In dynamic environments, animals must select actions based on sensory input as well as expected positive and negative consequences. This type of behavior is typically studied using perceptual decision making (PDM) tasks. The arguably most influential framework for describing the cognitive processes underlying PDM is signal detection theory (SDT). One central assumption of SDT is that observers make perceptual decisions by comparing sensory evidence to a static decision criterion. However, mounting evidence suggests that the criterion is in fact highly dynamic and that observers adjust it flexibly according to task demands. Nevertheless, the mechanisms by which observers integrate stimulus and reward information for adaptive criterion learning remain not well understood. Here, we systematically investigated the factors influencing criterion setting at the single-trial level. To that end, we first specified three SDT-based models that learn either from reward, reward omission, or both. Next, by concomitantly manipulating stimulus and reward probabilities, we constructed experimental conditions in which these models make divergent predictions. Finally, we subjected rats and pigeons to a PDM task comprising these conditions. We find that subjects adopted decision criteria that maximize total reward in all experimental conditions. Detailed behavioral analyses reveal that criterion learning is driven by the integration of rewards, not reward omissions, and that reward integration is influenced by two additional factors: first, the degree of stimulus uncertainty, and second, the difference in the relative reward rates (rather than the absolute reward rates) between the choice alternatives. A model incorporating these factors accounts well for criterion dynamics across experimental conditions for both species and links signal detection theory to a learning mechanism operating at the level of single trials which, in the steady state, produces behavior similar to the matching law, a central tenet of learning theory.

Designing interactions for human-AI teams (HATs) can be challenging due to an AI agent’s potential autonomy. Previous work suggests that higher autonomy does not always improve team performance, and situation-dependent autonomy adaptation might be beneficial. However, there is a lack of systematic empirical evaluations of such autonomy adaptation in human-AI interaction. Therefore, we propose a cooperative task in a simulated shared workspace to investigate effects of fixed levels of AI autonomy and situation-dependent autonomy adaptation on team performance and user satisfaction. We derive adaptation rules for AI autonomy from previous work and a pilot study. We implement these rule for our main experiment and find that team performance was best when humans collaborated with an agent adjusting its autonomy based on the situation. Additionally, users rated this agent highest in terms of perceived intelligence. From these results, we discuss the influence of varying autonomy degrees on HATs in shared workspaces.

In neuroscience, data are typically generated from neural network activity. The resulting time series represent measurements from spatially distributed subsystems with complex interactions, weakly coupled to a high-dimensional global system. We present a statistical framework to estimate the direction of information flow and its delay in measurements from systems of this type. Informed by differential topology, gaussian process regression is employed to reconstruct measurements of putative driving systems from measurements of the driven systems. These reconstructions serve to estimate the delay of the interaction by means of an analytical criterion developed for this purpose. The model accounts for a range of possible sources of uncertainty, including temporally evolving intrinsic noise, while assuming complex nonlinear dependencies. Furthermore, we show that if information flow is delayed, this approach also allows for inference in strong coupling scenarios of systems exhibiting synchronization phenomena. The validity of the method is demonstrated with a variety of delay-coupled chaotic oscillators. In addition, we show that these results seamlessly transfer to local field potentials in cat visual cortex.

Elimination by aspects (EBA) is a probabilistic choice model describing how humans decide between several options. The options from which the choice is made are characterized by binary features and associated weights. For instance, when choosing which mobile phone to buy the features to consider may be: long lasting battery, color screen, etc. Existing methods for inferring the parameters of the model assume pre-specified features. However, the features that lead to the observed choices are not always known. Here, we present a non-parametric Bayesian model to infer the features of the options and the corresponding weights from choice data. We use the Indian buffet process (IBP) as a prior over the features. Inference using Markov chain Monte Carlo (MCMC) in conjugate IBP models has been previously described. The main contribution of this paper is an MCMC algorithm for the EBA model that can also be used in inference for other non-conjugate IBP models---this may broaden the use of IBP priors considerably.