Caro

.gitignore

@@ -22,3 +22,5 @@ stimuli/strange_stories/clips
 stimuli/strange_stories/orig
 stimuli/liking/clips
 
+.idea/
+

MultiTaskBattery/task_file.py

@@ -1861,3 +1861,160 @@ def make_task_file(self,
 
         return trial_info
 
+
+class FingerRhythmic(TaskFile):
+    def __init__(self, const):
+        super().__init__(const)
+        self.name = 'finger_rhythmic'
+
+    def make_task_file(self,
+                       hand='right',
+                       responses=[1],
+                       run_number= None,
+                       task_dur = 70,
+                       trial_dur=35, # 2 sec trial start text, 27.95 sec tone train, ~5 sec buffer
+                       iti_dur=0,
+                       file_name=None):
+
+        # count number of trials
+        n_trials = int(np.floor(task_dur / (trial_dur + iti_dur)))
+        trial_info = []
+        t = 0
+
+        for n in range(n_trials):
+            trial = {}
+            trial['key_one'] = responses[0]
+            trial['trial_num'] = n
+            trial['hand'] = hand
+            trial['trial_dur'] = trial_dur
+            trial['iti_dur'] = iti_dur
+            trial['stim'] = 'generated'
+            trial['display_trial_feedback'] = False
+            trial['start_time'] = t
+            trial['end_time'] = t + trial_dur + iti_dur
+            trial_info.append(trial)
+            # Update for next trial:
+            t = trial['end_time']
+
+        trial_info = pd.DataFrame(trial_info)
+        if file_name is not None:
+            ut.dircheck(self.task_dir / self.name)
+            trial_info.to_csv(self.task_dir / self.name / file_name, sep='\t', index=False)
+
+        return trial_info
+
+class TimePerception(TaskFile):
+    def __init__(self, const):
+        super().__init__(const)
+        self.name = 'time_perception'  # folder: stimuli/perception/, tasks/perception/
+
+    def make_task_file(self,
+                       modality='time',          # 'time' or 'volume'
+                       responses=[1, 2],         # code 1 = left option, 2 = right option
+                       n_trials= 30,              # must be even
+                       trial_dur=4,            # tone + question window duration
+                       iti_dur=1.0,
+                       question_dur=2.0,
+                       display_feedback= True,
+                       run_number=None,
+                       file_name=None,
+                       **unused):
+
+        # sides per modality
+        if modality == 'time':
+            left_label, right_label = 'shorter', 'longer'
+        elif modality == 'volume':
+            left_label, right_label = 'quieter', 'louder'
+
+        sides = [left_label] * (n_trials // 2) + [right_label] * (n_trials // 2)
+        np.random.default_rng(run_number).shuffle(sides)
+
+        rows, t = [], 0.0
+        for i, side in enumerate(sides):
+            rows.append(dict(
+                trial_num=i,
+                modality=modality,                   # drives Task branching
+                side=side,                           # shorter/longer or softer/louder
+                key_one=int(responses[0]),           # instruction mapping only
+                key_two=int(responses[1]),
+                trial_type=1 if side in (left_label,) else 2,  # correct code
+                question_dur=float(question_dur),
+                trial_dur=float(trial_dur),
+                iti_dur=float(iti_dur),
+                display_trial_feedback= display_feedback,
+
+                # runtime logs (filled in Task)
+                comparison_ms=np.nan,
+                comparison_dba=np.nan,
+
+                start_time=float(t),
+                end_time=float(t + trial_dur + iti_dur),
+            ))
+            t = rows[-1]['end_time']
+
+        df = pd.DataFrame(rows)
+        if file_name:
+            ut.dircheck(self.task_dir / self.name)
+            df.to_csv(self.task_dir / self.name / file_name, sep='\t', index=False)
+        return df
+
+class SensMotControl(TaskFile):
+    def __init__(self, const):
+        super().__init__(const)
+        self.name = 'sensmot_control'
+
+    def make_task_file(self,
+                       hand='right',
+                       responses=[1, 2],
+                       run_number=None,
+                       task_dur=300,
+                       trial_dur=3,
+                       question_dur=2,
+                       iti_dur= 1,
+                       file_name=None,
+                       stim_file = None,
+                       condition=None):
+
+        # count number of trials
+        n_trials = int(np.floor(task_dur / (trial_dur + iti_dur)))
+        trial_info = []
+        t = 0
+
+        if stim_file:
+            stim = pd.read_csv(self.stim_dir / self.name / stim_file, sep='\t')
+        else:
+            stim = pd.read_csv(self.stim_dir / self.name / f'{self.name}_block1.csv', sep='\t')
+
+        if condition:
+            stim = stim[stim['condition'] == condition]
+        else:
+            stim = stim.loc[~stim['condition'].str.contains('practice', na=False)]
+
+        start_row = (run_number - 1) * n_trials
+        end_row = run_number * n_trials - 1
+        stim = stim.iloc[start_row:end_row + 1].reset_index(drop=True)
+
+        for n in range(n_trials):
+            trial = {}
+            trial['key_one'] = responses[0]
+            trial['key_two'] = responses[1]
+            trial['trial_num'] = n
+            trial['hand'] = hand
+            trial['trial_dur'] = trial_dur
+            trial['question_dur'] = question_dur
+            trial['iti_dur'] = iti_dur
+            trial['trial_type'] = stim['corr_resp'][n]
+            trial['stim'] = stim['color'][n]
+            trial['condition'] = stim['condition'][n]
+            trial['display_trial_feedback'] = True
+            trial['start_time'] = t
+            trial['end_time'] = t + trial_dur + iti_dur
+            trial_info.append(trial)
+
+            # Update for next trial:
+            t = trial['end_time']
+
+        trial_info = pd.DataFrame(trial_info)
+        if file_name is not None:
+            trial_info.to_csv(self.task_dir / self.name / file_name, sep='\t', index=False)
+        return trial_info

MultiTaskBattery/task_table.tsv

@@ -28,3 +28,6 @@ frith_happe	FrithHappe	triangle	frith_happe	"video clips of triangle animations,
 liking	Liking	meeting	liking	"video clips of people interacting, participants must determine if the interaction was positive or negative"	"Heerey, E.A. and Gilder, T.S.E. (2019) ‘The subjective value of a smile alters social behaviour’, PLoS ONE, 14(12), pp. 1–19. Available at: https://doi.org/10.1371/journal.pone.0225284."	"like,dislike"
 pong	Pong	pong	pong	pong game		NA
 affective	Affective	affective	affect	2 choice is the image pleasant or unpleasant		NA
+finger_rhythmic	FingerRhythmic	finger_rhythmic	fingrhytm	Assess the precision of internal timing mechanisms through a rhythmic tapping production task. Participants are instructed to produce a regular series of taps at a prescribed target interval.	Ivry, R. B., & Keele, S. W. (1989). Timing Functions of the Cerebellum. Journal of Cognitive Neuroscience, 1(2), 136-152.	self-paced tapping
+time_perception	TimePerception	time_perception	timeperc	Assess the precision of internal timing mechanisms by measuring participants’ perceptual acuity in discriminating short auditory intervals. Participants are presented with sequences of tones and must decide whether a comparison interval is longer or shorter than a standard reference interval.	Ivry, R. B., & Keele, S. W. (1989). Timing Functions of the Cerebellum. Journal of Cognitive Neuroscience, 1(2), 136–152.	time interval
+sensmot_control	SensMotControl	sensmot_control	sensmotcontrol	Assess basic sensorimotor control. Measure reaction time and response accuracy to evaluate low-level perceptual and motor processing. Part 1 is a Go/No-Go. Part 2 is a 2AFC	: Koechlin, E., & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. Trends in cognitive sciences, 11(6), 229-235.Koechlin 2007	Go/No-Go 2AFC.