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Cueing Effects in the Attentional Network Test: a Spotlight Diffusion Model Analysis

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Abstract

The attentional network test (ANT) uses flanker stimuli with different cue conditions to quantify differences in attentional processing. However, it is unclear precisely how the alerting and orienting cues in the task affect different decision processes. The present study leveraged computational modeling to identify the relationship between attentional cues and decision components. ANT data from a large sample of 156 participants were analyzed using the spotlight diffusion model, which quantifies decision components for response caution, motor/encoding time, perceptual processing, and attentional control. The spotlight analysis showed that the attentional cues had multiple effects on decision processing. Compared to the no cue condition, an alerting cue led to faster encoding/motor speed, improved perceptual processing, and increased attentional focusing. The orienting cue further led to a decrease in response caution and increased encoding/motor speed and attentional focusing to reduce interference from incompatible flankers. This analysis demonstrates that alerting and orienting cues have complex effects on decision processes that are not captured by simple differences in RTs, and that model-based analyses can delineate such effects to allow researchers to identify precisely how attentional processing varies across individuals or conditions in tasks like the ANT.

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References

  • Anderson, N. D., Lau, M. A., Segal, Z. V., & Bishop, S. R. (2007). Mindfulness based stress reduction and attentional control. Clinical Psychology & Psychotherapy, 14(6), 449–463.

    Article  Google Scholar 

  • Bellaera, L., & von Mühlenen, A. (2017). The effect of induced sadness and moderate depression on attention networks. Cognition and Emotion, 31(6), 1140–1152.

    Article  PubMed  Google Scholar 

  • Cavanagh, J. F., & Allen, J. J. (2008). Multiple aspects of the stress response under social evaluative threat: an electrophysiological investigation. Psychoneuroendocrinology, 33(1), 41–53.

    Article  PubMed  Google Scholar 

  • Fan, J., McCandliss, B. D., Sommer, T., Raz, A., & Posner, M. I. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14(3), 340–347.

    Article  PubMed  Google Scholar 

  • Gooding, D. C., Braun, J. G., & Studer, J. A. (2006). Attentional network task performance in patients with schizophrenia–spectrum disorders: evidence of a specific deficit. Schizophrenia Research, 88(1), 169–178.

    Article  PubMed  Google Scholar 

  • Grange, J. A. (2016). flankr: an R package implementing computational models of attentional selectivity. Behavioral Research Methods, 48(2), 528–541.

    Article  Google Scholar 

  • Hübner, R., Steinhauser, M., & Lehle, C. (2010). A dual-stage two-phase model of selective attention. Psychological Review, 117(3), 759–784.

    Article  PubMed  Google Scholar 

  • Jennings, J. M., Dagenbach, D., Engle, C. M., & Funke, L. J. (2007). Age-related changes and the attention network task: an examination of alerting, orienting, and executive function. Aging, Neuropsychology, and Cognition, 14(4), 353–369.

    Article  Google Scholar 

  • Konrad, K., Neufang, S., Thiel, C. M., Specht, K., Hanisch, C., Fan, J., Herpertz Dahlmann, B., & Fink, G. R. (2005). Development of attentional networks: an fMRI study with children and adults. Neuroimage, 28(2), 429–439.

    Article  PubMed  Google Scholar 

  • Leskin, L. P., & White, P. M. (2007). Attentional networks reveal executive function deficits in posttraumatic stress disorder. Neuropsychology, 21(3), 275–284.

    Article  PubMed  Google Scholar 

  • Posner, M. I., Rothbart, M. K., Vizueta, N., Levy, K. N., Evans, D. E., Thomas, K. M., & Clarkin, J. F. (2002). Attentional mechanisms of borderline personality disorder. Proceedings of the National Academy of Sciences, 99(25), 16366–16370.

    Article  Google Scholar 

  • Posner, M. I., Sheese, B. E., Odludaş, Y., & Tang, Y. (2006). Analyzing and shaping human attentional networks. Neural Networks, 19(9), 1422–1429.

    Article  PubMed  Google Scholar 

  • Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59 108.

    Article  Google Scholar 

  • Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356.

    Article  Google Scholar 

  • Ratcliff, R., Thapar, A., & McKoon, G. (2004). A diffusion model analysis of the effects of aging on recognition memory. Journal of Memory and Language, 50(4), 408–424.

    Article  Google Scholar 

  • Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106(2), 261–300.

    Article  PubMed  Google Scholar 

  • Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.

    Article  Google Scholar 

  • Rueda, M. R., Fan, J., McCandliss, B. D., Halparin, J. D., Gruber, D. B., Lercari, L. P., & Posner, M. I. (2004). Development of attentional networks in childhood. Neuropsychologia, 42(8), 1029–1040.

    Article  PubMed  Google Scholar 

  • Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X., & Kastner, S. (2012). The pulvinar regulates information transmission between cortical areas based on attention demands. Science, 337(6095), 753–756.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sato, H., Takenaka, I., & Kawahara, J. I. (2012). The effects of acute stress and perceptual load on distractor interference. The Quarterly Journal of Experimental Psychology, 65(4), 617–623.

    Article  PubMed  Google Scholar 

  • Schmitz, F., & Voss, A. (2012). Decomposing task-switching costs with the diffusion model. Journal of Experimental Psychology: Human Perception and Performance, 38(1), 222–250.

    PubMed  Google Scholar 

  • Schmitz, F., & Voss, A. (2014). Components of task switching: a closer look at task switching and cue switching. Acta Psychologica, 151, 184–196.

    Article  PubMed  Google Scholar 

  • Simon, J. R. (1969). Reactions toward the source of stimulation. Journal of Experimental Psychology, 81(1), 174–176. https://doi.org/10.1037/h0027448.

    Article  PubMed  Google Scholar 

  • Smith, P. L., Ellis, R., Sewell, D. K., & Wolfgang, B. J. (2010). Cued detection with compound integration-interruption masks reveals multiple attentional mechanisms. Journal of Vision, 10(5), 3–3.

    Article  PubMed  Google Scholar 

  • Smith, P. L., Ratcliff, R., & Wolfgang, B. J. (2004). Attention orienting and the time course of perceptual decisions: response time distributions with masked and unmasked displays. Vision Research, 44(12), 1297–1320.

    Article  PubMed  Google Scholar 

  • Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662. https://doi.org/10.1037/h0054651.

    Article  Google Scholar 

  • Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148–174.

    Article  PubMed  Google Scholar 

  • White, C. N., Ratcliff, R., & Starns, J. J. (2011). Diffusion models of the flanker task: discrete versus gradual attentional selection. Cognitive Psychology, 63(4), 210–238.

    Article  PubMed  PubMed Central  Google Scholar 

  • White, C. N., Servant, M., & Logan, G. D. (2018). Testing the validity of conflict drift-diffusion models for use in estimating cognitive processes: A parameter-recovery study. Psychonomic Bulletin & Review, 25(1), 286-301.

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Funding

This work was funded by NSF Grant 1650438 for Corey White.

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Correspondence to Corey N. White.

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White, C.N., Curl, R. Cueing Effects in the Attentional Network Test: a Spotlight Diffusion Model Analysis. Comput Brain Behav 1, 59–68 (2018). https://doi.org/10.1007/s42113-018-0004-6

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