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Automaticity is still ill-understood, and its relation to habit formation and skill acquisition is highly debated. Recently, the principle of caching has been advanced as a potentially promising avenue for studying automaticity. It is roughly understood as a means of storing direct input-output associations in a manner that supports instant lookup. We raise various concerns that should be addressed before the theoretical progress afforded by this principle can be evaluated. Is caching merely a metaphor for computer caching or is it a computational model that can be used to derive testable predictions? How do the short-term and long-term effects of automaticity relate to the distinction between working memory and long-term memory? Does caching apply to stimulus-response associations – as already suggested by Logan’s instance theory – or to algorithms, too? How much practice is required for caching and how does caching depend on the task’s type? What is the relation between control processes and caching as these pertain to the possible suppression of automatic processes? Dealing with these questions will arguably also advance our understanding of automaticity.

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time.

Addiction models theorise that alcohol attention bias (AAB) for alcohol-related cues develops through a process of classical conditioning and that attentional processes shift from controlled to automatically modulated responses. At the point of automaticity, alcohol cues grab the attention of problem drinkers beyond conscious control and can trigger alcohol use. To fully understand this shift, AAB should be thought of as developing on a continuum from when alcohol use commences. Despite this, little is known about AAB differences in younger populations who are at an early stage in their exposure to alcohol and related cues. This study compared AAB for alcohol cues across age groups (early adolescent, late adolescent, and young adult) and drinking groups (heavy drinkers, light drinkers, and non-drinkers) to provide a cross-sectional examination of differences in AAB and their relationship to alcohol use and age. Eye tracking was employed to measure several elements of attentional processing during exposure to alcohol cues. Differences across age groups and drinking groups were examined. Differences in controlled attention were found between heavy and light drinkers. As age increases, a shift towards automaticity can be seen with alcohol-related cues attracting the attention of young adult drinkers earlier in stimulus presentation. This cross-sectional approach provides an insight into AAB across a key developmental period. It highlights that influential processes underpinning AAB may change and how rapidly it may approach automaticity. The implications of these findings are discussed.

Addiction models theorise that alcohol attention bias (AAB) for alcohol-related cues develops through a process of classical conditioning and that attentional processes shift from controlled to automatically modulated responses. At the point of automaticity, alcohol cues grab the attention of problem drinkers beyond conscious control and can trigger alcohol use. To fully understand this shift, AAB should be thought of as developing on a continuum from when alcohol use commences. Despite this, little is known about AAB differences in younger populations who are at an early stage in their exposure to alcohol and related cues. This study compared AAB for alcohol cues across age groups (early adolescent, late adolescent, and young adult) and drinking groups (heavy drinkers, light drinkers, and non-drinkers) to provide a cross-sectional examination of differences in AAB and their relationship to alcohol use and age. Eye tracking was employed to measure several elements of attentional processing during exposure to alcohol cues. Differences across age groups and drinking groups were examined. Differences in controlled attention were found between heavy and light drinkers. As age increases, a shift towards automaticity can be seen with alcohol-related cues attracting the attention of young adult drinkers earlier in stimulus presentation. This cross-sectional approach provides an insight into AAB across a key developmental period. It highlights that influential processes underpinning AAB may change and how rapidly it may approach automaticity. The implications of these findings are discussed.

Theories of writing development and accumulating evidence indicate that handwriting automaticity is related to the development of effective writing skills, and that writing and reading skills are also associated with each other. However, less is known about the nature of these associations and the role of instructional factors in the early years. The present study examines: (1) the influence of handwriting automaticity in the writing and reading performance of Year 1 students, both concurrently and across time; (2) associations between students’ writing and reading performance and writing instruction. The current study involved 154 children enrolled in 24 classrooms from seven government-funded primary schools in Western Australia. Handwriting automaticity and word-reading were assessed at the end of kindergarten (Mage = 70 months, SD = 4.37 months) and a year later at the end of Year 1 (Mage = 82 months, SD = 3.64 months). Child-level measures of writing quality and production as well as teacher-reported measures of writing instruction were added in Year 1. Teachers reported on amount and type of writing instruction (i.e., teaching basic skills and teaching writing processes) and amount of writing practice in their classrooms. Data analyses included multilevel modelling. Handwriting automaticity predicted writing quality and production concurrently and across time after accounting for gender and initial word-reading skills. Handwriting automaticity predicted reading performance across time. Writing and reading performance were associated with amount of writing practice, while teaching planning and revising were positively associated with writing performance. Implications for writing development and writing instruction are discussed.

Alzheimer’s disease (AD) is characterized by a distinct pattern of cortical thinning and resultant changes in cognition and function. These result in prominent deficits in cognitive-motor automaticity. The relationship between AD-related cortical thinning and decreased automaticity is not well-understood. We aimed to investigate the relationship between cortical thickness regions-of-interest (ROI) and automaticity and attention allocation in AD using hypothesis-driven and exploratory approaches. We performed an ROI analysis of 46 patients with AD. Data regarding MR images, demographic characteristics, cognitive-motor dual task performance, and cognition were extracted from medical records. Cortical thickness was calculated from MR T1 images using FreeSurfer. Data from the dual task assessment was used to calculate the combined dual task effect (cDTE), a measure of cognitive-motor automaticity, and the modified attention allocation index (mAAI). Four hierarchical multiple linear regression models were conducted regressing cDTE and mAAI separately on (1) hypothesis-generated ROIs and (2) exploratory ROIs. For cDTE, cortical thicknesses explained 20.5% (p = 0.014) and 25.9% (p = 0.002) variability in automaticity in the hypothesized ROI and exploratory models, respectively. The dorsal lateral prefrontal cortex (DLPFC) (β =  − 0.479, p = 0.018) and superior parietal cortex (SPC) (β = 0.467, p = 0.003), and were predictors of automaticity. For mAAI, cortical thicknesses explained 20.7% (p = 0.025) and 28.3% (p = 0.003) variability in attention allocation in the hypothesized ROI and exploratory models, respectively. Thinning of SPC and fusiform gyrus were associated with motor prioritization (β =  − 0.405, p = 0.013 and β =  − 0.632, p = 0.004, respectively), whereas thinning of the DLPFC was associated with cognitive prioritization (β = 0.523, p = 0.022). Cortical thinning in AD was related to cognitive-motor automaticity and task prioritization, particularly in the DLPFC and SPC. This suggests that these regions may play a primary role in automaticity and attentional strategy during dual-tasking.

It is generally accepted that augmented feedback, provided by a human expert or a technical display, effectively enhances motor learning. However, discussion of the way to most effectively provide augmented feedback has been controversial. Related studies have focused primarily on simple or artificial tasks enhanced by visual feedback. Recently, technical advances have made it possible also to investigate more complex, realistic motor tasks and to implement not only visual, but also auditory, haptic, or multimodal augmented feedback. The aim of this review is to address the potential of augmented unimodal and multimodal feedback in the framework of motor learning theories. The review addresses the reasons for the different impacts of feedback strategies within or between the visual, auditory, and haptic modalities and the challenges that need to be overcome to provide appropriate feedback in these modalities, either in isolation or in combination. Accordingly, the design criteria for successful visual, auditory, haptic, and multimodal feedback are elaborated.

Second language (L2) listening requires efficient processing of continuing incoming information (Vandergrift & Goh, 2012). Even so, research into individual differences in L2 listening has mostly shed light on the role of linguistic knowledge measured without time pressure (e.g., Mecarty, 2000; Wang & Treffers-Daller, 2017; cf. Vafaee & Suzuki, 2020), leaving the role of processing speed and automaticity largely unexplored. To close this gap, we explored the determinants of successful listening using three processing tasks at lexical, syntactic, and propositional levels. Participants were 44 Chinese learners of English. Response accuracy afforded measures of vocabulary size, syntactic parsing skills, and formulation of propositional meaning. Reaction times and the coefficient of variation (Segalowitz & Segalowitz, 1993) afforded processing speed and automaticity measures at each level. We found a hierarchical relationship between different levels of processing, whereby lower-level, lexical effects cascade up and are mediated by propositional comprehension in accounting for listening comprehension. The results highlight the importance of considering processing accuracy and speed at different levels of the linguistic hierarchy to explain variability among L2 listeners. Different from most previous studies, we argue for a need to consider the temporal aspects of processing, along with linguistic knowledge, in modeling L2 listening.

We introduce the (extrinsic) relational Simon task as a tool for capturing automatic relational stimulus processing. In three experiments, participants responded to a perceptual relation between two stimuli. Results showed that participants were faster and more accurate to respond when the (task-irrelevant) conceptual relation between these stimuli was compatible (rather than incompatible) with the (extrinsic) relational meaning of the required responses. This effect was replicated irrespective of the type of stimulus materials used, irrespective of the similarity between the relational information that was task-relevant and the relational information that was task-irrelevant, and irrespective of the complexity of the task-irrelevant relational information. Our findings add to a growing body of evidence showing that relational stimulus processing can occur under conditions of automaticity.