Rule-based Task Research Articles

Analogical transfer in perceptual categorization

Analogical transfer is the ability to transfer knowledge despite significant changes in the surface features of a problem. In categorization, analogical transfer occurs if a classification strategy learned with one set of stimuli can be transferred to a set of novel, perceptually distinct stimuli. Three experiments investigated analogical transfer in rule-based and information-integration categorization tasks. In rule-based tasks, the optimal strategy is easy to describe verbally, whereas in information-integration tasks, accuracy is maximized only if information from two or more stimulus dimensions is integrated in a way that is difficult or impossible to describe verbally. In all three experiments, analogical transfer was nearly perfect in the rule-based conditions, but no evidence for analogical transfer was found in the information-integration conditions. These results were predicted a priori by the COVIS theory of categorization.

Development of implicit and explicit category learning

We present two studies that examined developmental differences in the implicit and explicit acquisition of category knowledge. College-attending adults consistently outperformed school-age children on two separate information-integration paradigms due to children’s more frequent use of an explicit rule-based strategy. Accuracy rates were also higher for adults on a unidimensional rule-based task due to children’s more frequent use of the irrelevant dimension to guide their behavior. Results across these two studies suggest that the ability to learn categorization structures may be dependent on a child’s ability to inhibit output from the explicit system.

Pigeons' categorization may be exclusively nonanalytic.

Recent theoretical and empirical developments in human category learning have differentiated an analytic, rule-based system of category learning from a nonanalytic system that integrates information across stimulus dimensions. In the present study, the researchers applied this theoretical distinction to pigeons' category learning. Pigeons learned to categorize stimuli varying in the tilt and width of their internal striping. The matched category problems had either a unidimensional (rule-based) or multidimensional (information-integration) solution. Whereas humans and nonhuman primates strongly dimensionalize these stimuli and learn rule-based tasks far more quickly than information-integration tasks, pigeons learned the two tasks equally quickly to the same accuracy level. Pigeons may represent a cognitive system in which the commitment to dimensional analysis and category rules was not strongly made. Their performance could suggest the character of the ancestral vertebrate categorization system from which that of primates emerged.

Information–integration category learning and the human uncertainty response

The human response to uncertainty has been well studied in tasks requiring attention and declarative memory systems. However, uncertainty monitoring and control have not been studied in multi-dimensional, information-integration categorization tasks that rely on non-declarative procedural memory. Three experiments are described that investigated the human uncertainty response in such tasks. Experiment 1 showed that following standard categorization training, uncertainty responding was similar in information-integration tasks and rule-based tasks requiring declarative memory. In Experiment 2, however, uncertainty responding in untrained information-integration tasks impaired the ability of many participants to master those tasks. Finally, Experiment 3 showed that the deficit observed in Experiment 2 was not because of the uncertainty response option per se, but rather because the uncertainty response provided participants a mechanism via which to eliminate stimuli that were inconsistent with a simple declarative response strategy. These results are considered in the light of recent models of category learning and metacognition.

When bad stress goes good: increased threat reactivity predicts improved category learning performance

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

Evidence for Cortical Automaticity in Rule-Based Categorization

There is evidence that rule-based category learning is supported by a broad neural network that includes the prefrontal cortex, the anterior cingulate cortex, the head of the caudate nucleus, and medial temporal lobe structures. Although thousands of studies have examined rule-based category learning, only a few have studied the development of automaticity in rule-based tasks. Categorizing by a newly learned rule makes heavy demands on declarative memory, but after thousands of repetitions rule-based categorizations are made with no apparent effort. Thus, it seems likely that the neural systems that mediate automatic rule-based categorization are substantially different from the systems that mediate initial learning. This research aims at identifying the neural systems responsible for early and late rule-based categorization performances. Toward this end, this article reports the results of an experiment in which human participants each practiced a rule-based categorization task for >10,000 trials distributed over 20 separate sessions. Sessions 1, 4, 10, and 20 were performed inside a magnetic resonance imaging scanner. The main findings are as follows: (1) cortical activation remained approximately constant throughout training, (2) subcortical activation increased with practice (i.e., there were more activated voxels in the striatum), and (3) only cortical activation was correlated with accuracy after extensive training. The results suggest an initial subcortical neural system centered around the head of the caudate that is gradually replaced by a cortical system centered around the ventrolateral prefrontal cortex. With extensive practice, the cortical system progressively becomes more caudal and dorsal, and is eventually centered around the premotor cortex.

Rule-based and information-integration category learning in normal aging

The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated processes, whereas information-integration is thought to involve implicit, striatally mediated processes. As a group, older adults showed rule-based and information-integration deficits. A series of models were applied that provided insights onto the type of strategy used to solve the task. Interestingly, when the analyses focused only on participants who used the task appropriate strategy in the final block of trials, the age-related rule-based deficit disappeared whereas the information-integration deficit remained. For this group of individuals, the final block information-integration deficit was due to less consistent application of the task appropriate strategy by older adults, and over the course of learning these older adults shifted from an explicit hypothesis-testing strategy to the task appropriate strategy later in learning. In addition, the use of the task appropriate strategy was associated with less interference and better inhibitory control for rule-based and information-information learning, whereas use of the task appropriate strategy was associated with greater working memory and better new verbal learning only for the rule-based task. These results suggest that normal aging impacts both forms of category learning and that there are some important similarities and differences in the explanatory locus of these deficits. The data also support a two-component model of information-integration category learning that includes a striatal component that mediated procedural-based learning, and a prefrontal cortical component that mediates the transition from hypothesis-testing to procedural-based strategies. Implications for independent vs. interactive category learning systems are discussed.

Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses

Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

Mechanisms and theory of “cognitive-type” cortical microcircuits

Event Abstract Back to Event Mechanisms and theory of “cognitive-type” cortical microcircuits Xiao-Jing Wang1* 1 Yale University School of Medicine, Kavli Institute for Neuroscience, United States What are the microcircuit properties that enable the prefrontal cortex to subserve cognitive functions, in contrast to early processing in primary sensory areas? In this presentation, I will discuss biophysically realistic microcircuit modeling and related experimental data that have begun to shed insights into this key issue. Recent work has led to a basic circuit mechanism that depends on slow, NMDA receptor mediated, recurrent synaptic excitation balanced by fast feedback inhibition. When recurrent connections exceed a threshold level, the network undergoes a sudden transition and becomes capable of self-sustained persistent activity for maintaining working memory. Additional cellular and synaptic processes play a role in generating robust persistent activity. The same circuit is also well suited for decision-making computations, which typically proceed from slow accumulation of information (by long transient signals in the form of stochastic ramping neural activity) toward categorical choice formation (through attractor network dynamics). Several subclasses of inhibitory neurons differentially contribute to different aspects of cognitive processes including stimulus selectivity, filtering behaviorally irrelevant distractors and winner-take-all competition. Moreover, such a circuit endowed with reward-dependent synaptic plasticity is able to account for adaptive learning in value-based economic choice behavior. Finally, I will show how this framework may be extended to describe “rule cells” and flexible rule-based tasks. Taken together, these results identify a set of core circuit properties, and suggest a common theory for understanding the prefrontal cortex and other association areas underlying cognitive functions.

Feedback Specificity Requirements for Learning in Younger and Older Adults: The Role of Cognitive Resources and Task Demand

The amount of feedback needed to learn a new task has long been debated ( cf. Schmidt & Bjork, 1992; Van Merrienboer & Sweller, 2005). One potential answer to the amount of feedback required is that feedback should be contingent upon the learner's cognitive resources and demands imposed by the task (McLaughlin, Rogers, & Fisk, 2006). To test this model, a study is proposed that accounts for the learner's cognitive resources by comparing samples of populations with known differences, older and younger adults (Horn & Cattell, 1967; Salthouse & Babcock, 1991). To account for task demands, a simple rule-based cue learning task has been created. Participants will be provided with varying levels of feedback specificty while learning the task. We predict younger adults will benefit from less feedback support while older adults benefit from more feedback support. Theoretical and applied contributions are also discussed.

Choking and excelling under pressure in experienced classifiers.

We extend previous work examining the effects of pressure on category learning to the effects of pressure on categorization performance in highly trained individuals. After extensive training on either a rule-based or an information-integration classification task, half of the participants performed the same task on a fifth day while under pressure to earn a monetary bonus ($50) for themselves and a partner. Performance of this group was compared with that of a low-pressure control group who performed without the pressure manipulation. Pressure caused performance decrements both for experienced classifiers performing rule-based tasks and for those performing information-integration tasks, as compared with control groups. These results contrast with those of previous research, where inexperienced classifiers choked on rule-based tasks but excelled on information-integration tasks. An additional "superpressure" block of trials was given at the end of the fifth session. Under this type of pressure, participants performing an information-integration task outperformed those performing rule-based tasks. Implications for theories of choking under pressure are discussed.

Design and evaluation of smart home user interface: effects of age, tasks and intelligence level

Smart homes are expected to promote productivity and enhance living experience, especially for old adults. To achieve this, the level of user interface intelligence should be designed to meet the needs of users and tasks. The aim of this study was to investigate the impact of user interface intelligence level on user performances for different tasks and different users. Two objectives were pursued. The first was to investigate how the intelligence of the user interface affects user performance when different cognitive tasks are performed. The second was to determine the differences between young and senior users when interacting with smart homes. A two-dimension conceptual model is proposed to describe the impact of these two factors, i.e., user interface intelligence and cognitive tasks, on smart home user performance. A 3-by-3 experiment was designed and three prototypes were developed. Thirty-six young and 36 senior people were recruited as participants. The results showed that different levels of intelligence of user interfaces facilitated user performance with different cognitive tasks. Users completed skill-based tasks using the least time and committing the least errors using the low-intelligence-level interface; when completing rule-based tasks, users exhibited best performance in terms of less error with the high intelligence interface. However, for senior people, because of the decline in cognitive abilities, their performance was not as clearly differentiated as that of younger people when using different smart home interfaces, especially for highly cognitively demanding tasks. Moreover, when senior people completed skill-based tasks, the task load they perceived increased together with the intelligence level of the smart home interface.

Data-logging and supervisory control in wireless sensor networks

Wireless Sensor Networks (WSNs) are being increasingly used in a multitude of applications such as environmental and structural health monitoring, and condition-based maintenance. Even though the sensors collect a vast amount of data, only a tiny fraction of this data may be useful. This paper proposes a Data-Logging and Supervisory Control (DSC) architecture to manage the information gathered by the WSN and make decisions based on this information. We present an application module which would be able to effectively manipulate the sensor data and to support a Discrete Event Controller (DEC). The DEC is responsible for generating rule-based tasks in order to address information-centric issues such as data-logging, alarm and event reporting and security. A combined DSC framework is proposed to address data pre-processing challenges such as acquiring and recording signals, online analysis, offline analysis, report generation and data sharing.

Category learning from equivalence constraints

Information for category learning may be provided as positive or negative equivalence constraints (PEC/NEC)-indicating that some exemplars belong to the same or different categories. To investigate categorization strategies, we studied category learning from each type of constraint separately, using a simple rule-based task. We found that participants use PECs differently than NECs, even when these provide the same amount of information. With informative PECs, categorization was rapid, reasonably accurate and uniform across participants. With informative NECs, performance was rapid and highly accurate for only some participants. When given directions, all participants reached high-performance levels with NECs, but the use of PECs remained unchanged. These results suggest that people may use PECs intuitively, but not perfectly. In contrast, using informative NECs enables a potentially more accurate categorization strategy, but a less natural, one which many participants initially fail to implement-even in this simplified setting.

Initial Training With Difficult Items Facilitates Information Integration, but Not Rule-Based Category Learning

Previous research has disagreed about whether a difficult cognitive skill is best learned by beginning with easy or difficult examples. Two experiments that clarify this debate are reported. Participants in both experiments received one of three types of training on a difficult perceptual categorization task. In one condition, participants began with easy examples, then moved to examples of intermediate difficulty, and finished with the most difficult examples. In a second condition, this order was reversed, and in a third condition, participants saw examples in a random order. The results depended on the type of categories that participants were learning. When the categories could be learned via explicit reasoning (a rule-based task), the three training procedures were equally effective. However, when the categorization rule was difficult to describe verbally (an information-integration task), participants who began with the most difficult items performed much better than participants in the other two conditions.

Cerebellar pathology does not impair performance on identification or categorization tasks.

In comparison to the basal ganglia, prefrontal cortex, and medial temporal lobes, the cerebellum has been absent from recent research on the neural substrates of categorization and identification, two prominent tasks in the learning and memory literature. To investigate the contribution of the cerebellum to these tasks, we tested patients with cerebellar pathology (seven with bilateral degeneration, six with unilateral lesions, and two with midline damage) on rule-based and information-integration categorization tasks and an identification task. In rule-based tasks, it is assumed that participants learn the categories through an explicit reasoning process. In information-integration tasks, optimal performance requires the integration of information from multiple stimulus dimensions, and participants are typically unaware of the decision strategy. The identification task, in contrast, required participants to learn arbitrary, color-word associations. The cerebellar patients performed similar to matched controls on all three tasks and performance did not vary with the extent of cerebellar pathology. Although the interpretation of these null results requires caution, these data contribute to the current debate on cerebellar contributions to cognition by providing boundary conditions on understanding the neural substrates of categorization and identification, and help define the functional domain of the cerebellum in learning and memory.

Differential effects of regulatory fit on category learning

Motivation affects the degree to which people engage in tasks as well as the processes that they bring to bear. We explore the proposal that a fit between a person’s situationally induced self-regulatory focus and the reward structure of the task that they are pursuing supports greater flexibility in processing than does a mismatch between regulatory focus and reward structure. In two experiments, we prime regulatory focus and manipulate task reward structure. Our participants perform a rule-based learning task whose solution requires flexible strategy testing as well as an information-integration task for which flexible strategy use hinders learning. Across two experiments, we predict and obtain a three-way interaction between regulatory focus, reward structure, and task. Relative to a mismatch, a match leads to better rule-based task performance, but worse performance on the information-integration task. We relate these findings to other work on motivation and choking under pressure.

The effects of positive versus negative feedback on information-integration category learning

A number of studies have shown that in category learning, providing feedback about errors allows faster learning than providing feedback about correct responses. However, these previous studies used explicit, rule-based tasks in which the category structures could be separated by a simple rule that was easily verbalized. Here, the results of the first experiment known to compare the efficacy of positive versus negative feedback during information-integration category learning are reported. Information-integration tasks require participants to integrate perceptual information from incommensurable dimensions, and evidence suggests that optimal responding recruits procedural learning. The results show that although nearly all of the full-feedback control participants demonstrated information-integration learning, participants receiving either positive-only or negative-only feedback generally used explicit, rule-based strategies. It thus appears that, unlike rule-based learning, consistent information-integration learning requires full feedback. The theoretical implications of these findings for current models of information-integration learning are discussed.

Choking and Excelling Under Pressure

Decrements in performance on cognitive tasks resulting from pressure to perform (i.e., choking) are thought to be caused by interference with the ability to use explicit strategies (the distraction theory). This view suggests that pressure should improve performance on tasks for which explicit strategies hamper performance. This hypothesis was tested by giving subjects one of two nearly identical learning tasks, a task that required learning a rule or one that required using a holistic information-integration strategy. Explicit rule use would hurt performance in the latter task. As predicted by the distraction theory, pressure decreased performance on the rule-based task but enhanced performance on the information-integration task.

Stimulus modality interacts with category structure in perceptual category learning

Two experiments were conducted that examined information integration and rule-based category learning, using stimuli that contained auditory and visual information. The results suggest that it is easier to perceptually integrate information within these sensory modalities than across modalities. Conversely, it is easier to perform a disjunctive rule-based task when information comes from different sensory modalities, rather than from the same modality. Quantitative model-based analyses suggested that the information integration deficit for across-modality stimulus dimensions was due to an increase in the use of hypothesis-testing strategies to solve the task and to an increase in random responding. The modeling also suggested that the across-modality advantage for disjunctive, rule-based category learning was due to a greater reliance on disjunctive hypothesis-testing strategies, as opposed to unidimensional hypothesis-testing strategies and random responding.