Future‐Bias: A (Qualified) Defense

The preferences of ordinary folks typically display a future‐bias. For instance, we care more about pains and pleasures in our future than pains and pleasures in our past. Indeed, this future‐bias is so pervasive, many have taken it for granted that the preferences of rational agents will, or at least can, display this future‐bias to some degree or other. However, the rationality of future‐biased preferences has recently come in for critique. However, in this article, I offer a defense of future‐biased preferences against three recent attempts – offered by David Brink, Preston Greene and Meghan Sullivan, and Tom Dougherty – to show such preferences irrational.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.