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It is widely acknowledged that opaque orthographies place additional demands on learning, often requiring many years to fully acquire. It is less widely recognized, however, that such opacity may offer certain benefits in the context of reading. For example, heterographic homophones such as ⟨knight⟩ and ⟨night⟩ (words that sound the same but which are spelled differently) impose additional costs in learning but reduce ambiguity in reading. Here, we consider the possibility that—left to evolve freely—writing systems will sometimes choose to forego some simplicity for the sake of informativeness when there is functional pressure to do so. We investigate this hypothesis by simulating the evolution of orthography as it is transmitted from one generation to the next, both with and without a communicative pressure for ambiguity avoidance. In addition, we consider two mechanisms by which informative heterography might be selected for: differentiation, in which new spellings are created to differentiate meaning (e.g., ⟨lite⟩ vs. ⟨light⟩), and conservation, in which heterography arises as a byproduct of sound change (e.g., ⟨meat⟩ vs. ⟨meet⟩). Under pressure from learning alone, orthographic systems become transparent, but when combined with communicative pressure, they tend to favor some additional informativeness. Nevertheless, our findings also suggest that, in the long term, simpler, transparent spellings may be preferred in the absence of top-down explicit teaching. [ABSTRACT FROM AUTHOR]

• Different languages concentrate information in different areas of the word. • We hypothesize that readers target words optimally to maximize information uptake. • We test this hypothesis with artificially constructed lexicons and eye tracking. • We support our analyses with a Bayesian cognitive model of visual word recognition. • Our findings suggest lexicon-level information might play a role in natural reading. Skilled readers use multiple heuristics to guide their eye movements during reading. One possible cue that readers may rely on is the way in which information about word identity is typically spread across words. In many (but not all) languages, words are, on average, more informative on the left, predicting that readers should have a preference for left-of-center fixation when targeting words. Any such effect will, however, be modulated by important perceptual constraints and may be masked by various confounding factors. In three experiments with artificially constructed lexicons, we provide causal evidence that the way in which a language distributes information affects how readers land on words. We further support our analyses with a Bayesian cognitive model of visual word recognition that predicts where readers ought to fixate in order to minimize uncertainty about word identity. Taken together, our findings suggest that global properties of the lexicon may play a role in isolated word targeting, and may therefore make a contribution to eye movement behavior in more natural reading settings. [ABSTRACT FROM AUTHOR]

A common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye-tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection. [ABSTRACT FROM AUTHOR]

Language maps signals onto meanings through the use of two distinct types of structure. First, the space of meanings is discretized into categories that are shared by all users of the language. Second, the signals employed by the language are compositional: The meaning of the whole is a function of its parts and the way in which those parts are combined. In three iterated learning experiments using a vast, continuous, open-ended meaning space, we explore the conditions under which both structured categories and structured signals emerge ex nihilo. While previous experiments have been limited to either categorical structure in meanings or compositional structure in signals, these experiments demonstrate that when the meaning space lacks clear preexisting boundaries, more subtle morphological structure that lacks straightforward compositionality-as found in natural languages-may evolve as a solution to joint pressures from learning and communication. [ABSTRACT FROM AUTHOR]

Recent research has shown that semantic category systems, such as color and kinship terms, find an optimal balance between simplicity and informativeness. We argue that this situation arises through pressure for simplicity from learning and pressure for informativeness from communicative interaction, two distinct pressures that often (but not always) pull in opposite directions. Another account argues that learning might also act as a pressure for informativeness, that learners might be biased toward inferring informative systems. This results in two competing hypotheses about the human inductive bias. We formalize these competing hypotheses in a Bayesian iterated learning model in order to simulate what kinds of languages are expected to emerge under each. We then test this model experimentally to investigate whether learners' biases, isolated from any communicative task, are better characterized as favoring simplicity or informativeness. We find strong evidence to support the simplicity account. Furthermore, we show how the application of a simplicity principle in learning can give the impression of a bias for informativeness, even when no such bias is present. Our findings suggest that semantic categories are learned through domain-general principles, negating the need to posit a domain-specific mechanism. [ABSTRACT FROM AUTHOR]

This study examined the migratory patterns of introduced rainbow trout (Oncorhynchus mykiss) in three rivers in Prince Edward Island, Canada, using acoustic telemetry and otolith microchemistry. Only 6% of acoustically tagged fish in three river systems left coastal embayments. A cohort of rainbow trout in all three rivers entered saline waters. Habitat use differed among migrants in the three rivers, as Montague River fish occupied estuary habitat (mean 20.79 PSU) more often than West River and Dunk River fish that tended to occupy both riverine tidal (mean 1.27 and 4.29 PSU, respectively) and freshwater habitats (<0.5 PSU), particularly during summer months (July and August). A second cohort of rainbow trout remained exclusively in fresh water. Migratory individuals were more likely to arise from anadromous mothers, but freshwater mothers produced migratory offspring in all sites. Migratory individuals were significantly larger than nonmigratory freshwater residents. This study suggests that partial residency was the primary strategy, with prominent tidal occupation, while secondary marine and freshwater contingents were included in the full range of successful migratory strategies. [ABSTRACT FROM AUTHOR]