices.",
    prompt="Did [[traded-away:the silk]] change location as a result of [[event:the trading]]?",
    options=["Yes", "No"],
)

item = add_spans_to_item(
    item,
    spans=[
        Span(
            span_id="span_0",
            segments=[SpanSegment(element_name="text", indices=[0, 1])],
            label=SpanLabel(label="trader"),
        ),
        Span(
            span_id="span_1",
            segments=[SpanSegment(element_name="text", indices=[2])],
            label=SpanLabel(label="event"),
        ),
        Span(
            span_id="span_2",
            segments=[SpanSegment(element_name="text", indices=[3, 4])],
            label=SpanLabel(label="traded-away"),
        ),
        Span(
            span_id="span_3",
            segments=[SpanSegment(element_name="text", indices=[6, 7])],
            label=SpanLabel(label="traded-for"),
        ),
    ],
)

{
  "type": "bead-binary-choice",
  "prompt": "Did <span class=\"bead-q-highlight\" style=\"background:#FFE0B2\">the silk<span class=\"bead-q-chip\" style=\"background:#E65100\">traded-away</span></span> change location as a result of <span class=\"bead-q-highlight\" style=\"background:#C8E6C9\">the trading<span class=\"bead-q-chip\" style=\"background:#2E7D32\">event</span></span>?",
  "stimulus": "<div class=\"stimulus-container\">...(span-highlighted tokens with subscript badges)...</div>",
  "choices": ["Yes", "No"],
  "metadata": {"trial_type": "binary_choice"}
}

Span + Free Text

Event summarization with a highlighted event span. The annotated span draws attention to the target event in a longer passage.

from bead.items.free_text import create_free_text_item
from bead.items.span_labeling import add_spans_to_item
from bead.items.spans import Span, SpanLabel, SpanSegment

item = create_free_text_item(
    text="The 1846 US occupation of Monterey put an end to any Mexican "
         "military presence at the Presidio. The fort was abandoned in 1866.",
    prompt="Summarize [[event:the highlighted event]] in one sentence.",
    multiline=True,
    min_length=5,
    max_length=200,
)

item = add_spans_to_item(
    item,
    spans=[
        Span(
            span_id="span_0",
            segments=[SpanSegment(element_name="text", indices=[21])],
            label=SpanLabel(label="event"),
        ),
    ],
)

{
  "type": "bead-free-text",
  "prompt": "Summarize <span class=\"bead-q-highlight\" style=\"background:#BBDEFB\">the highlighted event<span class=\"bead-q-chip\" style=\"background:#1565C0\">event</span></span> in one sentence.",
  "stimulus": "<div class=\"stimulus-container\">...(span-highlighted tokens)...</div>",
  "multiline": true,
  "rows": 3,
  "min_length": 5,
  "max_length": 200,
  "metadata": {"trial_type": "free_text"}
}

Relation Annotation

Span Relations (Fixed Labels)

Interactive span and relation annotation with searchable fixed label sets. Create spans, then use "Add Relation" to draw directed relations between them using thematic role labels.

from bead.items.span_labeling import create_interactive_span_item
from bead.items.spans import SpanSpec

item = create_interactive_span_item(
    text="The scientist presented the findings to the committee "
         "at the annual conference.",
    prompt="Create spans and relations for semantic role labeling.",
    label_set=[
        "Agent", "Patient", "Theme", "Recipient",
        "Instrument", "Location", "Time", "Predicate",
        "Stimulus", "Goal",
    ],
    span_spec=SpanSpec(
        interaction_mode="interactive",
        label_source="fixed",
        enable_relations=True,
        relation_label_source="fixed",
        relation_labels=[
            "agent-of", "patient-of", "theme-of",
            "recipient-of", "location-of", "time-of",
            "predicate-of",
        ],
        relation_directed=True,
    ),
)

{
  "type": "bead-span-label",
  "tokens": {
    "text": ["The", "scientist", "presented", "the", "findings",
             "to", "the", "committee", "at", "the", "annual",
             "conference", "."]
  },
  "span_spec": {
    "interaction_mode": "interactive",
    "label_source": "fixed",
    "labels": ["Agent", "Patient", "Theme", "Recipient",
               "Instrument", "Location", "Time", "Predicate",
               "Stimulus", "Goal"],
    "enable_relations": true,
    "relation_label_source": "fixed",
    "relation_labels": ["agent-of", "patient-of", "theme-of",
                        "recipient-of", "location-of", "time-of",
                        "predicate-of"],
    "relation_directed": true
  }
}

Span Relations (Wikidata)

Interactive entity linking and relation annotation with Wikidata search for both entity and relation labels. Useful for knowledge graph construction.

from bead.items.span_labeling import create_interactive_span_item
from bead.items.spans import SpanSpec

item = create_interactive_span_item(
    text="Marie Curie was born in Warsaw and later became "
         "a professor at the University of Paris.",
    prompt="Link entities via Wikidata and draw relations between them.",
    label_source="wikidata",
    span_spec=SpanSpec(
        interaction_mode="interactive",
        label_source="wikidata",
        enable_relations=True,
        relation_label_source="wikidata",
        relation_directed=True,
    ),
)

{
  "type": "bead-span-label",
  "tokens": {
    "text": ["Marie", "Curie", "was", "born", "in", "Warsaw",
             "and", "later", "became", "a", "professor", "at",
             "the", "University", "of", "Paris", "."]
  },
  "span_spec": {
    "interaction_mode": "interactive",
    "label_source": "wikidata",
    "enable_relations": true,
    "relation_label_source": "wikidata",
    "relation_directed": true,
    "wikidata_language": "en"
  }
}