The answer, according to the findings, is a clear no. Among the fifth-grade children who participated, stronger comprehension went hand in hand with markers of reading fluency. Children who understood the texts better tended to make fewer fixations overall, produced fewer regressions back to previously read material, and executed longer saccades, meaning their eyes jumped farther forward with each movement. These are classic signatures of an efficient, automatic decoding process. In contrast, the adults in the sample, who included twenty undergraduates and nine faculty members, showed a different pattern. For them, better comprehension was associated with slower reading, more fixations, and generally more attentive, effortful engagement with the text. Rather than fluency signaling understanding, in adults it was deeper cognitive investment that predicted how well the material was grasped.

This developmental reversal makes sense when situated within established theories of reading acquisition. The influential framework proposed by LaBerge and Samuels in the 1970s holds that reading depends on automaticity: when lower-level processes such as letter recognition and word decoding become automatic, attentional resources are freed for higher-level comprehension. For children still consolidating these lower-level skills, the efficiency of basic word recognition remains a bottleneck. A child who must laboriously assemble words letter by letter has little capacity left to build a coherent mental model of the passage. Consequently, in young readers, fluency measures function as a reasonable proxy for comprehension potential, a view consistent with the large body of evidence showing that text reading fluency mediates the relationship between decoding skill and comprehension throughout the elementary school years.

By adulthood, however, decoding is typically fully automatized for skilled readers, and variation in fluency among competent adult readers no longer constrains understanding in the same way. Instead, comprehension differences among adults reflect what happens after individual words are recognized: integrating sentences into a discourse model, drawing inferences, monitoring understanding, and relating new information to prior knowledge. Walter Kintsch’s construction-integration framework describes comprehension as the construction of a textbase and a situation model, processes that demand deliberate cognitive engagement. The finding that adults who read more slowly and fixated more often comprehended better suggests that these adults were allocating extra time to exactly these integrative operations. Slower reading in a skilled adult is not a sign of weakness but often a sign of deeper processing, particularly when texts are expository and informationally dense.

The distinction maps onto the difference between lexical and post-lexical processing. Fixation durations and word-based measures during a first pass through a sentence largely reflect lexical access and syntactic parsing, whereas regressions, second-pass reading, and selective reinspection often reflect repair, integration, and inference generation. In children, the study’s correlational pattern suggests that the first-pass efficiency measures dominate the comprehension picture. In adults, the engagement measures, including the willingness to slow down and dwell on the text, take over as the meaningful correlates of understanding. This shift from reliance on low-level skill to reliance on high-level processing represents a developmental progression in the very architecture of comprehension.

Several features of the study’s design merit attention when interpreting these results. Participants read age-appropriate expository texts, which means children and adults were not reading identical material. This choice, while necessary to avoid floor and ceiling effects, means the developmental comparison concerns comprehension of suitably calibrated texts rather than comprehension of a single fixed passage. Expository texts were a sensible choice because they place heavy demands on background knowledge and inference, and they resemble the kinds of reading required in educational settings from middle childhood onward. Eye-tracking data were collected during the participants’ first read of each text, which is important because rereading changes eye-movement patterns substantially; prior work by Hyönä and Kaakinen has shown that fixation durations drop and patterns reorganize when a text is encountered a second time. Capturing the first encounter ensures that the recorded eye movements reflect genuine online comprehension processes rather than memory-supported review.

The sample itself was modest but appropriate for a correlational study of this kind: twenty-one fifth-graders and twenty-nine adults. All participants were native Spanish speakers, which matters for several reasons. Spanish has a relatively shallow orthography, meaning that grapheme-to-phoneme correspondences are largely regular and decoding is typically mastered earlier and more completely than in deep orthographies such as English. Meta-analytic work, including the recent analysis by Leachman, Wolters, and Kim, has demonstrated that the relationship between text reading fluency and comprehension varies systematically with orthographic depth and developmental phase. In a transparent orthography, the fluency-comprehension link in children may reflect a somewhat different balance of skills than it would in English, where decoding struggles persist longer. The fact that fluency still emerged as the key correlate of comprehension in Spanish-speaking children underscores how central automatized word processing remains for comprehension even when the writing system is relatively friendly to the learner.

The study also connects to a broader movement in reading science away from overly simple models. The classic Simple View of Reading proposed by Gough and Tunmer in 1986 framed comprehension as the product of decoding and linguistic comprehension, a formulation that has been enormously productive but increasingly recognized as incomplete. More recent integrative models, such as Kim’s Direct and Indirect Effects Model of Reading and the framework advanced by Duke and Cartwright, emphasize that reading comprehension rests on a constellation of skills, including executive functions, vocabulary, background knowledge, and inference-making, whose relative contributions shift across development. The eye-tracking evidence from this study provides a behavioral, moment-to-moment signature of exactly such a shift: the same observable behavior, such as a high fixation count, carries different implications about comprehension depending on the reader’s developmental stage.

Executive functions deserve particular mention in this context. Butterfuss and Kendeou have argued that working memory, inhibition, and cognitive flexibility support the construction and maintenance of a coherent situation model, especially during the integration of information across sentences and paragraphs. The adult pattern observed here, in which more fixations and slower reading accompanied better comprehension, is consistent with readers strategically deploying these executive resources. Similarly, research on prior knowledge by Kaakinen and colleagues has shown that readers allocate longer fixations to text segments that are relevant to their reading goals, particularly when background knowledge must be actively coordinated with incoming information. Adults who engage in this kind of selective, effortful reading are, in effect, trading speed for depth, and the comprehension measures reward that trade.

Practically, the findings carry implications for assessment and instruction. For children in the intermediate grades, the results reinforce the value of building reading fluency, not as an end in itself but as the gateway to comprehension. Repeated reading, wide independent reading of connected text, and instruction that promotes automatic word recognition all serve to free cognitive resources for meaning-making. For adolescents and adults, especially those in teacher education programs where concerns about reading comprehension demands have recently been raised in the literature, the results suggest that interventions should target higher-level skills: inference generation, discourse integration, metacognitive monitoring, and the strategic slowing down that deep texts require. A one-size-fits-all approach that treats slow, effortful reading as a problem to be corrected would, for adult readers, mistake engagement for inefficiency.

The study’s methodological choices also illustrate good practice in eye-tracking research. The authors addressed outlier detection and treatment following established statistical guidance, an important step given that fixation distributions are typically skewed and sensitive to occasional anomalous measurements. The decision to include both undergraduate students and faculty professors within the adult group introduced a range of reading expertise among the adults, which may have sharpened the contrast between fluency-driven and engagement-driven comprehension profiles. The openness of the data on the Open Science Framework allows other researchers to verify the analyses, test alternative models, and pool evidence across studies, practices that strengthen the cumulative reliability of the field.

Some caveats remain. The correlational design cannot establish causal direction: it is possible that children who comprehend better become more fluent, rather than fluency driving comprehension, although longitudinal and intervention evidence elsewhere supports the latter pathway as well. The modest sample sizes, while adequate for regression-based analyses according to minimum sample size guidance, limit the precision of estimates and the ability to detect interactions. The inclusion of professors alongside undergraduates raises questions about whether the adult pattern is homogeneous or driven by the most expert readers. And because comprehension was measured after a single reading, the study cannot speak to how readers strategically reread or study text over time.

Nevertheless, the central contribution stands out clearly: the eye movements that betray comprehension are not universal but developmental. What looks like skill in a child, namely swift, forward-moving, uninterrupted reading, looks different in an adult, for whom comprehension is signaled by deliberate, resource-intensive engagement with the text. Understanding this progression helps researchers build more accurate models of reading development, helps educators match instruction to the reader’s current stage, and reminds us that the path from fluent decoding to deep understanding is not a single leap but a gradual reorganization of the entire reading system.

Subject of Research: Comprehension is in the Eye of the Reader: An Eye Tracking Study of Children and Adults

Article Title: Comprehension is in the Eye of the Reader: An Eye Tracking Study of Children and Adults

Article References: Tabullo, Á., Jofre, E. M. G., Del-Punta, J. A., Rodríguez, K. V., & Gasaneo, G. (2026). Comprehension is in the Eye of the Reader: An Eye Tracking Study of Children and Adults. Trends in Psychology. https://doi.org/10.1007/s43076-026-00537-4

Image Credits: AI Generated

DOI: 10.1007/s43076-026-00537-4

Keywords: Comprehension, Reader, Tracking, Children, Adults, scientific research

Much of the current understanding of cell fate comes from single-cell molecular measurements, particularly RNA sequencing. These methods can provide detailed profiles of gene expression and reveal regulatory networks associated with distinct cell states. Yet each measurement is usually a snapshot: it captures the molecular condition of a cell after the sample has been collected, rather than continuously observing the transition that produced it. Computational methods such as pseudotime analysis can arrange cells along an inferred developmental trajectory, but an inferred sequence is not the same as a direct record of change. Morphodynamics offers a complementary perspective by measuring features that can be observed repeatedly in living cells. Those features may include cell area, shape, movement, position within a tissue, nuclear geometry and the relationship between cellular structures. In principle, a time-resolved record of these variables can reveal transitional states that molecular sampling misses. The objective is not to replace molecular profiling, but to add the physical and behavioral dimension needed to understand how cell states are established in real tissues.

Tolonen and colleagues selected the Xenopus mucociliary epithelium because it differentiates rapidly and produces a complex, multilayered tissue. The model begins with an ectodermal cap that can be excised from an embryo and attached to a fibronectin-coated glass-bottom dish. Under culture conditions, the tissue proceeds through much of its differentiation program while remaining sufficiently thin for high-resolution, long-term live imaging. Over approximately 22 hours, it develops into a mature bilayer containing several specialized cell types. These include multiciliated cells, which help move material across an epithelial surface; small secretory cells; ionocytes; goblet cells; and basal stem cells. The cell types differ in their morphology and movement trajectories, creating observable physical signatures that can be measured during development. Because the explant reproduces important features of mucociliary epithelial development and resembles aspects of mammalian airway epithelium, it provides a tractable system for studying how cell behavior contributes to tissue organization.

To follow individual cells, the researchers used embryos injected with fluorescent markers labeling nuclei and cell membranes. Live imaging then captured the developing epithelium in three dimensions, while segmentation and tracking tools converted the image sequence into individual cell trajectories. Segmentation assigns image pixels or voxels to a particular cell, creating a digital mask that defines its boundaries. Tracking links those masks across successive frames, allowing researchers to estimate how each cell moves and changes over time. The analysis faced practical challenges. Cell shapes varied, the tissue was compact, and the available resolution along the imaging axis was limited. Membrane boundaries could therefore be difficult to identify consistently. Nuclear labeling provided a more reliable anchor, enabling accurate lineage tracking even when the surrounding cell geometry was ambiguous. From these trajectories, the team extracted morphometric and dynamic measurements and used them to define a morphodynamic state for each cell. This concept parallels a molecular state defined by gene expression, but it is based on physical features and behavior recorded in living tissue.

The first analysis produced an instructive negative result. When individual cellular features were considered without broader lineage information, the cells did not form sharply separated clusters corresponding to their eventual identities. The absence of clear clusters suggests that the relevant differences in this tissue are subtle rather than dramatic. Epithelial cells are also subject to physical constraints: they must pack together, share boundaries and maintain tissue integrity, which can make distinct cell types look similar at particular moments. Differentiation may therefore be encoded not in one conspicuous feature, but in combinations of modest changes distributed across time. To address this problem, the researchers turned to supervised machine-learning models. They generated a ground-truth dataset by fixing and immunostaining tissues at the endpoint, assigning final cell identities and then tracing those cells backward through their recorded lineages. This provided the models with known outcomes against which earlier morphodynamic patterns could be tested, transforming subtle physical trends into measurable associations with fate.

The supervised analysis used multivariate, multiclass prediction methods, including XGBoost and multinomial logistic regression implemented with scikit-learn. Rather than asking whether one measurement alone identified a cell type, these models evaluated combinations of features and their contribution to classification. XGBoost, an ensemble method based on decision-tree boosting, produced a mean cell-fate prediction accuracy of approximately 80 percent in the reported analysis. The most influential feature was the cell’s position along the Z axis. That result is biologically plausible because certain differentiated cell types occupy the apical surface of the multilayered epithelium. The model also identified the offset between nuclear and membrane centroids as informative. This measurement can reflect changes in cell polarity, shape and spatial organization during morphogenetic events such as radial intercalation, when cells move between tissue layers or rearrange relative to their neighbors. These signals were not necessarily strong enough to identify fate in isolation. Their predictive value emerged when the model considered several measurements together and interpreted them in the context of a cell’s lineage.

The findings illustrate why time-resolved phenomics could become an important partner to single-cell omics. Molecular data can show which genes and regulatory pathways are associated with a transition, whereas morphodynamic data can reveal when a cell changes position, how it reshapes itself and whether its movements are coordinated with those of nearby cells. Such information is especially relevant in epithelia, where fate is linked to tissue architecture, mechanical forces and collective behavior. A cell’s final identity may depend partly on the physical environment it experiences as it moves through a crowded, curved or multilayered tissue. Live imaging preserves this context, while computational pipelines make it possible to quantify many cells across extended developmental windows. Previous work has shown that single-cell phenomics can expose behavioral and mechanical heterogeneity during tissue remodeling. The current analysis extends that idea by showing that dynamic physical measurements can be scaled into a predictive framework resembling an omics workflow, even when individual features are relatively weak and cell states change continuously.

Several limitations remain before morphodynamics can provide a complete account of cell fate. The imaging system depends on fluorescent labeling, reliable segmentation and sufficient spatial and temporal resolution, and the authors note that variable morphology and limited Z resolution can affect the resulting masks. Prediction accuracy also depends on the quality and scope of the ground-truth dataset used for training. A model trained in one developmental system may not transfer directly to another tissue, species or disease state. Future studies could combine live morphodynamic measurements with molecular profiling of the same cells or closely matched lineages. Such integration may identify the precise time points at which physical changes coincide with decisive regulatory events. The approach could also help establish baseline patterns for biomedical phenotyping and early disease detection. In cancer research, for example, a detailed understanding of how normal cells change shape, position and behavior during differentiation could make it easier to recognize abnormal departures from that program. The broader significance is that cell fate may be read not only in molecular snapshots, but also in the evolving geometry and motion of living cells.

An important conceptual shift in this work is the treatment of morphology as a state variable rather than merely an endpoint description. A cell’s location, geometry and movement can be recorded repeatedly, preserving the order in which changes occur. This makes it possible to ask whether a physical feature precedes the appearance of a mature marker, rather than simply correlating the two after differentiation has finished. The distinction is especially valuable for identifying transition windows in which a cell may still be responsive to its environment or susceptible to developmental perturbation.

The study also shows why lineage information is central to interpreting phenotypic measurements. Cells sharing a tissue compartment may appear similar at one time point even when their later outcomes diverge. Conversely, the same feature may have different implications depending on where a cell came from and how it has moved. Linking measurements across a trajectory therefore supplies context that a collection of unrelated images cannot provide. Endpoint immunostaining served as the reference for assigning outcomes, while the preceding live record supplied the evidence used for prediction. This combination connects retrospective identity measurements with prospective dynamics without assuming that every visible difference is fate-determining.

Prediction should nevertheless be distinguished from mechanism. An informative feature, such as apical position or nuclear–membrane displacement, may report a process that accompanies fate commitment without causing it. The predictive pipeline can reveal when and where such associations occur, but perturbation experiments would be needed to test their functional importance. The framework could consequently serve as a way to prioritize developmental time points, cellular behaviors or physical transitions for experimental intervention. In this sense, morphodynamic analysis is not only a classification strategy: it can organize the complex sequence of events that connects progenitor behavior to the architecture of a differentiated epithelium.

Subject of Research: Using live-cell morphodynamics to predict cell fate during epithelial differentiation

Article Title: Tracking cell fate through morphodynamics

Article References: Dorr, J., & Mitchell, B. J. (2026). Tracking cell fate through morphodynamics. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00244-3

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00244-3

Keywords: cell fate, morphodynamics, live-cell imaging, epithelial development, Xenopus, single-cell phenomics, machine learning, cell differentiation, Tracking, cell, fate, scientific research