Intergenerational redistribution in the Great Recession

In this paper we construct a stochastic overlapping-generations general equilibrium model in which households are subject to aggregate shocks that affect both wages and asset prices. We use a calibrated version of the model to quantify how the welfare costs of severe recessions are distributed across different household age groups. The model predicts that younger cohorts fare better than older cohorts when the equilibrium decline in asset prices is large relative to the decline in wages, as observed in the data. Asset price declines hurt the old, who rely on asset sales to finance consumption, but benefit the young, who purchase assets at depressed prices. In our preferred calibration, asset prices decline more than twice as much as wages, consistent with the experience of the US economy in the Great Recession. A model recession is approximately welfare-neutral for households in the 20–29 age group, but translates into a large welfare loss of around 10% of lifetime consumption for households aged 70 and over.

A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm

Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http://dora.erbe-matzke.com/software.html

Averaging learning curves across and within participants

We examine recent concerns that averaged learning curves can present a distorted picture of individual learning. Analyses of practice curve data from a range of paradigms demonstrate that such concerns are well founded for fits of power and exponential functions when the arithmetic average is computed over participants. We also demonstrate that geometric averaging over participants does not, in general, avoid distortion. By contrast, we show that block averages of individual curves and similar smoothing techniques cause little or no distortion of functional form, while still providing the noise reduction benefits that motivate the use of averages. Our analyses are concerned mainly with the effects of averaging on the fit of exponential and power functions, but we also define general conditions that must be met by any set of functions to avoid distortion from averaging

Self-reported mind wandering reflects executive control and selective attention

Mind wandering is ubiquitous in everyday life and has a pervasive and profound impact on task-related performance. A range of psychological processes have been proposed to underlie these performance-related decrements, including failures of executive control, volatile information processing, and shortcomings in selective attention to critical task-relevant stimuli. Despite progress in the development of such theories, existing descriptive analyses have limited capacity to discriminate between the theories. We propose a cognitive-model based analysis that simultaneously explains self-reported mind wandering and task performance. We quantitatively compare six explanations of poor performance in the presence of mind wandering. The competing theories are distinguished by whether there is an impact on executive control and, if so, how executive control acts on information processing, and whether there is an impact on volatility of information processing. Across two experiments using the sustained attention to response task, we fnd quantitative evidence that mind wandering is associated with two latent factors. Our strongest conclusion is that executive control is impaired: increased mind wandering is associated with reduced ability to inhibit habitual response tendencies. Our nuanced conclusion is that executive control defcits manifest in reduced ability to selectively attend to the information value of rare but task-critical events