Scientific thinking is one of the most creative expressions of human cognition. This paper discusses my research contributions to the cognitive science of science. I have advanced the position that data on the cognitive practices of scientists drawn from extensive research into archival records of historical science or collected in extended ethnographic studies of contemporary science can provide valuable insight into the nature of scientific cognition and its relation to cognition in ordinary contexts. I focus on contributions of my research on analogy, model-based reasoning, and conceptual change and on how scientists enhance their natural cognitive capacities by creating modeling environments that integrate cognitive, social, material, and cultural resources. I provide an outline of my trajectory from a physicist to a philosopher of science to a hybrid cognitive scientist in my quest to understand the nature of scientific thinking.

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT-R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance-based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision-making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self-efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human-machine intelligence.

The interaction-dominant approach to perception and action, originally formulated in the mid-1990s, has matured and gained remarkable momentum as an entailment of the dynamical hypotheses proposed at that time. This framework seeks to explain the fluid and intricate interplay of causality spanning the entire organism by integrating high-dimensional details with low-dimensional constraints across various scales of behavior. Both Chemero (2024) and Wallot et al. (2024) have skillfully explored the theoretical implications and methodological challenges this perspective introduces. We echo Chemero's (2024) and Wallot et al.'s (2024) focus on multifractality, while also underscoring new efforts to model the synergetic relationships and cascading dynamics inherent in this interaction-dominant approach.

An enigma for human languages is that children learn to understand words in their mother tongue extremely fast. The cognitive sciences have not been able to fully understand the mechanisms behind this highly efficient learning process. In order to provide at least a partial answer to this problem, I have developed a cognitive model of the semantics of natural language in terms of conceptual spaces. I present a background to conceptual spaces and provide a brief summary of their main features, in particular how it handles learning of concepts. I then apply the model to give a geometric account of the semantics of different word classes. In particular, I propose a "single-domain hypotheses" for the semantics of all word classes except nouns. These hypotheses provide a partial answer to the enigma of how words are learned. Next, a dynamic cognitive model of events is introduced that replaces and extends the function of thematic roles. I apply it to analyze the meanings of different kinds of verbs. I argue that the model also explains some aspects of syntactic structure. In particular, I propose that a sentence typically refers to an event. Some further applications of conceptual spaces are briefly presented.

Retrieval practice-the process of actively calling information to mind rather than passively studying materials-has been proven to be a highly effective learning strategy. However, only recently, researchers have started to examine differences between learners in terms of the optimal conditions of retrieval practice in applied educational settings. In this study (N = 118), we focus on learners with dyslexia. We compare their performance to the performance of typical learners in an adaptive retrieval practice task using both typing-based and speech-based response conditions. We find that typical learners outperform learners with dyslexia when they are asked to respond by typing, but that this difference disappears when learners respond by speech. Using a mathematical model to decompose response times, we demonstrate that this typing-specific disadvantage in learners with dyslexia is mainly a consequence of processing delays, rather than poorer memory performance. These findings contribute to a better understanding of the mechanisms underlying declarative learning in dyslexia, and they can be used to tailor educational technology toward the needs of neurodiverse learners.

This paper explores the role of measurement as a cognitive technology across human history, emphasizing the coexistence of formal and informal measurement systems. While standardized systems dominate contemporary culture and are well documented across large-scale societies of the past, this manuscript highlights the less explored domain of informal measurement practices that have been integral to daily life from the past to the present. Through the examination of body-based measurement systems and proportional heuristics, we demonstrate how these informal strategies were not merely precursors to formal standards but essential adaptive tools for solving everyday problems. Often, these informal solutions come with practical advantages. This manuscript calls for a broader recognition of their significance in cultural and technological evolution.

Studies of homesigns have shed light on the human capacity for language and on the challenging problem of language acquisition. The study of homesign has evolved from a perspective grounded in gesture studies and child development to include sign language linguistics and the role of homesigns in language emergence at the community level. One overarching finding is that homesigns more closely resemble sign languages used by linguistic communities than they resemble the gestures produced by hearing people along with spoken language. Homesigns may not exhibit all of the linguistic properties of community languages, but the properties they do exhibit are language properties, and for the people who use them, homesigns are their language. Further, the linguistic structures in homesigns are innovated by the deaf people who use them and are imperfectly learned by their hearing communication partners. I close with a call to action: We cannot celebrate discoveries about the mind made possible by studies of homesigns and emerging languages while ignoring the pervasiveness of language deprivation among deaf people, and the relative lack of deaf participation in science, even in studies of sign languages. While the scientific community learns much from studying homesigns and sign languages, we also have a responsibility to work toward ensuring that every deaf person has access to language, communication, and education.

The 1998 article by van Gelder proposed a Dynamical Hypothesis (DH) in cognitive science consisting of Nature (cognitive agents are dynamical systems) and Knowledge (cognitive agents should be understood dynamically) hypotheses in contrast to the Computational Hypothesis (CH) that cognitive agents are computers. My commentary focuses on the contributions of Paxton and Necaise et al. in interpersonal motor coordination and radicalization across social media. I do not think that either contribution supports the Nature hypothesis but does conform with the Knowledge hypothesis. I conclude by describing cognitive agents as living systems (or nonliving systems that mimic aspects of living systems) that can be alternately viewed to support the DH or CH or both at the same time.

Standard models of lexical production assume that speakers access representations of meaning, grammar, and different aspects of sound in a roughly sequential manner (whether or not they admit cascading or interactivity). In contrast, we review evidence for a parallel activation model in which these representations are accessed in parallel. According to this account, word learning involves the binding of the meaning, grammar, and sound of a word into a single representation. This representation is then activated as a whole during production, and so all linguistic components are available simultaneously. We then note that language comprehension involves extensive use of prediction and argue that comprehenders use production mechanisms to determine (roughly) what they would say next if they were speaking. So far, theories of prediction-by-production have assumed sequential lexical production. We therefore reinterpret such evidence in terms of parallel lexical production.

The foundation of ancient, invented writing systems lies in the predominant iconicity of their sign shapes. However, these shapes are often used not for their referential meaning but in a metaphorical way, whereby one entity stands for another. Metaphor, including its subcategories pars pro toto and metonymy, plays a crucial role in the formation of the earliest pristine invented scripts, yet this mechanism has been understudied from a cognitive, contextual, and comparative perspective. This article aims to address issues pertaining to the definition, development, and application of these mechanisms in the formation of the Mesopotamian, Egyptian, and Chinese scripts. We analyze the local cases of metaphor-in-action in primary inventions, focusing first on visual metaphors and, second, on the typical or idiosyncratic uses of metonyms.

Automated moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach typically relies on language models to infer moral relevance or moral properties of a concept. This approach demands complex parameterization and costly computation, and it tends to disconnect with existing psychological accounts of moralization. We present a simple cognitive model for moral inference, Moral Association Graph (MAG), inspired by psychological work on moralization. Our model builds on word association network for inferring moral relevance and draws on rich psychological data. We demonstrate that MAG performs competitively to state-of-the-art language models when evaluated against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. We also show that our model yields interpretable outputs and is applicable to informing short-term moral change.

Predictive processing is an influential theoretical framework for understanding human and animal cognition. In the context of predictive processing, learning is often reduced to optimizing the parameters of a generative model with a predefined structure. This is known as Bayesian parameter learning. However, to provide a comprehensive account of learning, one must also explain how the brain learns the structure of its generative model. This second kind of learning is known as structure learning. Structure learning would involve true structural changes in generative models. The purpose of the current paper is to describe the processes involved upstream of these structural changes. To do this, we first highlight the remarkable compatibility between predictive processing and the processing fluency theory. More precisely, we argue that predictive processing is able to account for all the main theoretical constructs associated with the notion of processing fluency (i.e., the fluency heuristic, naïve theory, the discrepancy-attribution hypothesis, absolute fluency, expected fluency, and relative fluency). We then use this predictive processing account of processing fluency to show how the brain could infer whether it needs a structural change for learning the causal regularities at play in the environment. Finally, we speculate on how this inference might indirectly trigger structural changes when necessary.

Mastering how to convey meanings using language is perhaps the main challenge facing any language learner. However, satisfactory accounts of how this is achieved, and even of what it is for a linguistic item to have meaning, are hard to come by. Nick Chater was one of the pioneers involved in the early development of one of the most successful methodologies within the cognitive science of language for discovering meaning: distributional semantics. In this article, we review this approach and discuss its successes and shortcomings in capturing semantic phenomena. In particular, we discuss what we dub the "distributional paradox:" how can models that do not implement essential dimensions of human semantic processing, such as sensorimotor grounding, capture so many meaning-related phenomena? We conclude by providing a preliminary answer, arguing that distributional models capture the statistical scaffolding of human language acquisition that allows for communication, which, in line with Nick Chater's more recent ideas, has been shaped by the features of human cognition on the timescale of cultural evolution.

Engraved portable objects from Upper Palaeolithic and earlier sites are argued to be cognitive tools designed to store information for the purposes of calculation, record-keeping, or communication. This paper reviews the surprisingly long intellectual history of comparisons between these ancient objects and message sticks: marked graphic devices traditionally used for long-distance communication in Indigenous Australia. I argue that, while such comparisons have often been misguided, more cautious applications of ethnographic analogy may yield useful insights. A systematic analysis of historical observations together with more recent fieldwork, indicate that Australian message sticks are primarily tools of social cognition, as opposed to cognition tout court, and rely on orality and other context to become meaningful. Further, the practice of message stick communication may help clarify ongoing problems in the interpretation of Upper Palaeolithic objects including their possible role in aggregation activities, the distinction between decoration and notation, and the interplay between graphic sequences and speech.

Throughout the long history of Classic Maya hieroglyphs, a logosyllabic writing system used from the late first millennium BCE through the mid-second millennium CE in southern Mesoamerica, the most commonly recorded phonetic value was the syllable u (/ʔu/). With over a dozen different u hieroglyphs, Classic Maya scribes had more options for recording /ʔu/ than any other syllable or logograph. Cognitive approaches to writing systems typically attribute graphemic variation (i.e., alternation between signs with equivalent linguistic value) to semantic differences like animacy or to non-linguistic factors like identity. Distribution of Classic Maya u hieroglyphs, however, suggests that morphosyntactic context influenced which grapheme scribes wrote and when. This case suggests that scribal knowledge of Classic Maya hieroglyphs included ideas about writing's relationship to language. It also highlights the cognitive relevance of morphosyntax for a writing system's users as they differentiate among graphic signs with identical linguistic denotation.

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.

Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.

How do teachers' gestures influence students' learning? This article reviews research investigating the role of gestures in communication, focusing on teachers' communication with their students, primarily in mathematics and science instruction. We first briefly consider gesture's role in communication more generally as a backdrop for considering teaching as a special context for communication. We then describe teachers' spontaneous gesturing in teaching contexts, and we consider how teachers' spontaneous gestures might influence students' learning. We then consider experimental studies that provide causal support for the effects of teachers' gestures on students' learning. We conclude by discussing future directions and implications for educational practice.

This paper attempts to reconcile the claims that the mind is both flat (Chater, 2018) and highly rational (Oaksford & Chater, 2020). According to the flat mind hypothesis, the mind is a mass of inconsistent and contradictory fragments of experience. However, standard accounts of rationality from formal epistemology argue that to be rational, our beliefs must be consistent, and we must believe all the logical consequences of our beliefs. A social account of rationality is developed based on Brandom's (1994) logical expressivism, in which respecting the norms of logic and probability theory is still central but where these standards apply to our public commitments in social dialogical contexts rather than to our individual belief systems (Skovgaard-Olsen, 2017). According to this account, even if someone's individual beliefs are inconsistent, they cannot be condemned as irrational if they acknowledge the inconsistency and seek to resolve it. It is shown how this approach interacts with people's fragmented and shallow world knowledge, and its social distribution yields some counterintuitive consequences, such as it sometimes being rational individually not seeking to resolve contradictions. Other consequences of this social expressivist approach are considered, including for dual process theories of reasoning, our view of beliefs, the status of logic, and Fodor's (1983) view of central systems. It is concluded that people can have flat minds and yet be highly rational.

Ancient Roman "technical memory" is not (as much of the modern specialist literature would have it) a generative technology of association. Rather it is (as a literal reading of the texts would suggest) a specialized tool for precise serial recall. Modern experimental evidence both confirms the fitness for the purpose of the technique and shows why that purpose is not trivial, as some have suggested. While the mechanism(s) by which the technique operates are not fully understood, a review of the current literature suggests that it would have had the advantage over other mnemonic techniques by virtue of recruiting a variety of cognitive capacities. These likely include spatial/navigational mechanisms and possibly visual/imagery-based ones as well. Finally, small differences between the method as recorded in the ancient texts and similar methods that have been the subject of laboratory experiments are used to suggest possible directions for further experimentation.

Past research suggests that Working Memory plays a role in determining relative clause attachment bias. Disambiguation preferences may further depend on Processing Speed and explicit memory demands in linguistic tasks. Given that Working Memory and Processing Speed decline with age, older adults offer a way of investigating the factors underlying disambiguation preferences. Additionally, older adults might be subject to more severe similarity-based memory interference given their larger vocabularies and slower lexical access. Nevertheless, memory interference and sentence disambiguation have not been combined in studies on older adults before. We used a self-paced reading paradigm under memory load interference conditions. Older (n = 30) and Younger (n = 35) readers took part in the study online; reading times were recorded and measures of comprehension accuracy and load recall were collected. This setup allowed for the implicit measurement of attachment biases and memory interference effects interactively. Results show that similarity-based interference affected both age groups equally, but was more pronounced in NP2-biased structures, which took participants generally longer to read. Attachment preferences did not differ by group and were unaffected by Working Memory span. However, accuracy on recall prompts was predicted by Working Memory span in both groups. Findings of greater interference in syntactically dispreferred structures support unified processing models where parsing constraints naturally interact. The lack of age differences on our measures further aligns with research finding age-invariant implicit language processing.

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 that spirit.

Debates surrounding the origin of recovered memories of child abuse have traditionally focused on two conflicting arguments, namely that these memories are either false memories or instances of repressed memories (i.e., reflecting the idea that people can unconsciously block traumatic autobiographical experiences and eventually regain access). While scientific evidence for the first is clearly established, the second is the subject of a controversy in the academic, clinical, and legal fields. This controversy rages on today. In this introductory article to our topic "Beyond Repressed Memory: Current Alternative Solutions to the Controversy," we present alternative and more parsimonious explanations for repressed memories that we sorted into three categories: cognitive, motivational, and biological factors. Our aim is to provide a timely overview to help clinical and legal professionals, academics, and the general public to move beyond the idea that traumatic memories can be unconsciously repressed.

About 30 years ago, the Dynamical Hypothesis instigated a variety of insights and transformations in cognitive science. One of them was the simple observation that, quite unlike trial-based tasks in a laboratory, natural ecologically valid behaviors almost never have context-free starting points. Instead, they produce lengthy time series data that can be recorded with dense-sampling measures, such as heartrate, eye movements, EEG, etc. That emphasis on studying the temporal dynamics of extended behaviors may have been the trigger that led to a rethinking of what a "representation" is, and then of what a "cognitive agent" is. This most recent and perhaps most revolutionary transformation is the idea that a cognitive agent need not be a singular physiological organism. Perhaps a group of organisms, such as several people working on a joint task, can temporarily function as one cognitive agent - at least while they're working adaptively and successfully.

Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this "prediction boost" is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for individuals with still-developing executive functions. In this paper, we combined multiple analytic approaches to investigate the potential role of executive functions in elementary school-aged children's science learning. We performed an experiment that investigates children's science learning during a water displacement task where a "prediction boost" had previously been observed-children either made an explicit prediction or evaluated an event post hoc (i.e., postdiction). We then considered the relation of executive function measures and learning, which were collected following the main experiment. Via mixed effects regression models, we found that stronger executive function skills (i.e., stronger inhibition and switching scores) were associated with higher accuracy in Postdiction but not in the Prediction Condition. Using a theory-based Bayesian model, we simulated children's individual performance on the learning task (capturing "belief flexibility"), and compared this "flexibility" to the other measures to understand the relationship between belief revision, executive function, and prediction. Children in the Prediction Condition showed near-ceiling "belief flexibility" scores, which were significantly higher than among children in the Postdiction Condition. We also found a significant correlation between children's executive function measures to our "belief flexibility" parameter, but only for children in the Postdiction Condition. These results indicate that when children provided responses post hoc, they may have required stronger executive function capacities to navigate the learning task. Additionally, these results suggest that the "prediction boost" in children's science learning could be explained by increased metacognitive flexibility in the belief revision process.

Languages are neither designed in classrooms nor drawn from dictionaries-they are products of human minds and human interactions. However, it is challenging to understand how structure grows in these circumstances because generations of use and transmission shape and reshape the structure of the languages themselves. Laboratory studies on language emergence investigate the origins of language structure by requiring participants, prevented from using their own natural language(s), to create a novel communication system and then transmit it to others. Because the participants in these lab studies are already speakers of a language, it is easy to question the relevance of lab-based findings to the creation of natural language systems. Here, we take the findings from a lab-based language emergence paradigm and test whether the same pattern is also found in a new natural language: Nicaraguan Sign Language. We find evidence that signers of Nicaraguan Sign Language may show the same biases seen in lab-based language emergence studies: (1) they appear to condition word order based on the semantic dimension of intensionality and extensionality, and (2) they adjust this conditioning to satisfy language-internal order constraints. Our study adds to the small, but growing literature testing the relevance of lab-based studies to natural language birth, and provides convincing evidence that the biases seen in the lab play a role in shaping a brand new language.

This study investigates the role of locality (a task/material-related variable), demographic factors (age, education, and sex), cognitive capacities (verbal working memory [WM], verbal short-term memory [STM], speed of processing [SOP], and inhibition), and morphosyntactic category (time reference and grammatical aspect) in verb-related morphosyntactic production (VRMP). A sentence completion task tapping production of time reference and grammatical aspect in local and nonlocal configurations, and cognitive tasks measuring verbal WM capacity, verbal STM capacity, motor SOP, perceptual SOP, and inhibition were administered to 200 neurotypical Greek-speaking participants, aged between 19 and 80 years. We fitted generalized linear mixed-effects models and performed path analyses. Significant main effects of locality, age, education, verbal WM capacity, motor SOP, and morphosyntactic category emerged. Production of time reference and aspect did not interact with any of the significant factors (i.e., age, education, verbal WM capacity, motor SOP, and locality), and locality did not interact with any memory system. Path analyses revealed that the relationships between age and VRMP, and between education and VRMP were partly mediated by verbal WM; and the relationship between verbal WM and VRMP was partly mediated by perceptual SOP. Results suggest that subject-, task/material- and morphosyntactic category-specific factors determine accuracy performance on VRMP; and the effects of age, education, and verbal WM on VRMP are partly indirect. The fact that there was a significant main effect of verbal WM but not of verbal STM on accuracy performance in the VRMP task suggests that it is predominantly the processing component (and not the storage component) of verbal WM that supports VRMP. Lastly, we interpret the results as suggesting that VRMP is also supported by a procedural memory system whose efficiency might be reflected in years of formal education.

It is widely believed that play and curiosity are key ingredients as children develop models of the world. There is also an emerging consensus that children are Bayesian learners who combine their structured prior beliefs with estimations of the likelihood of new evidence to infer the most probable model of the world. An influential school of thought within developmental psychology, rational constructivism, combines these two ideas to propose that children learn intuitive theories of how the world works in part by engaging in play activities that allow them to gather new information for testing their theories. There are still, however, at least two pieces missing from rational constructivist theories of development. First, rational constructivism has so far devoted little attention to explaining why children's preferred form of learning, play, feels so fun, enjoyable, and rewarding. Rational constructivism may suggest that children are curious and like to play because reducing uncertainty and learning better theories of the causal workings of the world is enjoyable. What remains unclear, however, is why reducing uncertainty in play is interesting, fun, and joyful, while doing so in other forms of learning can be frustrating or boring. Second, rational constructivism may have overlooked how children, during play, will take control of and manipulate their environment, sometimes in an effort to create ideal niches for surprise-extraction, sometimes for developing strategies for making the world fit with their predictions. These missing elements from rational constructivism can be provided by understanding the contribution of play to development in terms of predictive processing, an influential framework in cognitive neuroscience that models many of the brain's cognitive functions as processes of model-based, probabilistic prediction.

What is the nature of lexical meanings such that they can both compose with others and also appear boundless? We investigate this question by examining the compositional properties of for-time adverbial as in "Ana jumped for an hour." At issue is the source of the associated iterative reading which lacks overt morphophonological support, yet, the iteration is not disconnected from the lexical meanings in the sentence. This suggests an analysis whereby the iterative reading is the result of the interaction between lexical meanings under a specific compositional configuration. We test the predictions of two competing accounts: Mismatch-and-Repair and Partition-Measure. They differ in their assumptions about lexical meanings: assumptions that have implications for the possible compositional mechanisms that each can invoke. Mismatch-and-Repair assumes that lexical meaning representations are discrete, separate from the conceptual system from which they originally emerged and brought into sentence meaning through syntactic composition. Partition-Measure assumes that lexical meanings are contextually salient conceptual structures substantially indistinguishable from the conceptual system that they inhabit. During comprehension, lexical meanings construe a conceptual representation, in parallel, morphosyntactic and morphophonological composition as determined by the lexical items involved in the sentence. Whereas both hypotheses capture the observed cost in the punctual predicate plus for-time adverbial composition (e.g., jump (vs. swim) for an hour), their predictions differ regarding iteration with durative predicates; for example, swim for a year (vs. for an hour). Mismatch-and-Repair predicts contrasting processing profiles and nonoverlapping activation patterns along punctuality differences. Partition-Measure predicts overlapping processing and cortical distribution profiles, along the presence of iterativity. Results from a self-paced reading and an functional Magnetic Resonance Imaging (fMRI) studies bear out the predictions of the Partition-Measure account, supporting a view of linguistic meaning composition in line with an architecture of language whereby combinatoriality and generativity are distributed, carried out in parallel across linguistic and nonlinguistic subsystems.