Relative cue encoding in the context of sophisticated models of categorization: Separating information from categorization

Acquisition of colour categories through learning: Differences between hue and lightness

Colour categories are acquired through learning, but the nature of this process is not fully understood. Some category distinctions are defined by hue (e.g. red/purple) but other by lightness (red/pink). The aim of this study was to investigate if the acquisition of key information for making accurate cross-boundary discriminations poses different challenges for hue-defined as opposed to lightness-defined boundaries. To answer this question, hue- and lightness-learners were trained on a novel category boundary within the GREEN region of colour space. After training, hue- and lightness-learners as well as untrained controls performed delayed same-different discrimination for lightness and hue pairs. In addition to discrimination data, errors during learning and category-labelling strategies were examined. Errors during learning distributed non-uniformly and in accordance with the Bezold-Brücke effect, which accounts for darker colours at the green-blue boundary appearing greener and lighter colours appearing bluer. Only hue-learners showed discrimination improvements due to category boundary acquisition. Thus, acquisition is more efficient for hue-category compared to lightness-category boundaries. Almost all learners reported using category-labelling strategies, with hue-learners almost exclusively using 'green'/'blue' and lightness learners using a wider range of labels, most often 'light'/'dark'. Thus, labels play an important role in colour category learning and such labelling does not conform to everyday naming: here, the label 'blue' is used for exemplars that would normally be named 'green'. In conclusion, labelling serves the purpose of highlighting key information that differentiates exemplars across the category boundary, and basic colour terms may be particularly effective in facilitating such attentional guidance.

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.

Using Rational Models to Interpret the Results of Experiments on Accent Adaptation

Exposure to unfamiliar non-native speech tends to improve comprehension. One hypothesis holds that listeners adapt to non-native-accented speech through distributional learning—by inferring the statistics of the talker's phonetic cues. Models based on this hypothesis provide a good fit to incremental changes after exposure to atypical native speech. These models have, however, not previously been applied to non-native accents, which typically differ from native speech in many dimensions. Motivated by a seeming failure to replicate a well-replicated finding from accent adaptation, we use ideal observers to test whether our results can be understood solely based on the statistics of the relevant cue distributions in the native- and non-native-accented speech. The simple computational model we use for this purpose can be used predictively by other researchers working on similar questions. All code and data are shared.

When context is and isn't helpful: A corpus study of naturalistic speech

Infants learn about the sounds of their language and adults process the sounds they hear, even though sound categories often overlap in their acoustics. Researchers have suggested that listeners rely on context for these tasks, and have proposed two main ways that context could be helpful: top-down information accounts, which argue that listeners use context to predict which sound will be produced, and normalization accounts, which argue that listeners compensate for the fact that the same sound is produced differently in different contexts by factoring out this systematic context-dependent variability from the acoustics. These ideas have been somewhat conflated in past research, and have rarely been tested on naturalistic speech. We implement top-down and normalization accounts separately and evaluate their relative efficacy on spontaneous speech, using the test case of Japanese vowels. We find that top-down information strategies are effective even on spontaneous speech. Surprisingly, we find that at least one common implementation of normalization is ineffective on spontaneous speech, in contrast to what has been found on lab speech. We provide analyses showing that when there are systematic regularities in which contexts different sounds occur in-which are common in naturalistic speech, but generally controlled for in lab speech-normalization can actually increase category overlap rather than decrease it. This work calls into question the usefulness of normalization in naturalistic listening tasks, and highlights the importance of applying ideas from carefully controlled lab speech to naturalistic, spontaneous speech.