skneuromsi.ndcollection.causes_acc module

Implementation of causes analysis in multisensory integration.

The NDResultCausesAcc class provides an accessor, NDResultCausesAcc, for analyzing causes in the context of multisensory integration. It offers methods to calculate causes and check the uniqueness of causes.

class skneuromsi.ndcollection.causes_acc.NDResultCollectionCausesAcc(ndcollection)

Bases: AccessorABC

Accessor for calculating causes in an NDResultCollection.

Causal analysis in the context of multisensory integration refers to the study of factors and conditions that contribute to how the brain combines and processes information from different senses, such as vision, hearing, touch, and others. It involves identifying these factors, studying their interactions, evaluating the consistency of sensory information, and developing models to explain how the brain integrates sensory inputs effectively.

Parameters:

ndcollection (NDResultCollection) – The NDResultCollection object to be accessed and analyzed.

causes_by_parameter(*, parameter=None)

Get causes by a specific parameter.

Parameters:

parameter (str, optional) – The parameter to group by, by default None. If None then the parameter with more than one value is selected. Check the documentation of the NDResultCollection.coerce_parameter.

Returns:

cdf – The DataFrame containing causes grouped by the specified parameter.

Return type:

pandas.DataFrame

unique_causes(*, parameter=None)

Get unique values of number of causes based on a specific parameter.

Parameters:

parameter (str, optional) – The parameter to group by, by default None. If None then the parameter with more than one value is selected. Check the documentation of the NDResultCollection.coerce_parameter.

Returns:

unique_causes – Array containing unique causes based on the specified parameter.

Return type:

numpy.ndarray

n_report(n, *, parameter=None)

Generate an N-report for a given number of causes.

Analogous to Unity’s “n_report,” this analysis function assesses how information from different sensory modalities converges or combines in the brain to create a arbitrary number of perceptual experiences.

For instance, if we consider three modalities - vision, hearing, and touch - and the brain identifies two experiences, two of these modalities are perceived as a single experience, while one remains separate.

The primary aim of this analysis is to determine how different modalities are integrated to form a coherent perceptual experience.

Parameters:

• n (int) – The number of causes for the N-report.

• parameter (str, optional) – The parameter to group by, by default None. If None then the parameter with more than one value is selected. Check the documentation of the NDResultCollection.coerce_parameter.

Returns:

the_report – The generated N-report DataFrame.

Return type:

pandas.DataFrame

unity_report(*, parameter=None)

Generate a unity report.

In the context of multisensory integration, the unity report typically refers to an analysis or visualization that assesses the extent to which information from different sensory modalities converges or combines in the brain to create a unified perceptual experience. This type of report aims to quantify the degree of integration or coherence between sensory inputs, such as visual, auditory, tactile, and other sensory stimuli.

For instance, researchers may use a unity report to analyze how well sensory information from vision and touch aligns when perceiving a textured object. They might measure the degree of synchronization or correlation between neural responses in visual and somatosensory brain regions to determine the level of multisensory integration.

The unity report in multisensory integration studies helps researchers understand the mechanisms underlying the brain’s ability to merge information from different senses, leading to a cohesive and unified perception of the environment. It provides quantitative insights into the integration process and contributes to our understanding of how the brain creates a seamless perceptual representation from diverse sensory inputs.

Parameters:

parameter (str, optional) – The parameter to group by, by default None. If None then the parameter with more than one value is selected. Check the documentation of the NDResultCollection.coerce_parameter.

Returns:

unity_report – The generated unity report DataFrame.

Return type:

pandas.DataFrame

mean_report(*, parameter=None)

Generate a mean report.

Similar to Unity’s “mean_report,” this analysis function evaluates how information from different sensory modalities converges or combines in the brain to create an average number of perceptual experiences.

For example, if we consider three modalities - vision, hearing, and touch - and the brain identifies 1.5 experiences, the model generally struggles to combine stimuli into a single sensory experience in most cases.

The main goal of this analysis is to determine how, in most instances, different modalities integrate to create a unified perceptual experience.

Parameters:

parameter (str, optional) – The parameter to group by, by default None. If None then the parameter with more than one value is selected. Check the documentation of the NDResultCollection.coerce_parameter.

Returns:

mean_report – The generated mean report DataFrame.

Return type:

pandas.DataFrame

describe_causes(*, parameter=None)

Generate descriptive statistics for causes.

This method generates descriptive statistics for all convergence values of the modalities concerning the given parameter.

These values aim to provide a general idea of how the integration model combined stimuli from different modalities.

Parameters:

parameter (str, optional) – The parameter to group by, by default None. If None then the parameter with more than one value is selected. Check the documentation of the NDResultCollection.coerce_parameter.

Returns:

describe_report – The generated descriptive statistics report DataFrame.

Return type:

pandas.DataFrame