___________________________________________________________________________________________________________________ *PROBABILISTIC SELECTION TASK* ___________________________________________________________________________________________________________________ Script Author: Katja Borchert, Ph.D. (katjab@millisecond.com) for Millisecond Software, LLC Date: 04-08-2014 last updated: 02-25-2022 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC Script Copyright © 02-25-2022 Millisecond Software ___________________________________________________________________________________________________________________ BACKGROUND INFO ___________________________________________________________________________________________________________________ This script implements a Probabilistic Selection Task, a learning-transfer task that uses different "win" probabilities of symbols presented in pairs during training to investigate participant's selections of 'winning' symbols when confronted with new symbol pairings during a transfer task. References: Frank, M.J., Seeberger, L.C. & O’Reilly. R.C. (2004). By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism. Science, 306, 1940-1943. Doll, B.B., Hutchison, K.E. & Frank, M.J. (2011). Dopaminergic Genes Predict Individual Differences in Susceptibility to Confirmation Bias. The Journal of Neuroscience, April 20, 2011 • 31(16):6188–6198. Millisecond Software thanks J. Bianchi for collaborating on this script! ___________________________________________________________________________________________________________________ TASK DESCRIPTION ___________________________________________________________________________________________________________________ Participants work through a learning and a test phase. Learning Phase: participants get presented with 3 symbol pairs (AB, CD, EF) and have to pick the "winning symbol" of each pair. The win-probabilities of each symbol are controlled in such a way that A wins over B in 80% of the times; C wins over D in 70% of the times, and E wins over F in 60% of the times. The learning phase gets repeated if participants don't select the winning symbol with a pre-determined frequency. Test Phase: by default, this scripts presents all possible pair combinations (15) during the test phase Note: This script provides the possibility to include an instruction manipulation by telling participants that one symbol (always the symbol being assigned to be symbol F) is either the symbol with the 'highest' or 'lowest' probability of winning (see Doll et al, 2011). To run a specific manipulation, go to section Editable Parameters and set parameters.InstructionManipulationCondition accordingly. By default, this script runs the Probabilistic Selection Task without any instruction manipulation (see parameters.InstructionManipulationCondition) ___________________________________________________________________________________________________________________ DURATION ___________________________________________________________________________________________________________________ Practice Block: ~3 minutes per practice block (repeated if learning criteria are not met) Test Block: ~5 minutes ___________________________________________________________________________________________________________________ DATA FILE INFORMATION ___________________________________________________________________________________________________________________ The default data stored in the data files are: (1) Raw data file: 'probabilisticselectiontask_raw*.iqdat' (a separate file for each participant) build: The specific Inquisit version used (the 'build') that was run computer.platform: the platform the script was run on (win/mac/ios/android) date, time: date and time script was run subject, group: with the current subject/groupnumber session: with the current session id (parameter) InstructionManipulationCondition: 0 = no manipulation; (parameter) minE_EF runs as set Manipulation Conditions, (parameter) minE_EF will automatically be set to 0 1 = participants are told that one symbol (symbol F) is the highest winning symbol 2 = participants are told that one symbol (symbol F) is the lowest winning symbol 3 = control group (no manipulation presented but (parameter) minE_EF will automatically be set to 0) (see Doll et al, 2011, for manipulation explanations) (parameter) minA_AB: proportion of A-selections in AB pairs that have to be met in a learning block (default: 0.65) (parameter) minC_CD: proportion of C-selections in CD pairs that have to be met in a learning block (default: 0.6) (parameter) minE_EF: proportion of E-selections in EF pairs that have to be met in a learning block (default: 0.5) !!!!Note: if you use the task within the Instruction Manipulation Framework, (parameter) minE_EF is automatically adjusted to 0 blockcode, blocknum: the name and number of the current block (built-in Inquisit variable) trialcode, trialnum: the name and number of the currently recorded trial (built-in Inquisit variable) Note: trialnum is a built-in Inquisit variable; it counts all trials run; even those that do not store data to the data file such as feedback trials. Thus, trialnum may not reflect the number of main trials run per block. stimulusitem.1/stimulusitem.2: the presented stimuli in order of trial presentation Note: does not need to correspond to spatial presentation of stimuli winletter: contains the winning letter during learning response: the scancode of the participant's response key 30 = A = left 38 = L = right 57 = spacebar selectedletter: contains the letter selected by participant during training and test phase correct: the correctness of the response (1 = correct; 0 = otherwise) latency: the response latency in ms; measured from onset of stimulus probA_ab_training- probC_CD_training: calculates the probentage A/C/E selections for the training pairs (current training block) A- F: contain the symbol itemnumbers assigned to letters A-F (randomly assigned during runtime) Letter A: highest win chance (during training wins with 80% probability) Letter B: lowest win chance (during training wins with 20% probability) countTrainingBlocks: counts the number of training blocks run TrainingPass: 0 = the training criteria were not met (performance should be flagged) 1 = the training criteria were met (2) Summary data file: 'probabilisticselectiontask_summary*.iqdat' (a separate file for each participant) inquisit.version: Inquisit version run computer.platform: the platform the script was run on (win/mac/ios/android) startDate: date script was run startTime: time script was started subjectid: assigned subject id number groupid: assigned group id number sessionid: assigned session id number elapsedTime: time it took to run script (in ms); measured from onset to offset of script completed: 0 = script was not completed (prematurely aborted); 1 = script was completed (all conditions run) (parameter) InstructionManipulationCondition: 0 = no manipulation; (parameter) minE_EF runs as set Manipulation Conditions, (parameter) minE_EF will automatically be set to 0 1 = participants are told that one symbol (symbol F) is the highest winning symbol 2 = participants are told that one symbol (symbol F) is the lowest winning symbol 3 = control group (no manipulation presented but (parameter) minE_EF will automatically be set to 0) (see Doll et al, 2011, for manipulation explanations) (parameter) minA_AB: proportion of A-selections in AB pairs that have to be met in a learning block (default: 0.65) (parameter) minC_CD: proportion of C-selections in CD pairs that have to be met in a learning block (default: 0.6) (parameter) minE_EF: proportion of E-selections in EF pairs that have to be met in a learning block (default: 0.5) !!!!Note: if you use the task within the Instruction Manipulation Framework, (parameter) minE_EF is automatically adjusted to 0 A- F: contain the symbol itemnumbers assigned to letters A-F (randomly assigned during runtime) Letter A: highest win chance (during training wins with 80% probability) Letter B: lowest win chance (during training wins with 20% probability) countTrainingBlocks: counts the number of training blocks run TrainingPass: 0 = the training criteria were not met (performance should be flagged) 1 = the training criteria were met //Training Performance: probA_ab_training: proportion A-selections in AB training pairs probC_CD_training: proportion C-selections in CD training pairs probE_EF_training: proportion E-selections in EF training pairs //Test Performance: probA_AB_test: proportion A-selections in AB_test pairs probA_AC_test: proportion A-selections in AC_test pairs probA_AD_test: proportion A-selections in AD_test pairs probA_AE_test: proportion A-selections in AE_test pairs probA_AF_test: proportion A-selections in AF_test pairs probB_BC_test: proportion B-selections in BC_test pairs probB_BD_test: proportion B-selections in BD_test pairs probB_BE_test: proportion B-selections in BE_test pairs probB_BF_test: proportion B-selections in BF_test pairs probC_CD_test: proportion C-selections in CD_test pairs probC_CE_test: proportion C-selections in CE_test pairs probC_CF_test: proportion C-selections in CF_test pairs probD_DE_test: proportion D-selections in DE_test pairs probD_DF_test: proportion D-selections in DF_test pairs probE_EF_test: proportion E-selections in EF_test pairs ___________________________________________________________________________________________________________________ EXPERIMENTAL SET-UP ___________________________________________________________________________________________________________________ Letter-Symbol-Assignment: each of the 6 symbols gets randomly assigned to 'play the role' of letters A-F Learning Phase: * each learning block runs for 60 trials (20 per symbol pair -> can be edited via parameters.trialnumber_learning) * for any given learning block the computer checks whether all learning criteria have been met during the given learning block -> learning criteria: symbol A is selected at least 65% of the times, symbol C is selected at least 60% of the times, symbol E is selected at least 50% of the times (unless the script is running within the InstructionManipulation Framework in which case no restrictions are placed on pair EF) If those criteria haven't been met in the block, a new learning block starts Test Phase: * the test block runs for 150 trials (10 trials for each of the 15 possible symbol pairs) * by default, this script runs all possible symbols pairs (see list.trialselection_test) in random order Trial Set-up: The left-right position of the symbol pairs is counterbalanced across each block -> symbol A appears on the left side in 50% of the trials (same for all symbols) ___________________________________________________________________________________________________________________ STIMULI ___________________________________________________________________________________________________________________ The stimuli are the 6 Hiragana characters presented in Frank et al (2004). Their size and location can be easily controlled via EDITABLE CODE -> Editable Parameters To use a different set of images, simply replace the stimuli under section Editable Stimuli -> item.symbols Two additional Hiragana symbols were selected for example symbols by Millisecond Software. ___________________________________________________________________________________________________________________ INSTRUCTIONS ___________________________________________________________________________________________________________________ Instructions are not original to the task. They are provided by Millisecond Software as htm/html pages and can be edited by changing the provided htm/html files. To edit htm/html-files: open the respective documents in simple Text Editors such as TextEdit (Mac) or Notepad (Windows). __________________________________________________________________________________________________________________ EDITABLE CODE ___________________________________________________________________________________________________________________ check below for (relatively) easily editable parameters, stimuli, instructions etc. Keep in mind that you can use this script as a template and therefore always "mess" with the entire code to further customize your experiment. The parameters you can change are: / InstructionManipulationCondition: 0 = no manipulation; parameters.minE_EF runs as set Manipulation Conditions, parameters.minE_EF will automatically be set to 0 1 = participants are told that one symbol (symbol F) is the highest winning symbol 2 = participants are told that one symbol (symbol F) is the lowest winning symbol 3 = control group (no manipulation presented but parameters.minE_EF will automatically be set to 0) (see Doll et al, 2011, for manipulation explanations) Learning Criteria: to remove a criterion, set the corresponding value to 0. /minA_AB: proportion of A-selections in AB pairs that have to be met in a learning block (default: 0.65) /minC_CD: proportion of C-selections in CD pairs that have to be met in a learning block (default: 0.6) /minE_EF: proportion of E-selections in EF pairs that have to be met in a learning block (default: 0.5) !!!!Note: if you use the task within the Instruction Manipulation Framework, parameters.minE_EF is automatically adjusted to 0 /maxNumberTrainingBlocks: the maximum number training blocks are run before participants proceed with the test. Performance of participants who fail the last training block will be flagged in the data file. /responsekey_left: response key for symbol on the left (default: "A") /responsekey_right: response key for symbol on the right (default: "L") /readyDuration: the duration (in ms) of the get-ready trial (default: 3000ms) /x_right: horizontal coordinate of the right symbol in screen probentages (default: 65%) /x_left: horizontal coordinate of the left symbol in screen probentages (default: 35%) /symbolheight: the height of the symbol image in screen height probentages (default: 40%) /trialnumber_learning: the number of trials in a learning session (default: 60 -> 20 per symbol pair: AB, CD, EF) Note: the minimum number for the default win probabilities and the balancing of left-right symbol presentation is 10 trials per symbol pair Note: the learning block gets repeated until specific learning criteria are met (see above) within a block /trialnumber_test: the number of trials in the test session (default: 150 -> 10 per symbol pair) !!!Note: by default, all 15 possible combinations of trials are run by this script for the test phase. To remove test combinations that are of little interest, go to list.trialselection_test under section EDITABLE LISTS /feedbackduration: the duration of the feedback stimuli (default: 1000ms) /winA_AB: the winning proportion of A in AB pairs (default: 0.8) /winC_CD: the winning proportion of C in CD pairs (default: 0.7) /winE_EF: the winning proportion of F in EF pairs (default: 0.6)