Decoding the contents and strength of imagery before volitional engagement.
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ABSTRACT:
SUBMITTER: Koenig-Robert R
PROVIDER: S-EPMC6401098 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We used a multi-voxel classification analysis of functional magnetic resonance imaging (fMRI) data to determine to what extent item-specific information about complex natural scenes is represented in several category-selective areas of human extrastriate visual cortex during visual perception and visual mental imagery. Participants in the scanner either viewed or were instructed to visualize previously memorized natural scene exemplars, and the neuroimaging data were subsequently subjected to a multi-voxel pattern analysis (MVPA) using a support vector machine (SVM) classifier. We found that item-specific information was represented in multiple scene-selective areas: the occipital place area (OPA), parahippocampal place area (PPA), retrosplenial cortex (RSC), and a scene-selective portion of the precuneus/intraparietal sulcus region (PCu/IPS). Furthermore, item-specific information from perceived scenes was re-instantiated during mental imagery of the same scenes. These results support findings from previous decoding analyses for other types of visual information and/or brain areas during imagery or working memory, and extend them to the case of visual scenes (and scene-selective cortex). Taken together, such findings support models suggesting that reflective mental processes are subserved by the re-instantiation of perceptual information in high-level visual cortex. We also examined activity in the fusiform face area (FFA) and found that it, too, contained significant item-specific scene information during perception, but not during mental imagery. This suggests that although decodable scene-relevant activity occurs in FFA during perception, FFA activity may not be a necessary (or even relevant) component of one's mental representation of visual scenes.
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Project description:IntroductionThe integration of AI in architectural design represents a significant shift toward creating emotionally resonant spaces. This research investigates AI's ability to evoke specific emotional responses through architectural imagery and examines the impact of professional training on emotional interpretation.MethodsWe utilized Midjourney AI software to generate images based on direct and metaphorical prompts across two architectural settings: home interiors and museum exteriors. A survey was designed to capture participants' emotional responses to these images, employing a scale that rated their immediate emotional reaction. The study involved 789 university students, categorized into architecture majors (Group A) and non-architecture majors (Group B), to explore differences in emotional perception attributable to educational background.ResultsFindings revealed that AI is particularly effective in depicting joy, especially in interior settings. However, it struggles to accurately convey negative emotions, indicating a gap in AI's emotional range. Architecture students exhibited a greater sensitivity to emotional nuances in the images compared to non-architecture students, suggesting that architectural training enhances emotional discernment. Notably, the study observed minimal differences in the perception of emotions between direct and metaphorical prompts among architecture students, indicating a consistent emotional interpretation across prompt types.ConclusionAI holds significant promise in creating spaces that resonate on an emotional level, particularly in conveying positive emotions like joy. The study contributes to the understanding of AI's role in architectural design, emphasizing the importance of emotional intelligence in creating spaces that reflect human experiences. Future research should focus on expanding AI's emotional range and further exploring the impact of architectural training on emotional perception.
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Project description:Human early visual cortex was traditionally thought to process simple visual features such as orientation, contrast, and spatial frequency via feedforward input from the lateral geniculate nucleus (e.g., [1]). However, the role of nonretinal influence on early visual cortex is so far insufficiently investigated despite much evidence that feedback connections greatly outnumber feedforward connections [2-5]. Here, we explored in five fMRI experiments how information originating from audition and imagery affects the brain activity patterns in early visual cortex in the absence of any feedforward visual stimulation. We show that category-specific information from both complex natural sounds and imagery can be read out from early visual cortex activity in blindfolded participants. The coding of nonretinal information in the activity patterns of early visual cortex is common across actual auditory perception and imagery and may be mediated by higher-level multisensory areas. Furthermore, this coding is robust to mild manipulations of attention and working memory but affected by orthogonal, cognitively demanding visuospatial processing. Crucially, the information fed down to early visual cortex is category specific and generalizes to sound exemplars of the same category, providing evidence for abstract information feedback rather than precise pictorial feedback. Our results suggest that early visual cortex receives nonretinal input from other brain areas when it is generated by auditory perception and/or imagery, and this input carries common abstract information. Our findings are compatible with feedback of predictive information to the earliest visual input level (e.g., [6]), in line with predictive coding models [7-10].
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Project description:Category information for visually presented objects can be read out from multi-voxel patterns of fMRI activity in ventral-temporal cortex. What is the nature and reliability of these patterns in the absence of any bottom-up visual input, for example, during visual imagery? Here, we first ask how well category information can be decoded for imagined objects and then compare the representations evoked during imagery and actual viewing. In an fMRI study, four object categories (food, tools, faces, buildings) were either visually presented to subjects, or imagined by them. Using pattern classification techniques, we could reliably decode category information (including for non-special categories, i.e., food and tools) from ventral-temporal cortex in both conditions, but only during actual viewing from retinotopic areas. Interestingly, in temporal cortex when the classifier was trained on the viewed condition and tested on the imagery condition, or vice versa, classification performance was comparable to within the imagery condition. The above results held even when we did not use information in the specialized category-selective areas. Thus, the patterns of representation during imagery and actual viewing are in fact surprisingly similar to each other. Consistent with this observation, the maps of "diagnostic voxels" (i.e., the classifier weights) for the perception and imagery classifiers were more similar in ventral-temporal cortex than in retinotopic cortex. These results suggest that in the absence of any bottom-up input, cortical back projections can selectively re-activate specific patterns of neural activity.
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Project description:The potential for human neuroimaging to read out the detailed contents of a person's mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimulus orientations the subject was seeing. Moreover, when subjects had to attend to one of two overlapping orthogonal gratings, feature-based attention strongly biased ensemble activity toward the attended orientation. These results demonstrate that fMRI activity patterns in early visual areas, including primary visual cortex (V1), contain detailed orientation information that can reliably predict subjective perception. Our approach provides a framework for the readout of fine-tuned representations in the human brain and their subjective contents.
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Project description:Mental imagery provides an essential simulation tool for remembering the past and planning the future, with its strength affecting both cognition and mental health. Research suggests that neural activity spanning prefrontal, parietal, temporal, and visual areas supports the generation of mental images. Exactly how this network controls the strength of visual imagery remains unknown. Here, brain imaging and transcranial magnetic phosphene data show that lower resting activity and excitability levels in early visual cortex (V1-V3) predict stronger sensory imagery. Further, electrically decreasing visual cortex excitability using tDCS increases imagery strength, demonstrating a causative role of visual cortex excitability in controlling visual imagery. Together, these data suggest a neurophysiological mechanism of cortical excitability involved in controlling the strength of mental images.
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Project description:Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert-Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.
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Project description:Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using (1) MI and (2) passive movements (PM) for training, separately for MEG and EEG. With MI training, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. With PM training, none of the inter-subject methods yielded above chance level (58.7%) accuracy. In conclusion, MTL and training with other subject's MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.
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Project description:The conscious field includes not only representations about external stimuli (e.g., percepts), but also conscious contents associated with internal states, such as action-related intentions (e.g., urges). Although understudied, the latter may provide unique insights into the nature of consciousness. To illuminate these phenomena, in a new experimental paradigm [Reflexive Imagery Task (RIT)], participants were instructed to not subvocalize the names of visually-presented objects. Each object was presented for 10 s on a screen. Participants indicated whenever they involuntarily subvocalized the object name. Research has revealed that it is difficult to suppress such subvocalizations, which occur on over 80% of the trials. Can the effect survive if one intentionally generates a competing (internally-generated) conscious content? If so, this would suggest that intentional and unintentional contents can co-exist simultaneously in consciousness in interesting ways. To investigate this possibility, in one condition, participants were instructed to reiteratively subvocalize a speech sound ("da, da, da") throughout the trial. This internally generated content is self-generated and intentional. Involuntary subvocalizations of object names still arose on over 80% of the trials. One could hypothesize that subvocalizations occurred because of the pauses between the intended speech sounds, but this is inconsistent with the observation that comparable results arose even when participants subvocalized a continuous, unbroken hum ("daaa….") throughout the trial. Regarding inter-content interactions, the continuous hum and object name seem to co-exist simultaneously in consciousness. This intriguing datum requires further investigation. We discuss the implications of this new paradigm for the study of internally-generated conscious contents.
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Project description: Not available
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