Oddity learning by African dwarf goats ( Capra hircus)

Seventeen African dwarf goats (adult females) were trained on oddity tasks using an automated learning device. One odd stimulus and three identical nonodd stimuli were presented on a screen divided into four sectors; the sector for the odd stimulus was varied pseudorandomly. Responses to the odd stimulus were deemed to be correct and were reinforced with food. In phase 1, the goats were trained on eight stimulus configurations. From trial to trial the odd discriminandum was either a + symbol or the letter S, and the nonodd discriminandum was the symbol not used as the odd one. In phase 2, the animals were similarly trained using an unfilled triangle or a filled (i.e., solid black) circle. In phase 3, three new discriminanda were used, an unfilled, small circle with radiating lines, an unfilled heart-shaped symbol, and an unfilled oval; which of the three discriminanda was odd and nonodd was varied from trial to trial. Following these training phases, a transfer test was given, which involved 24 new discriminanda sets. These were presented twice for a total of 48 transfer test trials. Results early in training showed approximately 25% correct, which might be expected by chance in a four-choice task. After 500-2,000 trials, results improved to approximately 40-44% correct. The best-performing subject reached 60-80% correct during training. On the transfer test, this subject had 47.9% correct and that significantly exceeded 25% expected by chance. This finding suggests that some exceptional individuals of African dwarf goats are capable of learning the oddity concept.     

Reflecting rules: A note on generalizing the deduction theorem

The purpose of this brief note is to prove a limitative theorem for a generalization of the deduction theorem. I discuss the relationship between the deduction theorem and rules of inference. Often when the deduction theorem is claimed to fail, particularly in the case of normal modal logics, it is the result of a confusion over what the deduction theorem is trying to show. The classic deduction theorem is trying to show that all so-called ‘derivable rules’ can be encoded into the object language using the material conditional. The deduction theorem can be generalized in the sense that one can attempt to encode all types of rules into the object language. When a rule is encoded in this way I say that it is reflected in the object language. What I show, however, is that certain logics which reflect a certain kind of rule must be trivial. Therefore, my generalization of the deduction theorem does fail where the classic deduction theorem didn't.     

On-road trust and perceived risk in Level 2 automation

To encourage appropriate use of driving automation, we need to understand and monitor driver’s trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events.30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined.Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving.Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation.At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in  < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.