ArtificialSubject model interface

class brainscore_language.artificial_subject.ArtificialSubject

class RecordingTarget

location to record from

language_system = 'language_system'

language_system_left_hemisphere = 'language_system_left_hemisphere'

language_system_right_hemisphere = 'language_system_right_hemisphere'

class RecordingType

method of recording

fMRI = 'fMRI'

functional magnetic resonance imaging

class Task

task to perform

next_word = 'next_word'

Predict the next word from the preceding context. Output a BehavioralAssembly with next-word predictions as the values and preceding stimulus in the stimulus coordinate.

Example:

Calling digest_text(text = ‘the quick brown’) could output

{'behavior': <xarray.BehavioralAssembly (presentation: 1)>
             array(['fox']), # the actual next word
             Coordinates:
               * presentation  (presentation) MultiIndex
               - stimulus      (presentation) object 'the quick brown'
               - stimulus_id   (presentation) int64 0}

Example:

Calling digest_text(text = [‘the quick brown’, ‘fox jumps’, ‘over the lazy’]) could output

{'behavior': <xarray.BehavioralAssembly (presentation: 3)>
             array(['fox', 'over', 'dog']), # the actual next words
             Coordinates:
               * presentation  (presentation) MultiIndex
               - stimulus      (presentation) object 'the quick brown' 'fox jumps', 'over the lazy'
               - stimulus_id   (presentation) int64 0 1 2}

reading_times = 'reading_times'

Output how long it took to read a given text, in milliseconds. Output a BehavioralAssembly with reading times in milliseconds as the values and text in the stimulus coordinate.

Example:

Calling digest_text(text = ‘the quick brown’) could output

{'behavior': <xarray.BehavioralAssembly (presentation: 1)>
             array([329.15]), # reading time in milliseconds
             Coordinates:
               * presentation  (presentation) MultiIndex
               - stimulus      (presentation) object 'the quick brown'
               - stimulus_id   (presentation) int64 0}

Example:

Calling digest_text(text = [‘the quick brown’, ‘fox jumps’, ‘over the lazy’]) could output

{'behavior': <xarray.BehavioralAssembly (presentation: 3)>
             array([329.15, 337.53, 341.13]), # reading times in milliseconds
             Coordinates:
               * presentation  (presentation) MultiIndex
               - stimulus      (presentation) object 'the quick brown' 'fox jumps', 'over the lazy'
               - stimulus_id   (presentation) int64 0 1 2}

digest_text(text: str | List[str]) → Dict[str, DataAssembly]

Parameters:

text – text to pass to the subject, either a single string (e.g. “the quick brown fox jumped”), or a list of strings (e.g. [“the quick”, “brown fox jumped”])

Returns:

a dictionary mapping from ‘behavior’ and ‘neural’ to a DataAssembly, if the subject was instructed to start_behavioral_task() and/or start_neural_recording() respectively. The DataAssembly always contains a presentation dimension, corresponding to the presentation of text stimuli (including a stimulus coordinate for the text, and a stimulus_id coordinate that uniquely identifies each stimulus). Values along the presentation dimension are ordered in the same way as the input. The behavior output has no other dimension, while the neural output has a neuroid dimension (including a neuroid_id coordinate to uniquely identify each recording unit), and sometimes a time_bin dimension (including time_bin_start and time_bin_end). Note that model state is not saved between every time that digest_text is called. For keeping states, between different inputs, text should be a list.

identifier() → str

The unique identifier for this model.

Returns:

e.g. ‘glove’, or ‘distilgpt2’

start_behavioral_task(task: Task)

Instruct the subject to output behavior when queried with digest_text(). Calling digest_text() will then output a BehavioralAssembly in the ‘behavior’ part of the output dict.

Parameters:

task – Which Task to use.

start_neural_recording(recording_target: RecordingTarget, recording_type: RecordingType)

Begin neural recordings from this subject. Calling digest_text() will then output a NeuroidAssembly in the ‘neural’ part of the output dict.

Parameters:

• recording_target – where in the language-involved parts of the brain to record from.

• recording_type – What recording method should be used.