潜意识暗示提高人机交互能力Word格式文档下载.docx

1、Enhanced Learning Pierre Chalfoun and Claude FrassonDpartement dInformatique et de Recherche Oprationnelle, Universit de Montral, Office 2194, Montral, Qubec, H3T 1J4, CanadaReceived 2 June 2010; Accepted 25 September 2010Academic Editor: Kenneth RevettCopyright 2011 Pierre Chalfoun and Claude Frass

2、on. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.AbstractThis paper presents results from an empirical study conducted with a subli

3、minal teaching technique aimed at enhancing learners performance in Intelligent Systems through the use of physiological sensors. This technique uses carefully designed subliminal cues （positive） and miscues （negative） and projects them under the learners perceptual visual threshold. A positive cue,

4、 called answer cue, is a hint aiming to enhance the learners inductive reasoning abilities and projected in a way to help them figure out the solution faster but more importantly better. A negative cue, called miscue, is also used and aims at obviously at the opposite （distract the learner or lead t

5、hem to the wrong conclusion）. The latest obtained results showed that only subliminal cues, not miscues, could significantly increase learner performance and intuition in a logic-based problem-solving task. Nonintrusive physiological sensors （EEG for recording brainwaves, blood volume pressure to co

6、mpute heart rate and skin response to record skin conductivity） were used to record affective and cerebral responses throughout the experiment. The descriptive analysis, combined with the physiological data, provides compelling evidence for the positive impact of answer cues on reasoning and intuiti

7、ve decision making in a logic-based problem-solving paradigm.1. IntroductionThe use of technology to build Intelligent User Interfaces （IUI） has revolutionized the way computers interact with human beings. Examples of these IUIs can be found in virtually every aspect of our lives, such as mobile med

8、ia sharing 1, 2, intelligent vehicular assistance 3, and mental spelling machines 4. One growing area of research within the HCI community recently has been focusing towards the design of smart interfaces for learning. Indeed, a smart interface should be able, in a learning context, to detect the us

9、ers emotional and cognitive states in order to adjust and adapt the teaching material accordingly. Such adaptive systems relying on efficient IUIs are known as intelligent tutoring systems and are comprised of multiple goal-specific modules to aid the learner. One of these modules, called the tutor,

10、 makes use of cognitive pedagogical strategies and affective states to properly model human cognition according to the learners constant needs and evolution. The tutor takes advantage of recent development in IUI to efficiently communicate with the learner. This multidisciplinary field of research t

11、ries to accomplish this daunting goal of user-modeling and adaptation by implementing the most recent evolutions and advances in various research areas such as artificial intelligence, neuroscience, and cognitive science. One of those recent advances this past decade has been the shift in emphasis f

12、or cognitive science from learners performance to learners experience during learning. Indeed, affective states, motivation, and knowledge construction have been extensively measured and explored 58 and have shown that emotions are an important component in skill acquisition and play a pivotal role

13、in learning. Indeed, researches in neurosciences and cognitive psychology have shown that emotions are widely related to diverse cognitive processes, such as attention, problem solving, and decision-making 9, 10. Emotions influence our behavior and play an important role in our every-day decision-ma

14、king processes 11. Cognitive activity is also fundamentally related to emotions 12. Cognitive process such as problem solving and decision making not only depend but are greatly intertwined with the individuals emotional state 13. Moreover, emotions are essential actors for creative thinking, inspir

15、ation, as well as concentration and motivation 10, 14. It then becomes vital for an HCI system to detect and recognize these emotional and cognitive states, via physiological sensors or otherwise, and relay them to the pertinent ITS modules. Hence, learning systems would be able to intelligently ada

16、pt their communication and interaction with learners through adaptive HCI systems. Nevertheless, a major component of learning and decision making when solving problems has been mostly neglected in this research field: human unconscious cognition. Indeed, the cognitive unconscious as a major player

17、is the integration and interpretation of complex material with regards to decision making and possibly learning. To that end, a large body of work in neuroscience and other fields has put forth compelling evidence that learning simple-to-complex information can be done without perception or complete

18、 awareness to the task at hand 1518. The idea that any information projected too fast to be consciously perceived by a learner （called subliminal projection） has been the focus of much research in neuroscience and cognitive psychology. A variety of IUIs have been designed for such a purpose, ranging

19、 from simple two-dimensional interfaces （2D） to complex 3D immersive ones 1923. Furthermore, the recording of the neural electrical activity, called EEG activity, present in the brain during the occurrence of these mental processes is also an active research area in HCI. Indeed, an IUI that detects

20、brain activity can recognize and quantify the users engagement level in specific activities 24. However, in the HCI community, EEG activity is mainly used to construct brain computer interfaces mainly aimed at character spelling or item recognition 4, 18, 2527. Unfortunately, very scarce research in

21、 the HCI community has employed affect, subliminal priming and EEG for educational purposes. Indeed, the existence of perceptual learning without perception has not only been proven, but also replicated in one study we did two years ago 19. In this study, we presented a novel subliminal priming tech

22、nique built into an adaptive IUI aimed at boosting performance by enhancing the learners deductive reasoning capabilities. Our findings concur with the literature: subliminal stimuli, which is stimuli projected below the threshold of conscious perception, can enhance learning and increase the deduct

23、ive reasoning of learners only if carefully and intelligently constructed before being integrated into an IUI. However, this initial study suffered from a major limitation: the exclusive use of positive subliminal priming （cues designed to help learning, not hinder or interfere with it）. Thus, we di

24、d not know what effect might negative cues, or miscues, have on learning and performance when integrated into our IUI system.In the present paper, we intend to follow up on our recent study by designing an evolved version of our adaptive IUI with our novel subliminal learning technique aimed at enha

25、ncing the learners inductive learning capabilities. This new IUI implemented in our tutoring system will marry positive and negative primes as well as affective sensors in the stringent 2D environment resembling online tests. Affective sensors will serve as important indicator of the learners emotio

26、nal and cerebral state when faced with the decision of correctly answering a question. When facing a question, learners can either （a） guess the answer or （b） correctly infer the solution （since this is an exam and the solution is not presented to them）. We believe that IUIs would be able to detect

27、both outcomes （a） and （b） by recording and analyzing emotional and cerebral data. We also believe, based on the extensive literature to that effect, that subliminal priming should influence both outcomes. We thus intend to investigate the relevance of augmenting our IUI with cerebral sensors and usi

28、ng our subliminal teaching technique by stating two HCI-research questions. First, does integrating different subliminal cuing types （positive, control, and miscue） into this newly designed IUI enhance or hamper the learners inductive reasoning capabilities and performance? Second, what significant

29、physiological impacts can this newly designed interface with our subliminal teaching technique have on learners performance during question answering as well as on the entire learning process?The organization of this paper is as follows: in the first section, we present previous work related to vari

30、ous aspects of our research. The second section will discuss priming in HCI, more specifically from an educational standpoint. The third section will lay the bases of physiological recordings and importance to education and learning in general. The fourth section describes the experiment setup and d

31、epicts the various aspects related to subliminal cues and miscues. The fifth section presents the obtained results which are discussed in section six leading to the last section where we conclude and present future work.2. Related WorkThe present work employs subliminal priming, affect, and EEG to i

32、nvestigate the possible contribution of priming to enhance learning and more specifically inductive reasoning. To the best of our knowledge, there are only two similar papers in the ITS/AIED （Artificial intelligence in education）/HCI community that employ subliminal priming and EEG in an educational context 28, 29. The first is our own work where we presented cerebral evidence, namely, a P300 component （see background on EEG and affective data below）, to confirm that both cues and miscues were interpreted in the brain, but with slightl