Phase diagram investigation and characterization of alloys in Bi-Ga10Sb90 section of Ga-Bi-Sb system

The results of phase equilibria investigation and characterization of the alloys in the Bi-Ga10Sb90 section of Ga-Bi-Sb system are presented in this paper. Phase diagram of the mentioned section has been calculated according to CALPHAD model, using Pandat software. In the frame of alloys characterization, structural, mechanical and electrical characteristics were determined using SEM-EDX analysis, microhardness and electroconductivity measurements

Phase diagram investigation and characterization of alloys in Bi-Ga10Sb90 section of Ga-Bi-Sb system

The results of phase equilibria investigation and characterization of the alloys in the Bi-Ga10Sb90 section of Ga-Bi-Sb system are presented in this paper. Phase diagram of the mentioned section has been calculated according to CALPHAD model, using Pandat software. In the frame of alloys characterization, structural, mechanical and electrical characteristics were determined using SEM-EDX analysis, microhardness and electroconductivity measurements

Saliency maps for finding changes in visual scenes?

Sudden changes in the environment reliably summon attention. This rapid change detection appears to operate in a similar fashion as pop-out in visual search, the phenomenon that very salient stimuli are directly attended, independently of the number of distracting objects. Pop-out is usually explained by the workings of saliency maps, i.e., map-like representations that code for the conspicuity at each location of the visual field. While past research emphasized similarities between pop-out search and change detection, our study highlights differences between the saliency computations in the two tasks: in contrast to pop-out search, saliency computation in change detection (i) operates independently across different stimulus properties (e.g., color and orientation), and (ii) is little influenced by trial history. These deviations from pop-out search are not due to idiosyncrasies of the stimuli or task design, as evidenced by a replication of standard findings in a comparable visual-search design. To explain these results, we outline a model of change detection involving the computation of feature-difference maps, which explains the known similarities and differences with visual search

Types of geometrical transformations and perceptual similarity of figures

In this study it was investigated which of several types of geometrical transformations emerge as dominant principle in grouping forms. It was assumed that forms which have more similar features would be grouped together more often. The stimuli consisted of basic form (O) and it’s transformation: (T1) rotation; (T2) projection onto horizontal plane; (T3) projection onto vertical plane; (T4) stretching and (T5) twisting. There were two groups of stimuli: (G1) asymmetrical planes and (G2) linear patterns. Stimuli varied in complexity (i.e. number of lines they contain): (S1) four; (S2) five and (S3) six.The sets of stimuli were created given by formula O-Tm-Tn. Subjects were asked to choose which of the stimuli from the set doesn’t go along with others. Results showed that the stimuli would be grouped together more often if they had more common features. These results were obtained for both groups of stimuli and all levels of complexity

Item-based selection is in good shape in visual compound search: a view from electrophysiology

We argue that although the framework put forward by Hulleman & Olivers (H&O) can successfully explain much of visual search behaviour, it appears limited to tasks without precise target identification demands. In particular, we contend that the unit of selection may be larger than a single item in standard detection tasks, whereas the unit may mandatorily be item-based in compound tasks

Item-based selection is in good shape in visual compound search: a view from electrophysiology

We argue that although the framework put forward by Hulleman & Olivers (H&O) can successfully explain much of visual search behaviour, it appears limited to tasks without precise target identification demands. In particular, we contend that the unit of selection may be larger than a single item in standard detection tasks, whereas the unit may mandatorily be item-based in compound tasks

Behavioral and electroencephalography recordings of human observers during a decision-making task with varying levels of task difficulty

Using computational modelling of human behavior together with concurrent recording of brain activity by means of electroencephalography, this project aims to characterize neural and cognitive mechanisms of human decision making

Self-relevance Facilitates Attention to Self-associated Targets on Feature-based Selective Attention Tasks

The ‘Self’ has a prioritized cognitive status, attributed to an automatic bottom-up attentional enhancement for self-relevant stimuli. Two predictions follow if self-relevant information is automatically boosted. First, processing should be enhanced for self- compared to other-relevant targets. Second, interference should be greater for self- compared to other-relevant distractors. To investigate these predictions, we adapted a motion reproduction task. Participants first learned to associate a colour (blue, pink) with themselves and a stranger (other), then viewed a label (YOU or OTHER) and two different coloured superimposed random dot kinematograms (RDKs; blue, pink). A response dial recorded participants’ reproduced direction of motion for the coloured RDK associated with the presented label. Facilitation and interference for self- and other-labelled features was assessed by the angular difference between the reported and true direction of motion (signed error magnitude). There was a small, but reliable response bias in direction of distractor motion showing that attentional selection was imperfect. Further regression-based analyses quantified the degree to which self and other-related stimuli influenced responses (decision weights). As predicted, decision weights for target stimuli showed a significant advantage for self- compared with other-relevant motions. By contrast distractor weights did not differ significantly between self- and other-relevant features, suggesting self-relevance did not modulate the degree of interference and self-relevant stimuli did not automatically capture attention. Overall, we show that feature-based attention is enhanced for self-associated sensory input, but only when task-relevant

White matter microstructure is associated with the precision of visual working memory

Visual working memory is critical for goal-directed behavior as it maintains continuity between previous and current visual input. Functional neuroimaging studies have shown that visual working memory relies on communication between distributed brain regions, which implies an important role for long-range white matter connections in visual working memory performance. Here, we characterized the relationship between the microstructure of white matter association tracts and the precision of visual working memory representations. To that purpose, we devised a delayed estimation task which required participants to reproduce visual features along a continuous scale. A sample of 80 healthy adults performed the task and underwent diffusion-weighted MRI. We applied mixture distribution modelling to quantify the precision of working memory representations, swap errors, and guess rates, all of which contribute to observed responses. Latent components of microstructural properties in sets of anatomical tracts were identified by principal component analysis. We found an interdependency between fibre coherence in the bilateral superior longitudinal fasciculus (SLF) I, SLF II, and SLF III, on one hand, and the bilateral inferior fronto-occipital fasciculus (IFOF), on the other, in mediating the precision of visual working memory in a functionally specific manner. We also found that individual differences in axonal density in a network comprising the bilateral inferior longitudinal fasciculus (ILF) and SLF III and right SLF II, in combination with a supporting network located elsewhere in the brain, form a common system for visual working memory to modulate response precision, swap errors, and random guess rates