Implicit and explicit learning in ACT-R

A useful way to explain the notions of implicit and explicit learning in ACT-R is to define implicit learning as learning by ACT-R's learning mechanisms, and explicit learning as the results of learning goals. This idea complies with the usual notion of implicit learning as unconscious and always active and explicit learning as intentional and conscious. Two models will be discussed to illustrate this point. First a model of a classical implicit memory task, the SUGARFACTORY scenario by Berry & Broadbent (1984) will be discussed, to show how ACT-R can model implicit learning. The second model is of the so-called Fincham task (Anderson & Fincham, 1994), and exhibits both implicit and explicit learning

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

The Value of Context-Based Interface Prototyping for the Autonomous Vehicle Domain: A Method Overview

Before autonomous vehicles (AVs; SAE levels 4 and 5) become broadly available, acceptance challenges such as trust and safety concerns must be overcome. In the development of appropriate HMIs that will tackle these challenges, physical and social context play essential roles. Contextual factors thus need to be considered in early prototyping stages. Based on a qualitative semi-systematic literature review and knowledge from our research, this paper elaborates on the value of context-based interface prototyping in the AV domain. It provides a comprehensive overview and a discussion of applicable methods, including physical lab-based prototyping (mock-up, ride simulation with virtual and mixed reality, and immersive video), social context simulation (actors, enactment, items and props, and sound), wizard-of-oz, and experimental vehicles. Finally, the paper discusses factors affecting the impact of prototyping and derives recommendations for the application of prototyping methods in future AV studies

Whether Skill Acquisition is Rule or Instance . . .

The traditional view of skill acquisition is that it can be explained by a gradual transition from behavior based on declarative rules in the form of examples and instructions towards general knowledge represented by procedural rules. This view is challenged by Logan's instance theory, which specifies that skill acquisition can be explained by the accumulation of examples or instances of the skill. The position defended in this paper is that both types of learning can occur, but their success depends on the respective task. In the Sugar Factory task, it is very hard to determine the rule guiding the system, so rule learning will fail and instance learning dominates. In the Fincham task, mainly rule learning occurs, but variations in the task show evidence for some instance learning as well. Experiments with both tasks are modeled using ACT-R, a hybrid cognitive architecture whose adaptive learning mechanisms seem to be well suited for modeling two very different tasks using the same methods