This is a commentary for a special issue on predictive processing and rational constructivist models of development. Mainly I use the opportunity to ask a bunch of questions about what these theoretical frameworks show us (and what they do not) and mostly where the open questions still are. To get meta for a moment, I thought these questions were the best way to maximize the value of my commentary: They have the highest probability of leading to the most uncertainty reduction for our field in the long term. Please read in thatspirit.

Our hands are always with us and are used for communication all over the world. When children do not have an established language model to learn from, they use their hands to gesture, and these gestures take on the forms of language. In this role, the hands reveal the fundamental properties of the mind that give shape to language. When children do learn an established language, they again use their hands to gesture. These gestures do not look like language but form an integrated system with language. In this role, the hands can convey ideas not found in the language they accompany. In both contexts, gesture provides a clear view of the mind hidden in our hands.

Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long-term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT-R models of performance on the stimulus-response learning, Reinforcement Learning Working Memory task. The task consisted of short 3-image, and long 6-image, feedback-based learning blocks. A no-feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single-mechanism RL and LTM models, and two integrated RL-LTM models: (a) RL-based meta-learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL-LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta-RL: 13.25%, bias-RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus-response task. Finally, long-term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3- vs 6-image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated-models, owing to the strength of our idiographic approach.

Modulation of visual attention in the Visual World Paradigm relies on parallel processing of linguistic and visual information. Previous studies have argued that the human linguistic capacity includes an aspect of anticipation of upcoming material. Such anticipation can be triggered by both lexical and grammatical/morphosyntactic cues. In this study, we investigated the relationship between comprehension and prediction by testing how subtle changes in visual representations can affect the processing of grammatical case cues in Russian by Russian‐German bilingual children (n = 49, age 8−13). The linguistic manipulation followed previous designs, contrasting SVO and OVS sentences, where the first NP (NP1) was marked with nominative or accusative case, respectively. Three types of visual displays were compared: (i) individual referents (potential agent/theme); (ii) pairs of referents (NP1 + potential agent/theme); and (iii) events (representing interactions between the referents). Participants were significantly more sensitive to the case manipulation when presented with events compared to the other two types of visual display. This suggests that they were able to quickly integrate the thematic role information signaled by grammatical case in the event representations. However, they were less likely to use the case information to anticipate upcoming arguments when the target pictures represented individual referents or pairs of noninteracting referents. We hypothesize that the process of argument anticipation is mediated by the activation of syntactic templates (SVO or OSV, depending on the case marking on NP1). The relatively weak anticipation effect observed may be attributed to the absence, or weak representation, of the noncanonical OVS template in the bilingual children's long‐term memory.

Decades of research have established that learners benefit when instruction includes hand gestures. This benefit is seen when learners watch an instructor gesture, as well as when they are taught or encouraged to gesture themselves. However, there is substantial individual variability with respect to this phenomenon-not all individuals benefit equally from gesture instruction. In the current paper, we explore the sources of this variability. First, we review the existing research on individual differences that do or do not predict learning from gesture instruction, including differences that are either context-dependent (linked to the particular task at hand) or context-independent (linked to the learner across multiple tasks). Next, we focus on one understudied measure of individual difference: the learner's own spontaneous gesture rate. We present data showing rates of "non-gesturers" across a number of studies and we provide theoretical motivation for why this is a fruitful area for future research. We end by suggesting ways in which research on individual differences will help gesture researchers to further refine existing theories and develop specific predictions about targeted gesture intervention for all kinds of learners.

Models of the explore-exploit problem have explained how children's decision making is weighed by a bias for information (directed exploration), randomness, and generalization. These behaviors are often tested in domains where a choice to explore (or exploit) is guaranteed to reveal an outcome. An often overlooked but critical component of the assessment of explore-exploit decisions lies in the expected success of taking actions in the first place-and, crucially, how such decisions might be carried out when learning from others. Here, we examine how children consider an informal teacher's beliefs about the child's competence when deciding how difficult a task they want to pursue. We present a simple model of this problem that predicts that while learners should follow the recommendation of an accurate teacher, they should exploit easier games when a teacher overestimates their abilities, and explore harder games when she underestimates them. We tested these predictions in two experiments with adults (Experiment 1) and 6- to 8-year-old children (Experiment 2). In our task, participants' performance on a picture-matching game was either overestimated, underestimated, or accurately represented by a confederate (the "Teacher"), who then presented three new matching games of varying assessed difficulty (too easy, too hard, just right) at varying potential reward (low, medium, high). In line with our model's predictions, we found that both adults and children calibrated their choices to the teacher's representation of their competence. That is, to maximize expected reward, when she underestimated them, participants chose games the teacher evaluated as being too hard for them; when she overestimated them, they chose games she evaluated as being too easy; and when she was accurate, they chose games she assessed as being just right. This work provides insight into the early-emerging ability to calibrate explore-exploit decisions to others' knowledge when learning in informal pedagogicalcontexts.

As organizations gravitate to group-based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, which individual attributes best predict group performance remains poorly understood. Here, we describe a preregistered experiment in which we simultaneously manipulated four widely studied attributes of group compositions: skill level, skill diversity, social perceptiveness, and cognitive style diversity. We find that while the average skill level of group members, skill diversity, and social perceptiveness are significant predictors of group performance, skill level dominates all other factors combined. Additionally, we explore the relationship between patterns of collaborative behavior and performance outcomes and find that any potential gains in solution quality from additional communication between the group members are outweighed by the overhead time cost, leading to lower overall efficiency. However, groups exhibiting more "turn-taking" behavior are considerably faster and thus more efficient. Finally, contrary to our expectation, we find that group compositional factors (i.e., skill level and social perceptiveness) are not associated with the amount of communication between group members nor turn-takingdynamics.

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.

Does the language you speak affect the way you think? According to the Principle of Linguistic Relativity, it does. Whether, however, the principle holds, has for several decades been one of the most controversial issues in cognitive science (for overviews, see Lucy, 1997; Wolff & Holmes, 2011). At the risk of oversimplifying its history, ideas linked to linguistic relativity can be traced back to philosophical writings in the German Romanticism of the 19th century (such as by Wilhelm von Humboldt) and became more widely known when ethno-linguists Edward Sapir and Benjamin Lee Whorf elaborated on them in their scientific work in the first half of the 20th century. While popular in the humanities, the proposition that speakers of different languages might perceive the world differently was untenable in early cognitive science. It was regarded as incommensurate with the assumption of a unified mind operating on a universal language of thought and hence simply inconceivable. When linguistic relativity eventually was put on the agenda in the 1990s (Gumperz & Levinson, 1991; Lucy, 1992), it was still met with substantial dissent (e.g., Pinker, 1994; overview in Gleitman & Papafragou, 2005); and although the antagonism of the early days has since given way to a more constructive exchange in which both opponents and supporters are increasingly seeking the middle ground and are even beginning to collaborate (e.g., Malt & Majid, 2013; Regier & Kay, 2009), the topic remains hotly debated. The current issue of Topics in Cognitive Science aims to make a productive contribution to this debate, both in format and content. In terms of format, this topic is one of only two that the journal has published featuring a target article, followed by a range of commentaries and a response to those commentaries by the author of the target article. This setup, known from Current Anthropology and Behavioral and Brain Sciences, is ideal for assembling multiple perspectives on controversial topics of broad interest across disciplines. The target article around which the current issue is structured (Kemmerer, 2023), is authored by linguist David Kemmerer (Purdue University, US). He develops a theoretical argument in support of linguistic relativity as deriving from the Grounded Cognition Model, a theory on conceptual knowledge that is highly influential within cognitive science. The argument entails a novel prediction that has the potential to inspire new experimental work across a range of academic communities that work on topics as diverse as perception, action, memory, emotion, language, culture, development, and evolution. For bringing together responses across such a wide field, Topics in Cognitive Science is the optimal forum. And, indeed, the eight commentaries accompanying this target article do reflect a wide variety of perspectives, authored by scholars who represent research communities on four continents and from across the social sciences, information sciences, and cognitive sciences, including linguistics, neuroscience, philosophy, and psychology. Credit for this topic goes to Asifa Majid (Oxford University, UK), a member of the Senior Editorial Board of Topics in Cognitive Science, who herself is a pioneer and leading figure in this subfield of cognitive science. She has been a strong voice for taking (linguistic) diversity and its consequences for cognition seriously (Majid, Bowerman, Kita, Haun, & Levinson, 2004), and yet she is also respected across camps for placing empirical evidence higher than theoretical ideology and for remaining open to a middle ground (e.g., Majid, 2021; Malt, Gennari, Imai, Ameel, Tsuda, & Majid, 2008). It was she who recruited the target article from David Kemmerer for Topics in Cognitive Science, suggested the format, handled the reviewing process, invited the commentaries with a clear objective for diversity, and monitored the compilation of the entire topic. topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstract and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).