To select or to wait? Response to the commentaries

A narrative review of the anatomy and function of the white matter tracts in language production and comprehension

Much is known about the role of cortical areas in language processing. The shift towards network approaches in recent years has highlighted the importance of uncovering the role of white matter in connecting these areas. However, despite a large body of research, many of these tracts’ functions are not well-understood. We present a comprehensive review of the empirical evidence on the role of eight major tracts that are hypothesized to be involved in language processing (inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, extreme capsule, middle longitudinal fasciculus, superior longitudinal fasciculus, arcuate fasciculus, and frontal aslant tract). For each tract, we hypothesize its role based on the function of the cortical regions it connects. We then evaluate these hypotheses with data from three sources: studies in neurotypical individuals, neuropsychological data, and intraoperative stimulation studies. Finally, we summarize the conclusions supported by the data and highlight the areas needing further investigation.

A little doubt saves many mistakes: Early and late error detection in copy-typing

Based on internal predictions, action-errors can be detected relatively early. Different kinds of sensory feedback further provide information about the occurrence of errors later on. To investigate the mechanisms underlying error detection in copy-typing, ten-finger-typists and hunt-and-peck-typists copy typed with and without visibility of the screen and keyboard. We expected that error detection evolves in slower typing before, during, and after an error. Results showed that more errors were reported with visible screen than with covered screen in both groups underpinning the importance of distal action-effects for error detection. Importantly, ten-finger-typists showed pre-error-slowing in the inter-keystroke-intervals (IKIs) before reported errors, but hunt-and-peck-typists did not. In both groups, error-slowing was observed in the last IKI before both reported and unreported errors. Hence, internal predictions play a role in error detection in both groups, but in ten-finger-typists, internal models may be more precise, leading to earlier error detection. Alternatively, slowing down may increase the probability of detecting errors. Finally, in both groups post-error-slowing indicates that sensory feedback from performing keystrokes contributes to error detection. In conclusion, feedback from distal action-effects (i.e., the screen), movement related feedback, and predictive mechanisms contribute to error detection in typing.

A Comprehension- or a Production-Based Monitor? Response to Roelofs (2020)

Roelofs (2020) has put forth a rebuttal of the criticisms raised against comprehension-based monitoring and has also raised a number of objections against production-based monitors. In this response, I clarify that the model defended by Roelofs is not a comprehension-based monitor, but belongs to a class of monitoring models which I refer to as production-perception models. I review comprehension-based and production-perception models, highlight the strength of each, and point out the differences between them. I then discuss the limitations of both for monitoring production at higher levels, which has been the motivation for production-based monitors. Next, I address the specific criticisms raised by Roelofs (2020) in light of the current evidence. I end by presenting several lines of arguments that preclude a single monitoring mechanism as meeting all the demands of monitoring in a task as complex as communication. A more fruitful avenue is perhaps to focus on what theories are compatible with the nature of representations at specific levels of the production system and with specific aims of monitoring in language production.