Video gameplay, personality and academic performance

The relationship between video gameplay, video game genre preference, personality, and GPA was investigated in an online correlational study with university students. In addition to administering self-report measures of GPA and personality, we asked three different questions regarding styles of video gameplay. The first asked the average time spent playing video games per week (habitual players), the second asked the total time spent playing favorite video games (selective players), and the third asked the number of different video games played in a year (diverse players). Students who were medium in selective player style (spent 11-50 h) had significantly higher GPAs than students low on selective player style (spent 0-10 h). Students high on habitual playing style (7 or more hours a week) showed significantly lower levels of Conscientiousness compared to students low on habitual playing style (0-1 h a week). Students who were high on the diverse style (i.e., 7 or more games played a year) showed significantly higher Openness scores than students low on the diverse style (0-3 games a year). Finally, several notable relations were found between video game genre preference, GPA, and personality. Results are discussed in terms of the positive implications of video gameplay on academic performance.     

Non-linear Control of Heave for an Unmanned Helicopter Using a Neural Network

This paper describes a new non-linear control technique applied to the heave control of an unmanned rotorcraft. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Desired trajectories are calculated to smoothly achieve a sequence of random step changes in desired height according to certain optimal criterion and plant limitations. Control inputs are then determined using the MATLAB^® optimisation toolbox to achieve those desired trajectories for the plant heave model. Finally, a neural network is trained to mimic the control inputs resulting from the optimisation process. The neural network controller produces trajectories closely resembling the results from the optimisation process but with a much reduced computation time. Flight test results of control of the heave dynamics of a helicopter confirm the neural network controller’s ability to operate in high disturbance conditions and outperform a proportional-derivative (PD) controller.     

Exploring probabilistic bisimulations, part I

We take a fresh look at strong probabilistic bisimulations for processes which exhibit both non-deterministic and probabilistic behaviour. We suggest that it is natural to interpret such processes as distributions over states in a probabilistic labelled transition system, a pLTS; this enables us to adapt the standard notion of contextual equivalence to this setting. We then prove that a novel form of bisimulation equivalence between distributions are both sound and complete with respect to this contextual equivalence. We also show that a very simple extension to HML, Hennessy–Milner Logic, provides finite explanations for inequivalences between distributions. Finally we show that our bisimulations between distributions in a pLTS are simply an alternative characterisation of a standard notion of probabilistic bisimulation equivalence, defined between states in a pLTS.     

Model checking agent programming languages

In this paper we describe a verification system for multi-agent programs. This is the first comprehensive approach to the verification of programs developed using programming languages based on the BDI (belief-desire-intention) model of agency. In particular, we have developed a specific layer of abstraction, sitting between the underlying verification system and the agent programming language, that maps the semantics of agent programs into the relevant model-checking framework. Crucially, this abstraction layer is both flexible and extensible; not only can a variety of different agent programming languages be implemented and verified, but even heterogeneous multi-agent programs can be captured semantically. In addition to describing this layer, and the semantic mapping inherent within it, we describe how the underlying model-checker is driven and how agent properties are checked. We also present several examples showing how the system can be used. As this is the first system of its kind, it is relatively slow, so we also indicate further work that needs to be tackled to improve performance.     

Liquid metal corrosion of 316L,Fe<Subscript>3</Subscript>Al,and FeCrSi in molten Zn-Al baths

Corrosion tests of 316L and two intermetallic compounds Fe₃Al and FeCrSi in industrial Galvanizing (Zn-0.18Al), GALFAN (Zn-5Al), GALVALUME (Zn-55Al), and Aluminizing (Al-8Si) baths and lab-scale static baths were conducted. In on-line tests in industrial hot-dip baths, 316L steel shows better corrosion resistance than Fe₃Al in Galvanizing, GALFAN, and GALVALUME baths. The corrosion resistance of 316L and Fe₃Al is similar in Aluminizing bath. In static tests, FeCrSi shows the best corrosion resistance in pure Zn, Zn-55Al, and Al-8Si baths. The corrosion resistance of 316L is better than that of Fe₃Al. In Zn-5Al bath, 316L shows no thickness loss after the test. For the same bath composition, the corrosion rates of the alloys in industrial baths are higher than those in static baths. Bath temperature and chemical composition play important roles in corrosion and intermetallic layer formation. Increasing bath temperature accelerates the corrosion process and changes the nature of intermetallic layers. A small amount of aluminum reduces the corrosion process by reducing the activity of Zn and forming inhibition layer. However, after aluminum content reaches the critical point, the dominant corrosion process changes from Zn-Fe reaction to Al-Fe reaction, and, consequently, the corrosion process accelerates by increasing aluminum content in the bath.     

Single neurons in the medial prefrontal cortex of the rat exhibit tonic and phasic coding during trace fear conditioning.

Trace fear conditioning is a learning task that requires the association of an auditory conditioned stimulus (CS) and a shock unconditioned stimulus (US) that are separated by a 20-s trace interval. Single neuron activity was recorded from the prelimbic and infralimbic areas of the medial prefrontal cortex in rats during trace fear conditioning or nonassociative unpaired training. Prelimbic neurons showed learning-related increases in activity to the CS and US, whereas infralimbic neurons showed learning-related decreases in activity to these stimuli. A subset of prelimbic neurons exhibited sustained increases in activity during the trace interval. These sustained prelimbic responses may provide a bridging code that allows for overlapping representations of CS and US information within the trace fear conditioning circuit. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Impairment in long-term retention but not short-term performance on a water maze reversal task following hippocampal or mediodorsal striatal n-methyl-d-aspartate receptor blockade.

Male Long-Evans rats were injected with 32 ng/μl of the N-methyl-D-aspartate (NMDA) receptor antagonist 3-(2-carboxypiperazin-4-yl) propyl-1-phosphonic acid (CPP) or vehicle and trained to locate a hidden platform in a different location (reversal training) than used on the initial 4 days of training. Rats treated with vehicle or CPP into the dorsal hippocampus, basolateral amygdala, or mediodorsal striatum had similar latencies to locate the platform on the reversal day. Rats infused with CPP into the dorsal hippocampus or mediodorsal striatum failed to search preferentially in the novel location during a 24-hr, drug-free retention test, whereas all other groups searched preferentially in this location. Therefore, blocking dorsal hippocampal or mediodorsal striatal NMDA receptors selectively blocked long-term spatial retention without producing short-term performance deficits. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dissociating Processes Supporting Causal Perception and Causal Inference in the Brain.

An understanding of relations between causes and effects is essential for making sense of the dynamic physical world. It has been argued that this understanding of causality depends on both perceptual and inferential components. To investigate whether causal perception and causal inference rely on common or on distinct processes, the authors tested 2 callosotomy (split-brain) patients and a group of neurologically intact participants. The authors show that the direct perception of causality and the ability to infer causality depend on different hemispheres of the divided brain. This finding implies that understanding causality is not a unitary process and that causal perception and causal inference can proceed independently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)