The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning.

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences-and the relations between the elements they comprise-are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for "chunking" of these letters into "words." The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.

The perceptual span is dynamically adjusted in response to foveal load by beginning readers.

The perceptual span describes the size of the visual field from which information is obtained during a fixation in reading. Its size depends on characteristics of writing system and reader, but-according to the foveal load hypothesis-it is also adjusted dynamically as a function of lexical processing difficulty. Using the moving window paradigm to manipulate the amount of preview, here we directly test whether the perceptual span shrinks as foveal word difficulty increases. We computed the momentary size of the span from word-based eye-movement measures as a function of foveal word frequency, allowing us to separately describe the perceptual span for information affecting spatial saccade targeting and temporal saccade execution. First fixation duration and gaze duration on the upcoming (parafoveal) word N + 1 were significantly shorter when the current (foveal) word N was more frequent. We show that the word frequency effect is modulated by window size. Fixation durations on word N + 1 decreased with high-frequency words N, but only for large windows, that is, when sufficient parafoveal preview was available. This provides strong support for the foveal load hypothesis. To investigate the development of the foveal load effect, we analyzed data from three waves of a longitudinal study on the perceptual span with German children in Grades 1 to 6. Perceptual span adjustment emerged early in development at around second grade and remained stable in later grades. We conclude that the local modulation of the perceptual span indicates a general cognitive process, perhaps an attentional gradient with rapid readjustment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Prediction and error in early infant speech learning: A speech acquisition model.

In the last two decades, statistical clustering models have emerged as a dominant model of how infants learn the sounds of their language. However, recent empirical and computational evidence suggests that purely statistical clustering methods may not be sufficient to explain speech sound acquisition. To model early development of speech perception, the present study used a two-layer network trained with Rescorla-Wagner learning equations, an implementation of discriminative, error-driven learning. The model contained no a priori linguistic units, such as phonemes or phonetic features. Instead, expectations about the upcoming acoustic speech signal were learned from the surrounding speech signal, with spectral components extracted from an audio recording of child-directed speech as both inputs and outputs of the model. To evaluate model performance, we simulated infant responses in the high-amplitude sucking paradigm using vowel and fricative pairs and continua. The simulations were able to discriminate vowel and consonant pairs and predicted the infant speech perception data. The model also showed the greatest amount of discrimination in the expected spectral frequencies. These results suggest that discriminative error-driven learning may provide a viable approach to modelling early infant speech sound acquisition.

Of mice and men: Speech sound acquisition as discriminative learning from prediction error, not just statistical tracking.

Despite burgeoning evidence that listeners are highly sensitive to statistical distributions of speech cues, the mechanism underlying learning may not be purely statistical tracking. Decades of research in animal learning suggest that learning results from prediction and prediction error. Two artificial language learning experiments test two predictions that distinguish error-driven from purely statistical models; namely, cue competition - specifically, Kamin's (1968) 'blocking' effect (Experiment 1) - and the predictive structure of learning events (Experiment 2). In Experiment 1, prior knowledge of an informative cue blocked learning of a second cue. This finding may help explain second language learners' difficulty in acquiring native-level perception of non-native speech cues. In Experiment 2, learning was better with a discriminative (cue-outcome) order compared to a non-discriminative (outcome-cue) order. Experiment 2 suggests that learning speech cues, including reversing effects of blocking, depends on (un)learning from prediction error and depends on the temporal order of auditory cues versus semantic outcomes. Together, these results show that (a) existing knowledge of acoustic cues can block later learning of new cues, and (b) speech sound acquisition depends on the predictive structure of learning events. When feedback from prediction error is available, this drives learners to ignore salient non-discriminative cues and effectively learn to use target cue dimensions. These findings may have considerable implications for the field of speech acquisition.