Cognitive Neuroscience Research Research Articles

Perception-action Dissociations as a Window into Consciousness.

Understanding the neural correlates of unconscious perception stands as a primary goal of experimental research in cognitive psychology and neuroscience. In this Perspectives paper, we explain why experimental protocols probing qualitative dissociations between perception and action provide valuable insights into conscious and unconscious processing, along with their corresponding neural correlates. We present research that utilizes human eye movements as a sensitive indicator of unconscious visual processing. Given the increasing reliance on oculomotor and pupillary responses in consciousness research, these dissociations also provide a cautionary tale about inferring conscious perception solely based on no-report protocols.

The wicked problem of naming the intangible: Abstract concepts, binary thinking, and computer vision labels

AbstractDelving into the intricate complexities of naming and categorizing the visual evocation of abstract concepts, this paper brings to light the limitations of relying on binary thinking to tackle these inherently intricate “wicked problems.” As computer vision applications rapidly expand, the pressing challenge of accurately labeling these abstract concepts in visual media comes into focus, necessitating a close examination of the interplay between visual data, nuanced cultural meanings, and artificial intelligence (AI). This work discusses the role these concepts play in automatic visual indexing, as well as the ways in which they expose how binary frameworks curtail technical performance and perpetuate power dynamics. To address this, the paper draws upon insights from recent cognitive neuroscience research and advocates for a more comprehensive, queer, and situated understanding of these concepts. This approach highlights the significance of humanistic and ethical perspectives in shaping the trajectory of AI development.

Understanding of facial features in face perception: insights from deep convolutional neural networks.

Face recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process. To address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees. Our findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN. Overall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.

Leveraging brain science for impactful advocacy and policymaking: The synergistic partnership between developmental cognitive neuroscientists and a parent-led grassroots movement to drive dyslexia prevention policy and legislation

Reading proficiency is crucial for academic, vocational, and economic success and has been closely linked to health outcomes. Unfortunately, in the United States, a concerning 63% of fourth-grade children are reading below grade level, with approximately 7%–10% exhibiting a disability in word reading, developmental dyslexia. Research in developmental cognitive neuroscience indicates that individuals with dyslexia show functional and structural brain alterations in regions processing reading and reading-related information, with some of these differences emerging as early as preschool and even infancy. This suggests that some children start schooling with less optimal brain architecture for learning to read, emphasizing the need for preventative education practices. This article reviews educational policies impacting children with dyslexia and highlights a decentralized parent-led grassroots movement, Decoding Dyslexia, which centers the voices of those directly impacted by dyslexia. It utilizes civic engagement practices, advocacy and lobbying on local, federal, and social media platforms, and strong partnerships with scientists to drive systems-level change in educational practices, leading to dyslexia prevention legislation across the U.S. The ongoing partnership continues to address the profound gaps between scientific findings and policymaking to drive systems-level change for contemporary challenges in educational practices within a learning disabilities framework.

Emotion language use in narratives of the 9/11 attacks predicts long-term memory.

Despite considerable cognitive neuroscience research demonstrating that emotions can influence the encoding and consolidation of memory, research has failed to demonstrate a relationship between self-reported ratings of emotions collected soon after a traumatic event and memory for the event over time. This secondary analysis of data from a multisite longitudinal study of memories of the September 11, 2001 terrorist attacks, asked the question of whether emotional language use could predict memory over time. In the 2 weeks following the 9/11 attacks, participants (N = 691; Mage = 36.8; 72% identifying as male; 76% identifying as white) wrote narratives about how they learned of the attacks and the impact of the attacks on them. Language features of these narratives were extracted using the Linguistic Inquiry Word Count program and used to predict three types of memory: (a) event memory accuracy, (b) flashbulb memory consistency, and (c) emotion memory consistency. These outcomes were assessed at the time of writing, 1, 3, and 10 years after the 9/11 attacks. Results of linear mixed-effects models indicate that greater use of negative emotion words in narratives predicts better event memory accuracy 3 and 10 years after the attacks and worse flashbulb memory consistency 10 years after the attacks. However, emotion word use does not predict emotion memory consistency across time. We also examine whether other exploratory linguistic predictors are associated with memory over time. These findings suggest that written language may serve as a potential early indicator of memory over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Real-time feedback improves imagined 3D primitive object classification from EEG

ABSTRACT Brain-computer interfaces (BCI) enable movement-independent information transfer from humans to computers. Decoding imagined 3D objects from electroencephalography (EEG) may improve design ideation in engineering design or image reconstruction from EEG for application in brain-computer interfaces, neuro-prosthetics, and cognitive neuroscience research. Object-imagery decoding studies, to date, predominantly employ functional magnetic resonance imaging (fMRI) and do not provide real-time feedback. We present four linked studies in a study series to investigate: (1) whether five imagined 3D primitive objects (sphere, cone, pyramid, cylinder, and cube) could be decoded from EEG; and (2) the influence of real-time feedback on decoding accuracy. Studies 1 (N = 10) and 2 (N = 3) involved a single-session and a multi-session design, respectively, without real-time feedback. Studies 3 (N = 2) and 4 (N = 4) involved multiple sessions, without and with real-time feedback. The four studies involved 69 sessions in total of which 26 sessions were online with real-time feedback (15,480 trials for offline and at least 6,840 trials for online sessions in total). We demonstrate that decoding accuracy over multiple sessions improves significantly with biased feedback (p = 0.004), compared to performance without feedback. This is the first study to show the effect of real-time feedback on the performance of primitive object-imagery BCI.

Reconstructing human gaze behavior from EEG using inverse reinforcement learning

Decoding eye movements from non-invasive electroencephalography (EEG) data is a challenging yet vital task for both scientific and practical purposes, especially for identifying neurodegenerative disorders like Alzheimer’s disease (AD). Our research tackles this complexity by adapting inverse reinforcement learning (IRL), a machine learning method, to infer decision-making strategies from observed behaviors. We implement this to understand the processes driving eye direction and movements during diverse cognitive tasks, providing new insights into this field. Our paper begins with a detailed description of the procedures for collecting and preprocessing EEG data related to gaze behavior. We then elaborate on the development of an IRL framework designed to predict the spatial and temporal dynamics of eye movements (scanpaths) in participants engaged in cognitive tasks of varying complexity. Our model is tailored to accommodate the complexities inherent in neural signals and the stochastic nature of human gaze patterns. Our research findings underscore IRL’s effectiveness in precisely forecasting gaze patterns based on a combination of EEG and image data. The correlation between the model’s predictions and the actual gaze behavior observed in controlled experiments reinforces the utility of IRL in cognitive neuroscience research. Notably, our IRL-EEG models demonstrated superior performance, especially in more complex cognitive tasks. We further delve into the implications of our results for enhancing the understanding of neural mechanisms that govern gaze behavior.

Cognitive phenomena measurement with time window-based multispectral brain mapping.

Cognitive neuroscience experiments require accurate and traceable methods of measuring cognitive phenomena, analyzing and processing data, and validating results, including measurement of impact of such phenomena on brain activity and consciousness. EEG measurement is the most widely used tool for evaluation of the experiment's progress. To extract more information from the EEG signal, continuous innovation is necessary to provide a broader range of information. This paper presents a new tool for measuring and mapping cognitive phenomena using time window-based multispectral brain mapping of electroencephalography (EEG) signals. The tool was developed using Python programming language and enables users to create brain maps images for six spectra (Delta, Theta, Alpha, Beta, Gamma, and Mu) of EEG signal. The system can accept an arbitrary number of EEG channels with standardized labels based on the 10-20 system, and users can select the channels, frequency bandwidth, type of signal processing, and time window length to perform the mapping. The key advantage of this tool is its ability to perform short-time brain mapping, which allows for the exploration and measurement of cognitive phenomena. The tool's performance was evaluated through testing on real EEG signals, and results demonstrated its effectiveness in accurately mapping cognitive phenomena. The developed tool can be used in various applications, including cognitive neuroscience research and clinical studies. Future work involves optimizing the tool's performance and expanding its capabilities.

A Two-Layer Self-Organizing Map with Vector Symbolic Architecture for Spatiotemporal Sequence Learning and Prediction.

We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm.

What looks dangerous? Reliability of anxiety and harm ratings of animal and tool visual stimuli

Background Visual stimuli are integral to psychology and cognitive neuroscience research, with growing numbers of image repositories tagged with their affective information like valence and arousal. However, more specific affective domains such as anxiousness and harm have not been empirically examined and reported for visual stimuli, despite their relevance to task paradigms investigating common psychiatric disorders like anxiety and obsessive-compulsive disorder (OCD). Methods In this study, we asked N = 80 participants to assess a set of 42 unique visual stimuli consisting of a variety of animals and tools on anxiety and harm scales. We then assessed the ratings’ psychometric properties. Results We found that animals were generally rated as more harm-perceiving and anxiety-inducing than tools, and were also higher in their inter-rater and test-retest reliabilities. Conclusions With this, we provide a database of affective information for these stimuli, which allows for their use in affective task paradigms using psychometrically validated visual stimuli.

Common and distinct patterns underlying different linguistic tasks: multivariate disconnectome symptom mapping in poststroke patients.

Numerous studies have been devoted to neural mechanisms of a variety of linguistic tasks (e.g. speech comprehension and production). To date, however, whether and how the neural patterns underlying different linguistic tasks are similar or differ remains elusive. In this study, we compared the neural patterns underlying 3 linguistic tasks mainly concerning speech comprehension and production. To address this, multivariate regression approaches with lesion/disconnection symptom mapping were applied to data from 216 stroke patients with damage to the left hemisphere. The results showed that lesion/disconnection patterns could predict both poststroke scores of speech comprehension and production tasks; these patterns exhibited shared regions on the temporal pole of the left hemisphere as well as unique regions contributing to the prediction for each domain. Lower scores in speech comprehension tasks were associated with lesions/abnormalities in the superior temporal gyrus and middle temporal gyrus, while lower scores in speech production tasks were associated with lesions/abnormalities in the left inferior parietal lobe and frontal lobe. These results suggested an important role of the ventral and dorsal stream pathways in speech comprehension and production (i.e. supporting the dual stream model) and highlighted the applicability of the novel multivariate disconnectome-based symptom mapping in cognitive neuroscience research.

The Agenda of The Reading Teacher Journal (RT) on reading skills: A Decade within a Corpus

The science of reading aims to explain the process of teaching and learning reading to educators, families, and students to enhance children's success in school and social life. Research in education, cognitive science, and cognitive neuroscience has provided insights into the cognitive processes of reading and the brain's functions during reading, facilitating the development of effective teaching methods and models. This study conducts a corpus-based analysis of the last decade of research trends related to reading to determine the frequently occurring collocations, their distribution over time, and the emerging and declining concepts in the field. Corpus linguistics is a quantitative approach that relies on analyzing large volumes of language data to uncover patterns and features of language use. It involves the preparation of corpora, which are digital collections of texts, and using statistical tools to analyze the data within them. RT's publications in the last ten years were recorded according to year, volume, and number. The data was harvested via online versions of journals and the collected data were transformed into raw text. Figures, tables, graphic information, and footnotes were excluded from the data. Each text was annotated with "year", "issue", "volume", "keyword 1" and "keyword 2" tags. TreeTagger was used for part-of-speech tagging and CQPWeb/CWB framework was used as the corpus interface. The whole data formed a corpus of 3,548,008 words. This study provides a corpus-based analysis of the concepts that frequently form collocations with the word "reading" in the Reading Teacher (RT) over the last decade. The findings reveal the most frequently used concepts and their distribution according to years and the concepts that have experienced a decline or an increase in usage. The study sheds light on the language and discourse of reading education and contributes to understanding the changes and trends in the field.

기호 인지 체계의 전환 측면에서 글자 그림책의 교육적 활용 방안

Objectives This study explored the educational use of lettered picture books in terms of shifting the symbol cog-nition system.
 Methods Based on research in cognitive neuroscience, we discuss the symbolic cognitive switch that accom-panies learning to read. Symbolic cognition shifts mean that the same visual and auditory symbols are processed in different ways than before. At the early reading level, the educational use of picture books needs to focus on building an expert system for skillful decoding.
 Results Letter picture books are effective in promoting visual identification of letter forms, recognition and com-bination of words and materials, and mastery of phonological and representational correspondences. In light of this, the study suggested the following educational uses of letter picture books: visual exposure to different letter forms, opportunities to practice separating and combining graphemes, and play for mastery of phonological and representational correspondences.
 Conclusions This study attempts to bridge the gap between research in cognitive neuroscience and early reading learning by focusing on lettered picture books. Cognitive neuroscience research on reading has provided insights into the development of reading skills and learning to read that cannot be ignored. We look forward to productive communication between various academic disciplines centered on reading.

Learning exceptions to category rules varies across the menstrual cycle

Ways in which ovarian hormones affect cognition have been long overlooked despite strong evidence of their effects on the brain. To address this gap, we study performance on a rule-plus-exception category learning task, a complex task that requires careful coordination of core cognitive mechanisms, across the menstrual cycle (N = 171). Results show that the menstrual cycle distinctly affects exception learning in a manner that parallels the typical rise and fall of estradiol across the cycle. Participants in their high estradiol phase outperform participants in their low estradiol phase and demonstrate more rapid learning of exceptions than a male comparison group. A likely mechanism underlying this effect is estradiol’s impact on pattern separation and completion pathways in the hippocampus. These results provide novel evidence for the effects of the menstrual cycle on category learning, and underscore the importance of considering female sex-related variables in cognitive neuroscience research.

The cross-race effect in automatic facial expression recognition violates measurement invariance.

Emotion has been a subject undergoing intensive research in psychology and cognitive neuroscience over several decades. Recently, more and more studies of emotion have adopted automatic rather than manual methods of facial emotion recognition to analyze images or videos of human faces. Compared to manual methods, these computer-vision-based, automatic methods can help objectively and rapidly analyze a large amount of data. These automatic methods have also been validated and believed to be accurate in their judgments. However, these automatic methods often rely on statistical learning models (e.g., deep neural networks), which are intrinsically inductive and thus suffer from problems of induction. Specifically, the models that were trained primarily on Western faces may not generalize well to accurately judge Eastern faces, which can then jeopardize the measurement invariance of emotions in cross-cultural studies. To demonstrate such a possibility, the present study carries out a cross-racial validation of two popular facial emotion recognition systems-FaceReader and DeepFace-using two Western and two Eastern face datasets. Although both systems could achieve overall high accuracies in the judgments of emotion category on the Western datasets, they performed relatively poorly on the Eastern datasets, especially in recognition of negative emotions. While these results caution the use of these automatic methods of emotion recognition on non-Western faces, the results also suggest that the measurements of happiness outputted by these automatic methods are accurate and invariant across races and hence can still be utilized for cross-cultural studies of positive psychology.

Multisensory subtypes of aphantasia: Mental imagery as supramodal perception in reverse

Cognitive neuroscience research on mental imagery has largely focused on the visual imagery modality in unimodal task contexts. Recent studies have uncovered striking individual differences in visual imagery capacity, with some individuals reporting a subjective absence of conscious visual imagery ability altogether (“aphantasia”). However, naturalistic mental imagery is often multi-sensory, and preliminary findings suggest that many individuals with aphantasia also report a subjective lack of mental imagery in other sensory domains (such as auditory or olfactory imagery). In this paper, we perform a series of cluster analyses on the multi-sensory imagery questionnaire scores of two large groups of aphantasic subjects, defining latent sub-groups in this sample population. We demonstrate that aphantasia is a heterogenous phenomenon characterised by dominant sub-groups of individuals with visual aphantasia (those who report selective visual imagery absence) and multi-sensory aphantasia (those who report an inability to generate conscious mental imagery in any sensory modality). We replicate our findings in a second large sample and show that more unique aphantasia sub-types also exist, such as individuals with selectively preserved mental imagery in only one sensory modality (e.g. intact auditory imagery). We outline the implications of our findings for network theories of mental imagery, discussing how unique aphantasia aetiologies with distinct self-report patterns might reveal alterations to various levels of the sensory processing hierarchy implicated in mental imagery.

Cognitive Control in Schizophrenia: Advances in Computational Approaches

Psychiatric research is undergoing significant advances in an emerging subspeciality of computational psychiatry, building on cognitive neuroscience research by expanding to neurocomputational modeling. Here, we illustrate some research trends in this domain using work on proactive cognitive control deficits in schizophrenia as an example. We provide a selective review of formal modeling approaches to understanding cognitive control deficits in psychopathology, focusing primarily on biologically plausible connectionist-level models as well as mathematical models that generate parameter estimates of putatively dissociable psychological or neural processes. We illustrate some of the advantages of these models in terms of understanding both cognitive control deficits in schizophrenia and the potential roles of effort and motivation. Further, we highlight critical future directions for this work, including a focus on establishing psychometric properties, additional work modeling psychotic symptoms and their interaction with cognitive control, and the need to expand both behavioral and neural modeling to samples that include individuals with different mental health conditions, allowing for the examination of dissociable neural or psychological substrates for seemingly similar cognitive impairments across disorders.

Fairness: from the guts to the brain – a critical examination by Atlantic fellows of the Global Brain Health Institute

In January 2023, the Global Brain Health Institute (GBHI) at UCSF hosted an online salon to discuss the relationship between fairness and brain health equity. We aimed to address two primary questions: first, how is fairness perceived by the public, and how does it manifest in societal constructs like equity and justice? Second, what are the neurobiological foundations of fairness, and how do they impact brain health? Drawing from interdisciplinary fields such as philosophy, psychology, and neuroscience, the salon served as a platform for participants to share diverse perspectives on fairness. Fairness is a multifaceted concept encompassing equity, justice, empathy, opportunity, non-discrimination, and the Golden Rule, but by delving into its evolutionary origins, we can verify its deep-rooted presence in both human and animal behaviors. Real-world experiments, such as Frans de Waal’s capuchin monkey study, have proven enlightening, elucidating many mechanisms that have shaped our neurobiological responses to fairness. Contemporary cognitive neuroscience research further emphasizes the role of neuroanatomical areas and neurotransmitters in encoding fairness-related processes. We also discussed the critical interconnection between fairness and healthcare equity, particularly its implications for brain health. These values are instrumental in promoting social justice and improving health outcomes. In our polarized social landscape, there are rising concerns about a potential decrease in fairness and prosocial behaviors due to isolated social bubbles. We stress the urgency for interventions that enhance perspective-taking, reasoning, and empathy. Overall, fairness is vital to fostering an equitable society and its subsequent influence on brain health outcomes.

Correlation between Teachers Knowledge, Beliefs, and Frequency of Implementation of Brain Based Learning Strategies

Brain-based learning (BBL) is grounded in an understanding of the structure and function of the brain, as well as empirical evidence derived from cognitive neuroscience research regarding optimal learning strategies for the brain. Educational programs that prioritize brain science in their curriculum and instructional methods are commonly referred to as "brain-based." The aim is to achieve efficient and expeditious education. As per the Education Reform, this particular approach is based on research that examines the brain's potential to restructure itself in order to enhance its capacity to retain and retrieve information while engaged in the process of learning. Enhancing one's capacity can be achieved through engaging in physical activity, maintaining a healthy diet, and effectively managing stress levels. The emotional state of an individual can also have an impact on their learning process. This study presents findings on the correlation between teachers' knowledge, beliefs, and frequency of implementation of brain-based learning strategies in primary Science classes. The research questions addressed in this paper are focused on determining the existence of such a correlation. The study comprised 207 educators from elementary schools in Croatia. The results suggest that educators at the primary level possess a sound understanding of brain-centered pedagogical approaches, yet they infrequently put them into practice. The educational process remains entrenched in a historically rooted paradigm that has not adequately shifted towards student-centered teaching. This is evidenced by the persistent employment of traditional, teacher-led instructional methods in elementary Science classrooms.

Toward an integrative account of internal and external determinants of event segmentation.

Our daily experiences unfold continuously, but we remember them as a series of discrete events through a process called event segmentation. Prominent theories of event segmentation suggest that event boundaries in memory are triggered by significant shifts in the external environment, such as a change in one's physical surroundings. In this review, we argue for a fundamental extension of this research field to also encompass internal state changes as playing a key role in structuring event memory. Accordingly, we propose an expanded taxonomy of event boundary-triggering processes, and review behavioral and neuroscience research on internal state changes in three core domains: affective states, goal states, and motivational states. Finally, we evaluate how well current theoretical frameworks can accommodate the unique and interactive contributions of internal states to event memory. We conclude that a theoretical perspective on event memory that integrates both external environment and internal state changes allows for a more complete understanding of how the brain structures experiences, with important implications for future research in cognitive and clinical neuroscience.