The mystery remains: breadth of attention in Flanker and Navon tasks unaffected by affective states induced by an appraisal manipulation

References

• Arslan, R. C., Walther, M. P., & Tata, C. S. (2020). Formr: A study framework allowing for automated feedback generation and complex longitudinal experience-sampling studies using R. Behavior Research Methods, 52(1), 376–387. https://doi.org/10.3758/s13428-019-01236-y
   PubMed Web of Science ®Google Scholar
• Badcock, J. C., Whitworth, F. A., Badcock, D. R., & Lovegrove, W. J. (1990). Low-frequency filtering and the processing of local-global stimuli. Perception, 19(5), 617–629. https://doi.org/10.1068/p190617
   PubMed Web of Science ®Google Scholar
• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01.
   Web of Science ®Google Scholar
• Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
   Web of Science ®Google Scholar
• Birk, J. L., Dennis, T. A., Shin, L. M., & Urry, H. L. (2011). Threat facilitates subsequent executive control during anxious mood. Emotion (Washington, D.C.), 11(6), 1291–1304. https://doi.org/10.1037/a0026152
   PubMed Web of Science ®Google Scholar
• Boer, L. C., & Keuss, P. J. (1982). Global precedence as a postperceptual effect: An analysis of speed–accuracy tradeoff functions. Perception & Psychophysics, 31(4), 358–366. https://doi.org/10.3758/BF03202660
   PubMedGoogle Scholar
• Botvinick, M., & Braver, T. (2015). Motivation and cognitive control: From behavior to neural mechanism. Annual Review of Psychology, 66(1), 83–113. https://doi.org/10.1146/annurev-psych-010814-015044
   PubMed Web of Science ®Google Scholar
• Bridges, D., Pitiot, A., MacAskill, M. R., & Peirce, J. W. (2020). The timing mega-study: Comparing a range of experiment generators, both lab-based and online. PeerJ, (8), e9414. https://doi.org/10.7717/peerj.9414
   Google Scholar
• Bruyneel, L., van Steenbergen, H., Hommel, B., Band, G. P. H., De Raedt, R., & Koster, E. H. W. (2013). Happy but still focused: Failures to find evidence for a mood-induced widening of visual attention. Psychological Research, 77(3), 320–332. https://doi.org/10.1007/s00426-012-0432-1
   PubMed Web of Science ®Google Scholar
• Carver, C. (2003). Pleasure as a sign you can attend to something else: Placing positive feelings within a general model of affect. Cognition and Emotion, 17(2), 241–261. https://doi.org/10.1080/02699930302294
   PubMed Web of Science ®Google Scholar
• Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: Between-subject and within-subject design. Journal of Economic Behavior & Organization, 81(1), 1–8. https://doi.org/10.1016/j.jebo.2011.08.009
   Web of Science ®Google Scholar
• Clore, G. L., & Huntsinger, J. R. (2007). How emotions inform judgment and regulate thought. Trends in Cognitive Sciences, 11(9), 393–399. https://doi.org/10.1016/j.tics.2007.08.005
   PubMed Web of Science ®Google Scholar
• Cohen, N., & Henik, A. (2012). Do irrelevant emotional stimuli impair or improve executive control? Frontiers in Integrative Neuroscience, 6, 33. https://doi.org/10.3389/fnint.2012.00033
   PubMedGoogle Scholar
• Dale, G., & Arnell, K. M. (2013). Investigating the stability of and relationships among global/local processing measures. Attention, Perception, & Psychophysics, 75(3), 394–406. https://doi.org/10.3758/s13414-012-0416-7
   PubMed Web of Science ®Google Scholar
• Easterbrook, J. A. (1959). The effect of emotion on cue utilization and the organization of behavior. Psychological Review, 66(3), 183–201. https://doi.org/10.1037/h0047707
   PubMed Web of Science ®Google Scholar
• Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16(1), 143–149. https://doi.org/10.3758/BF03203267
   Web of Science ®Google Scholar
• Eriksen, C. W., & James, J. D. S. (1986). Visual attention within and around the field of focal attention: A zoom lens model. Perception & Psychophysics, 40(4), 225–240. https://doi.org/10.3758/BF03211502
   PubMed Web of Science ®Google Scholar
• Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotions. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1449), 1367–1377. https://doi.org/10.1098/rstb.2004.1512
   PubMed Web of Science ®Google Scholar
• Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition & Emotion, 19(3), 313–332. https://doi.org/10.1080/02699930441000238
   PubMed Web of Science ®Google Scholar
• Friedman, R. S., & Förster, J. (2010). Implicit affective cues and attentional tuning: An integrative review. Psychological Bulletin, 136(5), 875–893. https://doi.org/10.1037/a0020495
   PubMed Web of Science ®Google Scholar
• Friedman, R. s., & Forster, J. (2011). Limitations of the motivational intensity model of attentional tuning: Reply to Harmon-Jones, Gable, and Price (2011). Psychological Bulletin, 137(3), 513–516. https://doi.org/10.1037/a0023088
   Web of Science ®Google Scholar
• Frijda, N. H., Kuipers, P., & ter Schure, E. (1989). Relations among emotion, appraisal, and emotional action readiness. Journal of Personality and Social Psychology, 57(2), 212–228. https://doi.org/10.1037/0022-3514.57.2.212
   Web of Science ®Google Scholar
• Gable, P., & Harmon-Jones, E. (2010a). The Blues Broaden, but the Nasty Narrows: Attentional Consequences of Negative Affects Low and High in Motivational Intensity. Psychological Science, 21(2), 211–215. https://doi.org/10.1177/0956797609359622
   PubMed Web of Science ®Google Scholar
• Gable, P., & Harmon-Jones, E. (2010b). The motivational dimensional model of affect: Implications for breadth of attention, memory, and cognitive categorisation. Cognition & Emotion, 24(2), 322–337. https://doi.org/10.1080/02699930903378305
   Web of Science ®Google Scholar
• Gable, P., & Harmon-Jones, E. (2013). Does arousal per se account for the influence of appetitive stimuli on attentional scope and the late positive potential? Psychophysiology, 50(4), 344–350. https://doi.org/10.1111/psyp.12023.
   PubMed Web of Science ®Google Scholar
• Gable, P. A., & Harmon-Jones, E. (2008). Approach-motivated positive affect reduces breadth of attention. Psychological Science, 19(5), 476–482. https://doi.org/10.1111/j.1467-9280.2008.02112.x
   PubMed Web of Science ®Google Scholar
• Gentsch, K., Grandjean, D., & Scherer, K. R. (2013). Temporal dynamics of event-related potentials related to goal conduciveness and power appraisals. Psychophysiology, 50(10), 1010–1022. https://doi.org/10.1111/psyp.12079
   PubMed Web of Science ®Google Scholar
• Grol, M., & Raedt, R. D. (2014). Effects of positive mood on attentional breadth for emotional stimuli. Frontiers in Psychology, 5, Article 1277. https://doi.org/10.3389/fpsyg.2014.01277.
   PubMed Web of Science ®Google Scholar
• Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E.-J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Comprehensive Results in Social Psychology, 2(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760
   Google Scholar
• Gross, J. J., Uusberg, H., & Uusberg, A. (2019). Mental illness and well-being: An affect regulation perspective. World Psychiatry, 18(2), 130–139. https://doi.org/10.1002/wps.20618
   PubMed Web of Science ®Google Scholar
• Han, S., & Humphreys, G. W. (2002). Segmentation and selection contribute to local processing in hierarchical analysis. The Quarterly Journal of Experimental Psychology Section A, 55(1), 5–21. https://doi.org/10.1080/02724980143000127
   PubMed Web of Science ®Google Scholar
• Huntsinger, J. R. (2013). Does Emotion Directly Tune the Scope of Attention? Current Directions in Psychological Science, 22(4), 265–270. https://doi.org/10.1177/0963721413480364
   Web of Science ®Google Scholar
• JASP Team. (2021). JASP (Version 0.16). https://jasp-stats.org/.
   Google Scholar
• Kaplan, R. L., Van Damme, I., & Levine, L. J. (2012). Motivation Matters: Differing Effects of Pre-Goal and Post-Goal Emotions on Attention and Memory. Frontiers in Psychology, 3, Article 404. https://doi.org/10.3389/fpsyg.2012.00404.
   Google Scholar
• Kimchi, R. (2015). The perception of hierarchical structure. In J. Wagemans (Ed.), The Oxford Handbook of perceptual organization (pp. 129–149). Oxford University Press.
   Google Scholar
• Kolnes, M., Naar, R., Allik, J., & Uusberg, A. (2019). Does goal congruence dilate the pupil over and above goal relevance? Neuropsychologia, 134, 107217. https://doi.org/10.1016/j.neuropsychologia.2019.107217
   PubMed Web of Science ®Google Scholar
• Kuznetsova, A., Brockhoff, P. B., Christensen, R. H. B., & Jensen, S. P. (2020). lmerTest: Tests in Linear Mixed Effects Models (3.1-3) [Computer software]. https://CRAN.R-project.org/package = lmerTest.
   Google Scholar
• Lacey, M. F., Wilhelm, R. A., & Gable, P. A. (2021). What is it about positive affect that alters attentional scope? Current Opinion in Behavioral Sciences, 39, 185–189. https://doi.org/10.1016/j.cobeha.2021.03.028.
   Web of Science ®Google Scholar
• Lerche, V., Voss, A., & Nagler, M. (2017). How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria. Behavior Research Methods, 49(2), 513–537. https://doi.org/10.3758/s13428-016-0740-2
   PubMed Web of Science ®Google Scholar
• Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013
   Web of Science ®Google Scholar
• Liu, F., Ding, J., & Zhang, Q.. (2016). Positive affect and selective attention: Approach-motivation intensity influences the early and late attention processing stages. Acta Psychologica Sinica, 48(7), 794–803. https://doi.org/10.3724/SP.J.1041.2016.00794
   Google Scholar
• Martin, M. (1979). Local and global processing: The role of sparsity. Memory & Cognition, 7(6), 476–484. https://doi.org/10.3758/BF03198264
   Web of Science ®Google Scholar
• Mather, M., & Sutherland, M. R. (2011). Arousal-biased competition in perception and memory. Perspectives on Psychological Science : A Journal of the Association for Psychological Science, 6(2), 114–133. https://doi.org/10.1177/1745691611400234
   PubMedGoogle Scholar
• Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition & Emotion, 23(2), 209–237. https://doi.org/10.1080/02699930802204677
   PubMed Web of Science ®Google Scholar
• Moors, A., Ellsworth, P. C., Scherer, K. R., & Frijda, N. H. (2013). Appraisal theories of emotion: State of the art and future development. Emotion Review, 5(2), 119–124. https://doi.org/10.1177/1754073912468165
   Web of Science ®Google Scholar
• Moriya, H., & Nittono, H. (2011). Effect of mood states on the breadth of spatial attentional focus: An event-related potential study. Neuropsychologia, 49(5), 1162–1170. https://doi.org/10.1016/j.neuropsychologia.2011.02.036
   PubMed Web of Science ®Google Scholar
• Müller, N. G., Bartelt, O. A., Donner, T. H., Villringer, A., & Brandt, S. A. (2003). A physiological correlate of the ‘Zoom Lens’ of visual attention. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23(9), 3561–3565. https://doi.org/10.1523/JNEUROSCI.23-09-03561.2003
   PubMed Web of Science ®Google Scholar
• Navon, D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive Psychology, 9(3), 353–383. https://doi.org/10.1016/0010-0285(77)90012-3
   Web of Science ®Google Scholar
• Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., & Lindeløv, J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51(1), 195–203. https://doi.org/10.3758/s13428-018-01193-y
   PubMed Web of Science ®Google Scholar
• Pessoa, L., & Engelmann, J. B. (2010). Embedding reward signals into perception and cognition. Frontiers in Neuroscience, 4(17), 1–8. https://doi.org/10.3389/fnins.2010.00017.
   PubMedGoogle Scholar
• Posner, M. I., & Rothbart, M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58(1), 1–23. https://doi.org/10.1146/annurev.psych.58.110405.085516
   PubMed Web of Science ®Google Scholar
• Pringle, H. L., Irwin, D. E., Kramer, A. F., & Atchley, P. (2001). The role of attentional breadth in perceptual change detection. Psychonomic Bulletin & Review, 8(1), 89–95. https://doi.org/10.3758/BF03196143
   PubMed Web of Science ®Google Scholar
• Quintana, D. S., & Williams, D. R. (2018). Bayesian alternatives for common null-hypothesis significance tests in psychiatry: A non-technical guide using JASP. BMC Psychiatry, 18(1), 178. https://doi.org/10.1186/s12888-018-1761-4
   PubMed Web of Science ®Google Scholar
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59
   Web of Science ®Google Scholar
• Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https://doi.org/10.1162/neco.2008.12-06-420
   PubMed Web of Science ®Google Scholar
• R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/.
   Google Scholar
• Rowe, G., Hirsh, J. B., & Anderson, A. K. (2007). Positive affect increases the breadth of attentional selection. Proceedings of the National Academy of Sciences of the United States of America, 104(1), 383–388. https://doi.org/10.1073/pnas.0605198104
   PubMed Web of Science ®Google Scholar
• RStudio Team. (2015). RStudio: Integrated Development for R. RStudio. http://www.rstudio.com/.
   Google Scholar
• Sasaki, Y., Hadjikhani, N., Fischl, B., Liu, A. K., Marret, S., Dale, A. M., & Tootell, R. B. H. (2001). Local and global attention are mapped retinotopically in human occipital cortex. Proceedings of the National Academy of Sciences, 98(4), 2077–2082. https://doi.org/10.1073/pnas.98.4.2077
   PubMed Web of Science ®Google Scholar
• Scherer, K. R. (2009). Emotions are emergent processes: They require a dynamic computational architecture. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3459–3474. https://doi.org/10.1098/rstb.2009.0141
   PubMed Web of Science ®Google Scholar
• Schimmack, U., & Derryberry, D. (2005). Attentional interference effects of emotional pictures: Threat, negativity, or arousal? Emotion, 5(1), 55–66. https://doi.org/10.1037/1528-3542.5.1.55
   PubMed Web of Science ®Google Scholar
• Shuman, V., Sander, D., & Scherer, K. R. (2013). Levels of Valence. Frontiers in Psychology, 4, Article 216. https://doi.org/10.3389/fpsyg.2013.00261
   PubMed Web of Science ®Google Scholar
• Siemer, M., Mauss, I., & Gross, J. (2007). Same situation–different emotions: How appraisals shape our emotions. Emotion. https://doi.org/10.1037/1528-3542.7.3.592.
   Google Scholar
• Steenbergen, H., Band, G., & Hommel, B. (2011). Threat but not arousal narrows attention: Evidence from pupil dilation and saccade control. Frontiers in Psychology, 2, Article 281. https://doi.org/10.3389/fpsyg.2011.00281.
   PubMed Web of Science ®Google Scholar
• Testing times: Which times tables do kids find the hardest? (2013, May 31). The Guardian. http://www.theguardian.com/news/datablog/2013/may/31/times-tables-hardest-easiest-children.
   Google Scholar
• Vanlessen, N., De Raedt, R., Koster, E. H. W., & Pourtois, G. (2016). Happy heart, smiling eyes: A systematic review of positive mood effects on broadening of visuospatial attention. Neuroscience and Biobehavioral Reviews, 68, 816–837. https://doi.org/10.1016/j.neubiorev.2016.07.001
   PubMed Web of Science ®Google Scholar
• van Peer, J., Grandjean, D., & Scherer, K. (2014). Sequential unfolding of appraisals: EEG evidence for the interaction of novelty and pleasantness. EMOTION, 14(1), 51–63. https://doi.org/10.1037/a0034566
   PubMed Web of Science ®Google Scholar
• van Steenbergen, H. (2015). Affective modulation of cognitive control: A biobehavioral perspective. In G. H. E. Gendolla, M. Tops, & S. L. Koole (Eds.), Handbook of biobehavioral approaches to self-regulation (pp. 89–107). Springer. https://doi.org/10.1007/978-1-4939-1236-0_7.
   Google Scholar
• van Steenbergen, H., & Bocanegra, B. R. (2016). Promises and pitfalls of web-based experimentation in the advance of replicable psychological science: A reply to Plant (2015). Behavior Research Methods, 48(4), 1713–1717. https://doi.org/10.3758/s13428-015-0677-x
   PubMed Web of Science ®Google Scholar
• von Hecker, U., & Meiser, T. (2005). Defocused attention in depressed mood: evidence from source monitoring. Emotion, 5(4), 456–463. https://doi.org/10.1037/1528-3542.5.4.456
   PubMed Web of Science ®Google Scholar
• Vuilleumier, P. (2015). Affective and motivational control of vision. Current Opinion in Neurology, 28(1), 29–35. https://doi.org/10.1097/WCO.0000000000000159
   PubMed Web of Science ®Google Scholar
• Wagenmakers, E.-J., Van Der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3–22. https://doi.org/10.3758/BF03194023
   PubMed Web of Science ®Google Scholar