Intentional fixation of behavioural learning,or how R-O learning blocks S-R learning

Four experiments deal with the acquisition of knowledge for the control of voluntary behaviour. Subjects had to accomplish a computer-controlled learning task that required them to learn which one of four actions (R) had to be performed in order to attain a certain one of four outcomes (O) in the presence of one of four situational contexts (S). Reinforcements were assigned to actions according to two schedules: In the S-R condition each of the four actions was consistently reinforced in the presence of a certain situation, whereas in the R-O condition each of the actions was either always (Experiments 1-3) or mostly (Experiment 4) reinforced if a certain outcome was required. Furthermore, the type of feedback was varied. In Experiment 1, only positive or negative reinforcements were fed back, whereas in Experiments 2-4 the actions resulted in outcomes that had to be compared with the required outcomes in order to determine successes and failures. The results indicate a preference for learning R-O contingencies over learning S-R contingencies. Most subjects were so fixated on learning R-O relations that they remained completely blind to the consistent reinforcement of S-R mappings. The data suggest that, in line with the ideomotor principle, the acquisition of behavioural competence is based primarily on the formation of bidirectional action-outcome relations. Specifications of the underlying learning mechanisms are discussed.     

Implicit visual learning and the expression of learning

Although the existence of implicit motor learning is now widely accepted, the findings concerning perceptual implicit learning are ambiguous. Some researchers have observed perceptual learning whereas other authors have not. The review of the literature provides different reasons to explain this ambiguous picture, such as differences in the underlying learning processes, selective attention, or differences in the difficulty to express this knowledge. In three experiments, we investigated implicit visual learning within the original serial reaction time task. We used different response devices (keyboard vs. mouse) in order to manipulate selective attention towards response dimensions. Results showed that visual and motor sequence learning differed in terms of RT-benefits, but not in terms of the amount of knowledge assessed after training. Furthermore, visual sequence learning was modulated by selective attention. However, the findings of all three experiments suggest that selective attention did not alter implicit but rather explicit learning processes.     

Effects of learning contexts on implicit and explicit learning

Two parallel tasks involving rule learning were identified in Experiment 1A and were used to assess implicit and explicit learning. In both tasks, subjects had to input numbers in order to reach the target values of outputs. The relationship between inputs and outputs was either simple (in the simple task) or complex (in the complex task), and the way in which target values were presented could be in the form of either numbers (in the simple task) or lines (in the complex task). Experiment 1B examined the validity of the explicit measure in the complex task. Experiments 2–4 investigated the interaction between learning contexts and the simple/complex learning tasks. Verbalization and instructions to search for the rules facilitated the simple-task learning and hurt or have no effect on the complex-task learning. In the observational-learning condition, no learning occurred for the simple task, and the complex task learning was impaired. These results suggest that the complex task and simple task involve two distinct learning systems. Other implications are also discussed.     

The theory of learning styles applied to distance learning

Distance Education (DE) associated with the use of Virtual Learning Environments (VLE) as interaction tools between the student and the educator has become a large research niche spread around the world. Techniques to improve learning effectiveness in VLEs seek to find relation-ships connections between pedagogical advances and educational technological resources available in VLEs. In this context, this work sought to associate the theory of Learning Styles with the behavior of the student of Distance Education, observing their interaction with the Virtual Learning Environment and trying to associate them with their learning style identified by the CHAEA questionnaire. For this purpose, the CHAEA questionnaire was applied to a group of distance learning students and the correlation between their interactions with VLE and their learning styles was verified. The results show that there is no correlation between these elements, which may show, in fact, the lack of coherence between these theories. Seeking a model for data, a linear regression was applied and the results were, then, confirmed.     

Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning, classification and inference learning, in terms of sensitivity to diagnostic and prototypical information. Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic, but prototypical, features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic, prototypical, features. Discussion focuses on aspects of the 2 learning tasks that might lead to this differential sensitivity and the implications for learning real-world categories.     

Earthquake disaster avoidance learning system using deep learning

The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system is based on the You Look Only Once (YOLO) deployed on our cloud-based server named Earthquake Situation Learning System (ESLS) for the detection of harmful objects associated with risk tags. ESLS is trained on our own indoor images dataset. The user interacts with the ESLS server through video or image files, and the object detection algorithm using YOLO recognizes the indoor objects with associated risk tags. Results show that the service time of ESLS is low enough to serve it to users in 0.8 s on average, including processing and communication times. Furthermore, the accuracy of the harmful object detection is 96% in the general indoor lighting situation. The results show that the proposed ESLS is applicable to real service for teaching the earthquake disaster avoidance.     

Category learning in the color-word contingency learning paradigm

In the typical color-word contingency learning paradigm, participants respond to the print color of words where each word is presented most often in one color. Learning is indicated by faster and more accurate responses when a word is presented in its usual color, relative to another color. To eliminate the possibility that this effect is driven exclusively by the familiarity of item-specific word-color pairings, we examine whether contingency learning effects can be observed also when colors are related to categories of words rather than to individual words. To this end, the reported experiments used three categories of words (animals, verbs, and professions) that were each predictive of one color. Importantly, each individual word was presented only once, thus eliminating individual color-word contingencies. Nevertheless, for the first time, a category-based contingency effect was observed, with faster and more accurate responses when a category item was presented in the color in which most of the other items of that category were presented. This finding helps to constrain episodic learning models and sets the stage for new research on category-based contingency learning.     

Feedback specificity, learning opportunities, and learning

Although increasing feedback specificity is generally beneficial for immediate performance, it can undermine certain aspects of the learning needed for later, more independent performance. The results of the present transfer experiment demonstrate that the effects of increasing feedback specificity on learning depended on what was to be learned, and these effects were partially mediated through the opportunities to learn how to respond to different task conditions during practice. More specific feedback was beneficial for learning how to respond to good performance and detrimental for learning how to respond to poor performance. The former relationship was partially mediated by feedback specificity's effect on learning opportunities during practice. The results have implications for designing feedback interventions and training to maximize the learning of various aspects of a task.     

Sequence learning

The ability to sequence information is fundamental to human performance. When subjects are asked to respond to one of several possible spatial locations of a stimulus, reaction times and error rates decrease when the target follows a sequence. In this article, we review the numerous theoretical and methodological perspectives that have been used to study sequence learning. The opportunity now exists to integrate evidence from different domains of cognitive science to begin to provide a comprehensive account of sequence learning. We suggest that subjects can learn sequences based on different information in a hierarchical representation, including either sequences of stimuli or sequences of responses. This learning can occur both with and without explicit awareness of the sequence. Multiple modes of learning exist and are subserved by different neural circuits.     

The procedural learning of action order is independent of temporal learning

How does learning the timing of actions influence our ability to learn the order of actions? A sequence of responses cued by spatial stimuli was learned in a serial reaction time task where the response-to-stimulus intervals (RSIs) were random, constant, or followed a fixed sequence. In this final sequenced-RSI condition, the response and RSI sequences were consistently matched in phase and could be integrated into a common sequence representation. The main result was that the response sequence was learned to a similar degree in all RSI training conditions, indicating that neither the predictability of RSIs nor the integration of the phase-matched response and timing sequences benefited learning of the response sequence. Nevertheless, temporal learning and integration speeded up performance without strengthening the representation of response order.     

Autonomic learning

Academic achievement was resolved into its intellectual and motivational components. IQ, personality variables, and parameters of operant and classical conditioning of autonomic nervous system controlled variables were examined in 99 black inner-city high school seniors. Six physiological variables(respiration, heart rate, finger plethysmogram, frontalis muscle potential, palmar skin conductance, and palmar skin potential) were analyzed by computer using analog-to-digital conversion and a Points of Interest (PI) program. Psychological measures were full WAIS IQ, Edwards Personality Inventory(EPI), Cattell Sixteen Personality Factor Questionnaire(16PF), McClelland need Achievement test(n Ach), Object Sorting test, Minnesota Counseling Inventory(MCI), Level of Aspiration Board, and Rotter—Miller Locus of Control. Achievement(Ach) was defined as the residuals of total grade point average(GPA) regressed on IQ. Each of 19 variables which discriminated significantly between high and low achievers was employed in step-wise regression analyses for GPA and Ach separately. In a regression analysis of variance of GPA, IQ=20.44%, EPI=9.08%, and physiological measures =21.37%(operant conditioning =18.70%; classical conditioning =2.67%). Combined measures of motivation(psychological and physiological) =30.45% of the variance in GPA. The entire psychological and physiological test battery, including IQ, contributed a total of 50.89% of the variance in GPA. In a regression analysis of variance in achievement, psychological measures, other than IQ(six EPI and one 16PF), =11.0% and physiological measures =30.3%. As a replication, this study demonstrated significant correlation between autonomic conditioning and school achievement.     

Procedural learning: A developmental study of motor sequence learning and probabilistic classification learning in school-aged children

In this study, we investigated motor and cognitive procedural learning in typically developing children aged 8–12 years with a serial reaction time (SRT) task and a probabilistic classification learning (PCL) task. The aims were to replicate and extend the results of previous SRT studies, to investigate PCL in school-aged children, to explore the contribution of declarative knowledge to SRT and PCL performance, to explore the strategies used by children in the PCL task via a mathematical model, and to see whether performances obtained in motor and cognitive tasks correlated. The results showed similar learning effects in the three age groups in the SRT and in the first half of the PCL tasks. Participants did not develop explicit knowledge in the SRT task whereas declarative knowledge of the cue–outcome associations correlated with the performances in the second half of the PCL task, suggesting a participation of explicit knowledge after some time of exposure in PCL. An increasing proportion of the optimal strategy use with increasing age was observed in the PCL task. Finally, no correlation appeared between cognitive and motor performance. In conclusion, we extended the hypothesis of age invariance from motor to cognitive procedural learning, which had not been done previously. The ability to adopt more efficient learning strategies with age may rely on the maturation of the fronto-striatal loops. The lack of correlation between performance in the SRT task and the first part of the PCL task suggests dissociable developmental trajectories within the procedural memory system.