Mia Šetić

The influence of vertical spatial orientation on property verification

According to the spatial registration hypothesis, the representation of stimulus location is automatically encoded during perception and it can interact with a more abstract linguistic representation. We tested this hypothesis in two experiments, using the semantic judgements of words. In the first experiment, words for animals that either fly or do not fly were presented either in the upper or lower part of a display relative to the fixation point. Reaction times showed significant interaction between the spatial position and the word type. The words for flying animals were judged faster when they were presented in the upper part while the words for non-flying animals were processed faster in the lower part of the display. In the second experiment we extended the stimulus set to words denoting non-living things which are associated with either upper or lower spatial position. Again, reaction times showed significant interaction between the actual spatial position where the words were presented, and their implicit association with upper or lower spatial position. The results provide support for the claim that spatial representation has an active role in lexical processing.

Effects of Isolation and Inbreeding on Human Quantitative Traits: An Example of Biochemical Markers of Hemostasis and Inflammation

Abstract Isolation is a known force in evolutionary biology and one of the main factors in speciation. One of the main consequences of severe isolation is reduced mate choice, which results in the occurrence of inbreeding as a result of isolation. We investigated the effects of individual genome-wide heterozygosity measured as the multilocus heterozygosity (MLH) on biochemical markers of hemostasis and inflammation in 1,041 individuals from the island of Vis, Croatia, where inbreeding is prevalent and a wide range of variation in the genome-wide heterozygosity is expected. Assessment of individual genome-wide heterozygosity was based on genome-wide scans using 800 microsatellite (STR) and 317,503 single nucleotide (SNP) polymorphic markers in each examinee. In addition, for each examinee we defined a personal genetic history (PGH) based on genealogical records. The association between PGH and MLH and fibrinogen, D-dimer (Dd), von Willebrand factor (vWF), tissue plasminogen activator (tPA), and C-reactive protein (CRP) was performed with a mixed model, controlling for possible confounding effects. PGH was a significant predictor only for tPA (P < 0.001), whereas neither of the two MLH measures exhibited significant association with any of the investigated traits. The effects of individual genome-wide heterozygosity are most likely expressed in highly polygenically determined traits or in traits that are mediated by rare and recessive genetic variants. Weak associations between PGH and MLH and markers of hemostasis and inflammation suggest that their genetic control may not be highly polygenic and that they could be promising targets for genetic association studies.

Modelling the statistical processing of visual information

Recent psychophysical investigations showed that humans have the ability to compute the mean size of a set of visual objects. The investigations suggest that the visual system is able to form an overall, statistical representation of a set of objects, while the information about individual members of the set is lost. We proposed a neural model that computes the mean size of a set of similar objects. The model is a feedforward, two-dimensional neural network with three layers. Computer simulations showed that the presented model of statistical processing is able to form abstract numerical representation and to compute the mean size independently from the visual appearance of objects. This is achieved in a fast, parallel manner without serial scanning of the visual field. The mean size is computed indirectly by comparing the total activity in the input layer and in the third layer. Therefore, the information about the size of individual elements is lost. An extended model is able to hold statistical information in the working memory and to handle the computation of the mean size for surfaces with empty interiors.

Modeling the top-down influences on the lateral interactions in the visual cortex

Attention modulates the amount of excitatory and inhibitory lateral interactions in the visual cortex. A recurrent neural network is proposed to account for modulatory influence of top-down signals. In the model, two types of inhibitions are distinguished: dendritic and lateral inhibitions. Dendritic inhibition regulates the amount of impact that surrounding cells may exert on a target cell via the dendrites of excitatory neurons and the dendrites of subpopulation of inhibitory neurons mediating lateral inhibition. Attention increases the amount of dendritic inhibition and prevents contextual interactions, while it has no effect on the target cell when there is no surround input. Computer simulations showed that the proposed model is able to exhibit properties of attentional gating. In the condition of focused attention, neural activity in the presence of surrounding stimuli is restored to the level as when the target stimulus is presented alone. Moreover, the model is able to show contrast gain and response gain on the contrast sensitivity function depending on the strength of the dendritic inhibition.

A neural model for attentional modulation of lateral interactions in the visual cortex

Neurophysiological investigations showed that attention influences neural responses in the visual cortex by modulating the amount of contextual interactions between cells. Attention acts as a gate that protects cells from lateral excitatory and inhibitory influences. A recurrent neural network based on dendritic inhibition is proposed to account for these findings. In the model, two types of inhibition are distinguished: dendritic and lateral inhibition. Dendritic inhibition regulates the amount of impact that surrounding cells may exert on a target cell via dendrites of excitatory neurons and dendrites of subpopulation of inhibitory neurons mediating lateral inhibition. Attention increases the amount of dendritic inhibition and prevents contextual interactions, while it has no effect on the target cell when there is no contextual input. Computer simulations showed that the proposed model reproduces the results of several studies about interaction between attention and horizontal connections in the visual cortex.

A model of the illusory contour formation based on dendritic computation

We proposed a new model of illusory contour formation based on the properties of dendritic computation. The basic elements of the network are a single-excitatory cell with two dendritic branches and an inhibitory cell. Both dendritic branches behave as an independent linear unit with a threshold. They sum all excitatory input from the nearby collinear cells, and the inhibition from one collateral of the corresponding inhibitory cell. Furthermore, the output of dendritic branches multiplicatively interacts before it is sent to the soma. The multiplication allows the excitatory cell to be active only if both of its branches receive enough excitation to reach the threshold. Computer simulations showed that the presented model of the illusory contour formation is able to perform perceptual grouping of nonadjacent collinear elements. It shows a linear response relationship with the input magnitude because dendritic inhibition counteracts recurrent excitation. The model can explain why illusory contours are stronger with irregular placement of inducing elements rather than regular placement and why top-down influences may prevent the illusory contour formation.

Computing the maximum using presynaptic inhibition with glutamate receptors

Neurophysiological investigations suggest that presynaptic ionotropic receptors are important mechanism for controlling synaptic transmission. In this paper, presynaptic kainate receptors are incorporated in a feedforward inhibitory neural network in order to investigate their role in the cortical information processing. Computer simulations showed that the proposed mechanism is able to compute the function maximum by disinhibiting the cell with the maximal amplitude. The maximum is computed with high precision even in the case where inhibitory synaptic weights are weak and (or) asymmetric. Moreover, the network is able to track time-varying input and to select multiple winners. These capabilities do not depend on the dimensionality of the network. Also, the model is able to implement the winner-take-all behaviour.

Numerical Congruency Effect in the Sentence-Picture Verification Task

In two experiments, we showed that irrelevant numerical information influenced the speed of sentence-picture verification. Participants were asked to verify whether the concept mentioned in a sentence matched the object presented in a subsequent picture. Concurrently, the number word attached to the concept in the sentence and the quantity of objects presented in the picture were manipulated (numerical congruency). The number of objects varied from one to four. In Experiment 1, participants read statements such as three dogs. In Experiment 2, they read sentences such as three dogs were wandering in the street. In both experiments, the verification speed revealed the interaction between response and numerical congruency. The verification times for concept-object match were faster when there was also numerical congruence (compared with incongruence) between the number word and quantity. On the other hand, there was no difference between numerical congruence and incongruence when the concept and object mismatched. The results are interpreted as evidence for the symbol grounding of number words in perceptual representation of small quantities, that is, quantities falling in the subitization range.

A Computational Model of Saliency Map Read-Out during Visual Search

When searching for a target in a visual scene filled with distractors, the mechanism of inhibition of return prevents revisiting previously attended locations. We proposed a new computational model for the inhibition of return, which is able to examine priority or saliency map in a manner consistent with psychophysical findings. The basic elements of the model are two neural integrators connected with two inhibitory interneurons. The integrators keep the saliency value of the currently attended location in the working memory. The inhibitory inter-neurons modulate a feedforward flow of information between the saliency map and the output map which points to the location of interest. Computer simulations showed that the model is able to read-out the saliency map when the objects are moving or when eye movements are present. Also, it is able to simultaneously select more then one location, even when they are non-contiguous. The model can be considered as a neural implementation of the episodic theory of attention.