Electrophysiological correlates of top-down effects facilitating natural image categorization are disrupted by the attenuation of low spatial frequency information

The modulatory effects of low and high spatial frequencies on the posterior C1, P1 and N1 event-related potential (ERP) amplitudes have long been known from previous electrophysiological studies. There is also evidence that categorization of complex natural images relies on top-down processes, probably by facilitating contextual associations during the recognition process. However, to our knowledge, no study has investigated so far how such top-down effects are manifested in scalp ERPs, when presenting natural images with attenuated low or high spatial frequency information. Twenty-one healthy subjects participated in an animal vs. vehicle categorization task with intact grayscale stimuli and images predominantly containing high (HSF) or low spatial frequencies (LSF). ERP scalp maps and amplitudes/latencies measured above occipital, parietal and frontocentral sites were compared among the three stimulus conditions. Although early occipital components (C1 and P1) were modulated by spatial frequencies, the time range of the N1 was the earliest to show top-down effects for images with unmodified low spatial frequency spectrum (intact and LSF stimuli). This manifested in ERP amplitude changes spreading to anterior scalp sites and shorter posterior N1 latencies. Finally, the frontocentral N350 and the centroparietal LPC were differently influenced by spatial frequency filtering, with the LPC being the only component to show an amplitude and latency modulation congruent with the behavioral responses (sensitivity index and reaction times). Our results strengthen the coarse-to-fine model of object recognition and provide electrophysiological evidence for low spatial frequency-based top-down effects within the first 200 ms of visual processing.

Computational model of visual hallucination in dementia with Lewy bodies

Patients with dementia with Lewy bodies (DLB) frequently experience visual hallucination (VH), which has been aptly described as people seeing things that are not there. The distinctive character of VH in DLB necessitates a new theory of visual cognition. We have conducted a series of studies with the aim to understand the mechanism of this dysfunction of the cognitive system. We have proposed that if we view the disease from the internal mechanism of neurocognitive processes, and if also take into consideration recent experimental data on conduction abnormality, at least some of the symptoms can be understood within the framework of network (or disconnection) syndromes. This paper describes the problem from a computational aspect and tries to determine whether conduction disturbances in a computational model can in fact produce a "computational" hallucination under appropriate assumptions.