Support for functional localization

.gitignore

@@ -136,7 +136,7 @@ dmypy.json
 
 ### project specific additions:
 
-brainscore_language/data
+# brainscore_language/data
 html
 .vscode
 *.code-workspace
@@ -149,4 +149,6 @@ cache
 .cache
 .idea/
 wandb/
-**/models/lm1b/resources
+**/models/lm1b/resources
+conda_score--*
+brainscore_language/data/*/*.csv

brainscore_language/artificial_subject.py

@@ -104,6 +104,8 @@ class RecordingType:
 
         fMRI = "fMRI"
         """ functional magnetic resonance imaging """
+        ECoG = "ECoG"
+        """ Electrocorticography """
 
     def start_neural_recording(self, recording_target: RecordingTarget, recording_type: RecordingType):
         """

brainscore_language/benchmarks/fedorenko2016/__init__.py

@@ -0,0 +1,5 @@
+from brainscore_language import benchmark_registry
+from .benchmark import Fedorenko2016_ridge, Fedorenko2016_linear
+
+benchmark_registry['Fedorenko2016-linear'] = Fedorenko2016_linear
+benchmark_registry['Fedorenko2016-ridge'] = Fedorenko2016_ridge

brainscore_language/benchmarks/fedorenko2016/benchmark.py

@@ -0,0 +1,56 @@
+import xarray as xr
+
+from brainscore_core.benchmarks import BenchmarkBase
+from brainscore_language import load_dataset, load_metric
+from brainscore_language.artificial_subject import ArtificialSubject
+from brainscore_language.benchmarks.blank2014.ceiling import ExtrapolationCeiling
+from brainscore_language.data.fedorenko2016 import BIBTEX
+from brainscore_language.utils.ceiling import ceiling_normalize
+
+from tqdm import tqdm
+
+def Fedorenko2016_linear():
+    return Fedorenko2016(metric="linear_pearsonr")
+
+def Fedorenko2016_ridge():
+    return Fedorenko2016(metric="ridge_pearsonr")
+
+class Fedorenko2016(BenchmarkBase):
+
+    def __init__(self, metric: str):
+        self.data = load_dataset('Fedorenko2016.language')
+
+        identifier = f"Fedorenko2016-{metric}"
+        self.metric = load_metric(metric)
+
+        ceiler = ExtrapolationCeiling(subject_column="subject_UID")
+        ceiling = ceiler(assembly=self.data, metric=self.metric)
+
+        super(Fedorenko2016, self).__init__(
+            identifier=identifier,
+            version=3,
+            parent='neural_language',
+            ceiling=ceiling,
+            bibtex=BIBTEX)
+
+    def __call__(self, candidate: ArtificialSubject):
+        candidate.start_neural_recording(recording_target=ArtificialSubject.RecordingTarget.language_system,
+                                         recording_type=ArtificialSubject.RecordingType.ECoG)
+
+        stimuli = self.data['stimulus']
+        sentences = self.data['sentence_id'].values
+        predictions = []
+        for sentence_id in tqdm(sorted(set(sentences))):  # go over individual stories, sorting to keep consistency across runs
+            sentence_indexer = [stimulus_sentence == sentence_id for stimulus_sentence in sentences]
+            sentence_stimuli = stimuli[sentence_indexer]
+            stimuli_values = sentence_stimuli.values
+            sentence_predictions = candidate.digest_text(stimuli_values)["neural"]
+            sentence_predictions['stimulus_id'] = 'presentation', sentence_stimuli['stimulus_id'].values
+            predictions.append(sentence_predictions)
+
+        predictions = xr.concat(predictions, dim='presentation')
+
+        raw_score = self.metric(predictions, self.data)
+        scores = ceiling_normalize(raw_score, self.ceiling)
+
+        return scores

brainscore_language/benchmarks/tuckute2024/__init__.py

@@ -0,0 +1,7 @@
+from brainscore_language import benchmark_registry
+from .benchmark import Tuckute2024_linear, Tuckute2024_ridge, Tuckute2024_rdm, Tuckute2024_cka
+
+benchmark_registry["Tuckute2024-linear"] = Tuckute2024_linear
+benchmark_registry["Tuckute2024-ridge"] = Tuckute2024_ridge
+benchmark_registry["Tuckute2024-rdm"] = Tuckute2024_rdm
+benchmark_registry["Tuckute2024-cka"] = Tuckute2024_cka

brainscore_language/benchmarks/tuckute2024/benchmark.py

@@ -0,0 +1,57 @@
+import xarray as xr
+
+from brainscore_core.benchmarks import BenchmarkBase
+from brainscore_core.metrics import Score
+from brainscore_language import load_dataset, load_metric
+from brainscore_language.artificial_subject import ArtificialSubject
+from brainscore_language.data.tuckute2024 import BIBTEX
+
+from tqdm import tqdm
+
+def Tuckute2024_linear():
+    return _Tuckute2024(metric='linear_pearsonr')
+
+def Tuckute2024_ridge():
+    return _Tuckute2024(metric='ridge_pearsonr')
+
+def Tuckute2024_rdm():
+    return _Tuckute2024(metric='rdm')
+
+def Tuckute2024_cka():
+    return _Tuckute2024(metric='cka')
+
+class _Tuckute2024(BenchmarkBase):
+
+    def __init__(self, metric):
+        identifier = f"Tuckute2024-{metric}"
+        self.data = load_dataset("Tuckute2024.language")
+        self.metric = load_metric(metric)
+
+        super(_Tuckute2024, self).__init__(
+            identifier=identifier,
+            version=1,
+            parent='neural_language',
+            ceiling=None,
+            bibtex=BIBTEX)
+
+    def __call__(self, candidate: ArtificialSubject):
+        candidate.start_neural_recording(recording_target=ArtificialSubject.RecordingTarget.language_system,
+                                         recording_type=ArtificialSubject.RecordingType.fMRI)
+
+        stimuli = self.data['stimulus']
+        sentences = self.data['stimulus_id'].values
+        predictions = []
+        for sentence_id in tqdm(sorted(set(sentences))):  # go over individual stories, sorting to keep consistency across runs
+            sentence_indexer = [stimulus_sentence == sentence_id for stimulus_sentence in sentences]
+            sentence_stimuli = stimuli[sentence_indexer]
+            stimuli_values = sentence_stimuli.values
+            sentence_predictions = candidate.digest_text(stimuli_values)["neural"]
+
+            sentence_predictions['stimulus_id'] = 'presentation', sentence_stimuli['stimulus_id'].values
+            predictions.append(sentence_predictions)
+
+        predictions = xr.concat(predictions, dim='presentation')
+
+        raw_score = self.metric(predictions, self.data)
+        return raw_score
+

brainscore_language/data/fedorenko2010_localization/__init__.py

@@ -0,0 +1,34 @@
+import pandas as pd
+
+from glob import glob
+from pathlib import Path
+
+from brainscore_language import data_registry
+
+BIBTEX = """@article{Fedorenko2010NewMF,
+  title={New method for fMRI investigations of language: defining ROIs functionally in individual subjects.},
+  author={Evelina Fedorenko and Po-Jang Hsieh and Alfonso Nieto-Castanon and Susan L. Whitfield-Gabrieli and Nancy G. Kanwisher},
+  journal={Journal of neurophysiology},
+  year={2010},
+  volume={104 2},
+  pages={1177-94},
+  url={https://api.semanticscholar.org/CorpusID:740913}
+}"""
+
+# Code adapted from: https://github.com/bkhmsi/brain-language-suma
+
+def load_data():
+    paths = glob(f"{Path(__file__).parent }/*.csv")
+    data = pd.read_csv(paths[0])
+    for path in paths[1:]:
+        run_data = pd.read_csv(path)
+        data = pd.concat([data, run_data])
+
+    data["sent"] = data["stim2"].apply(str.lower)
+
+    for stimuli_idx in range(3, 14):
+        # lowercase each stimulus/word then add it to the sentence
+        data["sent"] += " " + data[f"stim{stimuli_idx}"].apply(str.lower)
+    return data
+
+data_registry['Fedorenko2010.localization'] = load_data

brainscore_language/data/fedorenko2010_localization/langloc_fmri_run1_stim_set1.csv

@@ -0,0 +1,49 @@
+stim1,stim2,stim3,stim4,stim5,stim6,stim7,stim8,stim9,stim10,stim11,stim12,stim13,stim14
+1,JUST,THE,BAREST,SUGGESTION,OF,A,HEEL,IS,FOUND,ON,TEENAGE,PUMPS,S
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brainscore_language/data/fedorenko2010_localization/langloc_fmri_run1_stim_set2.csv

@@ -0,0 +1,49 @@
+stim1,stim2,stim3,stim4,stim5,stim6,stim7,stim8,stim9,stim10,stim11,stim12,stim13,stim14
+1,PART,OF,THE,FAMILY,SHOULD,BE,SLEEPING,WHILE,THE,REST,IS,AWAKE,S
+2,IT,MADE,NO,DIFFERENCE,THAT,MOST,EVIDENCE,POINTS,TO,AN,OPPOSITE,CONCLUSION,S
+3,SHIPMENTS,OF,ARMS,FROM,WESTERN,COUNTRIES,COULD,SIMILARLY,BE,SEIZED,AS,CONTRABAND,S
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+2,HU,BEXES,HUS,OPPREYES,OD,REE,BEROOF,FROT,OTUBLING,CAG,LILLOW,OTUBLING,N
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brainscore_language/data/fedorenko2010_localization/langloc_fmri_run1_stim_set3.csv

@@ -0,0 +1,49 @@
+stim1,stim2,stim3,stim4,stim5,stim6,stim7,stim8,stim9,stim10,stim11,stim12,stim13,stim14
+1,HE,ENLISTED,A,STAFF,OF,LOYAL,EXPERTS,AND,OF,MANY,ZEALOUS,VOLUNTEERS,S
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+8,AND,THIS,WAS,BEFORE,HE,BEGAN,TO,PLAY,HIS,STARTLINGLY,BEAUTIFUL,JAZZ,S
+9,IT,APPEARED,THAT,THE,THEORY,COULD,BE,SAVED,IN,ONE,WAY,ONLY,S
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+10,THE,PROBLEM,OF,SOLIDARITY,AND,MORALE,AGAIN,INVOLVES,THE,CONCEPT,OF,VALUES,S
+11,I,CROSSED,THE,BEAUTIFULLY,FURNISHED,LIVING,ROOM,TO,THE,PALE,YELLOW,PHONE,S
+12,NO,CLIENT,WOULD,EVER,THINK,OF,ASKING,HIM,TO,DO,SUCH,THINGS,S
+13,THEN,HE,NOTICED,THAT,THE,DRY,WOOD,OF,THE,WHEELS,HAD,SWOLLEN,S
+14,THE,HULL,WAS,ALSO,A,RESULT,OF,ALMOST,A,DECADE,OF,WORK,S
+15,THE,RED,GLOW,FROM,THE,COVE,HAD,DIED,OUT,OF,THE,SKY,S
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+15,PIFFURES,CAG,NE,LOZEN,EN,REE,PEMLIC,ETEOS,OSE,QUEN,OD,TEERS,N
+16,SHE,BLEW,HER,NOSE,ON,A,TISSUE,AND,OPENED,THE,COKE,BOTTLE,S
+17,IT,WAS,A,SIMPLE,IVORY,CRUCIFIX,WHICH,MY,MOTHER,HAD,GIVEN,ME,S
+18,THEY,ADOPTED,A,PROGRAM,BY,WHICH,LOUISIANA,WAS,DIVIDED,INTO,FIVE,DISTRICTS,S
+16,BELLER,SHOB,OT,RO,LIOTA,OSE,MEE,EF,HU,HUD,OCESTED,OTUBLING,N
+17,TRORE,WAM,REE,REDUCETION,EN,LIGET,WHISS,DE,STEVEPSED,DOD,VOT,ECITS,N
+18,CY,REE,TICE,HU,HUD,SMOLED,SQUEE,LEGORMETTES,HU,HUD,COYERS,DOIL,N
+19,WRO,CHIRPLY,CONWRERKING,SPOWES,DESOGGED,FOM,PEMLIC,ENHEPMENT,ONK,NOY,OD,DISTREY,N
+20,WHIKE,HOULE,HOGILCOTIVE,OYERS,DERE,STULL,CONVIDOTE,REE,BIGS,WOURN,PODE,INDECT,N
+21,AIRE,PUCKED,UD,REE,TIMEL,CHE,WAM,HEDDING,FOM,REE,FOBBITOL,GOULD,N
+19,THE,EXUBERANCE,ON,THIS,OCCASION,SET,A,STANDARD,FOR,SUBSEQUENT,DARTMOUTH,GATHERINGS,S
+20,THIS,IS,A,SIGNIFICANT,ADVANCE,BUT,ITS,IMPORTANCE,SHOULD,NOT,BE,EXAGGERATED,S
+21,IN,THIS,WAY,YOU,WILL,BE,ABLE,TO,DETECT,ANY,OBVIOUS,MISTAKES,S
+22,WHOLE,FAMILIES,ARE,MOVING,AND,REMOVAL,FIRMS,ARE,BOOKED,FOR,MONTHS,AHEAD,S
+23,THE,CONTINUING,MODERNIZATION,OF,THESE,FORCES,IS,A,COSTLY,BUT,NECESSARY,PROCESS,S
+24,THE,THIN,MAN,MOVED,SWIFTLY,TO,THE,PHONE,AND,DIALED,A,NUMBER,S
+22,HU,TONCED,HUS,HURTLY,FOPE,LOWOND,REE,DRE,BEP,OY,REE,RODER,N
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