Details

Autor(en) / Beteiligte

• Leh, Amalaswintha
• Langhanns, Christine
• Zhao, Fang
• Gaschler, Robert
• Müller, Hermann

Titel

Muscle activity in explicit and implicit sequence learning: Exploring additional measures of learning and certainty via tensor decomposition

Ist Teil von

• Acta psychologica, 2022-06, Vol.226, p.103587-103587, Article 103587

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Sequence learning in serial reaction time tasks (SRTTs) is usually inferred through the reaction time measured by a keyboard. However, this chronometric parameter offers no information beyond the time point of the button-press. We therefore examined whether sequence learning can be measured by muscle activations via electromyography (EMG) in a dual-task paradigm. The primary task was a SRTT, in which the stimuli followed a fixed sequence in some blocks, whereas the sequence was random in the control condition. The secondary task stimulus was always random. One group was informed about the fixed sequence, and the other not. We assessed three dependent variables. The chronometric parameter premotor time represents the duration between stimulus onset and the onset of EMG activity, which indicates the start of the response. The other variables describe the response itself considering the EMG activity after response start. The EMG integral was analyzed, and additionally, tensor decomposition was implemented to assess sequence dependent changes in the contribution of the obtained subcomponents. The results show explicit sequence learning in this dual-task setting. Specifically, the informed group show shorter premotor times in fixed than random sequences as well as larger EMG integral and tensor contributions. Further, increased activity seems to represent response certainty, since a decrease is found for both groups in trials following erroneous responses. Interestingly, the sensitivity to sequence and post-error effects varies between the subcomponents. The results indicate that muscle activity can be a useful indicator of response behavior in addition to chronometric parameters. •A sequence learning dual-task was replicated deriving responses from EMG signals.•Onsets of muscle activations show explicit, but no implicit sequence learning.•Tensor decomposition was possible with the response-related muscle activations.•Decomposition results indicate lower activity in random versus fixed sequences.•Lower activity in trials following errors indicates less certainty.