####################### PVTdemo.py Description ############################### # PVTdemo.py 10-09-06 replicating Psychomotor Vigilance Task (PVT) model by # Gunzelmann, G.Gluck, K., Van Dongen, H., & Dinges, D.F. in press (2006) # Decreased Arousal as a Result of Sleep Deprivation: The Unraveling of # Cognitive Control (chapter in Modeling Integrated Cognitive Systems) # reaction time model for letter X response X with trial interval 2-10 sec # experimental data collected 10 minutes every 2 hours for 88 hours # (missing latency feedback to the display) # simulating total sleep deprivation (TSD) for 0 1 2 3 days # PythonActor code by Terry Stewart and Bruce Landon #note: description from Loh,Laland,Dorrian,Roach and Dawson (2004) describing #their replication of Dinges and Powell (1985) which is not easily avialable. #PVT ran for a period of 10min. Subjects were required to reapond to a visual #stimulus presented at a variable interval (2000-10000ms) by pressing a button #with the thumb on the dominant hand. The visual stimulus was a four-digit #LED counter turning on and incrementing from 0 to 60s at 1ms. Button press #stopped the LED incrmenting allowing the subject 1s to read their reaction #time before the counter restarted. If a response was not made within 60s #the clock was reset and the counter restarted. If a response was made prior #to the presentaiton of the stimulus, a "False Start" message was displayed #If the button was not released after 3s a reminder message ("Release Button") #was displayed.(there is now a Palm verison of the PVT Saxena & George (2005) ######################## Define task setup ################################## goal_threshold = 1.98 #enable ccm simulation 1.98 1.80 1.66 1.58 TSD 0 1 2 3 utility_threshold = 1.84 #enable simulation 1.84 1.78 1.70 1.64 TSD 0 1 2 3 cycle_noise = 0.0125 # variability in fundamental cycle time of 50ms adjust_utility_threshold = 0.035 # reduce utility threshold after idle cycle repetitions=100 # the number of stimuli to show ####################### Editable parameters above ########################### from ccm.env.objects import *#http://www.carleton.ca/ics/ccmlab/ccmsuite.html import random import math class MatchEnvironment(ObjectEnvironment): def start(self): # define internal variables self.count=0 self.falseAlarm=0 self.lapse=0 self.sleepAttack=0 self.letter=Object(isa='letter',x=0.5,y=0.5,value=None) # initialize self.numrt=0 # for calculating okrtaverage self.sumrt=0 # for calculating okrtaverage self.rtsumX=0 # for calculating self.okrtsd self.rtsumXsqr=0 # for calculating self.okrtsd self.rtnum=0 # for calculating self.okrtsd yield random.uniform(2,10) # random wait 2-10 seconds self.letter.value='X' self.target=self.letter.value self.cueTime=self.now() # get the current time @ccm.parallelize def press(self,key): rt=self.now()-self.cueTime # calculate reaction time log.rt=rt # record the reaction time to the log if rt<0.150 or self.letter.value==None: # response before stimulus self.falseAlarm+=1 elif rt>30: self.sleepAttack+=1 elif rt>0.5: self.lapse+=1 else: self.numrt+=1 # ok rt between >0.15 (falseAlarm) <0.5 (lapse) self.sumrt+=rt self.rtsumX+=rt self.rtsumXsqr+=(rt * rt) self.rtnum+=1 self.letter.value=None self.count+=1 if self.count==repetitions: # convert to proportions scale=1.0/repetitions log.falseAlarm=self.falseAlarm*scale log.lapse=self.lapse*scale log.sleepAttack=self.sleepAttack*scale log.oktraverage=self.sumrt/self.numrt # over 150ms under 500ms log.self.okrtsd= math.sqrt((self.rtnum * self.rtsumXsqr -self.rtsumX * self.rtsumX)/ (self.rtnum * (self.rtnum-1))) self.stop() else: self.model.pgc.goal=goal_threshold # reset goal at start of trial yield random.uniform(2,10) # random inter-trial interval 2-10s self.letter.value='X' # the cue stimuls (for rt counter digits) self.target=self.letter.value self.cueTime=self.now() # start trial reaction timer from ccm.lib.actr import * # http://www.carleton.ca/ics/ccmlab/ccmsuite.html class PMpgcGoalReducer(PMpgc): # enables falling asleep or micro sleeps def below_threshold(self): if env.letter.value==None: return # don't reduce G if no stimuli self.goal-=adjust_utility_threshold # reduce threshold after idle cycle if self.goal < 0 : # self.goal is G which is motivational goal value self.goal=0.0001 #keep G positive even during sleep attack ################# Define the ACT-R Model Components ########################## class MyModel: # minimal model (with no memory,mood,feeling,pain or alertness) goal=Buffer() visual=Buffer() env=DirectEnvironment() vision=SOSVision(visual,delay=0.085, delay_sd=0.01) procedural=Procedural(threshold=utility_threshold, delay_sd=cycle_noise) pgc=PMpgcGoalReducer(procedural,goal=goal_threshold) #sleepless adjust G pnoise=PMNoise(procedural,0.25) # add in the noise part of the utility ################# Define ACT-R Model Production Memories ##################### def attend(goal='attend',vision='busy:False'): # looking for stimulus X vision.request('isa:letter') goal.set('attend') def respond(goal='attend',visual='isa:letter value:?letter'): #see X env.press(letter) # model presses key X to stop the reaction time clock visual.clear() def just_click(goal='attend',vision='busy:False'): # enables false alarms env.press('X') # press X before stimulus appears due to model noise visual.clear() ####################### Define Environment setup ############################ log=ccm.log() # enable log to print out results env=MatchEnvironment() # don't automatically try to log things env.model=ACTR(MyModel,logging=False) #turn off logging internal cycle states env.model.goal.set('attend') # set starting goal buffer # needed to set the P value for 'just_click' to zero env.model.pgc.set('just_click',successes=0,failures=1,lock=True) # turn on visual display or just run env.run() #import ccm.display #ccm.display.Display(env) #env.run(rate=1) # to run at real-time env.run() # run simulation with model