For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands. The requisite ability to dynamically modify or cancel planned actions is known as inhibitory control in psychology. We formalize inhibitory control as a rational decision-making problem, and apply to it to the classical stop-signal task. Using Bayesian inference and stochastic control tools, we show that the optimal policy systematically depends on various parameters of the problem, such as the relative costs of different action choices, the noise level of sensory inputs, and the dynamics of changing environmental demands. Our normative model accounts for a range of behavioral data in humans and animals in the stop-signal task, suggesting that the brain implements statistically optimal, dynamically adaptive, and reward-sensitive decision-making in the context of inhibitory control problems.
An anticipated increase in the number of people withdementia will lead to an escalation in health and socialcare spending unless it is altered by a major breakthroughin treatment or prevention. Behavioral symptomsassociated with dementia (BSD) are some of themost difficult problems faced by caregivers. Severalmeasurement issues have hampered the progress oftimely intervention for BSD. Sensor technology mayoffer a solution to the early detection of BSD that willguide the development of tailored interventions. Similarly,a clinical conceptualization of BSD and its measurementissues can facilitate the engineering of sensornetworks and algorithms for activity recognition. Multidisciplinarycollaboration and the consideration of ethicalissues will improve the adoption of these technologiesin healthcare research.
IBM Watson is known for its work in identifying cancer treatments and beating contestants on Jeopardy! But now the computing system has expertise in a new area of research: neuroscience. Watson discovered five genes linked to ALS, sometimes called Lou Gehrig's disease, IBM announced on Wednesday. The tech company worked with researchers at the Barrow Neurological Institute in Phoenix, Arizona. The discovery is Watson's first in any type of neuroscience, and suggests that Watson could make discoveries in research of other neurological diseases.
In 1983, the IBM PC XT debuted with 128K of RAM and a 10MB hard disk. In that same year, the first mobile phone debuted weighing about 2.5 pounds and with a $4,000 price tag. Fast forward to today and the average person unlocks their smartphone 76-80 times a day and relies on it for every aspect of their lives. These amazing pieces of hardware are millions of times more capable than all of NASA's computing power in the 1960s. Now that we have a supercomputer that never leaves people's sides, maybe it's time that we do some more innovation and see how that device can be used for "mobile health".