This paper uses the Process Dissociation Procedure to explore whether people can acquire unconscious knowledge in the serial reaction time task [Destrebecqz, A., & Cleeremans, A. . Can sequence learning be implicit? New evidence with the Process Dissociation Procedure. Psychonomic Bulletin & Review, 8, 343–350; Wilkinson, L., & Shanks, D. R. . Intentional control and implicit sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 354–369]. Experiment 1 showed that people generated legal sequences above baseline levels under exclusion (...) instructions. Reward moved exclusion performance towards baseline, indicating that the extent of motivation in the test phase influenced the expression of unconscious knowledge. Experiments 2 and 3 revealed that even with reward, adding noise to the sequences or shortening training led to above-baseline exclusion performance, suggesting that task difficulty and the amount of training also affected the expression of unconscious knowledge. The results help resolve some current debates about the role of conscious awareness in sequence learning. (shrink)

This paper investigates the conscious status of both the knowledge that an item is legal and the knowledge of why it is legal in sequence learning. We compared ability to control use of knowledge with stated awareness of the knowledge as measures of the conscious status of knowledge. Experiment 1 showed that when people could control use of judgment knowledge they were indeed conscious of having that knowledge according to their own statements. Yet Experiment 2 showed that people could exert (...) such control over the use of judgment knowledge when claiming they had no structural knowledge: i.e. conscious judgment knowledge could be based on unconscious structural knowledge. Further implicit learning research should be clear over whether judgment or structural knowledge is claimed to be unconscious as the two dissociate in sequence learning. (shrink)

This paper investigated how implicit and explicit knowledge is reflected in event-related potentials in sequence learning. ERPs were recorded during a serial reaction time task. The results showed that there were greater RT benefits for standard compared with deviant stimuli later than early on, indicating sequence learning. After training, more standard triplets were generated under inclusion than exclusion tests and more standard triplets under exclusion than chance level, indicating that participants acquired both explicit and implicit knowledge. However, deviant targets elicited (...) enhanced N2 and P3 components for targets with explicit knowledge but a larger N2 effect for targets with implicit knowledge, revealing that implicit knowledge expresses itself in relatively early components and explicit knowledge in additional P3 components. The results help resolve current debate about the neural substrates supporting implicit and explicit learning. (shrink)

We investigated the extent to which people could generate sequences of responses based on knowledge acquired from the Serial Reaction Time task, depending on whether it felt subjectively like the response was based on pure guessing, intuition, conscious rules or memories. Norman and Price argued that in the context of our task, intuition responses were the same as guessing responses. In reply, we argue that not only do subjects apparently claim to be experiencing different phenomenologies when saying intuition versus guess, (...) but also intuition and guess responses are associated with different behaviors. We found that people could control the knowledge when generating responses felt to be based on intuition but not those felt to be pure guessing. We present further evidence here that triplets associated with intuition but not guessing were also processed fluently. (shrink)

Although more and more researches have shown that edge-based information is more important than surface-based information in object recognition, it remains unclear whether edge-based features play a more crucial role than surface-based features in category learning. To address this issue, a modified prototype distortion task was adopted in the present study, in which each category was defined by a rule or similarity about either the edge-based features (i.e., contours or shapes) or the corresponding surface-based features (i.e., color and textures). The (...) results of Experiments 1 and 2 showed that when the category was defined by a rule, the performance was significantly better in the edge-based condition than in the surface-based condition in the testing phase, and increasing defined dimensions enhanced rather than reduced performance in the edge-based condition but not in the surface-based condition. The results of Experiment 3 showed that when each category was defined by similarity, there was also a larger learning effect when the category was defined by edge-based dimensions than by surface-based dimensions in the testing phase. The current study is the first to provide convergent evidence that the edge-based information matters more than surface-based information in incidental category learning. (shrink)

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 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 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. (shrink)