The Visual Advantage Effect in Comparing Uni-Modal and Cross-Modal Probabilistic Category Learning.

People rely on multiple learning systems to complete weather prediction (WP) tasks with visual cues. However, how people perform in audio and audiovisual modalities remains elusive. The present research investigated how the cue modality influences performance in probabilistic category learning and conscious awareness about the category knowledge acquired. A modified weather prediction task was adopted, in which the cues included two dimensions from visual, auditory, or audiovisual modalities. The results of all three experiments revealed better performances in the visual modality relative to the audio and audiovisual modalities. Moreover, participants primarily acquired unconscious knowledge in the audio and audiovisual modalities, while conscious knowledge was acquired in the visual modality. Interestingly, factors such as the amount of training, the complexity of visual stimuli, and the number of objects to which the two cues belonged influenced the amount of conscious knowledge acquired but did not change the visual advantage effect. These findings suggest that individuals can learn probabilistic cues and category associations across different modalities, but a robust visual advantage persists. Specifically, visual associations can be learned more effectively, and are more likely to become conscious. The possible causes and implications of these effects are discussed.

Multisensory transfer effects in implicit and explicit category learning.

In category learning, transfer emerges when individuals apply the knowledge or strategy learned with one set of stimuli to a novel set. Under multisensory circumstances, it remains unclear whether the transfer occurs differently in implicit and explicit category learning, as it remains controversial whether explicit knowledge is indispensable for transfer. To address this issue, we adopted multisensory stimuli in implicit (i.e., information integration, II) and explicit (i.e., conjunctive rule-based, RB) category learning tasks. Experiment 1 showed that when training and novel sets shared the same feature modalities, spontaneous transfer without feedback was perfect in the II condition but impaired in the RB condition. However, with feedback, the dependent transfer occurred equally between conditions. Experiment 2 revealed that when across different feature modalities, no spontaneous transfer was observed in any of the two conditions. A dependent transfer occurred in the RB but not in the II condition. Experiment 3 found that delayed feedback disrupted II task performance but not RB task performance, indicating that people did not learn the II task using an explicit rule. The current study demonstrated that transfer to novel stimuli could emerge in explicit and implicit category learning within feature modalities. However, the transfer across feature modalities could only arise with feedback in explicit learning. We further discuss these findings' meaning with the current category learning theory.

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.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.