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Activity: Go/No-Go#

Documentation for tasks.go_no_go.py, part of the Tasks utilities in the M2C2 DataKit package.

Module Summary#

This module contains functions to score and summarize the Go/No-Go task data. The Letter Go/No-Go is a continuous performance task where participants are asked to press a button at the bottom of the screen every time a letter appears in the center of the screen. The Go/No-Go task measures attention and inhibitory control by requiring participants to respond to "GO" stimuli, while withholding responses to "NOGO" stimuli.

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Citations#

[INSERT CITATIONS HERE]

Public API#

go_no_go #

score_errors(row) #

Identifies and returns the type of error in a Go/No-Go task trial.

The function classifies the trial based on the correctness and the type of response provided. It returns "omission" for a missed response to a "GO" trial and "commission" for an incorrect response to a "NOGO" trial. "correct" is returned if the response is correct.

Parameters:

Name Type Description Default
row Series

A single row of the trial-level Go/No-Go task dataframe containing the columns "trial_type", "trial_response", and "correct".

 required

Returns:

Name Type Description
str str | None
  • "omission" if the trial is a "GO" trial and incorrectly responded as "NOGO".
  • "commission" if the trial is a "NOGO" trial and incorrectly responded as "GO".
  • "correct" if the response is correct.

Raises:

Type Description
ValueError

If the trial type or response values are invalid.
Source code in m2c2_datakit/tasks/go_no_go.py 
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def score_errors(row: pd.Series) -> str | None:
    """
    Identifies and returns the type of error in a Go/No-Go task trial.

    The function classifies the trial based on the correctness and the type of response
    provided. It returns "omission" for a missed response to a "GO" trial and "commission"
    for an incorrect response to a "NOGO" trial. "correct" is returned if the response is correct.

    Args:
        row (pd.Series): A single row of the trial-level Go/No-Go task dataframe containing
                         the columns "trial_type", "trial_response", and "correct".

    Returns:
        str:
            * "omission" if the trial is a "GO" trial and incorrectly responded as "NOGO".
            * "commission" if the trial is a "NOGO" trial and incorrectly responded as "GO".
            * "correct" if the response is correct.

    Raises:
        ValueError: If the trial type or response values are invalid.
    """

    try:
        trial_type: str = row["trial_type"]
        trial_response: str = row["trial_response"]
        trial_correct: bool = row["correct"]

        # If trial_correct is false return "Omission" or "Commission"
        if trial_correct is False:
            if trial_type == "GO" and trial_response == "NOGO":
                return "omission"
            elif trial_type == "NOGO" and trial_response == "GO":
                return "commission"
            else:
                raise ValueError(
                    f"Invalid trial_type or trial_response values: {trial_type}, {trial_response}"
                )
        # If trial_correct is true return None
        elif trial_correct is True:
            return "correct"
        else:
            raise ValueError(f"Invalid trial_correct value: {trial_correct}")

    except Exception as e:
        print(f"Error processing row: {e}")
        return None

summarize(x, trials_expected=30, rt_outlier_low=100) #

Summarizes the Go/No-Go task data by calculating statistics such as omission errors, commission errors, correct responses, and response times.

This function computes the number of omission errors, commission errors, correct responses, and overall response times for the Go/No-Go task.

Parameters:

Name Type Description Default
x DataFrame

The trial-level scored dataset for the Go/No-Go task.

 required
trials_expected int

The expected number of trials. Defaults to 30.

 30

Returns:

Type Description
Series

A Pandas Series containing the summary statistics for the Go/No-Go task.
Source code in m2c2_datakit/tasks/go_no_go.py 
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def summarize(x, trials_expected=30, rt_outlier_low=100):
    """
    Summarizes the Go/No-Go task data by calculating statistics such as omission errors, commission errors, correct responses, and response times.

    This function computes the number of omission errors, commission errors, correct responses, and overall response times for the Go/No-Go task.

    Args:
        x (pd.DataFrame): The trial-level scored dataset for the Go/No-Go task.
        trials_expected (int, optional): The expected number of trials. Defaults to 30.

    Returns:
        (pd.Series): A Pandas Series containing the summary statistics for the Go/No-Go task.
    """

    d = helpers.summarize_common_metadata(x, trials_expected)

    # Summarize errors for omissions and commissions or correct responses
    d["n_errors_omission"] = (x["metric_accuracy"] == "omission").sum()
    d["n_errors_commission"] = (x["metric_accuracy"] == "commission").sum()
    d["n_trials_correct"] = (x["metric_accuracy"] == "correct").sum()

    # Unfiltered RT for whole task
    d["median_response_time_all"] = x["response_time_ms"].median()

    # Unfiltered RT for omission and commission errors and correct responses
    for metric in ["omission", "commission", "correct"]:
        d[f"median_response_time_{metric}"] = x.loc[
            x["metric_accuracy"] == metric, "response_time_ms"
        ].median()

    # Max RT for whole task to see if any trials were displayed longer than expected
    d["max_response_time_all"] = x["response_time_ms"].max()

    # * Only using low filter since the stimulus is only on screen for 1 second
    # Filter out outliers: RT < 100 ms for whole task
    rt_filtered = x.loc[
        (x["response_time_ms"] >= rt_outlier_low),
        "response_time_ms",
    ]
    d["median_response_time_low_filtered"] = rt_filtered.median()

    # Count of outliers filtered out
    d["n_trials_rt_filtered"] = x["response_time_ms"].shape[0] - rt_filtered.shape[0]

    # Filtered RT for omission and commission errors and correct responses
    for metric in ["omission", "commission", "correct"]:
        d[f"median_response_time_{metric}_low_filtered"] = x.loc[
            (x["metric_accuracy"] == metric)
            & (x["response_time_ms"] >= rt_outlier_low),
            "response_time_ms",
        ].median()

    return pd.Series(d)