Dual-learning systems during speech category learning

Abstract

Dual-system models of visual category learning posit the existence of an explicit, hypothesis-testing reflective system, as well as an implicit, procedural-based reflexive system. The reflective and reflexive learning systems are competitive and neurally dissociable. Relatively little is known about the role of these domain-general learning systems in speech category learning. Given the multidimensional, redundant, and variable nature of acoustic cues in speech categories, our working hypothesis is that speech categories are learned reflexively. To this end, we examined the relative contribution of these learning systems to speech learning in adults. Native English speakers learned to categorize Mandarin tone categories over 480 trials. The training protocol involved trial-by-trial feedback and multiple talkers. Experiments 1 and 2 examined the effect of manipulating the timing (immediate vs. delayed) and information content (full vs. minimal) of feedback. Dual-system models of visual category learning predict that delayed feedback and providing rich, informational feedback enhance reflective learning, while immediate and minimally informative feedback enhance reflexive learning. Across the two experiments, our results show that feedback manipulations that targeted reflexive learning enhanced category learning success. In Experiment 3, we examined the role of trial-to-trial talker information (mixed vs. blocked presentation) on speech category learning success. We hypothesized that the mixed condition would enhance reflexive learning by not allowing an association between talker-related acoustic cues and speech categories. Our results show that the mixed talker condition led to relatively greater accuracies. Our experiments demonstrate that speech categories are optimally learned by training methods that target the reflexive learning system.

Figure 1

Example of multiple-talker stimuli used in the category training study. Fundamental frequency contours of the four tones (T1 = high-level; T2 = low-rising; T3 = low-dipping; T4 = falling) produced by four native Mandarin speakers (2 f). Tone contours were obtained using Praat (Boersma & Weenink, 2011).

Figure 2

Experimental procedures. In Experiments 1-3 we examined the effects of reflexive (top) or reflective (bottom) training manipulations on tone category learning success.

Figure 3

Category learning curves across reflexive vs. reflective conditions in all three experiments: (a) Experiment 1: feedback delay (immediate vs. delayed); (b) Experiment 2: feedback information (minimal vs. full); (c) Experiment 3: talker variability (mixed vs. blocked). Plotted in solid bold lines are the proportions of correct responses across participants within each condition over the course of learning. The black lines denote the reflexive conditions and the red, the reflective conditions. For purposes of visualization of trial-by-trial data, each point in the line denotes the average number of correct responses in a sliding 80-trial window. For trials preceding the 80^th trial, cumulative averages were used. Plotted in thin lines are the ranges of standard error of the averages used in the sliding windows. Visual assessment of the learning curves suggest that both conditions result in equivalent degrees of category learning towards the earlier phase of experiment, but the reflexive condition leads to greater learning than does the reflective condition towards the later phase of the experiment. This pattern is consistent across all three experiments.