On the efficiency of instruction-based rule encoding

Ruge, Hannes; Karcz, Tatjana; Mark, Tony; Martin, Victoria C.; Zwosta, Katharina; Wolfensteller, Uta

doi:10.1016/j.actpsy.2017.04.005

Cited by 20 publications

(23 citation statements)

References 43 publications

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“…Interestingly, Ruge et al [19] used a larger task-rule set-size of four S-R rules (and thus presumably complex-enough to be sensitive to WM capacity differences), and manipulated WM load as well. They found that instruction-based learning was generally influenced by WM load, such that performance was worse under a WM-demanding condition.…”

Section: Introductionmentioning

confidence: 99%

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Pereg

Meiran

2019

PLoS ONE

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The ability to efficiently perform actions immediately following instructions and without prior practice has previously been termed Rapid Instructed Task Learning (RITL). In addition, it was found that instructions are so powerful that they can produce automatic effects, reflected in activation of the instructions in an inappropriate task context. RITL is hypothesized to rely on limited working memory (WM) resources for holding not-yet implemented task rules. Similarly, automatic effects of instructions presumably reflect the operation of task rules kept in WM. Therefore, both were predicted to be influenced by WM load. However, while the involvement of WM in RITL is implicated from prior studies, evidence regarding WM involvement in instructions-based automaticity is mixed. In the current study, we manipulated WM load by increasing the number of novel task rules to be held in WM towards performance in the NEXT paradigm. In this task, participants performed a series of novel tasks presented in mini-blocks, each comprising a) instructions of novel task rules; b) a NEXT phase measuring the automatic activation of these instructed rules, in which participants advance the screen using a key-press; and c) a GO phase in which the new rules are first implemented and RITL is measured. In three experiments, we show a dissociation: While RITL (rule implementation) was impaired by increased WM load, the automatic effects of instructions were not robustly influenced by WM load. Theoretical implications are discussed.

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Section: Introductionmentioning

confidence: 99%

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Pereg

Meiran

2019

PLoS ONE

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“…The task rules were explicitly instructed at the beginning of each block. For this reason, participants did not need to rely on trial‐and‐error feedback but instead could start encoding and using the current rules right after the instruction (Ruge & Wolfensteller, ; Ruge, Karcz, Mark, Martin, Zwosta, & Wolfensteller, ). The present study asked whether instructed rule changes, instructed task switches, and possible interactions might be associated with dissociable neural processes possibly as a function of practice following rule changes.…”

Section: Introductionmentioning

confidence: 99%

When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi‐level demand changes

Shi

Wolfensteller

Schubert

et al. 2017

Human Brain Mapping

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Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching).

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“…Subjects performed a simple stimulus-response learning task with deterministic feedback (N = 85), see also (25). All subjects were informed about the purpose and procedure of the experiment and gave written informed consent prior to taking part in the experiment, in accordance with the Declaration of Helsinki.…”

Section: Resultsmentioning

confidence: 99%

Deterministic response strategies in trial-and-error learning

Mohr

Zwosta

Marković

et al. 2018

Preprint

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Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in trial-and-error learning go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning. Trial-and-error learning | Reinforcement learning | Q-learning | Associative learning | Computational modelingCorrespondence: holger.mohr@tu-dresden.de IntroductionLearning rewarded stimulus-response associations via trialand-error can be a powerful strategy, which has been employed successfully in complex learning tasks (1). However, human learning strategies in trial-and-error learning tasks typically go beyond merely associating stimuli and responses via reinforcement. Instead, it has been shown that humans employ high-level cognitive capabilities like working memory and attention to make learning more efficient by exploiting hidden or overt structure in the environment (2-6). For example, it was shown that subjects can quickly reactivate previously learned response strategies (7) and incorporate information on unselected response options to improve learning efficiency (8-10). Building on a long history of research on associative learning (11,12), recent studies increasingl...

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On the efficiency of instruction-based rule encoding

Cited by 20 publications

References 43 publications

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi‐level demand changes

Deterministic response strategies in trial-and-error learning

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