Dražen Domijan

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.

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.

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

A computational model was developed to explain a pattern of results of fMRI activation in the intraparietal sulcus (IPS) supporting visual working memory for multiobject scenes. The model is based on the hypothesis that dendrites of excitatory neurons are major computational elements in the cortical circuit. Dendrites enable formation of a competitive queue that exhibits a gradient of activity values for nodes encoding different objects, and this pattern is stored in working memory. In the model, brain imaging data are interpreted as a consequence of blood flow arising from dendritic processing. Computer simulations showed that the model successfully simulates data showing the involvement of inferior IPS in object individuation and spatial grouping through representation of objects’ locations in space, along with the involvement of superior IPS in object identification through representation of a set of objects’ features. The model exhibits a capacity limit due to the limited dynamic range for nodes and the operation of lateral inhibition among them. The capacity limit is fixed in the inferior IPS regardless of the objects’ complexity, due to the normalization of lateral inhibition, and variable in the superior IPS, due to the different encoding demands for simple and complex shapes. Systematic variation in the strength of self-excitation enables an understanding of the individual differences in working memory capacity. The model offers several testable predictions regarding the neural basis of visual working memory.

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.

Concurrent Dynamics of Category Learning and Metacognitive Judgments

In two experiments, we examined the correspondence between the dynamics of metacognitive judgments and classification accuracy when participants were asked to learn category structures of different levels of complexity, i.e., to learn tasks of types I, II, and III according to Shepard et al. (1961). The stimuli were simple geometrical figures varying in the following three dimensions: color, shape, and size. In Experiment 1, we found moderate positive correlations between confidence and accuracy in task type II and weaker correlation in task type I and III. Moreover, the trend analysis in the backward learning curves revealed that there is a non-linear trend in accuracy for all three task types, but the same trend was observed in confidence for the task type I and II but not for task type III. In Experiment 2, we found that the feeling-of-warmth judgments (FOWs) showed moderate positive correlation with accuracy in all task types. Trend analysis revealed a similar non-linear component in accuracy and metacognitive judgments in task type II and III but not in task type I. Our results suggest that FOWs are a more sensitive measure of the progress of learning than confidence because FOWs capture global knowledge about the category structure, while confidence judgments are given at the level of an individual exemplar.

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.

Resonant Dynamics of Grounded Cognition: Explanation of Behavioral and Neuroimaging Data Using the ART Neural Network

Research on grounded cognition suggests that the processing of a word or concept reactivates the perceptual representations that are associated with the referent object. The objective of this work is to demonstrate how behavioral and functional neuroimaging data on grounded cognition can be understood as different manifestations of the same cortical circuit designed to achieve stable category learning, as proposed by the adaptive resonance theory (ART). We showed that the ART neural network provides a mechanistic explanation of why reaction times in behavioral studies depend on the expectation or attentional priming created by the word meaning (Richter and Zwaan, 2009). A mismatch between top-down expectation and bottom-up sensory data activates an orienting subsystem that slows execution of the current task. Furthermore, we simulated the data from functional neuroimaging studies of color knowledge retrieval that showed anterior shift (Chao and Martin, 1999; Thompson-Schill, 2003) and an overlap effect (Simmons et al., 2007; Hsu et al., 2011) in the left fusiform gyrus. We explain the anterior effect as a result of the partial activation of different components of the same ART circuit in the condition of passive viewing. Conversely, a demanding perceptual task requires activation of the whole ART circuit. This condition is reflected in the fMRI image as an overlap between cortical activation during perceptual and conceptual processing. We conclude that the ART neural network is able to explain how the brain grounds symbols in perception via perceptual simulation.

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.

The influence of rule availability and item similarity on metacognitive monitoring during categorisation

ABSTRACT In four experiments, we examined the sources of metacognitive judgments during the categorisation of new items after the learning of old items was completed. In the rule condition, the categorisation rule was explicitly given to the participants during learning, while in the no-rule condition participants relied on feedback to infer category membership. In the transfer phase, two types of novel items were used: good and bad transfer items. Transfer items also differed in the level of similarity to their training pairs. In the rule condition, all types of transfer items were classified with high accuracy and confidence. In the no-rule condition, a dissociation between accuracy and confidence was revealed. Good transfer items were classified more accurately than bad transfer items, whereas similar items were classified with higher confidence than dissimilar items. The obtained results suggest the utilisation of two potential cues for metacognitive judgments: declarative knowledge if the correct rule is explicitly available, and item similarity if it is difficult to infer the correct rule from feedback.