Gist in time: Scene semantics and structure enhance recall of searched objects

Previous work has shown that recall of objects that are incidentally encountered as targets in visual search is better than recall of objects that have been intentionally memorized (Draschkow, Wolfe, & Võ, 2014). However, this counter-intuitive result is not seen when these tasks are performed with non-scene stimuli. The goal of the current paper is to determine what features of search in a scene contribute to higher recall rates when compared to a memorization task. In each of four experiments, we compare the free recall rate for target objects following a search to the rate following a memorization task. Across the experiments, the stimuli include progressively more scene-related information. Experiment 1 provides the spatial relations between objects. Experiment 2 adds relative size and depth of objects. Experiments 3 and 4 include scene layout and semantic information. We find that search leads to better recall than explicit memorization in cases where scene layout and semantic information are present, as long as the participant has ample time (2500ms) to integrate this information with knowledge about the target object (Exp. 4). These results suggest that the integration of scene and target information not only leads to more efficient search, but can also contribute to stronger memory representations than intentional memorization.

Visual information representation and rapid-scene categorization are simultaneous across cortex: An MEG study

Perceiving the visual world around us requires the brain to represent the features of stimuli and to categorize the stimulus based on these features. Incorrect categorization can result either from errors in visual representation or from errors in processes that lead to categorical choice. To understand the temporal relationship between the neural signatures of such systematic errors, we recorded whole-scalp magnetoencephalography (MEG) data from human subjects performing a rapid-scene categorization task. We built scene category decoders based on (1) spatiotemporally resolved neural activity, (2) spatial envelope (SpEn) image features, and (3) behavioral responses. Using confusion matrices, we tracked how well the pattern of errors from neural decoders could be explained by SpEn decoders and behavioral errors, over time and across cortical areas. Across the visual cortex and the medial temporal lobe, we found that both SpEn and behavioral errors explained unique variance in the errors of neural decoders. Critically, these effects were nearly simultaneous, and most prominent between 100 and 250ms after stimulus onset. Thus, during rapid-scene categorization, neural processes that ultimately result in behavioral categorization are simultaneous and co-localized with neural processes underlying visual information representation.

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.