Stimulus-response Rules Research Articles

Conditioning task switching behavior

Most reward studies focus on the reinforcement of simple tasks or stimulus-response rules. However, recent theories (re)emphasized that cognitive control representations should adhere to the same reinforcement learning principles as do more basic stimulus and response representations. This study focused on the act of switching between different tasks, and investigated the effects of disproportionally rewarding task alternations or repetitions in a cued task switching paradigm on subsequent voluntary task switching behavior (i.e., when participants could choose which task to perform). The results show that subjects who were more rewarded for task alternations (relative to those more rewarded for repetitions) showed more task switching behavior. Moreover, this increased task switching behavior also came with a cost, with participants more rewarded for task repetitions showing a better task focus (i.e., smaller task-rule congruency effects). These results demonstrate that reward can reinforce more abstract control representations, beyond low-level stimulus or response representations.

On the efficiency of instruction-based rule encoding

Instructions have long been considered a highly efficient route to knowledge acquisition especially compared to trial-and-error learning. We aimed at substantiating this claim by identifying boundary conditions for such an efficiency gain, including the influence of active learning intention, repeated instructions, and working memory load and span. Our experimental design allowed us to not only assess how well the instructed stimulus-response (S-R) rules were implemented later on, but also to directly measure prior instruction encoding processes. This revealed that instruction encoding was boosted by an active learning intention which in turn entailed better subsequent rule implementation. As should be expected, instruction-based learning took fewer trials than trial-and-error learning to reach a similar performance level. But more importantly, even when performance was measured relative to the identical number of preceding correct implementation trials, this efficiency gain persisted both in accuracy and in speed. This suggests that the naturally greater number of failed attempts in the initial phase of trial-and-error learning also negatively impacted learning in subsequent trials due to the persistence of erroneous memory traces established beforehand. A single instruction trial was sufficient to establish the advantage over trial-and-error learning but repeated instructions were better. Strategic factors and inter-individual differences in WM span – the latter exclusively affecting trial-and-error learning presumably due to the considerably more demanding working memory operations – could reduce or even abolish this advantage, but only in error rates. The same was not true for response time gains suggesting generally more efficient task automatization in instruction-based learning.

There are limits to the effects of task instructions: Making the automatic effects of task instructions context-specific takes practice.

Unlike other animals, humans have the unique ability to share and use verbal instructions to prepare for upcoming tasks. Recent research showed that instructions are sufficient for the automatic, reflex-like activation of responses. However, systematic studies into the limits of these automatic effects of task instructions remain relatively scarce. In this study, the authors set out to investigate whether this instruction-based automatic activation of responses can be context-dependent. Specifically, participants performed a task of which the stimulus-response rules and context (location on the screen) could either coincide or not with those of an instructed to-be-performed task (whose instructions changed every run). In 2 experiments, the authors showed that the instructed task rules had an automatic impact on performance-performance was slowed down when the merely instructed task rules did not coincide, but, importantly, this effect was not context-dependent. Interestingly, a third and fourth experiment suggests that context dependency can actually be observed, but only when practicing the task in its appropriate context for over 60 trials or after a sufficient amount of practice on a fixed context (the context was the same for all instructed tasks). Together, these findings seem to suggest that instructions can establish stimulus-response representations that have a reflexive impact on behavior but are insensitive to the context in which the task is known to be valid. Instead, context-specific task representations seem to require practice. (PsycINFO Database Record

Rule induction performance in amnestic mild cognitive impairment and Alzheimer’s dementia: examining the role of simple and biconditional rule learning processes

ABSTRACTIntroduction: Rule induction tests such as the Wisconsin Card Sorting Test require executive control processes, but also the learning and memorization of simple stimulus–response rules. In this study, we examined the contribution of diminished learning and memorization of simple rules to complex rule induction test performance in patients with amnestic mild cognitive impairment (aMCI) or Alzheimer’s dementia (AD). Method: Twenty-six aMCI patients, 39 AD patients, and 32 control participants were included. A task was used in which the memory load and the complexity of the rules were independently manipulated. This task consisted of three conditions: a simple two-rule learning condition (Condition 1), a simple four-rule learning condition (inducing an increase in memory load, Condition 2), and a complex biconditional four-rule learning condition—inducing an increase in complexity and, hence, executive control load (Condition 3). Results: Performance of AD patients declined disproportionately when the number of simple rules that had to be memorized increased (from Condition 1 to 2). An additional increment in complexity (from Condition 2 to 3) did not, however, disproportionately affect performance of the patients. Performance of the aMCI patients did not differ from that of the control participants. In the patient group, correlation analysis showed that memory performance correlated with Condition 1 performance, whereas executive task performance correlated with Condition 2 performance. Conclusions: These results indicate that the reduced learning and memorization of underlying task rules explains a significant part of the diminished complex rule induction performance commonly reported in AD, although results from the correlation analysis suggest involvement of executive control functions as well. Taken together, these findings suggest that care is needed when interpreting rule induction task performance in terms of executive function deficits in these patients.

Network mechanisms of intentional learning

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

Multi-modal representation of effector modality in frontal cortex during rule switching.

We report a functional magnetic resonance imaging (fMRI) study which investigated whether brain areas involved in updating task rules within the frontal lobe of the cerebral cortex show activity related to the modality of motor response used in the task. Participants performed a rule switching task using different effector modalities. In some blocks participants responded with left/right button presses, whilst in other blocks left/right saccades were required. The color of a Cue event instructed a left or right response based upon a rule, followed by a Feedback which indicated whether the rule was to stay the same or “Flip” on the next trial. The findings revealed variation in the locus of activity within the ventrolateral frontal cortex dependent upon effector modality. Other frontal areas showed no significant difference in activity between response epochs but changed their pattern of connectivity with posterior cortical areas dependent upon response. Multivariate analysis revealed that the pattern of activity evoked by Flip rule Feedbacks within an apparently supra modal frontal region (dorsolateral frontal cortex) discriminated between response epochs. The results are consistent with the existence of multi-modal representations of stimulus-response (SR) rules within the frontal cerebral cortex.

Is it harder to switch among a larger set of tasks?

When stimuli afford multiple tasks, switching among them involves promoting one of several task-sets in play into a most-active state. This process, often conceptualized as retrieving task parameters and stimulus-response (S-R) rules into procedural working memory, is a likely source of the reaction time (RT) cost of a task-switch, especially when no time is available for task preparation before the stimulus. We report 2 task-cuing experiments that asked whether the time consumed by task-set retrieval increases with the number of task-sets in play, while unconfounding the number of tasks with their frequency and recency of use. Participants were required to switch among 3 or 5 orthogonal classifications of perceptual attributes of an object (Experiment 1) or of phonological/semantic attributes of a word (Experiment 2), with a 100 or 1,300 ms cue-stimulus interval. For 2 tasks for which recency and frequency were matched in the 3- and 5-task conditions, there was no effect of number of tasks on the switch cost. For the other tasks, there was a greater switch cost in the 5-task condition with little time for preparation, attributable to effects of frequency/recency. Thus, retrieval time for active task-sets is not influenced by the number of alternatives per se (unlike several other kinds of memory retrieval) but is influenced by recency or frequency of use.

Response selection difficulty modulates the behavioral impact of rapidly learnt action effects.

It is well-established that we can pick up action effect associations when acting in a free-choice intentional mode. However, it is less clear whether and when action effect associations are learnt and actually affect behavior if we are acting in a forced-choice mode, applying a specific stimulus–response (S–R) rule. In the present study, we investigated whether response selection difficulty imposed by S–R rules influences the initial rapid learning and the behavioral expression of previously learnt but weakly practiced action effect associations when those are re-activated by effect exposure. Experiment 1 showed that the rapid learning of action effect associations is not directly influenced by response selection difficulty. By contrast, the behavioral expression of re-activated action effect associations is prevented when actions are directly activated by highly over-learnt response cues and thus response selection difficulty is low. However, all three experiments showed that if response selection difficulty is sufficiently high during re-activation, the same action effect associations do influence behavior. Experiments 2 and 3 revealed that the effect of response selection difficulty cannot be fully reduced to giving action effects more time to prime an action, but seems to reflect competition during response selection. Finally, the present data suggest that when multiple novel rules are rapidly learnt in succession, which requires a lot of flexibility, action effect associations continue to influence behavior only if response selection difficulty is sufficiently high. Thus, response selection difficulty might modulate the impact of experiencing multiple learning episodes on action effect expression and learning, possibly via inducing different strategies.

Selective influence of working memory load on exceptionally slow reaction times.

The rate of exceptionally slow reaction times (RTs), described by the long tail of the RT distribution, was found to be amplified in a variety of special populations with cognitive deficits (e.g., early-stage Alzheimer's disease, attention-deficit/hyperactivity disorder, low intelligence, elderly). Previous individual differences studies found high correlations between working memory (WM) and parameters that characterize the magnitude of the long-RT tail. However, the causal direction remains unknown. In 3 choice-reaction task experiments, we examined this relationship by directly manipulating WM availability. In Experiment 1, the stimulus-response rules were either arbitrary (WM demanding) or nonarbitrary. In Experiment 2, the arbitrary rules were either novel (demanding) or practiced. In Experiment 3, WM was loaded with either declarative (stimulus-stimulus) or procedural (stimulus-response) arbitrary rules. Using an ex-Gaussian model fitting, we found across all experiments that WM demands uniquely influenced the τ parameter, mostly responsible for the long-RT distribution tail. Evidence accumulation modeling of the choice process indicated that WM load had little influence on the decision process itself and primarily affected the duration of an exponentially distributed nondecision component, assumed to reflect the process of rule retrieval. Theoretical interpretations and implications are discussed.

Reflexive activation of newly instructed stimulus-response rules: evidence from lateralized readiness potentials in no-go trials.

Previous behavioral and electrophysiological evidence has suggested that the instructions for a new choice task are processed even when they are not currently required, indicating intention-based reflexivity. Yet these demonstrations were found in experiments in which participants were set to execute a response (go). In the present experiment, we asked whether intention-based reflexivity would also be observed under unfavorable conditions in which participants were set not to respond (no-go). In each miniblock of our paradigm, participants received instructions for a task in which two new stimuli were mapped to right/left keys. Immediately after the instructions, a no-go phase began, which was immediately followed by a go phase. We found a significant stimulus-locked lateralized readiness potential in the first no-go trial, indicating reflexive operation of the new instructions. These results show that representing instructions in working memory provides sufficient conditions for stimuli to launch task processing, proceeding all the way until motor response-specific brain activation, which takes place even under unfavorable, no-go conditions.

Industry Clusters Innovation Action Model Based on the CAS Theory – Shanxi Magnesium Industry

Based on complex adaptive systems (CAS) theory, using the method of case studies to explore the Shaanxi Province magnesium industry cluster knowledge innovation behavior. Based on the complexity stimulus—response rules, build the knowledge innovation system of metallic magnesium industry cluster main body behavior patterns; Based on complex adaptive - learning, building the overall behavior pattern of magnesium metal industry cluster knowledge innovation system; and based on the complex level—emerge theory, build the magnesium industry cluster evolution behavior pattern of knowledge innovation system. Use CAS theory to solve the problem of knowledge innovation in industrial cluster, which offers a basis for realize the sustainable and health development of industry cluster better.

Don’t Do It Again! Directed Forgetting of Habits

Most daily routines are determined by habits. However, the experienced ease and automaticity of habit formation and execution come at a cost when habits that are no longer appropriate must be overcome. So far, proactive and reactive control strategies that prevent inappropriate habit execution either by preparation or “on the fly” have been identified. Here, we present evidence for a third, retroactive control strategy. In two experiments using the list method of directed forgetting, the accessibility of newly learned and practiced stimulus-response rules was significantly reduced when participants were cued to forget the rules rather than to remember them. The results thus show that directed forgetting, so far observed and investigated only for episodic memory traces, can also be applied to habits. The findings further emphasize the adaptive value of forgetting and can be taken as evidence of a retroactive strategy of habit control.

The speed of learning instructed stimulus-response association rules in human: Experimental data and model

Humans can learn associations between visual stimuli and motor responses from just a single instruction. This is known to be a fast process, but how fast is it? To answer this question, we asked participants to learn a briefly presented (200ms) stimulus-response rule, which they then had to rapidly apply after a variable delay of between 50 and 1300ms. Participants showed a longer response time with increased variability for short delays. The error rate was low and did not vary with the delay, showing that participants were able to encode the rule correctly in less than 250ms. This time is close to the fastest synaptic learning speed deemed possible by diffusive influx of AMPA receptors. Learning continued at a slower pace in the delay period and was fully completed in average 900ms after rule presentation onset, when response latencies dropped to levels consistent with basic reaction times. A neural model was proposed that explains the reduction of response times and of their variability with the delay by (i) a random synaptic learning process that generates weights of average values increasing with the learning time, followed by (ii) random crossing of the firing threshold by a leaky integrate-and-fire neuron model, and (iii) assuming that the behavioural response is initiated when all neurons in a pool of m neurons have fired their first spike after input onset. Values of m=2 or 3 were consistent with the experimental data. The proposed model is the simplest solution consistent with neurophysiological knowledge. Additional experiments are suggested to test the hypothesis underlying the model and also to explore forgetting effects for which there were indications for the longer delay conditions.This article is part of a Special Issue entitled Neural Coding 2012.

Are stimulus–response rules represented phonologically for task-set preparation and maintenance?

Accounts of task-set control generally assume that the current task's stimulus-response (S-R) rules must be elevated to a privileged state of activation. How are they represented in this state? In 3 task-cuing experiments, we tested the hypothesis that phonological working memory is used to represent S-R rules for task-set control by getting participants to switch between 2 sets of arbitrary S-R rules and manipulating the articulatory duration (Experiment 1) or phonological similarity (Experiments 2 and 3) of the names of the stimulus terms. The task cue specified which of 2 objects (Experiment 1) or consonants (Experiment 2) in a display to identify with a key press. In Experiment 3, participants switched between identifying an object/consonant and its color/visual texture. After practice, neither the duration nor the similarity of the stimulus terms had detectable effects on overall performance, task-switch cost, or its reduction with preparation. Only in the initial single-task training blocks was phonological similarity a significant handicap. Hence, beyond a very transient role, there is no evidence that (declarative) phonological working memory makes a functional contribution to representing S-R rules for task-set control, arguably because once learned, they are represented in nonlinguistic procedural working memory.

Irrelevant Stimulus Processing When Switching Between Tasks

Frequent switching between two tasks afforded by the same stimuli is associated with between-task congruency effects, that is, relatively impaired performance when a stimulus affords different responses as compared to the same responses in both tasks. These congruency effects indicate some form of application of the stimulus-response (S-R) rules of the currently irrelevant task. Between-task congruency effects are usually enhanced on task switch trials compared with task repetition trials. Here we investigate whether this interaction reflects stronger proactive interference from the irrelevant task on switch trials or whether performance on switch trials is characterized by generally enhanced susceptibility to task-irrelevant information processing. To this end, we contrasted between-task congruency effects with interference exerted from flanker stimuli taken from the current task (Experiment 1) and from spatial-numerical association of response codes (SNARC; Experiment 2). In both experiments, between-task congruency effects were larger on switch trials than on repetition trials, whereas interference from the other source remained constant, thus demonstrating that switch trials are not characterized by generally increased distractibility.

Global Consensus Theorem and Self-Organized Criticality: Unifying Principles for Understanding Self-Organization, Swarm Intelligence and Mechanisms of Carcinogenesis

Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. Generally, each level in a biological hierarchy possesses its own systemic properties and requires its own way of observation, conceptualization, and modeling. In this work, an attempt is made to outline general guiding principles in exploration of a wide range of seemingly dissimilar phenomena observed in large communities of individuals devoid of any personal intelligence and interacting with each other through simple stimulus-response rules. Mathematically, these guiding principles are well captured by the Global Consensus Theorem (GCT) equally applicable to neural networks and to Lotka-Volterra population dynamics. Universality of the mechanistic principles outlined by GCT allows for a unified approach to such diverse systems as biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, social networks and many other systems. Another cluster of universal laws governing the self-organization in large communities of locally interacting individuals is built around the principle of self-organized criticality (SOC). The GCT and SOC, separately or in combination, provide a conceptual basis for understanding the phenomena of self-organization occurring in large communities without involvement of a supervisory authority, without system-wide informational infrastructure, and without mapping of general plan of action onto cognitive/behavioral faculties of its individual members. Cancer onset and proliferation serves as an important example of application of these conceptual approaches. In this paper, the point of view is put forward that apparently irreconcilable contradictions between two opposing theories of carcinogenesis, that is, the Somatic Mutation Theory and the Tissue Organization Field Theory, may be resolved using the systemic approaches provided by GST and SOC.

A carry-over task rule in task switching: An ERP investigation using a Go/Nogo paradigm

Investigations of executive control using a task-switching paradigm have consistently found longer reaction times for task-switch trials than task-repetition trials. This switch cost may result from interference by a stimulus-response (SR) rule carried over from the preceding alternative task. We examined event-related brain potential (ERP) evidence for such carry-over effects using a combined paradigm of task switching with Go/Nogo; Nogo trials, which require no response execution, should expose carry-over effects from preceding trials. On Go trials, twelve participants performed a button-pressing task in compatible (hand and signal direction consistent) and incompatible conditions, which switched predictably every three trials. Reaction times were longer on switch than on repetition trials. On compatible switch trials, a stimulus-locked lateralized readiness potential (sLRP) for Nogo stimuli revealed a positive dip, suggesting incorrect response activation in the early automatic process that was induced by a SR rule carried over from the preceding task.

Generalized lessons about sequence learning from the study of the serial reaction time task.

Over the last 20 years researchers have used the serial reaction time (SRT) task to investigate the nature of spatial sequence learning. They have used the task to identify the locus of spatial sequence learning, identify situations that enhance and those that impair learning, and identify the important cognitive processes that facilitate this type of learning. Although controversies remain, the SRT task has been integral in enhancing our understanding of implicit sequence learning. It is important, however, to ask what, if anything, the discoveries made using the SRT task tell us about implicit learning more generally. This review analyzes the state of the current spatial SRT sequence learning literature highlighting the stimulus-response rule hypothesis of sequence learning which we believe provides a unifying account of discrepant SRT data. It also challenges researchers to use the vast body of knowledge acquired with the SRT task to understand other implicit learning literatures too often ignored in the context of this particular task. This broad perspective will make it possible to identify congruences among data acquired using various different tasks that will allow us to generalize about the nature of implicit learning.

Abstract Rule Learning: The Differential Effects of Lesions in Frontal Cortex (IN4-2.003)

Objective: To evaluate whether lesions in progressively more anterior portions of lateral frontal cortex selectively impair the acquisition of progressively more abstract action rules. Background Simple 1st-order stimulus-response rules (e.g. a mapping between a color and a button press) are less abstract than 2nd-order rules (e.g. a mapping between a color and a button press that varies with context). Dorsal premotor cortex (PMd) and pre-premotor cortex (prePMd) have been implicated in the execution of 1st and 2nd-order rules, respectively. We predicted that subjects with lesions of the prePMd would demonstrate impaired learning of 2nd-order but not 1st-order rules. Design/Methods: In this behavioral and functional MRI study, 18 subjects with a history of brain injury (14 with a history of stroke, 3 with traumatic brain injury, and 1 status post tumor resection) were tested in a learning paradigm with two conditions: one in which only simple 1st-order stimulus-response relationships could be learned, and one in which 2nd-order relationships were also available. The primary behavioral outcome measure was accuracy at the end of learning. Secondary measures included changes in connectivity between lateral prefrontal cortical regions as measured by functional MRI. Planned statistical analyses included paired T-tests for accuracy between 1st and 2nd-order rule conditions, and standardized multivariate statistical analysis of fMRI data. Results: Subjects with prePMd lesions were unable to acquire 2nd-order rules. Unexpectedly, they identified alternate 1st-order rules that allowed them to perform better than other subjects when 2nd-order rules were available (delta(accuracy) = 0.10, T(7) = 5.35, p = 0.001). Across all subjects, a reduction in functional connectivity between left pre-PMd and adjacent regions in the hierarchy correlated with improved performance. Conclusions: These findings support the theory that specific regions within lateral frontal cortex shapes the search for relevant stimulus-response mappings. Supported by: National Institutes of Health. Disclosure: Dr. Kayser has nothing to disclose. Dr. D9Esposito has nothing to disclose.

Strategy-effects in prefrontal cortex during learning of higher-order S–R rules

All of us regularly face situations that require the integration of the available information at hand with the established rules that guide behavior in order to generate the most appropriate action. But where individuals differ from one another is most certainly in terms of the different strategies that are adopted during this process. A previous study revealed differential brain activation patterns for the implementation of well established higher-order stimulus-response (S-R) rules depending on inter-individual strategy differences (Wolfensteller and von Cramon, 2010). This raises the question of how these strategies evolve or which neurocognitive mechanisms underlie these inter-individual strategy differences. Using functional magnetic resonance imaging (fMRI), the present study revealed striking strategy-effects across regions of the lateral prefrontal cortex during the implementation of higher-order S-R rules at an early stage of learning. The left rostrolateral prefrontal cortex displayed a quantitative strategy-effect, such that activation during rule integration based on a mismatch was related to the degree to which participants continued to rely on rule integration. A quantitative strategy ceiling effect was observed for the left inferior frontal junction area. Conversely, the right inferior frontal gyrus displayed a qualitative strategy-effect such that participants who at a later point relied on an item-based strategy showed stronger activations in this region compared to those who continued with the rule integration strategy. Together, the present findings suggest that a certain amount of rule integration is mandatory when participants start to learn higher-order rules. The more efficient item-based strategy that evolves later appears to initially require the recruitment of additional cognitive resources in order to shield the currently relevant S-R association from interfering information.