No abstract

Computational models of neurons and behavior are key tools in the feedback cycle between theory development, experimentation, and data analysis.The drift diffusion model (DDM) and other types of sequential sampling models have proven to be useful ways of quantifying and characterizing individual differences and the effects of experimental manipulations on various types of behavior in humans and other animals.To date, partially for the sake of simplicity, most modeling studies have assumed a constant rate of evidence accumulation when fitting the data and testing theoretical predictions.Nevertheless, there are a growing number of theories and empirical data that suggest that evidence accumulation rates may vary within the time course of a decision because of changes in attention, arousal, goals, or other factors. Fitting a DDM with a time-varying evidence accumulation rate can be much more computationally demanding and time consuming than fitting the standard DDM if there is no analytical solution for the time-varying DDM.Here, we demonstrate how to mathematically reformulate an influential subset of time-varying DDMs into standard DDMs with constant drift rates. This simple yet powerful reformulation allows time-varying DDMs with piecewise-constant drift rates to be easily and rapidly estimated within existing hierarchical Bayesian frameworks.We show detailed examples of this process using data from both computer simulations and humans in two separate empirical studies. Our results demonstrate that the method can quickly and accurately recover parameters from simulations and fit hierarchical Bayesian models to real data.

Empirical evidence has shown that visually enhancing the saliency of reward probabilities can ease the cognitive demands of value comparisons and improve value-based decisions in old age. In the present study, we used a time-varying drift diffusion model that includes starting time parameters to better understand (1) how increasing the saliency of reward probabilities may affect the dynamics of value-based decision-making and (2) how these effects may interact with age. We examined choices made by younger and older adults in a mixed lottery choice task. On a subset of trials, we used a color-coding scheme to highlight the saliency of reward probabilities, which served as a decision-aid. The results showed that, in control trials, older adults started to consider probability relative to magnitude information sooner than younger adults, but that their evidence accumulation processes were less sensitive to reward probabilities than that of younger adults. This may indicate a noisier and more stochastic information accumulation process during value-based decisions in old age. The decision-aid increased the influence of probability information on evidence accumulation rates in both age groups, but did not alter the relative timing of accumulation for probability versus magnitude in either group.

A standard assumption in neuroscience is that low-effort model-free learning is automatic and continuously employed, while more complex model-based strategies are only used when the rewards they generate are worth the additional effort. We present evidence refuting this assumption. First, we demonstrate flaws in previous reports of combined model-free and model-based reward prediction errors in the ventral striatum that likely led to spurious results. More appropriate analyses yield no evidence of a model-free prediction errors in this region. Second, we find that task instructions generating more correct model-based behaviour reduce rather than increase mental effort. This is inconsistent with cost-benefit arbitration between model-based and model-free strategies. Together, our data suggest that model-free learning may not be automatic. Instead, humans can reduce mental effort by using a model-based strategy alone rather than arbitrating between multiple strategies. Our results call for re-evaluation of the assumptions in influential theories of learning and decision-making.