Update.

README.md

@@ -16,8 +16,20 @@ To keep participants focused on the task,
 there is also a behavioral task that participants will perform throughout the localizer.
 There are three options for this task: oddball, one-back, and two-back.
 
+The task does not include any kind of jitter.
+
+### Configuration
+
+You should modify the configuration file (``config.yml``) based on the following elements:
+
+1.  The current configuration has 4-minute runs. The VPN lab recommends acquiring at least 4 runs.
+2.  The repetition time (TR) of fMRI data for the localizer experiment must be a factor of its miniblock duration (6 s by default).
+    The original paper used a TR of 2 seconds.
+3.  If you want, you can replace the VPN lab's stimuli with another set.
+    Each stimulus set *must* have a "baseline" category (for all tasks) and a "scrambled" category (for the oddball task).
+
 ### Differences from the original task
 
 While we have attempted to maximize similarity between this task and the VPN lab's task,
 there are differences.
-I'm not sure what those differences are, unfortunately.
+I'm not sure what those differences are, unfortunately, since I wrote this task a few years ago.

bids/generate_task_description_files.py

@@ -38,14 +38,14 @@
                 'Also referred to as a "hit".'
             ),
             "false_positive": (
-                "An incorrect button-press on a baseline trial. "
+                "An incorrect button-press on a baseline or category trial. "
                 'Also referred to as a "false alarm".'
             ),
             "false_negative": (
                 "An incorrect non-press on a task target trial. "
                 'Also referred to as a "miss".'
             ),
-            "true_negative": ("A correct non-press on a baseline trial."),
+            "true_negative": ("A correct non-press on a baseline or category trial."),
         },
     },
 }

config.yml

@@ -2,20 +2,20 @@
 constants:
   # Countdown presented at the beginning of each run, in seconds
   COUNTDOWN_DURATION: 12
-  # Final screen presented at the end of each run, in seconds
-  END_SCREEN_DURATION: 2
+  # Proportion of trials in which to do the task (default = 50%)
+  TASK_RATE: 0.5
   # How long each stimulus is presented, in seconds
   IMAGE_DURATION: 0.4
-  # Number of single-category blocks
-  N_BLOCKS: 36
-  # Number of stimuli per block
-  N_STIMULI_PER_BLOCK: 12
+  # Interstimulus interval. Trial duration is IMAGE_DURATION + TARGET_ISI.
+  TARGET_ISI: 0.1
   # How long to wait after start of presentation of stimulus to accept response
   RESPONSE_WINDOW: 1
-  # Interstimulus interval
-  TARGET_ISI: 0.1
-  # Proportion of trials in which to do the task
-  TASK_RATE: 0.5
+  # Number of stimuli per block
+  N_STIMULI_PER_BLOCK: 12
+  # Number of single-category blocks per run
+  N_BLOCKS: 36
+  # Final screen presented at the end of each run, in seconds
+  END_SCREEN_DURATION: 2
   # Total run duration, in seconds
   TOTAL_DURATION: 240
 # The different sets of categories that determine which stimuli are used

fLoc.py

@@ -72,7 +72,8 @@ def allocate_responses(events_df, response_times, response_window=1.0):
         # Looping backwards lets us keep earliest response for RT
         for rt in response_times[::-1]:
             if onset <= rt < next_onset:
-                # Ignore response window and use current trial's duration only
+                # Ignore response window and use current trial's duration only,
+                # since we really don't know which trial elicited the false positive.
                 events_df.loc[trial_idx, "accuracy"] = 0
                 events_df.loc[trial_idx, "classification"] = "false_positive"
                 events_df.loc[trial_idx, "response_time"] = rt - onset
@@ -393,6 +394,7 @@ def main(config):
             ]
             stimuli[category] = stimulus_files
         else:
+            # TODO: Support stimulus for baseline trials
             stimuli[category] = None  # baseline trials just have fixation
 
     # Determine which trials will be task