SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.     

Goal‐Proximity Decision‐Making

Reinforcement learning (RL) models of decision‐making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal‐option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal‐Proximity Decision‐making (GPD), is implemented within the ACT‐R cognitive framework. GPD is found to be more efficient than RL in three maze‐navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL.     

A Lambek Automaton

We define an automata-theoretic counterpart of (type-logical)grammars based on the (associative) Lambek-calculus L, a prominentformalism in computational linguistics. While the usual push-downautomaton (PDA) has the same weak generative power as the L-basedgrammars (Pentus, 1995), there is no direct relationship betweenthe computations of a PDA for some language L and the derivationsof an L-based grammar for L. In the Lambek-automaton, on theother hand, there is a tight relation (1-1) between automatoncomputations and grammar derivations. The automaton exhibitsa novel mode of operation, using hypothetical steps, directlyinspired by the hypothetical reasoning embodied by L.     

Functional Equivalence of Sleep Loss and Time on Task Effects in Sustained Attention

Research on sleep loss and vigilance both focus on declines in cognitive performance, but theoretical accounts have developed largely in parallel in these two areas. In addition, computational instantiations of theoretical accounts are rare. The current work uses computational modeling to explore whether the same mechanisms can account for the effects of both sleep loss and time on task on performance. A classic task used in the sleep deprivation literature, the Psychomotor Vigilance Test (PVT), was extended from the typical 10‐min duration to 35 min, to make the task similar in duration to traditional vigilance tasks. A computational cognitive model demonstrated that the effects of time on task in the PVT were equivalent to those observed with sleep loss. Subsequently, the same mechanisms were applied to a more traditional vigilance task—the Mackworth Clock Task—providing a good fit to existing data. This supports the hypothesis that these different types of fatigue may produce functionally equivalent declines in performance.     

Further Evidence That Sleep Deprivation Effects and the Vigilance Decrement Are Functionally Equivalent: Comment on Altmann (2018)

Veksler and Gunzelmann (2018) argue that the vigilance decrement and the deleterious effects of sleep loss reflect functionally equivalent degradations in cognitive processing and performance. Our account is implemented in a cognitive architecture, where these factors produce breakdowns in goal‐directed cognitive processing that we refer to as microlapses. Altmann (2018) raises a number of challenges to microlapses as a unified account of these deficits. Under scrutiny, however, the challenges do little to discredit the theory or conclusions in the original paper. In our response, we address the most serious challenges. In so doing, we provide additional support for the theory and mechanisms, and we highlight opportunities for extending their explanatory breadth.