nafc

Classes:

Nafc([stage_block, stim, reward, ...])

A Two-alternative forced choice task.

class Nafc(stage_block=None, stim=None, reward=50, req_reward=False, punish_stim=False, punish_dur=100, correction=False, correction_pct=50.0, bias_mode=False, bias_threshold=20, stim_light=True, **kwargs)[source]

Bases: autopilot.tasks.task.Task

A Two-alternative forced choice task.

(can’t have number as first character of class.)

Stages

request - compute stimulus, set request trigger in center port.
discrim - respond to input, set reward/punishment triggers on target/distractor ports
reinforcement - deliver reward/punishment, end trial.

Variables

target ("L", "R") – Correct response
distractor ("L", "R") – Incorrect response
stim – Current stimulus
response ("L", "R") – Response to discriminand
correct (0, 1) – Current trial was correct/incorrect
correction_trial (bool) – If using correction trials, last trial was a correction trial
trial_counter (itertools.count) – Which trial are we on?
discrim_playing (bool) – Is the stimulus playing?
bailed (0, 1) – Subject answered before stimulus was finished playing.
current_stage (int) – As each stage is reached, update for asynchronous event reference

Parameters

stage_block (threading.Event) – Signal when task stages complete.

stim (dict) –

Stimuli like:

"sounds": {
    "L": [{"type": "Tone", ...}],
    "R": [{"type": "Tone", ...}]
}

reward (float) – duration of solenoid open in ms
req_reward (bool) – Whether to give a water reward in the center port for requesting trials
punish_stim (bool) – Do a white noise punishment stimulus
punish_dur (float) – Duration of white noise in ms
correction (bool) – Should we do correction trials?
correction_pct (float) – (0-1), What proportion of trials should randomly be correction trials?
bias_mode (False, “thresholded_linear”) – False, or some bias correction type (see managers.Bias_Correction )
bias_threshold (float) – If using a bias correction mode, what threshold should bias be corrected for?
current_trial (int) – If starting at nonzero trial number, which?
stim_light (bool) – Should the LED be turned blue while the stimulus is playing?
**kwargs

Attributes:

`STAGE_NAMES`
`PARAMS`
`PLOT`
`HARDWARE`

Classes:

TrialData

Trialwise Data for a Two-Alternative Forced Choice Task

Methods:

`request`(args, *kwargs)	Stage 0: compute stimulus, set request trigger in center port.
`discrim`(args, *kwargs)	Stage 1: respond to input, set reward/punishment triggers on target/distractor ports
`reinforcement`(args, *kwargs)	Stage 2 - deliver reward/punishment, end trial.
`punish`()	Flash lights, play punishment sound if set
`respond`(pin)	Set self.response
`stim_start`()	mark discrim_playing = true
`stim_end`()	called by stimulus callback
`flash_leds`()	flash lights for punish_dir

STAGE_NAMES = ['request', 'discrim', 'reinforcement']

PARAMS = OrderedDict([ ('reward', {'tag': 'Reward Duration (ms)', 'type': 'int'}), ('req_reward', {'tag': 'Request Rewards', 'type': 'bool'}), ( 'punish_stim', {'tag': 'White Noise Punishment', 'type': 'bool'}), ( 'punish_dur', {'tag': 'Punishment Duration (ms)', 'type': 'int'}), ('correction', {'tag': 'Correction Trials', 'type': 'bool'}), ( 'correction_pct', { 'depends': {'correction': True}, 'tag': '% Correction Trials', 'type': 'int'}), ( 'bias_mode', { 'tag': 'Bias Correction Mode', 'type': 'list', 'values': { 'None': 0, 'Proportional': 1, 'Thresholded Proportional': 2}}), ( 'bias_threshold', { 'depends': {'bias_mode': 2}, 'tag': 'Bias Correction Threshold (%)', 'type': 'int'}), ('stim', {'tag': 'Sounds', 'type': 'sounds'})])

PLOT = { 'chance_bar': True, 'data': {'correct': 'rollmean', 'response': 'segment', 'target': 'point'}, 'roll_window': 50}

pydantic model TrialData[source]

Bases: autopilot.data.models.protocol.Trial_Data

Trialwise Data for a Two-Alternative Forced Choice Task

Show JSON schema

{
   "title": "TrialData",
   "description": "Trialwise Data for a Two-Alternative Forced Choice Task",
   "type": "object",
   "properties": {
      "group": {
         "title": "Group",
         "description": "Path of the parent step group",
         "type": "string"
      },
      "session": {
         "title": "Session",
         "description": "Current training session, increments every time the task is started",
         "type": "integer"
      },
      "session_uuid": {
         "title": "Session Uuid",
         "description": "Each session gets a unique uuid, regardless of the session integer, to enable independent addressing of sessions when session numbers might overlap (eg. reassignment)",
         "type": "string"
      },
      "trial_num": {
         "title": "Trial Num",
         "description": "Trial data is grouped within, well, trials, which increase (rather than resetting) across sessions within a task",
         "datajoint": {
            "key": true
         },
         "type": "integer"
      },
      "target": {
         "title": "Target",
         "description": "Which side is the correct side this trial",
         "datajoint": {
            "datatype": "enum",
            "kwargs": {
               "args": [
                  "L",
                  "R"
               ]
            }
         },
         "enum": [
            "L",
            "R"
         ],
         "type": "string"
      },
      "response": {
         "title": "Response",
         "description": "The side that was poked",
         "datajoint": {
            "datatype": "enum",
            "kwargs": {
               "args": [
                  "L",
                  "R"
               ]
            }
         },
         "enum": [
            "L",
            "R"
         ],
         "type": "string"
      },
      "correct": {
         "title": "Correct",
         "description": "Whether the subject's response matched the target",
         "type": "boolean"
      },
      "correction": {
         "title": "Correction",
         "description": "Whether this trial was a correction trial or not",
         "type": "boolean"
      },
      "RQ_timestamp": {
         "title": "Rq Timestamp",
         "description": "The time where the stimulus was presented and the trial was requested",
         "type": "string",
         "format": "date-time"
      },
      "DC_timestamp": {
         "title": "Dc Timestamp",
         "description": "The time when the subject responded",
         "type": "string",
         "format": "date-time"
      },
      "bailed": {
         "title": "Bailed",
         "description": "Whether the subject bailed the trial from a timeout or any other reason they did not finish",
         "type": "boolean"
      }
   },
   "required": [
      "session",
      "trial_num",
      "target",
      "response",
      "correct",
      "correction",
      "RQ_timestamp",
      "DC_timestamp",
      "bailed"
   ]
}

Fields

DC_timestamp (datetime.datetime)
RQ_timestamp (datetime.datetime)
bailed (bool)
correct (bool)
correction (bool)
response (Literal['L', 'R'])
target (Literal['L', 'R'])

field target: Literal['L', 'R'] [Required]: Which side is the correct side this trial

field response: Literal['L', 'R'] [Required]: The side that was poked

field correct: bool [Required]: Whether the subject’s response matched the target

field correction: bool [Required]: Whether this trial was a correction trial or not

field RQ_timestamp: datetime.datetime [Required]: The time where the stimulus was presented and the trial was requested

field DC_timestamp: datetime.datetime [Required]: The time when the subject responded

field bailed: bool [Required]: Whether the subject bailed the trial from a timeout or any other reason they did not finish

HARDWARE = { 'LEDS': {'C': 'LED_RGB', 'L': 'LED_RGB', 'R': 'LED_RGB'}, 'POKES': {'C': 'Digital_In', 'L': 'Digital_In', 'R': 'Digital_In'}, 'PORTS': {'C': 'Solenoid', 'L': 'Solenoid', 'R': 'Solenoid'}}

request(*args, **kwargs)[source]

Stage 0: compute stimulus, set request trigger in center port.

Returns

With fields:

{
'target': self.target,
'trial_num' : self.current_trial,
'correction': self.correction_trial,
'type': stimulus type,
**stim.PARAMS
}

Return type

data (dict)

discrim(*args, **kwargs)[source]

Stage 1: respond to input, set reward/punishment triggers on target/distractor ports

Returns

With fields::: { ‘RQ_timestamp’: datetime.datetime.now().isoformat(), ‘trial_num’: self.current_trial, }

Return type

data (dict)

reinforcement(*args, **kwargs)[source]

Stage 2 - deliver reward/punishment, end trial.

Returns

With fields:

 {
'DC_timestamp': datetime.datetime.now().isoformat(),
'response': self.response,
'correct': self.correct,
'bailed': self.bailed,
'trial_num': self.current_trial,
'TRIAL_END': True
}

Return type

data (dict)

punish()[source]: Flash lights, play punishment sound if set

respond(pin)[source]

Set self.response

Parameters: pin – Pin to set response to

stim_start()[source]: mark discrim_playing = true

stim_end()[source]

called by stimulus callback

set outside lights blue

flash_leds()[source]: flash lights for punish_dir