DIFFERENTIAL CONTRIBUTIONS OF IMPLICIT AND EXPLICIT LEARNING MECHANISMS TO VARIOUS CONTEXTUAL CUES IN DUAL ADAPTATION ---------- **GITHUB:** https://github.com/ayalamar/sequence This GitHub repository contains all scripts necessary to run the experiments, and recreate figures and analyses for this project. **The files on GitHub are:** 1) [Analysis][1] (contains all R scripts for analysing and plotting data) - `dataPrepro_single.R` preprocesses data and `dataAnalysis_single.R` plots and runs statistics on Single control groups - `dataPrepro.R` preprocesses data and `dataAnalysis.R` plots and runs statistics on Experiments 1a, 1b, and the Non-instructed condition in Experiment 2 - `dataAnalysis_explicit.R` plots and runs statistics on the Instructed condition in Experiment 2 - `dataAnalysis_Omnibus.R` runs omnibus tests between Single control groups and Dual conditions. - `plotTrajectories_dual.R` plots splined cursor trajectories for all dual conditions - `plotTrajectories_single.R` plots splined cursor trajectories for Single control conditions 2. [Experiments][2] (contains all Python code for running the experiments in their own respective directories) 3. The folder ["PreQuence data prepre"][3] which contains scripts that add some missing stamps to raw data of Experiment 1b ---------- **OSF:** https://osf.io/v2hwp/files/ The project OSF contains all data necessary to recreate plots and analysis, along with some supplementary files and preprint. The files on OSF are: 1. `DA_sequence_preMS.pdf` - preprint 2. `DA_sequence_preMS_Supplementary tables.pdf` - supplementary tables for preprint 3. `CWONLY_allTaggedData_n10_ALL.csv `- Single CCW-only dataset ** 4. `CCWONLY_allTaggedData_n10_ALL.csv ` - Single CW-only dataset ** 5. `ACTIVEMOVEMENT_allTaggedData_n28_ALL.csv` & `PASSIVEMOVEMENT_allTaggedData_n30_ALL.csv ` - Experiment 1a dataset (Active Follow-through and Passive Follow-through conditions, respectively) ** 6. `ACTIVELEADIN_allTaggedData_n31_ALL.csv ` - Experiment 1b dataset ** 7. `STATICVISUAL_allTaggedData_n12_ALL.csv ` & `INSTRUCTION_allTaggedData_n12_ALL.csv ` - Experiment 2 dataset (Non-instructed and Instructed conditions, respectively) ** 8. `post_exp_interview.xlsx` - Post experiment questions and notes about participant responses **ALL DATA ON OSF CONTAIN EXCLUDED TRIALS FROM PRESCREENING (COLUMN "SELECTION_1") ---------- **ABSTRACT:** The ability to switch between different tasks accurately and efficiently is an invaluable feature to a flexible and adaptive human motor system. This can be examined in dual adaptation paradigms where the motor system is challenged to perform under randomly switching, opposing perturbations. Typically, dual adaptation doesn’t proceed unless each mapping is trained in association with a predictive cue. To investigate this, we first explored whether dual adaptation occurs under a variety of contextual cues including active follow-through movements, passive follow-through movements, active three-part lead-in movements, and static visual cues. In a final intervention, we provided our Instructed group with a compensatory strategy about the perturbations (30° CW/CCW rotations) and their relationships to each context (static visual cues). This allowed us to explore the extent by which dual learning is supported by both implicit and explicit mechanisms, regardless of whether or not they elicited dual adaptation across all the various cues. To this end, following perturbed training, participants from all experiments were asked to either use or ignore the strategy as they reached without visual feedback. This Process Dissociation Procedure teased apart the implicit and explicit contributions to dual adaptation. We found that active movement cues, but not passive ones, elicited dual adaptation. Expectedly, static visual cues didn’t elicit dual adaptation, but those in the Instruction group compensated by implementing aiming strategies. Critically, we found no implicit contributions in this Instruction group, but an effect of instruction, suggesting that explicit aiming strategies inhibit implicit mechanisms in dual adaptation. Thus, by implementing conscious strategies, dual adaptation can be easily facilitated even in cases where learning would not occur otherwise. [1]: https://github.com/ayalamar/sequence/tree/master/analysis [2]: https://github.com/ayalamar/sequence/tree/master/experiments [3]: https://github.com/ayalamar/sequence/tree/master/analysis/PreQuence%20data%20prepre