The perception of false self scale for adolescents: Reliability,validity, and longitudinal relationships with depressive and anxious symptoms

A scale assessing generalized false self‐perceptions (Perceptions of False Self, POFS) was developed and tested across three studies involving a total of 331 adolescents (11–16 years). In Study 1, interviews were conducted to develop items for the scale. In Study 2, psychometric techniques were used to derive a scale composed of 16 items. Study 2 also assessed the validity of the scale: depressive symptoms were found to increase POFS across 10 weeks, whereas false self‐perceptions had a reciprocal effect on anxiety. In Study 3, the convergent validity of the POFS scale was established. Overall, the evidence suggests that the POFS scale is a reliable and valid measure of generalized false self‐perceptions.     

Attitudes,Tracing, and Control

There is an apparent tension in our everyday moral responsibility practices. On the one hand, it is commonly assumed that moral responsibility requires voluntary control: an agent can be morally responsible only for those things that fall within the scope of her voluntary control. On the other hand, we regularly praise and blame individuals for mental states and conditions that appear to fall outside the scope of their voluntary control, such as desires, emotions, beliefs, and other attitudes. In order to resolve this apparent tension, many philosophers appeal to a tracing principle to argue that agents are morally responsible (only) for those attitudes whose existence can be traced back, causally, to a voluntary action or omission in the past. My aim in this article is to critically evaluate this tracing strategy and to argue that it gives us a misguided picture of when and why we are morally responsible for our attitudes. I argue that we should accept a ‘judgment sensitivity’ condition of moral responsibility rather than a ‘voluntary control’ condition, and defend this account against various objections.     

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.