Navigating cognitive parameter space

In fitting computational models to data, we represent the data (e.g. reaction time distributions) in terms of a small set of psychologically meaningful latent dimensions (e.g. parameters in an evidence accumulation model). Fitting a model to observed data then involves finding a point in this "cognitive parameter space" that is likely to have generated the data. Typically that is where the mechanistic explanation ends and we do not specify or, indeed, ask how an agent ended up at that point. The (often implicit) assumption is that agents try to maximise some objective function through some optimisation process (e.g. gradient descent). However, we rarely take the agent's perspective and consider the information and cognitive mechanisms available to conduct their search through the parameter space. This search is subject to several constraints. Sampling the objective is necessarily serial, local and time-consuming: objective estimates at a given location are likely to be uncertain and the agent may need several interactions with the environment to reduce this uncertainty. In light of these constraints, we explore a cognitively more plausible (i.e. minimal) search strategy. This strategy is based on local sampling of an objective function and making ordinal comparisons with only the most recently visited location in the parameter space. We report simulation results for the behaviour of this algorithm for optimising and satisficing agents, under a range of boundary conditions (e.g. noise in the objective estimates and the granularity with which objective comparisons can be made). Our overall argument is that identifying the information and mechanisms available to agents for navigating the cognitive parameter space is critical for understanding variation in cognition and behaviour over time, between different environmental conditions and between different populations or individuals.