Uncovering the richness of the stimulus: structure dependence and indirect statistical evidence

The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses of child-directed speech showing that there is indirect statistical information useful for correct auxiliary fronting in polar interrogatives and that such information is sufficient for distinguishing between grammatical and ungrammatical generalizations, even in the absence of direct evidence. We further show that there are simple learning devices, such as neural networks, capable of exploiting such statistical cues, producing a bias toward correct AUX questions when compared to their ungrammatical counterparts. The results suggest that the basic assumptions of the poverty of stimulus argument may need to be reappraised.     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.     

The role of levels of processing in disentangling the ERP signatures of conscious visual processing

We aimed to distinguish electrophysiological signatures of visual awareness from other task-related processes through manipulating the level of processing of visual stimuli. During an event-related EEG experiment, 36 subjects performed either color (low-level condition) or magnitude (high-level condition) evaluations of masked digits. Participants also assessed subjective visibility of each stimulus using the Perceptual Awareness Scale (PAS). Mean amplitude of the components of interest was analyzed (VAN − 140–240 ms; LP − 380–480 ms) with weighted regression mixed model. In the VAN component time window the mean amplitude correlated with PAS rating in both conditions. Mean amplitude in the LP time window correlated with PAS ratings in the high-level condition, but not in the low-level condition. Our results support the temporal unfolding of ERP makers of conscious processing, with an early component reflecting the initial perceptual experience and a late component being a correlate of the conscious experience of non-perceptual information.     

A learning rule that explains how rewards teach attention

Many theories propose that top-down attentional signals control processing in sensory cortices by modulating neural activity. But who controls the controller? Here we investigate how a biologically plausible neural reinforcement learning scheme can create higher order representations and top-down attentional signals. The learning scheme trains neural networks using two factors that gate Hebbian plasticity: (1) an attentional feedback signal from the response-selection stage to earlier processing levels; and (2) a globally available neuromodulator that encodes the reward prediction error. We demonstrate how the neural network learns to direct attention to one of two coloured stimuli that are arranged in a rank-order. Like monkeys trained on this task, the network develops units that are tuned to the rank-order of the colours and it generalizes this newly learned rule to previously unseen colour combinations. These results provide new insight into how individuals can learn to control attention as a function of reward contingency.     

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.     

Powered-Two-Wheelers kinematic characteristics and interactions during filtering and overtaking in urban arterials

The present paper focuses on the Powered-Two-Wheelers (PTWs) kinematic characteristics and their interactions with the rest of traffic in urban arterials. The factors that may affect the likelihood of PTW drivers to accept critical spacing during filtering and overtaking are also investigated using trajectory data collected from video recordings. The distributional characteristics of the PTW kinematic parameters showed that the patterns of filtering and overtaking have several differences. Further results using Logit models show that PTW speed difference with the rest of traffic, spacing, the existence of heavy vehicles and the occurrence of platoon of moving PTWs (in which the leader is the reference PTW) are significant factors related to the probability of driving in critical spaces through traffic. The likelihood of accepting critical lateral distance from the vehicle being overtaken may be related to the adjacent lane spacing, the speed difference and the existence of a platoon of PTWs. A comparative study between Logit models and equivalent structures of neural networks showed that, in the specific application, neural networks were found to perform better than the Logit models in terms of the model’s discrimination power.     

I’ll just watch: Do the pro-social effects of coordination really generalize to non-actors?

ABSTRACT

Moving in time together has been shown to cultivate pro-social effects in co-actors, such as cooperation and helping. But less is known about who these effects apply to – whether they are restricted only to co-actors, or whether they generalize to those not involved in the coordination. One difference between past work finding generalized vs. restricted effects is whether these “outsiders” were present for the coordination or not. The present study explores whether the pro-social effects of coordination are seen towards observers as well as co-actors, and whether the absence or presence of observers during the coordination is a determining factor. Results show that greater cooperation following coordination is only seen amongst co- actors, regardless of whether the observers were present during the task or not. Findings are discussed in the context of the literature and alternative explanations for research showing generalized effects are suggested.