Visual Spatial Information Research Articles

Evaluation of mental performance and cognitive functions of children and adolescents diagnosed with radiologically isolated syndrome

BackgroundRadiologically isolated syndrome (RIS) is a condition characterized by asymptomatic, incidentally detected demyelinating plaques in the CNS in a patient without typical clinical findings of multiple sclerosis (MS). This study aimed to compare the mental status and cognitive functions of child and adolescent RIS cases with healthy controls and to investigate the relationship between psychometric test results and the demyelinating lesion characteristics. MethodsThe mental status and cognitive functions of 12 RIS cases and 12 healthy controls were compared. Semi-structured interviews, behavioral evaluations, depression and anxiety scales, neuropsychological test battery, and an intelligence test were applied for the evaluation of mental state and cognitive functions. These results were compared with the number and localization of demyelinating lesions. ResultsSustained attention, visual-motor coordination, short-term memory skills, and ability to use visual-spatial information were found worse in the RIS group. There was no correlation between mental state and cognitive functions, and the number and localization of demyelinating lesions. ConclusionOur study showed that pediatric RIS cases may have worse cognitive performance than healthy controls, but no correlation was found between the number and location of demyelinating lesions and psychiatric findings. Although it is controversial whether psychiatric disorders and cognitive disabilities have predictive value in terms of MS conversion in pediatric RIS cases, these subjects were not included in the scope of this study.

Altered effective connectivity on rapid automatized naming deficits in Chinese children with developmental dyslexia: An rs-fMRI study with Ganger causality analysis

Rapid Automatized Naming (RAN) is the core defect of developmental dyslexia (DD), requiring collaboration among brain areas to complete. However, it's still unclear which effective connectivity (EC) among brain areas are crucial for RAN deficits in Chinses children with DD. The current study aims to explore the EC among brain areas related to RAN deficits in Chinese children with DD. We recruited 36 Chinese children with DD and 64 typically developing (TD) children aged 8–12 to complete resting-state functional magnetic resonance imaging (rs-fMRI) scan. Granger causality analysis (GCA) was employed to analysis the EC among brain areas related to RAN, and to calculate the relationship between EC and RAN scores. Compared to TD group, the DD group exhibited significantly decreased EC from left precentral gyrus (PG) to right precuneus, left anterior cingulate and paracingulate gyrus (ACG), left calcarine and right angular, from left middle frontal gyrus (MFG) to left calcarine. Additionally, the DD group showed increased EC from right cuneus to left inferior frontal gyrus triangular part (IFGtri). The EC from left PG to left ACG was positively correlated with letters-RAN score. The results showed Chinese children with DD had both defect and compensatory mechanisms for their RAN deficits. The decreased EC output from left PG may be the core problem of the RAN deficits, which may influence the integration of visual-spatial information, attention, memory retrieval, and speech motor in speech production. The current study has important clinic implications for establishing intervention measures targeted brain.

A study of pedestrian wayfinding behavior based on desktop VR considering both spatial knowledge and visual information

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.

Cross-modal generative model for visual-guided binaural stereo generation

Binaural stereo audio is recorded by imitating the way the human ear receives sound, which provides people with an immersive listening experience. Existing approaches leverage autoencoders and directly exploit visual spatial information to synthesize binaural stereo, resulting in a limited representation of visual guidance. For the first time, we propose a visually guided generative adversarial approach for generating binaural stereo audio from mono audio. Specifically, we develop a Stereo Audio Generation Model (SAGM), which utilizes shared spatio-temporal visual information to guide the generator and the discriminator to work separately. The shared visual information is updated alternately in the generative adversarial stage, allowing the generator and discriminator to deliver their respective guided knowledge while visually sharing. The proposed method learns bidirectional complementary visual information, which facilitates the expression of visual guidance in generation. In addition, spatial perception is a crucial attribute of binaural stereo audio, and thus the evaluation of stereo spatial perception is essential. However, previous metrics failed to measure the spatial perception of audio. To this end, a metric to measure the spatial perception of audio is proposed for the first time. The proposed metric is capable of measuring the magnitude and direction of spatial perception in the temporal dimension. Further, considering its function, it is feasible to utilize it instead of demanding user studies to some extent. The proposed method achieves state-of-the-art performance on 2 datasets and 5 evaluation metrics. Qualitative experiments and user studies demonstrate that the method generates space-realistic stereo audio.

Implementing an electromagnetic tracking navigation system improves the precision of endoscopic transgastric necrosectomy in an ex vivo model

Endoscopic transgastric necrosectomy is crucial in the management of complications resulting from necrotizing pancreatitis. However, both real-time and visual-spatial information is lacking during the procedure, thereby jeopardizing a precise positioning of the endoscope. We conducted a proof-of-concept study with the aim of overcoming these technical difficulties. For this purpose, a three-dimensional (3D) phantom of a stomach and pancreatic necroses was 3D-printed based on spatial information from individual patient CT scans and subsequently integrated into a silicone torso. An electromagnetic (EM) sensor was adjusted inside the endoscope´s working channel. A software interface enabled real time visualization. The accuracy of this novel assistant system was tested ex vivo by four experienced interventional endoscopists who were supposed to reach seven targets inside the phantom in six different experimental runs of simulated endoscopic transgastric necrosectomy. Supported by endoscopic camera view combined with real-time 3D visualization, all endoscopists reached the targets with a targeting error ranging between 2.6 and 6.5 mm in a maximum of eight minutes. In summary, the EM tracking system might increase efficacy and safety of endoscopic transgastric necrosectomy at the experimental level by enhancing visualization. Yet, a broader feasibility study and further technical improvements are mandatory before aiming at implementation into clinical setting.

인공지능(AI)을 활용한 건축디자인 정보처리 모델에 관한 연구

Recently, there is a lot of discussion in the architectural world about the impact that artificial intelligence image generation models will have on architectural design. Artificial intelligence uses information processing theory to analyze and model human cognitive abilities. According to information processing theory, architectural design is an information processing activity that establishes reasonable concepts to solve spatial problems and processes visual and conceptual information that corresponds to them. In this study, the five information processing processes of Akin's (1986) information processing model, DIPS (design information processing system), 'Information acquisition', 'Information representation', 'Information projection', 'Information confirmation', and 'Regulation of control'. The characteristics of architectural design thinking types, ‘convergent thinking’, ‘divergent thinking’, and ‘analogical thinking’ were examined. Through this, I identified the characteristics of representative design-related artificial intelligence NLP, GAN, and DNN and proposed an architectural design information processing model using artificial intelligence. The purpose of this paper is to examine the architectural design information processing model and compare the characteristics of the thinking style that occurs in the process with the characteristics of the artificial intelligence model to analyze the possibility of using artificial intelligence in architectural design and to provide architectural design information using artificial intelligence. The goal is to propose a processing model.

Temporally Dissociable Mechanisms of Spatial, Feature, and Motor Selection during Working Memory-guided Behavior.

Working memory (WM) is a capacity- and duration-limited system that forms a temporal bridge between fleeting sensory phenomena and possible actions. But how are the contents of WM used to guide behavior? A recent high-profile study reported evidence for simultaneous access to WM content and linked motor plans during WM-guided behavior, challenging serial models where task-relevant WM content is first selected and then mapped on to a task-relevant motor response. However, the task used in that study was not optimized to distinguish the selection of spatial versus nonspatial visual information stored in memory, nor to distinguish whether or how the chronometry of selecting nonspatial visual information stored in memory might differ from the selection of linked motor plans. Here, we revisited the chronometry of spatial, feature, and motor selection during WM-guided behavior using a task optimized to disentangle these processes. Concurrent EEG and eye position recordings revealed clear evidence for temporally dissociable spatial, feature, and motor selection during this task. Thus, our data reveal the existence of multiple WM selection mechanisms that belie conceptualizations of WM-guided behavior based on purely serial or parallel visuomotor processing.

Gestures cued by demonstratives in speech guide listeners' visual attention during spatial language comprehension.

Gestures help speakers and listeners during communication and thinking, particularly for visual-spatial information. Speakers tend to use gestures to complement the accompanying spoken deictic constructions, such as demonstratives, when communicating spatial information (e.g., saying "The candle is here" and gesturing to the right side to express that the candle is on the speaker's right). Visual information conveyed by gestures enhances listeners' comprehension. Whether and how listeners allocate overt visual attention to gestures in different speech contexts is mostly unknown. We asked if (a) listeners gazed at gestures more when they complement demonstratives in speech ("here") compared to when they express redundant information to speech (e.g., "right") and (b) gazing at gestures related to listeners' information uptake from those gestures. We demonstrated that listeners fixated gestures more when they expressed complementary than redundant information in the accompanying speech. Moreover, overt visual attention to gestures did not predict listeners' comprehension. These results suggest that the heightened communicative value of gestures as signaled by external cues, such as demonstratives, guides listeners' visual attention to gestures. However, overt visual attention does not seem to be necessary to extract the cued information from the multimodal message. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Single-neuron and population measures of neuronal activity in working memory tasks.

Information represented in working memory is reflected in the firing rate of neurons in the prefrontal cortex and brain areas connected to it. In recent years, there has been an increased realization that population measures capture more accurately neural correlates of cognitive functions. We examined how single neuron firing in the prefrontal and posterior parietal cortex of two male monkeys compared with population measures in spatial working memory tasks. Persistent activity was observed in the dorsolateral prefrontal and posterior parietal cortex and firing rate predicted working memory behavior, particularly in the prefrontal cortex. These findings had equivalents in population measures, including trajectories in state space that became less separated in error trials. We additionally observed rotations of stimulus representations in the neuronal state space for different task conditions, which were not obvious in firing rate measures. These results suggest that population measures provide a richer view of how neuronal activity is associated with behavior, largely confirming that persistent activity is the core phenomenon that maintains visual-spatial information in working memory.NEW & NOTEWORTHY Recordings from large numbers of neurons led to a reevaluation of neural correlates of cognitive functions, which traditionally were defined based on responses of single neurons or averages of firing rates. Analysis of neuronal recordings from the dorsolateral prefrontal and posterior parietal cortex revealed that properties of neuronal firing captured in classical studies of persistent activity can account for population representations, though some population characteristics did not have clear correlates in single neuron activity.

The contralateral organization of the human nervous system as a quantum unfolded, holographic-like, artifactual representation of the underlying dynamics of a fundamentally two-dimensional universe.

A working hypothesis is put forward in this article that the contralateral organization of the human nervous system appears to function like a quantum unfolded holographic apparatus by appearing to invert and reverse quantum unfolded visual and non-visual spatial information. As such, the three-dimensional contralateral organization would be an artifactual representation of the underlying dynamics of a fundamentally two-dimensional universe. According to the holographic principle, nothing that is experienced as three-dimensional could have been processed in a three-dimensional brain. Everything we would experience at a two-dimensional level would appear as a three-dimensional holographic representation, including the architecture of our brains. Various research observations reported elsewhere are reviewed and interpreted here as they may be related in a process that is fundamental to the underlying two-dimensional dynamics of the contralateral organization. The classic holographic method and characteristics of image formation contained by a holograph are described as they relate to the working hypothesis. The double-slit experiment is described and its relevance to the working hypothesis.

HEAT: Incorporating hierarchical enhanced attention transformation into urban road detection

AbstractRoad detection plays a vital role in automated driving and advanced driver assistance systems. In recent years, mainstream frameworks have suffered from the restrictions of a receptive field and the limitation of modelling long‐range relations. Previous methods fail to segment precise road boundaries when urban roads with similar surface textures are presented. Moreover, road regions are perceived as non‐road areas due to the shadow effect, which affects the completeness of the road in the traffic environment. To this end, for urban road detection, a hierarchical enhanced attention transformation (HEAT) architecture, which holds both fine details (road edges) and global contextual information (road structure) is proposed. The symmetrical data‐fusion residual network fuses visual semantic and spatial structure information. The attention consolidation units model global contextual information at different layers to realize the feature enhancement from coarse to fine. In addition, corresponding local and global features are fused hierarchically in the progressive up‐sampling modules. Comprehensive empirical studies are conducted to compare other mainstream methods in the KITTI and the Cityscapes dataset. HEAT shows highly competitive performance in that confusable areas are correctly distinguished in the presence of obstacles, shadows, and similar road textures. HEAT outperforms state‐of‐the‐art methods in urban road detection.

Learning How to Detect Salient Objects in Nighttime Scenes

The detection of salient objects in nighttime scene settings is an essential research issue in computer vision. None of the known approaches can accurately anticipate salient objects in the nighttime scenes. Due to the lack of visible light, spatial visual information cannot be accurately perceived by traditional and deep network models. This paper proposed a Mountain Basin Network (MBNet) to identify salient objects for distinguishing the pixel-level saliency of low-light images. To improve the objects localizations and pixel classification performances, the proposed model incorporated a High-Low Feature Aggregation Module (HLFA) to synchronize the information from a high-level branch (named Bal-Net) and a low-level branch (called Mol-Net) to fuse the global and local context, and a Hierarchical Supervision Module (HSM) was embedded to aid in obtaining accurate salient objects, particularly the small ones. In addition, a multi-supervised integration technique was explored to optimize the structure and borders of salient objects. In the meantime, to facilitate more investigation into nighttime scenes and assessment of visual saliency models, we created a new nighttime dataset consisting of thirteen categories and a total of one thousand low-light images. Our experimental results demonstrated that the suggested MBNet model outperforms seven current state-of-the-art methods for salient object detection in nighttime scenes.

Visual-spatial and visuomotor functioning in adults with neurofibromatosis type 1.

Neurofibromatosis type 1 (NF1) is a neurodevelopmental genetic disorder associated with visual-spatial and visuomotor deficits, which have not been studied well in adults with NF1. In 22 adults with NF1 and 31 controls, visuomotor functioning was assessed by measuring eye latency, hand latency and hand accuracy during visuomotor tasks. Visual-spatial functioning was assessed by measuring eye movement responses during the Visual Threshold Task. The NF1 group had a significantly shorter eye latency than the control group and was less accurate in their hand movements during specific visuomotor tasks. The groups showed no differences in eye movement responses during the Visual Threshold Task and in hand latency during the visuomotor tasks. In contrast to studies in children with NF1, we found no alterations in visual-spatial information processing in adults. Impairments in eye latency and hand accuracy during specific visuomotor tasks may indicate deficits in visuomotor functioning in adults with NF1.

Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.

Кластеризация нейропсихологических данных: оценка соотношения состояния когнитивных функций у детей 6–9 лет

The aim of this project is an investigation of the state of cognitive functions in older preschool and primary-school-age children. At this age, there is a considerable development and maturation of various components of cognitive functions, and large individual variability of their condition is observed. In the article, we tried to divide a large sample of children with normative development into groups with different neuropsychological profiles according to the strengths and weaknesses of various components of cognitive functions.
 A sample of 484 children between the ages of 6 and 9 years old underwent a Lurian face-to-face neuropsychological examination and the computerized neuropsychological assessment. In each of the tests, key parameters were identified to evaluate three groups of cognitive functions: executive functions, visual-spatial information processing, auditory information processing. Then a cluster analysis was carried out, which allowed us to identify five groups of subjects: children with a high level of cognitive functions; children with executive function weakness; children with low auditory information processing; children with low visual-spatial information processing; children with a low level of cognitive functions. With the help of other complex neuropsychological tests, we verified this division into groups. The data obtained confirm the idea of uneven development of cognitive functions in children aged 6–9 years with normative development. These results can be used in further research of theoretical issues of cognitive development, and for practical purposes in neuropsychologist correction.

Gaze Target Estimation Inspired by Interactive Attention

As an essential nonverbal cue, the human gaze reveals human intentions and plays a crucial role in human daily activities. Therefore, automatic detection of the person’s gaze target has drawn the interests of the computer vision community. This is useful not only for identifying whether children are attentive in class but also for locating items of interest to humans in retail settings. Existing gaze-following methods have only explored and exploited the scenes context and the head cues. Considering the significance of human-object interaction in understanding human intentions, we present the Visual-Spatial Graph and introduce a graph attention network to analyze the interaction probability between the human and elements in the scene. Then the interaction probability inferred from the visual-spatial information that is aggregated by the attention mechanism can be transformed into an interactive attention map that depicts the areas people care about. In addition, we construct a transformer as an encoder to integrate the features extracted by the scene and head pathways aiming to decode the gaze target. After introducing interactive attention, our proposed method achieves outstanding performance on two benchmarks: GazeFollow and VideoAttentionTarget. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nkuhzx/VSG-IA</uri> .

Interdigitated Columnar Representation of Personal Space and Visual Space in Human Parietal Cortex.

Personal space (PS) is the space around the body that people prefer to maintain between themselves and unfamiliar others. Intrusion into personal space evokes discomfort and an urge to move away. Physiologic studies in nonhuman primates suggest that defensive responses to intruding stimuli involve the parietal cortex. We hypothesized that the spatial encoding of interpersonal distance is initially transformed from purely sensory to more egocentric mapping within human parietal cortex. This hypothesis was tested using 7 Tesla (7T) fMRI at high spatial resolution (1.1 mm isotropic), in seven subjects (four females, three males). In response to visual stimuli presented at a range of virtual distances, we found two categories of distance encoding in two corresponding radially-extending columns of activity within parietal cortex. One set of columns (P columns) responded selectively to moving and stationary face images presented at virtual distances that were nearer (but not farther) than each subject's behaviorally-defined personal space boundary. In most P columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining P columns, BOLD responses decreased with increasing proximity. A second set of parietal columns (D columns) responded selectively to disparity-based distance cues (near or far) in random dot stimuli, similar to disparity-selective columns described previously in occipital cortex. Critically, in parietal cortex, P columns were topographically interdigitated (nonoverlapping) with D columns. These results suggest that visual spatial information is transformed from visual to body-centered (or person-centered) dimensions in multiple local sites within human parietal cortex.SIGNIFICANCE STATEMENT Recent COVID-related social distancing practices highlight the need to better understand brain mechanisms which regulate "personal space" (PS), which is defined by the closest interpersonal distance that is comfortable for an individual. Using high spatial resolution brain imaging, we tested whether a map of external space is transformed from purely visual (3D-based) information to a more egocentric map (related to personal space) in human parietal cortex. We confirmed this transformation and further showed that it was mediated by two mutually segregated sets of columns: one which encoded interpersonal distance and another that encoded visual distance. These results suggest that the cortical transformation of sensory-centered to person-centered encoding of space near the body involves short-range communication across interdigitated columns within parietal cortex.

Feature Extraction Method of Art Visual Communication Image Based on 5G Intelligent Sensor Network

5G intelligent sensor network technology realizes the perception, processing, and transmission of information. It forms the three pillars of information technology together with computer technology and communication technology and is an important part of the Internet of Things technology. The 5G smart sensor network is a wireless communication module added to the sensor nodes, and a wireless communication network is formed by a large number of stationary or movable sensor nodes in the form of self-organization and multihop transmission. This paper proposes a keypoint feature extraction method based on deep learning, which can extract keypoint local features for matching. This method uses the convolutional network structure, which is pretrained based on the Siamese network structure and then adjusted to the ternary network structure to continue training to improve the accuracy. This paper proposes a high-art visual communication image classification based on multifeature extraction and classification decision fusion. In the data preprocessing stage, the correlation alignment algorithm is performed on the datasets of different domains (source domain and target domain) to reduce the difference in spatial distribution, and then, a multifeature extractor is designed to extract artistic visual communication images and spatial information. In the process, the multitask learning method is introduced to jointly train the networks of multiple data sets to reduce the degree of overfitting of the model, solve the problem of insufficient labeled samples in the target domain data set, and affect the classification accuracy of high-art visual communication images. Finally, the classification results are obtained through the fusion of voting decisions. The experimental results show that the advantage of this framework is that it utilizes the artistic visual communication image and spatial structure information from the source and target scenes, which can significantly reduce the dependence on the number of labeled samples in the target domain and improve the classification performance. In this paper, a dual-channel deep residual convolutional neural network is designed. The multiple convolution layers of the residual module in the network use hard parameters to share, so that the deep feature representation on the joint spatial spectrum dimension can be automatically extracted. The features extracted by the network are transferred to maximize the auxiliary role of the labeled samples in the source domain and avoid the negative transfer problem caused by the forced transfer between irrelevant samples.

The perspective of Speech and Language Pathologist about Outcomes of an Early Language Delay in Children Less than 5 Years Old

Background: Language is the systematic and standard use of sounds (or signs or written symbols) for communication or expression purpose. In humans language is controlled primarily by the left cerebral hemisphere, and the right hemisphere is specialized for processing visual-spatial information. This actually depends on the handedness of humans. It is evaluated that around 95% of right-handers and 70% of left-handers appear left-hemisphere dominant for language. Early language delay is a condition that called a common developmental disorder. Persistent delay may lead to adverse impacts on the emotional, behavioral and social abilities of children.&#x0D; Objective: The objective of the present study was to find out the perspective of speech and language pathologists/therapists about the outcomes of an early language delay in children less than 5 years old.&#x0D; Materials &amp; Methods: This descriptive study was carried out at NUR International University, from 1st March 2021 to 30 September 2021 to determine the outcomes of an early language delay in less than 5 years old. For this purpose data was collected from almost 50 speech therapist working with the language delaychildrensthrough self-designed questionnaire. Questionnaire which was used as data collecting instrument that was designed by expert opinion and literature review.&#x0D; Results: Results show that the most facing outcomes are difficulty in understanding verbal communication 30(60%), turn-taking during conversation as well as playing 32(64%), focus and concentration difficulties 28(56%), difficulty in linguists use 30(60%), difficulty in imitation 25(50%), performing cognitive tasks30(60%), academic issues 30(60%), arranging syntactic and semantics 25(50%), non-compliance 29(58%), emotional tantrums 35(70%) and physical aggression on siblings and animals 31(62%).&#x0D; Conclusion: The children are facing difficulties in their academic life and extremely confront the behavioral, social and, emotional problems in their daily life.

Processing of visual and non-visual naturalistic spatial information in the "parahippocampal place area"

The “parahippocampal place area” (PPA) in the human ventral visual stream exhibits increased hemodynamic activity correlated with the perception of landscape photos compared to faces or objects. Here, we investigate the perception of scene-related, spatial information embedded in two naturalistic stimuli. The same 14 participants were watching a Hollywood movie and listening to its audio-description as part of the open-data resource studyforrest.org. We model hemodynamic activity based on annotations of selected stimulus features, and compare results to a block-design visual localizer. On a group level, increased activation correlating with visual spatial information occurring in the movie is overlapping with a traditionally localized PPA. Activation correlating with semantic spatial information occurring in the audio-description is more restricted to the anterior PPA. On an individual level, we find significant bilateral activity in the PPA of nine individuals and unilateral activity in one individual. Results suggest that activation in the PPA generalizes to spatial information embedded in a movie and an auditory narrative, and may call for considering a functional subdivision of the PPA.