T. Florian Jaeger

Exogenous attention is a rapid, involuntary mechanism that automatically reallocates processing resources toward salient stimuli. It enhances visual sensitivity in the vicinity of the salient stimulus, both in extrafoveal regions and within the high-acuity foveola. While the spatial frequencies (SFs) modulated by exogenous attention in extrafoveal vision are well characterized, it remains unknown how this mechanism operates within the foveola, which can resolve SFs up to 30 cycles per degree (CPD). Here, we examined which SFs were enhanced by fine-grained deployments of exogenous attention within this highest-acuity region of the visual field. Using high-precision eye-tracking to precisely localize gaze during attentional allocation, we found that exogenous attention at the foveal scale selectively enhances contrast sensitivity for low- to mid-range SFs (4–8 CPD), with no significant benefits for higher SFs (12–20 CPD). In contrast, attention-related benefits on asymptotic performance at the highest contrast were observed across a wide range of SFs. These results indicate that, despite the high-resolution capacity of the foveola, exogenous attention remains an inflexible mechanism that, even at this scale, selectively enhances contrast gain for lower SFs—mirroring its behavior in extrafoveal vision.

Human speech recognition tends to be robust, despite substantial cross-talker variability. Believed to be critical to this ability are auditory normalization mechanisms whereby listeners adapt to individual differences in vocal tract physiology. This study investigates the computations involved in such normalization. Two 8-way alternative forced-choice experiments assessed L1 listeners' categorizations across the entire US English vowel space—both for unaltered and synthesized stimuli. Listeners' responses in these experiments were compared against the predictions of 20 influential normalization accounts that differ starkly in the inference and memory capacities they imply for speech perception. This includes variants of estimation-free transformations into psycho-acoustic spaces, intrinsic normalizations relative to concurrent acoustic properties, and extrinsic normalizations relative to talker-specific statistics. Listeners' responses were best explained by extrinsic normalization, suggesting that listeners learn and store distributional properties of talkers' speech. Specifically, computationally simple (single-parameter) extrinsic normalization best fit listeners' responses. This simple extrinsic normalization also clearly outperformed Lobanov normalization—a computationally more complex account that remains popular in research on phonetics and phonology, sociolinguistics, typology, and language acquisition.

Accurate word recognition is facilitated by context. Some relevant context, however, occurs after the word. Rational use of such “right context” would require listeners to have maintained uncertainty or subcategorical information about the word, thus allowing for consideration of possible alternatives when they encounter relevant right context. A classic study continues to be widely cited as evidence that subcategorical information maintenance is limited to highly ambiguous percepts and short time spans (Connine et al., 1991). More recent studies, however, using other phonological contrasts, and sometimes other paradigms, have return mixed results. We identify procedural and analytical issues that provide an explanation for existing results. We address these issues in two reanalyses of previously published results and two new experiments. In all four cases, we find consistent evidence against both limitations reported in Connine et al.’s seminal work: at least within the classic paradigms. Key to our approach is the introduction of an ideal observer framework to derive normative predictions for human word recognition expected if listeners maintain and integrate subcategorical information about preceding speech input optimally with subsequent context. We test these predictions in Bayesian generalized linear mixed-effect analyses, including at the level of individual participants.

This erratum concerns a coding mistake in the implementation of the generalized additive mixed model (GAMM) analysis of the original paper. This mistake does no

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 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 common 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.

Head-Marking and Agreement: Evidence from Yucatec Maya was published in Explorations of the Syntax-Semantics Interface on page 169.

Speech perception depends on the ability to generalize previously experienced input effectively across talkers. How such cross-talker generalization is achieved has remained an open question. In a seminal study, Bradlow & Bent (2008, henceforth BB08) found that exposure to just five minutes of accented speech can elicit improved recognition that generalizes to an unfamiliar talker of the same accent (N=70 participants). Cross-talker generalization was, however, only observed after exposure to multiple talkers of the accent, not after exposure to a single accented talker. This contrast between single- and multi-talker exposure has been highly influential beyond research on speech perception, suggesting a critical role of exposure variability in learning and generalization. We assess the replicability of BB08’s findings in two large-scale perception experiments (total N=640) including 20 unique combinations of exposure and test talkers. Like BB08, we find robust evidence for cross-talker generalization after multi-talker exposure. Unlike BB08, we also find evidence for generalization after single-talker exposure. The degree of cross-talker generalization depends on the specific combination of exposure and test talker. This and other recent findings suggest that exposure to cross-talker variability is not necessary for cross-talker generalization. Variability during exposure might affect generalization only indirectly, mediated through the informativeness of exposure about subsequent speech during test: similarity-based inferences can explain both the original BB08 and the present findings. We present Bayesian data analysis, including Bayesian meta-analyses and replication tests for generalized linear mixed models. All data, stimuli, and reproducible literate (R markdown) code are shared via OSF.