Sozial-, Wirtschafts- und Entscheidungspsychologie


Social, Economic, and Decision Psychology Blog

Archive for June, 2015

The flexibility of models of recognition memory

David Kellen has a new paper out in the Journal of Mathematical Psychology on recognition memory modelling. David’s paper deals with an old problem in model comparison, namely how to penalize models according to their flexibility (i.e., how to implement a reasonable Ockham’s razor?). Specifically, the paper reports an efficient method for the computation of the Normalized Maximum Likelihood statistic for models of categorical data. This statistic is then applied to the domain of recognition-memory models, in which model flexibility is known to play a large role when comparing model fits to empirical data.

The flexibility of models of recognition memory: The case of confidence ratings

The normalized maximum likelihood (NML) index is a model-selection index derived from the minimum-description length principle. In contrast to traditional model-selection indices, it also quantifies differences in flexibility between models related to their functional form. We present a new method for computing the NML index for models of categorical data that parameterize multinomial or product-multinomial distributions and apply it to comparing the flexibility of major models of recognition memory for confidence-rating based receiver-operating-characteristic (ROC) data. NML penalties are tabulated for datasets of typical sizes and interpolation functions are fitted that allow one to interpolate NML penalties for datasets with sizes between the tabulated ones. Recovery studies suggest that the NML index performs better than traditional model-selection indices in model selection from ROC data. In an NML-based meta-analysis of 850 ROC datasets, versions of the dual-process signal detection models received most support followed by the finite mixture signal detection model and constrained versions of two-high threshold models.

Klauer, K. C. & Kellen, D. (2015). The flexibility of models of recognition memory: The case of confidence ratings. Journal of Mathematical Psychology, 67, 8-25.

Age-performance relations

At CDS, we study the life span development of performance in different domains, particularly decision making, and are interested in the interplay between, on the one hand, the gains in knowledge from a life time of experience, and, on the other, the age-related losses in physical and cognitive abilities due to biological aging. An ecological perspective on aging suggests that the result of such gain/loss interactions on performance is a function of task demands.

I’ve been trying to find examples of such task-dependent age-performance patterns and last weekend’s Champions League final gave me an incentive to look into the age distribution of soccer players and managers. Gianluigi Buffon, Juventus’ goalkeeper, at 37, was the oldest player on the pitch… Could such longevity be found in other positions?

I got the player data from the 2014/2015 edition of the tournament at UEFA.com and plotted the age distributions of players as a function of position and whether they actually played in the tournament (as opposed to just being on the roster). I also searched Wikipedia for the managers’ ages.

champions2015

 

Here’s the 3 main points I take from this figure:

1) The results suggest that Buffon’s longevity can be matched in other positions but the age distributions do vary systematically, with Forwards being the youngest and Goalkeepers the oldest group on average. This makes sense because past research suggests that Forwards typically perform many more high intensity activity plays relative to, say, Goalkeepers.  This is an example of different tasks (i.e., soccer positions) posing different challenges to aging individuals.

2) The age differences between the players that do and do not play (0 vs. > 0 Appearances) seem to suggest that aging is not only associated with losses but also gains – most likely experience.

3) The fact that players are much younger than managers suggests that there is a limit to experience offsetting physical decline. Notwithstanding, experience as a player can prove useful later on: Thirty out of 32 managers in the 2014/2015 Champions League edition are ex-professional soccer players!

One would also like to link age to actual performance. This is not always trivial because objective measures of performance are often not available. However, for Forwards in the Champions League one can look at goals scored, which I’ve plotted below as a function of age (circle diameters are proportional to the number of players in that age bracket). The results show a peak/plateau starting before 30s as would be expected from the age distribution of Forwards in the previous figure…

champions2015_forwards

Ideally, one would have a full description of task demands and age-related changes in physical prowess, cognitive abilities, and knowledge, in order to make predictions about the shape of age-performance relations in other domains, such as professional occupations, something I hope to do in the near future…

Andreas Wilke

Andreas Wilke

Andreas Wilke from Clarkson University, USA, is visiting CDS for the next two weeks and giving a talk in the Social, Economic, and Decision Psychology Colloquium this Thursday (June 11).

Hot-hand bias in rhesus monkeys

Human decision-makers often exhibit the hot-hand phenomenon, a tendency to perceive positive serial autocorrelations in independent sequential events. We hypothesize that this bias reflects a strong and stable tendency among primates (including humans) to perceive positive autocorrelations in temporal sequences, that this bias is an adaptation to clumpy foraging environments, and that it may even be ecologically rational. Several studies support this idea in humans, but a stronger test would be to determine whether non-human primates also exhibit a hot hand bias. Here we report behavior of three monkeys performing a novel gambling task in which correlation between sequential gambles (i.e., temporal clumpiness) is systematically manipulated. We find that monkeys have better performance (meaning, more optimal behavior) for clumped (positively correlated) than for dispersed (negatively correlated) distributions. These results identify and quantify a new bias in monkeys’ risky decisions, support accounts that specifically incorporate cognitive biases into risky choice, and support the suggestion that the hot-hand phenomenon is an evolutionary ancient bias.