Associations do not energize behavior: on the forgotten legacy of Kurt Lewin

Hundred years ago, Kurt Lewin published a series of articles in which he vehemently argued against the idea that associations between stimuli and responses motivate behavior. This article reviews his empirical work and theory and the cogency of Lewin’s conclusion according to modern standards. We conclude that Lewin’s criticism of the contiguity principle of associationism is still valid, and is now supported by a broad range of theories on learning, motivation, and action control. Implications for modern dual-system theory and modern theories on motivated action and (instructed) task sets are discussed.

Effects of Continuous vs Discrete Frequency Scaling and Core Allocation on Energy Efficiency of Static Schedules for Moldable Tasks

Models for energy-efficient static scheduling of parallelizable tasks with deadlines on frequency-scalable parallel machines comprise moldable vs. malleable tasks and continuous vs. discrete frequency levels, plus preemptive vs. non-preemptive task execution with or without task migration. We investigate the tradeoff between scheduling time and energy efficiency when going from continuous to discrete core allocation and frequency levels on a multicore processor, and from preemptive to non-preemptive task execution. To this end, we present a tool to convert a schedule computed for malleable tasks on machines with continuous frequency scaling [Sanders and Speck, Euro-Par (2012)] into one for moldable tasks on a machine with discrete frequency levels. We compare the energy efficiency of the converted schedule to the energy consumed by a schedule produced by the integrated crown scheduler [Melot et al., ACM TACO (2015)] for moldable tasks and a machine with discrete frequency levels. Our experiments with synthetic and application-based task sets indicate that the converted Sanders Speck schedules, while computed faster, consume more energy on average than crown schedules. Surprisingly, it is not the step from malleable to moldable tasks that is responsible but the step from continuous to discrete frequency levels. One-time frequency scaling during a task’s execution can compensate for most of the energy overhead caused by frequency discretization.

A Novel Feasibility Test for Energy Minimization of Real-Time Mixed Task Sets for DVS-Enabled Uniprocessor System

With advancements in computing and communication technologies on mobile devices, the performance requirements of embedded processors have significantly increased, resulting in a corresponding increase in its energy consumption. Dynamic scaling of operating voltage and operating frequency has a strong correlation to energy minimization in CMOS real-time circuits. Simultaneous optimization of ([Formula: see text], [Formula: see text] pairs under dynamic activity levels is thus extensively investigated over several years. The supply voltage is tuned dynamically during runtime (DVS), with a fixed threshold voltage, to achieve energy minimization. This work addresses the issue of maximizing the energy efficiency of real-time periodic, aperiodic and mixed task sets, in a uniprocessor system, by developing a novel task feasibility methodology, with a novel processor performance-based constraint, to generate the optimal operating supply voltage to the individual task of task sets. The energy minimization of real-time mixed task sets is formulated as Geometric Programming (GP) problem, by varying frequency for periodic tasks sets and keeping fixed frequency for aperiodic tasks set, over a range of task sets and hence computing optimal operating voltages. Simulation experiments show energy savings on the cumulative basis of 50%, 38% and 29% for periodic, aperiodic and mixed task sets, respectively, based on the processing timing constraints of task sets.

EXPRESS: Rethinking attentional reset: Task sets determine the boundaries of adaptive control

Adaptive control processes that minimize distraction often operate in a context-specific manner. For example, they may minimize distraction from irrelevant conversations during a lecture but not in the hallway afterwards. It remains unclear, however, whether (a) salient perceptual features or (b) task sets based on such features serve as contextual boundaries for adaptive control in standard distractor-interference tasks. To distinguish between these possibilities, we manipulated whether the structure of a standard, visual distractor-interference task allowed (Experiment 1) or did not allow (Experiment 2) participants to associate salient visual features (i.e., color patches and color words) with different task sets. We found that changing salient visual features across consecutive trials reduced a popular measure of adaptive control in distractor-interference tasks – the congruency sequence effect (CSE) – only when the task structure allowed participants to associate these visual features with different task sets. These findings extend prior support for the task set hypothesis from somewhat atypical cross-modal tasks to a standard unimodal task. Conversely, they pose a challenge to an alternative “attentional reset” hypothesis, and related views, wherein changing salient perceptual features always results in a contextual boundary for the CSE.

A Crowd-Powered Task Generation Method for Study of Struggling Search

Evaluation of interactive search systems and study of users’ struggling search behaviors require a significant number of search tasks. However, generation of such tasks is inherently difficult, as each task is supposed to trigger struggling search behavior rather than simple search behavior. To the best of our knowledge, there has not been a commonly used task set for research in struggling search. Moreover, the everchanging landscape of information needs would render old task sets less ideal if not unusable for evaluation. To deal with this problem, we propose a crowd-powered task generation method and develop a platform to efficiently generate struggling search tasks on basis of online wikis such as Wikipedia. Our experiments and analysis show that the generated tasks are qualified to emulate struggling search behaviors consisting of “repeated similar queries” and “quick-back clicks”; tasks of diverse topics, high quality and difficulty can be created using this method. For benefit of the community, we publicly released a task generation platform TaskGenie, a task set of 80 topically diverse struggling search tasks with “baselines,” and the corresponding anonymized user behavior logs.

Dissociating the Neural Correlates of Planning and Executing Hierarchical Task Sets

Task processing and task representation, two facets of cognitive control, are both supported by lateral frontal cortex (LFC). However, processing and representation have largely been investigated separately, so it is unknown if they are distinguishable aspects of control or if they are complementary descriptions of the same mechanism. Here, we explored this by combining a hierarchical task mapping with a pre-cueing procedure. Participants made match/non-match judgments on features of pairs of stimuli. Cues presented at the start of each trial indicated the judgment domain (spatial/non-spatial), the response hand, both, or neither, giving variable amounts of information to the subject at each time point in the trial. Our results demonstrated that regions throughout LFC supported task processing, indicated by an influence of time point on their BOLD activity levels. A subset of regions in left caudal LFC also supported task representation, indicated by an interaction between time point and cue information; we termed this subgroup the "CuexTime" group. This interaction effect was not seen in the remaining LFC regions, which only showed a main effect of time consistent with involvement in task processing; we termed this subgroup the "Time" group. These results suggest that task representation is one component of task processing, confined to the "CuexTime group" in left caudal LFC, while other regions in our task support other aspects of task processing. We further conducted an exploratory investigation of connectivity between regions in the "CuexTime" and "Time" groups and their potential relationship to networks that support distinct cognitive control functions.

Computational models of adaptive behavior and prefrontal cortex

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

No-go trials in task switching: effects on the task-set and task-space level

A common marker for inhibition processes in task switching are n − 2 repetition costs. The present study aimed at elucidating effects of no-go trials on n − 2 repetition costs. In contrast to the previous studies, no-go trials were associated with only one of the three tasks in the present two experiments. High n − 2 repetition costs occurred if the no-go task had to be executed in trial n − 2, irrespective of whether a response had to be withheld or not. In contrast, no n − 2 repetition costs were visible if the other two tasks were relevant in n − 2. Whereas this n − 2 effect was unaffected by whether participants could reliably exclude a no-go trial or not, effects of no-gos in trial n were determined by this knowledge. The results differ from effects of no-go trials that are not bound to a specific task. It is assumed that the present no-go variation exerted its effect not on the response level, but on the level of task sets, resulting in enhanced salience of the no-go task that leads to higher activation and, as a consequence, to stronger inhibition. The dissociation of the effects on no-gos in trials n − 2 and n as a function of foreknowledge suggests that the balance between activation and inhibition is shifted not only for single trials and tasks, but for the whole task space.