AIMC Topic: Hallucinations

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What can we learn about the psychiatric diagnostic categories by analysing patients' lived experiences with Machine-Learning?

BMC psychiatry
BACKGROUND: To deliver appropriate mental healthcare interventions and support, it is imperative to be able to distinguish one person from the other. The current classification of mental illness (e.g., DSM) is unable to do that well, indicating the p...

Image Hallucination From Attribute Pairs.

IEEE transactions on cybernetics
Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based generation methods confront a dilemma. On the one hand, the image generator should b...

Dual-Path Deep Fusion Network for Face Image Hallucination.

IEEE transactions on neural networks and learning systems
Along with the performance improvement of deep-learning-based face hallucination methods, various face priors (facial shape, facial landmark heatmaps, or parsing maps) have been used to describe holistic and partial facial features, making the cost o...

Robotically-induced hallucination triggers subtle changes in brain network transitions.

NeuroImage
The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in patients with Parkinson's disease, it has been argued to be indicative of a more severe and rapidly ...

Tinnitus-like "hallucinations" elicited by sensory deprivation in an entropy maximization recurrent neural network.

PLoS computational biology
Sensory deprivation has long been known to cause hallucinations or "phantom" sensations, the most common of which is tinnitus induced by hearing loss, affecting 10-20% of the population. An observable hearing loss, causing auditory sensory deprivatio...

De novo protein design by deep network hallucination.

Nature
There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences. Here we investigate whether the information captured by such networks is sufficiently...

Negative content in auditory verbal hallucinations: a natural language processing approach.

Cognitive neuropsychiatry
INTRODUCTION: Negative content of auditory verbal hallucinations (AVH) is a strong predictor of distress and impairment. This paper quantifies emotional voice-content in order to explore both subjective (i.e. perceived) and objectively (i.e. linguist...

Deep HDR Hallucination for Inverse Tone Mapping.

Sensors (Basel, Switzerland)
Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas must be expanded and any missing information due to over/under-exposure...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Scientific reports
Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors determine individual proneness to psychotic symptoms. Schizotypy is a multifaceted personality trait rela...

Inner speech.

Wiley interdisciplinary reviews. Cognitive science
Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "little voice in the head," and so on. It is both a familiar element of first-person experience and a psy...