Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention

We present the case for a role of biologically plausible neural network modelling in bridging the gap between physiology and behavior. We argue that spiking level networks can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity (Study 1) to ‘whole system’ behavior in human visual search (Study 2), while also capturing effects at ‘intermediate level’, including performance break down after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data

Cognitive modelling of attentional networks: efficiencies, interactions, impairments and development

According to the attention network theory, attention is viewed as an organ system comprising specialised networks that carry out functions of alerting, orienting and executive control. The Attention Network Test (ANT) is a simple and popular experiment that measures the efficiencies and interactions of these three subcomponents of attention in a single task, and has been used for adults, children and attention deficit patients. In this thesis, cognitive modelling is used as a research tool to simulate the performance of subjects on the ANT, as well as variations of the ANT using ACT-R 6.0 cognitive architecture. All models are validated against human data using various goodness-of-fit criteria at multiple measures of the latency, accuracy and efficiency of the three networks. Once the simulation of healthy human performance on the ANT is established, modifications inspired by psychology literature are made to simulate the performance on ANT by children and patients affected with Alzheimer‘s disease (AD) and mild traumatic brain injury (mTBI). The implementation of networks, their interactions and impairments in the models are shown to be theoretically grounded. Based on the simulation results and the understanding gained through model processes, a number of novel predictions are made, behaviour of the networks and a few discrepancies in human data are explained. The model predicts that in the case of Alzheimer‘s disease, the orienting network may be impaired and cueing may have a positive effect on conflict resolution. Also, in the case of mTBI, it was predicted that the validity effect may be impaired only in the earlier weeks after the injury. For children, a possible relationship between processing speed and mechanism of inhibitory control is predicted. It is posited that there is not always a 'global clock' that controls processing speed and further different processes may be running with different processing times

Cognitive Control in Majority Search: A Computational Modeling Approach

Despite the importance of cognitive control in many cognitive tasks involving uncertainty, the computational mechanisms of cognitive control in response to uncertainty remain unclear. In this study, we develop biologically realistic neural network models to investigate the instantiation of cognitive control in a majority function task, where one determines the category to which the majority of items in a group belong. Two models are constructed, both of which include the same set of modules representing task-relevant brain functions and share the same model structure. However, with a critical change of a model parameter setting, the two models implement two different underlying algorithms: one for grouping search (where a subgroup of items are sampled and re-sampled until a congruent sample is found) and the other for self-terminating search (where the items are scanned and counted one-by-one until the majority is decided). The two algorithms hold distinct implications for the involvement of cognitive control. The modeling results show that while both models are able to perform the task, the grouping search model fit the human data better than the self-terminating search model. An examination of the dynamics underlying model performance reveals how cognitive control might be instantiated in the brain for computing the majority function

Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries

In earlier work, we developed the Selective Attention for Identification Model (SAIM [16]). SAIM models the human ability to perform translation-invariant object identification in multiple object scenes. SAIM suggests that central for this ability is an interaction between parallel competitive processes in a selection stage and a object identification stage. In this paper, we applied the model to visual search experiments involving simple lines and letters. We presented successful simulation results for asymmetric and symmetric searches and for the influence of background line orientations. Search asymmetry refers to changes in search performance when the roles of target item and non-target item (distractor) are swapped. In line with other models of visual search, the results suggest that a large part of the empirical evidence can be explained by competitive processes in the brain, which are modulated by the similarity between target and distractor. The simulations also suggest that another important factor is the feature properties of distractors. Finally, the simulations indicate that search asymmetries can be the outcome of interactions between top-down (knowledge about search items) and bottom-up (feature of search items) processing. This interaction in VS-SAIM is dominated by a novel mechanism, the knowledge-based on-centre-off-surround receptive field. This receptive field is reminiscent of the classical receptive fields but the exact shape is modulated by both, top-down and bottom-up processes. The paper discusses supporting evidence for the existence of this novel concept

Objects, spatial compatibility, and affordances: A connectionist study

In two Artificial Life simulations we evolved artificial organisms possessing a visual and a motor system, and whose nervous system was simulated with a neural network. Each organism could see four objects, either upright or reversed, with a left or a right handle. In Task 1 they learned to reach the object handle independently of the handle\u27s position. In Task 2 they learned to reach one of two buttons located below the handle either to decide either where the handle was (Simulation 1) or whether the object was upright or reversed (Simulation 2). Task 1 simulated real life experience, Task 2 replicated either a classic spatial compatibility task (Simulation 1) or an experiment by Tucker & Ellis (1998) (Simulation 2). In both simulations learning occurred earlier in the Compatible condition, when the button to reach and the handle were on the same side, than in the Incompatible condition

Understanding person acquisition using an interactive activation and competition network

Face perception is one of the most developed visual skills that humans display, and recent work has attempted to examine the mechanisms involved in face perception through noting how neural networks achieve the same performance. The purpose of the present paper is to extend this approach to look not just at human face recognition, but also at human face acquisition. Experiment 1 presents empirical data to describe the acquisition over time of appropriate representations for newly encountered faces. These results are compared with those of Simulation 1, in which a modified IAC network capable of modelling the acquisition process is generated. Experiment 2 and Simulation 2 explore the mechanisms of learning further, and it is demonstrated that the acquisition of a set of associated new facts is easier than the acquisition of individual facts in isolation of one another. This is explained in terms of the advantage gained from additional inputs and mutual reinforcement of developing links within an interactive neural network system. <br/