CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Clark, H. H. Using Language (Cambridge University Press, Cambridge, 1996).


    Google Scholar
     

  • 2.

    Kluger, A. N. & DeNisi, A. The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol. Bull. 119, 254–284 (1996).


    Google Scholar
     

  • 3.

    Pashler, H., Cepeda, N. J., Wixted, J. T. & Rohrer, D. When does feedback facilitate learning of words?. J. Exp. Psychol. Learn. Mem. Cogn. 31, 3 (2005).

    PubMed 

    Google Scholar
     

  • 4.

    Hattie, J. & Timperley, H. The power of feedback. Rev. Educ. Res. 77, 81–112 (2007).


    Google Scholar
     

  • 5.

    Shute, V. J. Focus on formative feedback. Rev. Educ. Res. 78, 153–189 (2008).


    Google Scholar
     

  • 6.

    Kelley, C. M. & McLaughlin, A. C. Individual differences in the benefits of feedback for learning. Hum. Factors 54, 26–35 (2012).

    PubMed 

    Google Scholar
     

  • 7.

    Shneiderman, B. & Plaisant, C. Quality of services. In Designing the User Interface: Strategies for Effective Human–Computer Interaction, 4 edn, 453–475 (Pearson Addison Wesley, Boston, 2005).

  • 8.

    Kohrs, C., Hrabal, D., Angenstein, N. & Brechmann, A. Delayed system response times affect immediate physiology and the dynamics of subsequent button press behavior. Psychophysiology 51, 1178–1184 (2014).

    PubMed 

    Google Scholar
     

  • 9.

    Kohrs, C., Angenstein, N., Scheich, H. & Brechmann, A. Human striatum is differentially activated by delayed, omitted, and immediate registering feedback. Front. Hum. Neurosci. 6, 243 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 10.

    Kohrs, C., Angenstein, N. & Brechmann, A. Delays in human–computer interaction and their effects on brain activity. PLoS ONE 11, 1–14 (2016).


    Google Scholar
     

  • 11.

    Behne, N., Scheich, H. & Brechmann, A. The left dorsal striatum is involved in the processing of neutral feedback. NeuroReport 19, 1497–1500 (2008).

    PubMed 

    Google Scholar
     

  • 12.

    Hollerman, J. R. & Schultz, W. Dopamine neurons report an error in the temporal prediction of reward during learning. Nat. Neurosci. 1, 304–309 (1998).

    CAS 
    PubMed 

    Google Scholar
     

  • 13.

    McClure, S. M., Berns, G. S. & Montague, P. R. Temporal prediction errors in a passive learning task activate human striatum. Neuron 38, 339–346 (2003).

    CAS 
    PubMed 

    Google Scholar
     

  • 14.

    Thorndike, E. L. The law of effect. Am. J. Psychol. 39, 212–222 (1927).


    Google Scholar
     

  • 15.

    Skinner, B. F. Science and Human Behavior (Macmillan, New York, 1953).


    Google Scholar
     

  • 16.

    Kulhavy, R. W. & Wager, W. Feedback in programmed instruction: historical context and implications for practice. In Interactive Instruction and Feedback (eds Dempsey, J. V. & Sales, G. C.) 3–20 (Educational Technology Publications, Englewood Cliffs, 1993).


    Google Scholar
     

  • 17.

    Schultz, W. Updating dopamine reward signals. Curr. Opin. Neurobiol. 23, 229–238 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 18.

    Bromberg-Martin, E. S., Matsumoto, M. & Hikosaka, O. Dopamine in motivational control: rewarding, aversive, and alerting. Neuron 68, 815–834 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 19.

    Delgado, M. R. Reward-related responses in the human striatum. Ann. N. Y. Acad. Sci. 1104, 70–88 (2007).

    ADS 
    PubMed 

    Google Scholar
     

  • 20.

    Balleine, B. W., Delgado, M. R. & Hikosaka, O. The role of the dorsal striatum in reward and decision-making. J. Neurosci. 27, 8161–8165 (2007).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 21.

    O’Doherty, D. Reward representations and reward-related learning in the human brain: insights from neuroimaging. Curr. Opin. Neurobiol. 14, 769–776 (2004).

    PubMed 

    Google Scholar
     

  • 22.

    Tricomi, E. M., Delgado, M. R. & Fiez, J. A. Modulation of caudate activity by action contingency. Neuron 41, 281–292 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • 23.

    Haruno, M. & Kawato, M. Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning. J. Neurophysiol. 95, 948–959 (2006).

    PubMed 

    Google Scholar
     

  • 24.

    Aron, A. R. et al. Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning. J. Neurophysiol. 92, 1144–1152 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • 25.

    Rodriguez, P. F., Aron, A. R. & Poldrack, R. A. Ventral-striatal/nucleus-accumbens sensitivity to prediction errors during classification learning. Hum. Brain Mapp. 27, 306–313 (2006).

    CAS 
    PubMed 

    Google Scholar
     

  • 26.

    McGovern, R. A. et al. Human substantia nigra neurons encode decision outcome and are modulated by categorization uncertainty in an auditory categorization task. Physiol. Rep. 3, e12422 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 27.

    Mory, E. H. Feedback research revisited. In Handbook of Research on Educational Communications and Technology 2nd edn (ed. Jonassen, D. H.) 745–783 (Lawrence Erlbaum Associates Publishers, Mahwah, 2004).


    Google Scholar
     

  • 28.

    Wolff, S. & Brechmann, A. Carrot and stick 2.0: the benefits of natural and motivational prosody in computer-assisted learning. Comput. Hum. Behav. 43, 76–84 (2015).


    Google Scholar
     

  • 29.

    Clark, H. H. & Brennan, S. E. Grounding in communication. In Perspectives on Socially Shared Cognition (eds Resnick, L. B. et al.) 127–149 (American Psychological Association, Washington DC, 1991).


    Google Scholar
     

  • 30.

    Miller, R. B. Response time in man–computer conversational transactions. In Proceedings AFIPS Spring Joint Computer Conference, 267–277 (Montvale, 1968).

  • 31.

    Pérez-Quinones, M. A. & Sibert, J. L. A collaborative model of feedback in human–computer interaction. In Proceedings Conference on Human Factors in Computing Systems, 316–323 (Vancouver, British Columbia, Canada, 1996).

  • 32.

    Allen, J. et al. Towards conversational human–computer interaction. AI Mag. 22, 27–37 (2001).


    Google Scholar
     

  • 33.

    Graesser, A. C., VanLehn, K., Rosé, C. P., Jordan, P. W. & Harter, D. Intelligent tutoring systems with conversational dialogue. AI Mag. 22, 39–51 (2001).


    Google Scholar
     

  • 34.

    Nass, C. & Brave, S. Wired for Speech: How Voice Activates and Advances the Human–Computer Relationship (The MIT Press, Cambridge, 2005).


    Google Scholar
     

  • 35.

    DePasque, S. & Tricomi, E. Effects of intrinsic motivation on feedback processing during learning. NeuroImage 119, 175–186 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 36.

    Kirsch, P. et al. Anticipation of reward in a nonaversive differential conditioning paradigm and the brain reward system: an event-related fMRI study. NeuroImage 20, 1086–1095 (2003).

    PubMed 

    Google Scholar
     

  • 37.

    Daniel, R. & Pollmann, S. Comparing the neural basis of monetary reward and cognitive feedback during information-integration category learning. J. Neurosci. 30, 47–55 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 38.

    Delgado, M. R., Nystrom, L. E., Fissell, C., Noll, D. C. & Fiez, J. A. Tracking the hemodynamic responses to reward and punishment in the striatum. J. Neurophysiol. 84, 3072–3077 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • 39.

    Elliott, R., Friston, K. J. & Dolan, R. J. Dissociable neural responses in human reward systems. J. Neurosci. 20, 6159–6165 (2000).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Knutson, B., Westdorp, A., Kaiser, E. & Hommer, D. FMRI visualization of brain activity during a monetary incentive delay task. NeuroImage 12, 20–27 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • 41.

    Nieuwenhuis, S. et al. Activity in human reward-sensitive brain areas is strongly context dependent. NeuroImage 25, 1302–1309 (2005).

    PubMed 

    Google Scholar
     

  • 42.

    Puschmann, S., Brechmann, A. & Thiel, C. M. Learning-dependent plasticity in human auditory cortex during appetitive operant conditioning. Hum. Brain Mapp. 34, 2841–2851 (2013).

    PubMed 

    Google Scholar
     

  • 43.

    Weis, T., Brechmann, A., Puschmann, S. & Thiel, C. M. Feedback that confirms reward expectation triggers auditory cortex activity. J. Neurophysiol. 110, 1860–1868 (2013).

    PubMed 

    Google Scholar
     

  • 44.

    Lempert, K. M. & Tricomi, E. The value of being wrong: intermittent feedback delivery alters the striatal response to negative feedback. J. Cogn. Neurosci. 28, 261–274 (2016).

    PubMed 

    Google Scholar
     

  • 45.

    Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y. & Platt, M. L. Posterior cingulate cortex: adapting behavior to a changing world. Trends Cogn. Sci. 15, 143–151 (2011).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 46.

    Leech, R. & Sharp, D. J. The role of the posterior cingulate cortex in cognition and disease. Brain 137, 12–32 (2014).

    PubMed 

    Google Scholar
     

  • 47.

    Bzdok, D. et al. Subspecialization in the human posterior medial cortex. NeuroImage 106, 55–71 (2015).

    PubMed 

    Google Scholar
     

  • 48.

    Shulman, G. L. et al. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J. Cogn. Neurosci. 9, 648–663 (1997).

    CAS 
    PubMed 

    Google Scholar
     

  • 49.

    Raichle, M. E. et al. A default mode of brain function. Proc. Natl. Acad. Sci. U. S. Am. 98, 676–82 (2001).

    ADS 
    CAS 

    Google Scholar
     

  • 50.

    Greicius, M. D., Krasnow, B., Reiss, A. L. & Menon, V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100, 253–258 (2003).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 51.

    Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. 102, 9673–9678 (2005).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 52.

    Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).

    ADS 
    PubMed 

    Google Scholar
     

  • 53.

    Mak, L. E. et al. The default mode network in healthy individuals: a systematic review and meta-analysis. Brain Connect. 7, 25–33 (2017).

    PubMed 

    Google Scholar
     

  • 54.

    Vogt, B. A., Vogt, L. & Laureys, S. Cytology and functionally correlated circuits of human posterior cingulate areas. NeuroImage 29, 452–466 (2006).

    PubMed 

    Google Scholar
     

  • 55.

    Vogt, B. A. Regions and subregions of the cingulate cortex. In Cingulate Neurobiology and Disease 1st edn (ed. Vogt, B. A.) (Oxford University Press, Oxford, 2009).


    Google Scholar
     

  • 56.

    Leech, R., Braga, R. & Sharp, D. J. Echoes of the brain within the posterior cingulate cortex. J. Neurosci. 32, 215–222 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 57.

    Fan, Y. et al. Dorsal and ventral posterior cingulate cortex switch network assignment via changes in relative functional connectivity strength to noncanonical networks. Brain Connect. 9, 77–94 (2019).

    PubMed 

    Google Scholar
     

  • 58.

    Brodmann, K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Barth, Leipzig, 1909).


    Google Scholar
     

  • 59.

    Brewer, J., Garrison, K. & Whitfield-Gabrieli, S. What about the self is processed in the posterior cingulate cortex?. Front. Hum. Neurosci. 7, 647 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 60.

    Northoff, G. & Bermpohl, F. Cortical midline structures and the self. Trends Cogn. Sci. 8, 102–107 (2004).

    PubMed 

    Google Scholar
     

  • 61.

    Northoff, G. et al. Self-referential processing in our brain—a meta-analysis of imaging studies on the self. NeuroImage 31, 440–457 (2006).

    PubMed 

    Google Scholar
     

  • 62.

    Golland, Y. et al. Extrinsic and intrinsic systems in the posterior cortex of the human brain revealed during natural sensory stimulation. Cereb. Cortex 17, 766–777 (2007).

    PubMed 

    Google Scholar
     

  • 63.

    Andrews-Hanna, J. R. The brain’s default network and its adaptive role in internal mentation. Neuroscientist 18, 251–270 (2012).

    PubMed 

    Google Scholar
     

  • 64.

    Delgado, M. R., Locke, H. M., Stenger, V. A. & Fiez, J. A. Dorsal striatum responses to reward and punishment: effects of valence and magnitude manipulations. Cogn. Affect. Behav. Neurosci. 3, 27–38 (2003).

    CAS 
    PubMed 

    Google Scholar
     

  • 65.

    van Veen, V., Holroyd, C. B., Cohen, J. D., Stenger, V. A. & Carter, C. S. Errors without conflict: implications for performance monitoring theories of anterior cingulate cortex. Brain Cogn. 56, 267–276 (2004).

    PubMed 

    Google Scholar
     

  • 66.

    Nieuwenhuis, S., Slagter, H. A., Alting von Geusau, N. J., Heslenfeld, D. J. & Holroyd, C. B. Knowing good from bad: differential activation of human cortical areas by positive and negative outcomes. Eur. J. Neurosci. 21, 3161–3168 (2005).

    PubMed 

    Google Scholar
     

  • 67.

    Reeves, B. & Nass, C. The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places (Cambridge University Press, New York, 1996).


    Google Scholar
     

  • 68.

    Nass, C. & Moon, Y. Machines and mindlessness: social responses to computers. J. Soc. Issues 56, 81–103 (2000).


    Google Scholar
     

  • 69.

    Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh Inventory. Neuropsychologia 9, 97–113 (1971).

    CAS 

    Google Scholar
     

  • 70.

    Goebel, R., Esposito, F. & Formisano, E. Analysis of functional image analysis contest (FIAC) data with BrainVoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Hum. Brain Mapp. 27, 392–401 (2006).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 71.

    Baumgart, F. et al. Electrodynamic headphones and woofers for application in magnetic resonance imaging scanners. Med. Phys. 25, 2068–2070 (1998).

    CAS 
    PubMed 

    Google Scholar
     

  • 72.

    Bethmann, A. & Brechmann, A. On the definition and interpretation of voice selective activation in the temporal cortex. Front. Hum. Neurosci. 8, 1–14 (2014).


    Google Scholar
     

  • 73.

    Talairach, J. & Tournoux, P. Co-Planar Stereotaxic Atlas of the Human Brain (Thieme, New York, 1988).


    Google Scholar
     



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *