Effects of learning duration on implicit transfer

Interleaved practice benefits implicit sequence learning and transfer

Compared to blocked practice, interleaved practice of different tasks leads to superior long-term retention despite poorer initial acquisition performance. This phenomenon, the contextual interference effect, is well documented in various domains but it is not yet clear if it persists in the absence of explicit knowledge in terms of fine motor sequence learning. Additionally, while there is some evidence that interleaved practice leads to improved transfer of learning to similar actions, transfer of implicit motor sequence learning has not been explored. The present studies used a serial reaction time task where participants practiced three different eight-item sequences that were either interleaved or blocked on Day 1 (training) and Day 2 (testing). In Experiment 1, the retention of the three training sequences was tested on Day 2 and in Experiment 2, three novel sequences were performed on Day 2 to measure transfer. We assessed whether subjects were aware of the sequences to determine whether the benefit of interleaved practice extends to implicitly learned sequences. Even for participants who reported no awareness of the sequences, interleaving led to a benefit for both retention and transfer compared to participants who practiced blocked sequences. Those who trained with blocked sequences were left unprepared for interleaved sequences at test, while those who trained with interleaved sequences were unaffected by testing condition, revealing that learning resulting from blocked practice may be less flexible and more vulnerable to testing conditions. These results indicate that the benefit of interleaved practice extends to implicit motor sequence learning and transfer.

A Dual Simple Recurrent Network Model for Chunking and Abstract Processes in Sequence Learning

Although many studies have provided evidence that abstract knowledge can be acquired in artificial grammar learning, it remains unclear how abstract knowledge can be attained in sequence learning. To address this issue, we proposed a dual simple recurrent network (DSRN) model that includes a surface SRN encoding and predicting the surface properties of stimuli and an abstract SRN encoding and predicting the abstract properties of stimuli. The results of Simulations 1 and 2 showed that the DSRN model can account for learning effects in the serial reaction time (SRT) task under different conditions, and the manipulation of the contribution weight of each SRN accounted for the contribution of conscious and unconscious processes in inclusion and exclusion tests in previous studies. The results of human performance in Simulation 3 provided further evidence that people can implicitly learn both chunking and abstract knowledge in sequence learning, and the results of Simulation 3 confirmed that the DSRN model can account for how people implicitly acquire the two types of knowledge in sequence learning. These findings extend the learning ability of the SRN model and help understand how different types of knowledge can be acquired implicitly in sequence learning.

Impact of Response Stimulus Interval on Transfer of Non-local Dependent Rules in Implicit Learning: An ERP Investigation

In the literature on implicit learning, controversy exists regarding whether the knowledge obtained from implicit sequence learning consists of context-bound superficial features or context-free structural rules. To explore the nature of implicit knowledge, event related potentials (ERP) recordings of participants’ performances in a non-local dependent transfer task under two response-stimulus-interval (RSI) conditions (250 and 750 ms) were obtained. In the behavioral data, a transfer effect was found in the 750 ms RSI condition but not in the 250 ms RSI condition, suggesting that a long RSI is the basis for the occurrence of non-local dependent transfer, as which might have provided enough reaction time for participants to process and capture the implicit rule. Moreover, P300 amplitude was found to be sensitive to the impact of RSI on the training process (i.e., the longer RSI elicited higher P300 amplitudes), while variations in both N200 (i.e., a significant increase) and P300 amplitudes (i.e., a significant decrease) were found to be related to the presence of a transfer effect. Our results supported the claim that implicit learning can involve abstract rule knowledge acquisition under an appropriate RSI condition, and that amplitude variation in early ERP components (i.e., N200 and P300) can be useful indexes of non-local dependent learning and transfer effects.

Impacts of visuomotor sequence learning methods on speed and accuracy: Starting over from the beginning or from the point of error

The present study examined whether sequence learning led to more accurate and shorter performance time if people who are learning a sequence start over from the beginning when they make an error (i.e., practice the whole sequence) or only from the point of error (i.e., practice a part of the sequence). We used a visuomotor sequence learning paradigm with a trial-and-error procedure. In Experiment 1, we found fewer errors, and shorter performance time for those who restarted their performance from the beginning of the sequence as compared to those who restarted from the point at which an error occurred, indicating better learning of spatial and motor representations of the sequence. This might be because the learned elements were repeated when the next performance started over from the beginning. In subsequent experiments, we increased the occasions for the repetitions of learned elements by modulating the number of fresh start points in the sequence after errors. The results showed that fewer fresh start points were likely to lead to fewer errors and shorter performance time, indicating that the repetitions of learned elements enabled participants to develop stronger spatial and motor representations of the sequence. Thus, a single or two fresh start points in the sequence (i.e., starting over only from the beginning or from the beginning or midpoint of the sequence after errors) is likely to lead to more accurate and faster performance.