Perceptuo-motor, cognitive, and description-based decision-making seem equally good

Jarvstad et al (2013) show that when perceptuo-motor and ‘cognitive’ decisions are assessed in the same way there are no marked differences in performances.

The context for this is the difference between studies of perceptual-motor and perceptual decision making (which have emphasised the optimality of human performance) and studies of more cognitive choices (for which the ‘heuristics and biases’ tradition has purported to demonstrate substantial departures from optimality).

Jarvstad and colleagues note that experiments in these two domains differ in several important ways. One is the difference between basing decisions on probabilities derived from descriptions verses derived from experience (which has its own literature; Hertwig & Erev, 2009). Another is that perceptual-motor tasks often involve extensive training, with feedback, whereas cognitive decision making task are often one-shot and/or without feedback.

The definition of optimality employed also varies across the domains. Perceptual-motor tasks usually compare performance to that of an optimal agent, often modelled incorporating some constraints on task performance (e.g. motor noise). Cognitive tasks have often sought to compare performance to the standard of rational utility maximisers, designing choices in the experiments precisely to demonstrate violation of axioms on which rational choice rests (e.g. transitivity).

In short, claiming a difference in decision making across these two domains may be premature if other influences on both task performance and task assessment are not comparable.

To carry out a test of performance in the two domains, Jarvstad et al carried out the following experiment. They compared a manual aiming task (A) and a numerical arithmetic task (B). During a learning phase they assessed variability on the two tasks (ie frequency and range of error in physical (A) or numerical (B) distance). Both kinds of stimuli varied in the ease with which the required response could be successfully produced (ie. they varied in difficulty). They also elicited explicit judgements of stimuli that participants judged would match set levels of success (e.g. that they thought they would have a 50% or a 75%, say, chance of getting right).

During a decision phase they asked participants to choose between pairs of stimuli with different rewards (upon success) and different difficulties. Importantly, the difficulties were chosen – using the data provided by the learning phase – so as to match certain explicit probabilities (such as might be provided in a traditional decision from description experiment on risky choice. They also tested such decisions from explicit probabilities, in a task labelled ‘C’).

The results show that all three tasks had a comparable proportion of decisions which were optimal, in the sense of maximising chance of reward (Fig 3A). For all three tasks more optimal decisions were made on those decision which were more consequential (ie which had a bigger opportunity cost and which, consequentially, were presumably easier to discriminate between, Fig 3B – shown).

Using individual participant data, it is possible to recover – via model fitting – the subjective weights for value and probability functions. These show an underweighting of low objective probabilities in the perceptual-motor task (Fig 4D) and a overweighting of low objective probabilities in the classical probability-from-description cask (Fig 4F). This is in line with previous literature reporting a divergence between the domains in the way low probability events are treated (Hertwig et al, 2004). However, Jarvstad use the explicit judgements obtained in the learning phase to show that the apparent discrepancy in weighting results from differences in the subjective probability function (ie how likely success is judged in the perceptual-motor domain) rather than in the weighting given to this probability. If probability estimations are held constant, then similar weightings to low probability events are found across the domains.

They also show that an individual performance on a task is better predicted by their performance on a task in a different domain than by the average performance in that domain – ie that individual differences are more important than task differences in nature and extent of divergence from optimality.

REFERENCES

Jarvstad, A., Hahn, U., Rushton, S. K., & Warren, P. A. (2013). Perceptuo-motor, cognitive, and description-based decision-making seem equally good. Proceedings of the National Academy of Sciences, 110(40), 16271-16276.

Hertwig R, Barron G, Weber EU, Erev I (2004) Decisions from experience and the effect of rare events in risky choice. Psychol Sci 15(8):534–539.

Hertwig R, Erev I (2009) The description-experience gap in risky choice. Trends Cogn Sci 13(12):517–523.