David Rothlein

The similarity structure of distributed neural responses reveals the multiple representations of letters.

Most cognitive theories of reading and spelling posit modality-specific representations of letter shapes, spoken letter names, and motor plans as well as abstract, amodal letter representations that serve to unify the various modality-specific formats. However, fundamental questions remain regarding the very existence of abstract letter representations, the neuro-topography of the different types of letter representations, and the degree of cortical selectivity for orthographic information. We directly test quantitative models of the similarity/dissimilarity structure of distributed neural representations of letters using Multivariate Pattern Analysis-Representational Similarity Analysis (MVPA-RSA) searchlight methods to analyze the BOLD response recorded from single letter viewing. These analyses reveal a left hemisphere ventral temporal region selectively tuned to abstract letter representations as well as substrates tuned to modality-specific (visual, phonological and motoric) representations of letters. The approaches applied in this research address various shortcomings of previous studies that have investigated these questions and, therefore, the findings we report serve to advance our understanding of the nature and format of the representations that occur within the various sub-regions of the large-scale networks used in reading and spelling.

Tracking behavioral and neural fluctuations during sustained attention: A robust replication and extension

&NA; Novel paradigms have allowed for more precise measurements of sustained attention ability and fluctuations in sustained attention over time, as well as the neural basis of fluctuations and lapses in performance. However, in recent years, concerns have arisen over the replicability of neuroimaging studies and psychology more broadly, particularly given the typically small sample sizes. One recently developed paradigm, the gradual‐onset continuous performance task (gradCPT) has been validated behaviorally in large samples of participants. Yet neuroimaging studies investigating the neural basis of performance on this task have only been collected in small samples. The present study completed both a robust replication of the original neuroimaging findings and extended previous results from the gradCPT task using a large sample of 140 Veteran participants. Results replicate findings that fluctuations in attentional stability are tracked over time by BOLD activity in task positive (e.g., dorsal and ventral attention networks) and task negative (e.g., default network) regions. Extending prior results, we relate this coupling between attentional stability and on‐going brain activity to overall sustained attention ability and demonstrate that this coupling strength, along with across‐network coupling, could be used to predict individual differences in performance. Additionally, the results extend previous findings by demonstrating that temporal dynamics across the default and dorsal attention networks are associated with lapse‐likelihood on subsequent trials. This study demonstrates the reliability of the gradCPT, and underscores the utility of this paradigm in understanding attentional fluctuations, as well as individual variation and deficits in sustained attention. HighlightsA robust fMRI replication with N=140 of the gradCPT sustained attention paradigm.Neural coupling with fluctuations in reaction time variability predicts performance.Neural trajectories prior to target onset vary with likelihood of attention lapses.

Sensitivity to stimulus similarity is associated with greater sustained attention ability

Sustained attention is critical for tasks where perceptual information must be continuously processed, like reading or driving; however, the cognitive processes underlying sustained attention remain incompletely characterized. In the experiments that follow, we explore the relationship between sustaining attention and the contents and maintenance of task-relevant features in an attentional template. Specifically, we administered the gradual onset continuous performance task (gradCPT), a sensitive measure of sustained attention, to a large web-based sample (N>20,000) and a smaller laboratory sample for validation and extension. The gradCPT requires participants to respond to most stimuli (city scenes – 90 %) and withhold to rare target images (mountain scenes – 10 %). By using stimulus similarity to probe the representational content of task-relevant features—assuming either exemplar- or category-based feature matching—we predicted that RTs for city stimuli that were more “mountain-like” would be slower and “city-like” mountain stimuli would elicit more erroneous presses. We found that exemplar-based target-nontarget (T-N) similarity predicted both RTs and erroneous button presses, suggesting a stimulus-specific feature matching process was adopted. Importantly, individual differences in the degree of sensitivity to these similarity measures correlated with conventional measures of attentional ability on the gradCPT as well as another CPT that is perceptually less demanding. In other words, individuals with greater sustained attention ability (assessed by two tasks) were more likely to be influenced by stimulus similarity on the gradCPT. These results suggest that sustained attention facilitates the construction and maintenance of an attentional template that is optimal for a given task.

The Role of Allograph Representations in Font-Invariant Letter Identification

The literate brain must contend with countless font variants for any given letter. How does the visual system handle such variability? One proposed solution posits stored structural descriptions of basic letter shapes that are abstract enough to deal with the many possible font variations of each letter. These font-invariant representations, referred to as allographs in this paper, while frequently posited, have seldom been empirically evaluated. The research reported here helps to address this gap with 2 experiments that examine the possible influence of allograph representations on visual letter processing. In these experiments, participants respond to pairs of letters presented in an atypical font in 2 tasks—visual similarity judgments (Experiment 1) and same/different decisions (Experiment 2). By using representational similarity analysis (RSA) in conjunction with linear mixed effect models (LMEM; RSA-LMEM) we show that the similarity structure of the responses to the atypical font is influenced by the predicted similarity structure of allograph representations even after accounting for font-specific visual shape similarity. Similarity due to symbolic (abstract) identity, name, and motor representations of letters are also taken into account providing compelling evidence for the unique influence of allograph representations in these tasks. These results provide support for the role of allograph representations in achieving font-invariant letter identification.

Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants (n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars (n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.