Extending Dynamical Systems Theory to Model Embodied Cognition

We define a mathematical formalism based on the concept of an ‘‘open dynamical system” and show how it can be used to model embodied cognition. This formalism extends classical dynamical systems theory by distinguishing a ‘‘total system’’ (which models an agent in an environment) and an ‘‘agent system’’ (which models an agent by itself), and it includes tools for analyzing the collections of overlapping paths that occur in an embedded agent's state space. To illustrate the way this formalism can be applied, several neural network models are embedded in a simple model environment. Such phenomena as masking, perceptual ambiguity, and priming are then observed. We also use this formalism to reinterpret examples from the embodiment literature, arguing that it provides for a more thorough analysis of the relevant phenomena.     

Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images

Alzheimer’s disease, the most common form of dementia is a neurodegenerative brain order that has currently no cure for it. Hence, early diagnosis of such disease using computer-aided systems is a subject of great importance and extensive research amongst researchers. Nowadays, deep learning or particularly convolutional neural network (CNN) is getting more attention due to its state-of-the-art performances in variety of computer vision tasks such as visual object classification, detection and segmentation. Several recent studies, that have used brain MRI scans and deep learning have shown promising results for diagnosis of Alzheimer’s disease. However, most common issue with deep learning architectures such as CNN is that they require large amount of data for training. In this paper, a mathematical model P_FS_EC_TL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task. Experimentation is performed on data collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The accuracy of the 3-way classification using the described method is 95.73% for the validation set.     

Learning representations in a gated prefrontal cortex model of dynamic task switching

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients.     

Hemispheric asymmetries in the split-fovea model of semantic processing

We report a series of neural network models of semantic processing of single English words in the left and the right hemispheres of the brain. We implement the foveal splitting of the visual field and assess the influence of this splitting on a mapping from orthography to semantic representations in single word reading. The models were trained on English four-letter words, presented according to their frequency in all positions encountered during normal reading. The architecture of the model interacted with the training set to produce processing asymmetries comparable to those found in behavioral studies. First, the cueing effects of dominant and subordinate meanings of ambiguous words were different for words presented to the left or to the right of the input layer. Second, priming effects of groups of related words were stronger in the left input than the right input of the model. These effects were caused by coarser-coding in the right half compared with the left half of the model, an emergent effect of the split model interacting with informational asymmetries in the left and right parts of words in the lexicon of English. Some or all of the behavioral data for reading single words in English may have a similar origin.     

Exploring the effects of EEG signals on collision cases happening in the process of young drivers’ braking

Detecting mental states in drivers offers an opportunity to reduce accidents by triggering alerts and signaling the need for rest or renewed focus. Here we used electroencephalography (EEG) to measure brain signals in young drivers operating a driving simulator to detect mental states and predict accidents. We measured reaction times to unexpected hazardous events and correlated them with EEG signals measured from the frontal, parietal, and temporal cortices as well as the central sulcus (corresponding to motor cortex). We found that EEG signals in the relative beta (power in beta (13–30 Hz) relative to total power of the EEG (0.5–30 Hz)), alpha/delta, alpha/theta, beta/delta, beta/theta frequency bands were higher for collisions than successful collision avoidance, and that the key decision-making period is the 2nd second before braking. Importantly, a decision tree classifier trained on these neural signals predicted collision avoidance outcomes. Then based on random forest model, we extracted three critical neural signals (beta/delta_frontal, relative beta_parietal and relative beta_central Sulcus) to classify collision avoidance outcomes. Our findings suggest measuring EEG during driving may provide useful signals for enhancing driver safety.     

Learning Łukasiewicz logic

The integration between connectionist learning and logic-based reasoning is a longstanding foundational question in artificial intelligence, cognitive systems, and computer science in general. Research into neural-symbolic integration aims to tackle this challenge, developing approaches bridging the gap between sub-symbolic and symbolic representation and computation. In this line of work the core method has been suggested as a way of translating logic programs into a multilayer perceptron computing least models of the programs. In particular, a variant of the core method for three valued Łukasiewicz logic has proven to be applicable to cognitive modelling among others in the context of Byrne’s suppression task. Building on the underlying formal results and the corresponding computational framework, the present article provides a modified core method suitable for the supervised learning of Łukasiewicz logic (and of a closely-related variant thereof), implements and executes the corresponding supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle. The resulting system is then evaluated in several empirical test cases, and recommendations for future developments are derived.     

Turing redux: Enculturation and computation

Many of our cognitive capacities are shaped by enculturation. Enculturation is the acquisition of cognitive practices such as symbol-based mathematical practices, reading, and writing during ontogeny. Enculturation is associated with significant changes to the organization and connectivity of the brain and to the functional profiles of embodied actions and motor programs. Furthermore, it relies on scaffolded cultural learning in the cognitive niche. The purpose of this paper is to explore the components of symbol-based mathematical practices. Phylogenetically, these practices are the result of concerted organism-niche interactions that have led from approximate number estimations to the emergence of discrete, symbol-based mathematical operations. Ontogenetically, symbol-based mathematical practices are associated with plastic changes to neural circuitry, action schemata, and motor programs. It will be suggested that these practices rely on previously acquired capacities such as subitizing and counting. With these considerations in place, I will argue that computations, understood in the sense of Turing (1936), are a specific kind of symbol-based mathematical practices that can be realized by human organisms, machines, or by hybrid organism-machine systems. In sum, this paper suggests a new way to think about mathematical cognition and computation.     

Hierarchies of Self-Organizing Maps for action recognition

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and learns to represent action prototypes. The third- and last-layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-layer SOM and is independent – to certain extent – of the camera’s angle and relative distance to the actor. The experiments were carried out with encouraging results with action movies taken from the INRIA 4D repository. In terms of representational accuracy, measured as the recognition rate over the training set, the architecture exhibits 100% accuracy indicating that actions with overlapping patterns of activity can be correctly discriminated. On the other hand, the architecture exhibits 53% recognition rate when presented with the same actions interpreted and performed by a different actor. Experiments on actions captured from different view points revealed a robustness of our system to camera rotation. Indeed, recognition accuracy was comparable to the single viewpoint case. To further assess the performance of the system we have also devised a behavioural experiments in which humans were asked to recognise the same set of actions, captured from different points of view. Results form such a behavioural study let us argue that our architecture is a good candidate as cognitive model of human action recognition, as architectural results are comparable to those observed in humans.     

Life span changes: Performing a continuous 1:2 bimanual coordination task

The experiment was conducted to determine the influence of mirror movements in bimanual coordination during life span. Children, young adults, and older adults were instructed to perform a continuous 1:2 bimanual coordination task by performing flexion–extension wrist movements over 30 s where symmetrical and non-symmetrical coordination patterns alternate throughout the trial. The vision of the wrists was covered and Lissajous-feedback was provided online. All age groups had to perform 10 trials under three different load conditions (0 kg, .5 kg, 1.0 kg: order counterbalanced). Load was manipulated to determine if increased load increases the likelihood of mirror movements. The data indicated that the performance of the young adults was superior compared to the children and older adults. Children and older adults showed a stronger tendency to develop mirror movements and had particular difficulty in performing the non-symmetrical mode. This type of influence may be attributed to neural crosstalk.