Learning mechanisms in cue reweighting

What do you learn from a single cue? Dimensional reweighting and cue reassociation from experience with a newly unreliable phonetic cue

In language comprehension, we use perceptual cues to infer meanings. Some of these cues reside on perceptual dimensions. For example, the difference between bear and pear is cued by a difference in voice onset time (VOT), which is a continuous perceptual dimension. The present paper asks whether, and when, experience with a single value on a dimension behaving unexpectedly is used by the learner to reweight the whole dimension. We show that learners reweight the whole VOT dimension when exposed to a single VOT value (e.g., 45 ms) and provided with feedback indicating that the speaker intended to produce a /b/ 50% of the time and a /p/ the other 50% of the time. Importantly, dimensional reweighting occurs only if 1) the 50/50 feedback is unexpected for the VOT value, and 2) there is another dimension that is predictive of feedback. When no predictive dimension is available, listeners reassociate the experienced VOT value with the more surprising outcome but do not downweight the entire VOT dimension. These results provide support for perceptual representations of speech sounds that combine cues and dimensions, for viewing perceptual learning in speech as a combination of error-driven cue reassociation and dimensional reweighting, and for considering dimensional reweighting to be reallocation of attention that occurs only when there is evidence that reallocating attention would improve prediction accuracy (Harmon, Z., Idemaru, K., & Kapatsinski, V. 2019. Learning mechanisms in cue reweighting. Cognition, 189, 76-88.).

Transfer of statistical learning from passive speech perception to speech production

Communicating with a speaker with a different accent can affect one's own speech. Despite the strength of evidence for perception-production transfer in speech, the nature of transfer has remained elusive, with variable results regarding the acoustic properties that transfer between speakers and the characteristics of the speakers who exhibit transfer. The current study investigates perception-production transfer through the lens of statistical learning across passive exposure to speech. Participants experienced a short sequence of acoustically variable minimal pair (beer/pier) utterances conveying either an accent or typical American English acoustics, categorized a perceptually ambiguous test stimulus, and then repeated the test stimulus aloud. In the canonical condition, /b/-/p/ fundamental frequency (F0) and voice onset time (VOT) covaried according to typical English patterns. In the reverse condition, the F0xVOT relationship reversed to create an "accent" with speech input regularities atypical of American English. Replicating prior studies, F0 played less of a role in perceptual speech categorization in reverse compared with canonical statistical contexts. Critically, this down-weighting transferred to production, with systematic down-weighting of F0 in listeners' own speech productions in reverse compared with canonical contexts that was robust across male and female participants. Thus, the mapping of acoustics to speech categories is rapidly adjusted by short-term statistical learning across passive listening and these adjustments transfer to influence listeners' own speech productions.

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.

What we do (not) know about the mechanisms underlying adaptive speech perception: A computational framework and review

Speech from unfamiliar talkers can be difficult to comprehend initially. These difficulties tend to dissipate with exposure, sometimes within minutes or less. Adaptivity in response to unfamiliar input is now considered a fundamental property of speech perception, and research over the past two decades has made substantial progress in identifying its characteristics. The mechanisms underlying adaptive speech perception, however, remain unknown. Past work has attributed facilitatory effects of exposure to any one of three qualitatively different hypothesized mechanisms: (1) low-level, pre-linguistic, signal normalization, (2) changes in/selection of linguistic representations, or (3) changes in post-perceptual decision-making. Direct comparisons of these hypotheses, or combinations thereof, have been lacking. We describe a general computational framework for adaptive speech perception (ASP) that-for the first time-implements all three mechanisms. We demonstrate how the framework can be used to derive predictions for experiments on perception from the acoustic properties of the stimuli. Using this approach, we find that-at the level of data analysis presently employed by most studies in the field-the signature results of influential experimental paradigms do not distinguish between the three mechanisms. This highlights the need for a change in research practices, so that future experiments provide more informative results. We recommend specific changes to experimental paradigms and data analysis. All data and code for this study are shared via OSF, including the R markdown document that this article is generated from, and an R library that implements the models we present.

Short-term perceptual reweighting in suprasegmental categorization

Segmental speech units such as phonemes are described as multidimensional categories whose perception involves contributions from multiple acoustic input dimensions, and the relative perceptual weights of these dimensions respond dynamically to context. For example, when speech is altered to create an "accent" in which two acoustic dimensions are correlated in a manner opposite that of long-term experience, the dimension that carries less perceptual weight is down-weighted to contribute less in category decisions. It remains unclear, however, whether this short-term reweighting extends to perception of suprasegmental features that span multiple phonemes, syllables, or words, in part because it has remained debatable whether suprasegmental features are perceived categorically. Here, we investigated the relative contribution of two acoustic dimensions to word emphasis. Participants categorized instances of a two-word phrase pronounced with typical covariation of fundamental frequency (F0) and duration, and in the context of an artificial "accent" in which F0 and duration (established in prior research on English speech as "primary" and "secondary" dimensions, respectively) covaried atypically. When categorizing "accented" speech, listeners rapidly down-weighted the secondary dimension (duration). This result indicates that listeners continually track short-term regularities across speech input and dynamically adjust the weight of acoustic evidence for suprasegmental decisions. Thus, dimension-based statistical learning appears to be a widespread phenomenon in speech perception extending to both segmental and suprasegmental categorization.

Atypical Reinforcement Learning in Developmental Dyslexia

OBJECTIVES

According to the Procedural Deficit Hypothesis, abnormalities in corticostriatal pathways could account for the language-related deficits observed in developmental dyslexia. The same neural network has also been implicated in the ability to learn contingencies based on trial and error (i.e., reinforcement learning [RL]). On this basis, the present study tested the assumption that dyslexic individuals would be impaired in RL compared with neurotypicals in two different tasks.

METHODS

In a probabilistic selection task, participants were required to learn reinforcement contingencies based on probabilistic feedback. In an implicit transitive inference task, participants were also required to base their decisions on reinforcement histories, but feedback was deterministic and stimulus pairs were partially overlapping, such that participants were required to learn hierarchical relations.

RESULTS

Across tasks, results revealed that although the ability to learn from positive/negative feedback did not differ between the two groups, the learning of reinforcement contingencies was poorer in the dyslexia group compared with the neurotypicals group. Furthermore, in novel test pairs where previously learned information was presented in new combinations, dyslexic individuals performed similarly to neurotypicals.

CONCLUSIONS

Taken together, these results suggest that learning of reinforcement contingencies occurs less robustly in individuals with developmental dyslexia. Inferences for the neuro-cognitive mechanisms of developmental dyslexia are discussed.

Differences in perceptual assimilation following training

Learning to perceive non-native speech sounds is difficult for adults. One method to improve perception of non-native contrasts is through a distributional learning paradigm. Three groups of native-English listeners completed a perceptual assimilation task in which they mapped French vowels onto English vowel categories: Two groups (bimodal, unimodal distribution) completed a perceptual learning task for the French /œ/-/o/ contrast and a third completed no training. Both trained groups differed from the untrained group, but participants in the bimodal group showed a different perceptual mapping for the targeted /œ/ vowel, suggesting that the bimodal condition may maximize perception of non-native contrasts.

Training a non-native vowel contrast with a distributional learning paradigm results in improved perception and production

Previous distributional learning research suggests that adults can improve perception of a non-native contrast more efficiently when exposed to a bimodal than a unimodal distribution. Studies have also suggested that perceptual learning can transfer to production. The current study tested whether the addition of visual images to reinforce the contrast and active learning with feedback would result in lcearning in both conditions and would transfer to gains in production. Native English-speaking adults heard stimuli from a bimodal or unimodal /o/-/œ/ continuum. No group differences were found on a discrimination task, possibly suggesting that the supports eliminated previously documented group differences. On an identification task, listeners in the bimodal group showed better performance than the unimodal group on the endpoint stimuli. Production results indicated that both groups showed increased Euclidean distance between the target vowels after training, suggesting that perceptual training improved production skills in both conditions. Contrary to expectations, degree of perception and production learning were not correlated. Together, these results suggest that a bimodal distribution may aid learning, but that adding images to reinforce the contrast and active learning to the training paradigm could mitigate disadvantages found previously for participants exposed to a unimodal distribution.