State-Trait Anxiety Inventoryによるペインクリニック外来患者の不安の評価

Mental imagery can be advantageous, unnecessary and even clinically disruptive. With methodological constraints now overcome, research has shown that visual imagery involves a network of brain areas from the frontal cortex to sensory areas, overlapping with the default mode network, and can function much like a weak version of afferent perception. Imagery vividness and strength range from completely absent (aphantasia) to photo-like (hyperphantasia). Both the anatomy and function of the primary visual cortex are related to visual imagery. The use of imagery as a tool has been linked to many compound cognitive processes and imagery plays both symptomatic and mechanistic roles in neurological and mental disorders and treatments. Mental imagery plays a role in a variety of cognitive processes such as memory recall. In this review, Joel Pearson discusses recent insights into the neural mechanisms that underlie visual imagery, how imagery can be objectively and reliably measured, and how it affects general cognition.

The human imagination: the cognitive neuroscience of visual mental imagery

This is a high impact journal published by Annual Reviews, Inc., a nonprofit scientific publisher established in 1929 to promote the advancement of the sciences through critical reviews. Covering all areas of psychology research and practice, the Annual Review of Psychology is planned by a rotating committee of eight distinguished psychologists who invite qualified authors to contribute reviews of significant developments in the discipline.

Annual Review of Psychology

Historically, mental imagery has been defined as an experiential state-as something necessarily conscious. But most behavioural or neuroimaging experiments on mental imagery-including the most famous ones-do not actually take the conscious experience of the subject into consideration. Further, recent research highlights that there are very few behavioural or neural differences between conscious and unconscious mental imagery. I argue that treating mental imagery as not necessarily conscious (as potentially unconscious) would bring much needed explanatory unification to mental imagery research. It would also help us to reassess some of the recent aphantasia findings inasmuch as at least some subjects with aphantasia would be best described as having unconscious mental imagery. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2019.0689

Unconscious mental imagery.

Despite the past few decades of research providing convincing evidence of the similarities in function and neural mechanisms between imagery and perception, for most of us, the experience of the two are undeniably different, why? Here, we review and discuss the differences between imagery and perception and the possible underlying causes of these differences, from function to neural mechanisms. Specifically, we discuss the directional flow of information (top-down versus bottom-up), the differences in targeted cortical layers in primary visual cortex and possible different neural mechanisms of modulation versus excitation. For the first time in history, neuroscience is beginning to shed light on this long-held mystery of why imagery and perception look and feel so different. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Why do imagery and perception look and feel so different

The ability to control one's thoughts is crucial for attention, focus, ideation, and mental well-being. Although there is a long history of research into thought control, the inherent subjectivity of thoughts has made objective examination, and thus mechanistic understanding, difficult. Here, we report a novel method to objectively investigate thought-control success and failure by measuring the sensory strength of visual thoughts using binocular rivalry, a perceptual illusion. Across five experiments (N = 67), we found that thought-control failure may occur because of the involuntary and antithetical formation of nonreportable sensory representations during attempts at thought suppression but not during thought substitution. Notably, thought control was worse in individuals with high levels of anxiety and schizotypy but more successful in mindful individuals. Overall, our study offers insight into the underlying mechanisms of thought control and suggests that individual differences play an important role in the ability to control thoughts.

Measuring Thought-Control Failure: Sensory Mechanisms and Individual Differences:

Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is still unclear to which extent artificial neural networks approximate human object vision at the behavioral and neural levels. Here, we investigated whether machine object vision mimics the representational hierarchy of human object vision with an experimental design that allows testing within-domain representations for animals and scenes, as well as across-domain representations reflecting their real-world contextual regularities such as animal-scene pairs that often co-occur in the visual environment. We found that DCNNs trained in object recognition acquire representations, in their late processing stage, that closely capture human conceptual judgements about the co-occurrence of animals and their typical scenes. Likewise, the DCNNs representational hierarchy shows surprising similarities with the representational transformations emerging in domain-specific ventrotemporal areas up to domain-general frontoparietal areas. Despite these remarkable similarities, the underlying information processing differs. The ability of neural networks to learn a human-like high-level conceptual representation of object-scene co-occurrence depends upon the amount of object-scene co-occurrence present in the image set thus highlighting the fundamental role of training history. Further, although mid/high-level DCNN layers represent the category division for animals and scenes as observed in VTC, its information content shows reduced domain-specific representational richness. To conclude, by testing within- and between-domain selectivity while manipulating contextual regularities we reveal unknown similarities and differences in the information processing strategies employed by human and artificial visual systems. Author Summary Computational object vision represents the new frontier of brain models, but do current artificial visual systems known as deep convolutional neural networks (DCNNs) represent the world as humans do? Our results reveal that DCNNs are able to capture important representational aspects of human vision both at the behavioral and neural levels. At the behavioral level, DCNNs are able to pick up contextual regularities of objects and scenes thus mimicking human high-level semantic knowledge such as learning that a polar bear “lives” in ice landscapes. At the neural representational level, DCNNs capture the representational hierarchy observed in the visual cortex all the way up to frontoparietal areas. Despite these remarkable correspondences, the information processing strategies implemented differ. In order to aim for future DCNNs to perceive the world as humans do, we suggest the need to consider aspects of training and tasks that more closely match the wide computational role of human object vision over and above object recognition.

https://doi.org/10.1371/journal.pcbi.1011086

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

The ability to control one9s thoughts is important for mental wellbeing, attention, focus, future planning and ideation. While there is a long history of research into thought control, the inherent subjectivity of thoughts has made objective investigation, and thus mechanistic understanding difficult. Here, we report a novel method to empirically investigate thought control success and failure by objectively measuring the sensory strength of visual thoughts. We use the perceptual illusion binocular rivalry to assess emergent images in mind during two common thought control strategies: thought suppression and thought substitution. Thought suppression was ineffective, suppressed thoughts primed subsequent rivalry dominance at the same level as intentionally imagined thoughts. This priming effect was disrupted by concurrent uniform luminance and changes in retinotopic location, suggesting early visual representations/traces. While individuals showed some metacognition of thought suppression, strikingly, the perceptual effects remained even when thoughts were reported as successfully suppressed, indicating these thoughts may exist outside of reportable awareness. In contrast, thought substitution was more effective in controlling the perceptual effects and showed good metacognition. A thought control index predicted greater levels of trait mindfulness, while high levels of anxiety and schizotypy were related to poor thought control. Overall, our findings offer a novel method to track thoughts before and after they emerge into awareness and suggest that non-reportable and involuntary thoughts form visual representations pivotal to thought control failure.

https://www.biorxiv.org/content/biorxiv/early/2018/04/03/293761.full.pdf

Thought Control Failure: Sensory Determinants and Functional Effects

The human visual system has a seemingly unique tendency to interpret zoomorphic objects as animals, not as objects. This animal appearance bias is very strong in the ventral visual pathway as measured through functional magnetic resonance imaging (fMRI), but it is absent in feedforward deep convolutional neural networks. Here we investigate how this bias emerges over time by probing its representational dynamics through multivariate electroencephalography (EEG). The initially activated representations to lookalike zoomorphic objects are very similar to the representations activated by animal pictures and very different from the neural responses to regular objects. Neural responses that reflect the true identity of the zoomorphic objects as inanimate objects are weaker and appear later, as do effects of task context. The strong early emergence of an animal appearance bias strongly supports a feedforward explanation, indicating that lack of recurrence in deep neural networks is not an explanation for their failure to show this bias.

/pdf/the-representational-dynamics-of-the-animal-appearance-bias-3b9971ab.pdf

The representational dynamics of the animal appearance bias in human visual cortex are indicative of fast feedforward processing

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Measuring Thought-Control Failure: Sensory Mechanisms and Individual Differences:

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

Thought Control Failure: Sensory Determinants and Functional Effects

The representational dynamics of the animal appearance bias in human visual cortex are indicative of fast feedforward processing