Hallucination as Mental Imagery

Hallucination is a big deal in contemporary philosophy of perception. The main reason for this is that the way hallucination is treated marks an important stance in one of the most hotly contested debates in this subdiscipline: the debate between 'relationalists' and 'representationalists'. I argue that if we take hallucinations to be a form of mental imagery, then we have a very straightforward way of arguing against disjunctivism: if hallucination is a form of mental imagery and if mental imagery and perception have some substantive common denominator, then a fortiori, perception and hallucination will also have a substantive common denominator

How Much Do We Learn about Hallucinations from Thought-Experiments?

The idea that our sensory experience cannot serve as a ground for knowledge lingers on within philosophical thinking from its very beginning. Since even the ancient sceptics argued against the possibility of knowledge based on sense perception due to its potentially illusory or hallucinatory character, it seems reasonable to address the issue of hallucination itself. The purpose of this paper is to discuss upon the philosophical account of hallucination present in current debates. I will mainly work on the so-called ‘argument from hallucination’ which provides a prevalent objection both against the direct realism theory of perception, and externalist theories of content of experience. My primary intention will be to single out the ontological claims concerning hallucinatory experience that constitute the core of the argument from hallucination. Moreover, the legitimacy of philosophical theses concerning hallucination will be discussed both by means of philosophical analysis, and in the light of chosen empirical findings.Numer został przygotowany przy wsparciu Ministerstwa Nauki i Szkolnictwa Wyższego

Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson's disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD. Author SummaryInefficient integration of information between external stimuli and internal perceptual predictions may lead to misperceptions or visual hallucinations in Parkinson's disease (PD). In this study, we show that hallucinatory behavior in PD patients is associated with marked alterations in structural network topology. Severity of hallucinatory behavior was associated with decreased connectivity in a large subnetwork that included the majority of the diverse club, nodes with a high number of between-module connections. Furthermore, changes in between-module connectivity were found across brain regions involved in visual processing, top-down prediction centers, and endogenous attention, including the occipital, orbitofrontal, and posterior cingulate cortex. Together, these findings suggest that impaired integration across different sides across different perceptual processing regions may result in inefficient transfer of information

Attention-Aware Face Hallucination via Deep Reinforcement Learning

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations

A PSYCHOLINGUISTIC ANALYSIS OF SCHIZOPHRENIC SPEECH REFLECTING HALLUCINATION AND DELUSION IN THE CAVEMAN’S VALENTINE

The objectives of this research are (1) to explain the speech abnormalities of a schizophrenic character, Romulus, in The Caveman’s Valentine; and (2) to present the characteristics of schizophrenia represented by Romulus in his speech. This research employed a descriptive qualitative method. It was concerned with the description of the data in the form of utterances produced by the schizophrenic character, Romulus, in which the phenomena of schizophrenic speech abnormalities exist. Quantification of the data was also done in this research, only to strengthen the answer of the first objective. Meanwhile, for the second objective, the explanation is without number. Finally, in order to support the credibility of the data findings, data trustworthiness was maintained in the form of triangulation and peer discussion (peer debriefing). The findings of this research show that first, among the eight types of schizophrenic speech abnormalities, only four of them occur. They are looseness, perseveration of ideas, peculiar use of words, and non-logical reasoning (peculiar logic). Looseness is the first most-often appearing phenomenon, followed by perseveration of ideas, peculiar use of words, and non-logical reasoning (peculiar logic). Second, all characteristics of schizophrenia, i.e. hallucination and delusion, are also shown in the movie. Hallucination is represented by the occurrence of visual and auditory hallucination, while delusion is represented by the occurrence of paranoid delusion and delusion of reference. In addition, for the characteristics of schizophrenia, the number of the occurrence of each phenomenon is not important since the existence of each characteristic is enough to judge that someone suffers from schizophrenia. Keywords : schizophrenia, looseness, perseveration of ideas, peculiar use of words, non-logical reasoning (peculiar logic), hallucination, delusion, The Caveman’s Valentin

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL