Several studies of procedural learning in Parkinson's disease (PD) have demonstrated that these patients are impaired with respect to age-matched control subjects. In order to examine more closely the specific impairment, we considered three dimensions along which a procedural learning task could vary. These are: (1) implicit vs explicit learning, (2) instance vs rule learning, and (3) learning with internal vs external error correction. We consider two hypotheses that could explain the impairments observed in PD for different types of explicit motor learning: (H1) an impairment related to the acquisition of rules vs specific instances, and (H2) an impairment in learning when no explicit error feedback is provided. In order to examine the condition of rule learning with external error feedback, we developed a modified version of the serial reaction time (SRT) protocol that tests analogical transfer in sequence learning (ATSL). Reaction times are measured for responses to visual stimuli that appear in several different repeating sequences. While these isomorphic sequences are different, they share a common rule. Verbatim learning of a sequence would result in negative transfer from one sequence to a different one, while rule learning would result in positive transfer. Parkinson's patients and age-matched controls demonstrate significant acquisition and positive transfer of the rule between sequences. Our results demonstrate that PD patients are capable of learning and transferring rule or schema-based representations in an explicit learning format, and that this form of learning may be functionally distinct from learning mechanisms that rely on representations of the verbatim or statistical structure of sequences.

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{{Explor lien
   |wiki=    Wicri/Sante
   |area=    ParkinsonV1
   |flux=    France
   |étape=   Analysis
   |type=    RBID
   |clé=     ISTEX:228779B3C2E8F6FECF5C68D951FD16CEBFA0D972
   |texte=   Analogical transfer is effective in a serial reaction time task in Parkinson's disease: Evidence for a dissociable form of sequence learning
}}