A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used

A Review and a Proposal About Socio-economic Impacts of Artificial Intelligence

There is a high potential for artificial intelligence (AI) to become the ena-bling technology of the new socio-economical paradigm. The huge amount of academic literature produced in the last few years leaves little room for doubts about the revolutionary nature of AI. Some scientific papers are fo-cused on negative aspects of a complete or partial substitution of the work-force by the intelligent machines, while others are more optimistic and are focused on the ideas revolving around the universal basic income and the decrease in the number of full-time working hours. The proposed analysis, both of existing literature and new statistical evidence, aims at the further exploration of the possibility for artificial intelligence to become the ena-bling technology of the next technological revolution