Manual rotation training improves direction-estimations in a virtual environmental space

This study investigated in a virtual environment, whether the training of a small-scale ability, i.e., manual or mental rotation, has an influence on the large-scale ability to estimate a direction. Ninety-six participants completed a direction estimation task as a pretest and then received either a manual rotation or a mental rotation training or played a nonspatial computer game. After that they completed the direction estimation task once again. The results showed that the direction estimation error decreased from the pre- to posttest only for the manual rotation training group. For that, the small-scale spatial ability was at least partially related to the large-scale ability, which supports the Partial Dissociation Model.     

From human to artificial cognition and back: New perspectives on cognitively inspired AI systems

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.     

A real‐world rational agent: unifying old and new AI

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.     

Moving words: dynamic representations in language comprehension

Eighty-two participants listened to sentences and then judged whether two sequentially presented visual objects were the same. On critical trials, participants heard a sentence describe the motion of a ball toward or away from the observer (e.g., “The pitcher hurled the softball to you”). Seven hundred and fifty milliseconds after the offset of the sentence, a picture of an object was presented for 500 ms, followed by another picture. On critical trials, the two pictures depicted the kind of ball mentioned in the sentence. The second picture was displayed 175 ms after the first. Crucially, it was either slightly larger or smaller than the first picture, thus suggesting movement of the ball toward or away from the observer. Participants responded more quickly when the implied movement of the balls matched the movement described in the sentence. This result provides support for the view that language comprehension involves dynamic perceptual simulations.     

Evidence against integration of spatial maps in humans: generality across real and virtual environments

A real-world open-field search task was implemented with humans as an analogue of Blaisdell and Cook’s (Anim Cogn 8:7–16, 2005) pigeon foraging task and Sturz, Bodily, and Katz’s (Anim Cogn 9:207–217, 2006) human virtual foraging task to 1) determine whether humans were capable of integrating independently learned spatial maps and 2) make explicit comparisons of mechanisms used by humans to navigate real and virtual environments. Participants searched for a hidden goal located in one of 16 bins arranged in a 4 × 4 grid. In Phase 1, the goal was hidden between two landmarks (blue T and red L). In Phase 2, the goal was hidden to the left and in front of a single landmark (blue T). Following training, goal-absent trials were conducted in which the red L from Phase 1 was presented alone. Bin choices during goal-absent trials assessed participants’ strategies: association (from Phase 1), generalization (from Phase 2), or integration (combination of Phase 1 and 2). Results were inconsistent with those obtained with pigeons but were consistent with those obtained with humans in a virtual environment. Specifically, during testing, participants did not integrate independently learned spatial maps but used a generalization strategy followed by a shift in search behavior away from the test landmark. These results were confirmed by a control condition in which a novel landmark was presented during testing. Results are consistent with the bulk of recent findings suggesting the use of alternative navigational strategies to cognitive mapping. Results also add to a growing body of literature suggesting that virtual environment approaches to the study of spatial learning and memory have external validity and that spatial mechanisms used by human participants in navigating virtual environments are similar to those used in navigating real-world environments.     

Impulse processing: a dynamical systems model of incremental eye movements in the visual world paradigm

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP.     

The role of prediction in mental processing: A process approach

Although prediction plays a prominent role in mental processing, we have only limited understanding of how the brain generates and employs predictions. This paper develops a theoretical framework in three steps. First I propose a process model that describes how predictions are produced and are linked to behavior. Subsequently I describe a generative mechanism, consisting of the selective amplification of neural dynamics in the context of boundary conditions. I hypothesize that this mechanism is active as a process engine in every mental process, and that therefore each mental process proceeds in two stages: (i) the formation of process boundary conditions; (ii) the bringing about of the process function by the operation – within the boundary conditions – of a relatively ‘blind’ generative process. Thirdly, from this hypothesis I derive a strategy for describing processes formally. The result is a multilevel framework that may also be useful for studying mental processes in general.