# The 800-pound gorilla in the print: Inattentional blindness in perceptual experts and novices ## Rationale Inattentional blindness is a failure to notice visible but unexpected objects when one’s attention is engaged in another task. In a classic study by Simons and Chabris (1999), viewers watched a video of three basketball players and were asked to count how many times these players passed the ball to one another. But many participants failed to notice someone dressed as a gorilla walking through the scene while they counted. Although the characteristics of the task, and the nature of the stimuli one is viewing, may affect inattentional blindness (e.g. the degree of congruency between the known target and the unexpected object; Most et al., 2001), individual differences between people can predict whether one will experience inattentional blindness. One is more likely to see an unexpected object if they have more experience in a domain relevant to the task. Basketball players, for instance, are more likely to notice the gorilla passing through the scene mentioned above than non-basketballers (Memmert, 2006). Expertise therefore appears to play a role in how susceptible one is to inattentional blindness. Turning to the field of diagnostic medicine, the story appears quite similar. A majority of radiologists (60%) failed to notice a clavicle (collarbone) removed from a chest X-ray (Potchen, 2006) and many health care professionals failed to detect a misplaced femoral line when evaluating a case (Lum, Fairbanks, Pennington & Zwemer, 2005). It is quite clear therefore that expertise does not immunise one against inattentional blindness, but how do novices fair on equivalent tasks? In a study by Drew, Võ and Wolfe (2013), radiologists were instructed to spot lung nodules across several slices of a Computed Tomography (CT) scan. A majority (83%) of radiologists did not detect a gorilla inserted into a number of the slices despite the gorilla being 48 times larger than the average nodule. But none of the novice controls spotted the gorilla either. This finding is a striking demonstration of inattentional blindness among experts, but it says little about the role of expertise. The low rates of detection among participants suggests a floor effect. The gorillas were certainly visible (confirmed by a manipulation check), but evidently quite difficult to spot when engaged in challenging task without any expectation of a gorilla. The floor effect may have arisen because the gorilla was quite faint relative to its surroundings or because it was located in very few of the slices. In any case, it is premature to speculate about the effects of expertise on inattentional blindness in such contexts, and what it reveals about perceptual expertise and expert attention. Many have postulated that experts are less susceptible to inattentional blindness than novices because their attentional capacity is less occupied by the primary task (e.g. Drew, Võ and Wolfe, 2013). This explanation converges with dual-process theory (Kahneman 2011; James, 1890) and with the literature on skill acquisition (Feltovich, Prietula & Ericsson, 2006), which suggests that skills become more automatic with practice. For example, when first learning to read or drive a car, the expereince can feel effortful, and requires a great deal of attention and concentration. With more practice, however, these skills become increasingly effortless. Across many domains of skill acquisition, it is common to see novices engage effortfully in the same task that experts complete with ease, in domains like music (Bangert, Schubert, & Fabian, 2014), and sport (Beilock, Carr, MacMahon & Starkes, 2002) to fingerprint examination (Thompson, Tangen & Searston, 2014) to name a few. When skills like reading or playing an instrument become automatic, it frees up more attentional resources. In turn, one is more receptive to unexpected objects and events that may arise. This reduced cognitive load explains why making the primary task easier (Simons & Jensen , 2009) or training more on the primary task (Richards, Hannon & Derakshan, 2010), reduces one's susceptibility to inattentional blindness. This general expert efficiency framework suggests that experts ought to spot unexpected objects more readily than novices wherever the object is located and however it is placed within the image. ### Shallow processing versus deep processing To correctly categorise or identify novel instances we must use our knowledge of the structural regularities of a domain gained from prior experience (Day & Goldstone, 2012). Many experts possess an ability to recruit these structured representations and make inferences about new instances because they perceive objects and identities more abstractly than their surface features (Day & Goldstone, 2012; Gentner, 1983). In other words, experts pay attention to deeper underlying structural similarities and less to superficial features. A seminal study by Chi, Feltovich, and Glaser (1981) examined differences between experts (advanced physics PhD students) and nonexperts (undergraduates). They found that experts overwhelmingly tended to group physics problems on the basis of general principles underlying the solution (e.g., conservation of energy, Newton’s second law) whereas undergraduates were more likely to group problems based on surface features (e.g., the presence of springs, inclined planes, etc.). Similarly, expert programmers sort computer programs based on underlying algorithms whereas novices sort based on application type (Weiser & Shertz, 1983). And trained musicians group musical pieces by melodic or harmonic structure whereas non-musicians group primarily by the similarity of the instruments (Wolpert, 1990). An ability to attend to underlying, abstract detail and to filter out irrelevant surface information may drive expert problem solving. What about visual expertise? One can attempt to identify or classify objects and scenes by relying on perceptual similarities in shape, colour and other surface properties (Tarr and Cheng, 2003). However, visual experts too tend to look beyond visual similarity and focus on deeper characteristics. A marker of perceptual expertise is an ability to discern features or dimensions of a category that remain constant across different instances from other features or dimensions that are more flexible across instances. That is, experts wield the ability to filter within-category (or within-identity) variability and pay attention to between-category (or between-identity) variability. To illustrate, consider faces. Almost all of us recognise familiar faces at the level of the individual very quickly and accurately, despite variations in lighting, viewpoint, make-up or facial hair across different occasions (Tarr and Cheng, 2003; Burton, Kramer, Ritchie & Jenkins, 2016). Expertise enables perceivers to more effectively use available information (Gibson & Gibson, 1955). This advantage stems from a fine-tuning that happens during learning, which leaves an individual more sensitive to features and patterns that are useful for performing well (Gauthier, Tarr, & Bub, 2010). Experts, because of their training, have learned to efficiently 'see through' irrelevant information and detect and select distinguishing features most relevant to the task (Hoffman & Rehder, 2010; Kellman & Garrigan, 2012; Schneider & Schiffrin, 1977; Wang, Cavanaugh & Green, 1994). ### Selective attention Across a broad range of domains, including medicine (Sheridan & Reingold, 2017), chess (Sheridan & Reingold, 2014) and fingerprint examination (Roads, Mozer & Busey, 2016), experts attend to highly constrained regions or features more than novices. The features these experts attend to are thought to be more distinctive, task-relevant or diagnostic (Haider & Frensch, 1999). A constrained search of important local detail (learned attention) and a lack of attention to irrelevant detail (learned inattention) may aid experts in making good decisions quickly and accurately. When considering expert-novice differences in inattentional blindness it is therefore essential to consider where an unexpected, anomalous object is located. In the study by Drew, Võ and Wolfe (2013), the gorillas were placed “near a lung nodule such that both were clearly visible.” In other words, the gorillas were located in fairly ‘distinctive’ or ‘task-relevant’ regions within the image. It is interesting to consider whether rates of detection change depending on where an unexpected object is located. That is, if the object is located in a distinctive or nondistinctive area. Experienced CCTV operators and novices displayed similar rates of inattentional blindness regardless of whether an unexpected event was task-relevant (e.g. a person with a suspicious parcel) or not (e.g. a person in a pirate costume; Nasholm, Rohlfing & Sauer, 2014). However, the likely explanation for this non-effect is that the stimuli were not particularly novel to most people. Experienced operators, although more familiar with the task, are not more familiar with watching crowds or television screens compared to the average person. A change detection paradigm provides more compelling evidence. Change blindness is a failure to notice changes in a visual stimulus while it flickers off and on again. American football experts were more sensitive to semantic changes to local objects in football-related images than football novices, but were not more sensitive to semantically irrelevant changes or changes to images unrelated to football (Werner & Thies, 2000). These results suggest that experts only encode semantically relevant information quicker or more efficiently than novices. Indeed, experts demonstrate finer perceptual discriminations for stimuli related to their area of expertise in many contexts (Buttle & Raymond, 2003; Curby, Glazek & Gauthier, 2009; Diamond & Carey, 1986; Myles-Worsley et al., 1988). By extension, experts, ought to be sensitive to anomalous, unexpected objects when these objects are located in regions that experts are likely to attend; regions that are task-relevant or contain distinctive information. However, the rates of inattentinal blindness among experts may rise to that of novices, or even higher, if an unexpected object appears in an irrelevant or nondistinctive region. Because experts constrain their attention to critical or distinctive local features to enable efficient performance, they may move on without much further search, consistent with a satisfaction of search hypothesis (Berbaum et al., 1998). A novice, on the other hand, because they are less sensitive to distinctive information, may inspect wider portions of the image and spot the unexpected object. ### Global attention Within and between variation can consist of variation of both local or global features. For example, the same face one day can be blemished with a pimple formed the previous night, by lipstick chosen at the whim of its wearer or by a haircut received a few days prior. These are changes to local features. However, a face can also appear very different depending on the lighting in the room or the perspective from which we observe it. These are changes to global features. As face experts, however, we must see beyond both of these sources of variation and pay attention to features that are ‘sticky’ across different instances of the same face. For example, the shape of local features like the nose or global configurations like the distance between the eyes and mouth. It is well-established that people process faces holistically (e.g. Richler, Palmeri & Gauthier, 2012; Richler, Bukach & Gauthier, 2009). That is, we identify faces as wholes rather than as a summation of their parts. A common illustration of holistic face processing is the fact that inverting a face disrupts our ability to process configural information more than individual features (e.g. Diamond & Carey, 1986; Farah, Tanaka & Drain, 1995; Freire, Lee & Symons, 2000; Goodrich and Yonelinas 2019; Leder & Carbon, 2006; Maurer et al., 2002; Rhodes, Brake & Atkinson, 1993). There is, however, also evidence that experts in some domains perceive non-face objects (like dogs and Greebles) holistically whereas novices still process these objects in a piecemeal, feature-based way (e.g. Curby, Glazek & Gauthier, 2009; Diamond & Carey, 1986; Richler, Bukach and Gauthier, 2009). If one perceives an object holistically, they are often unable to attend to its individual parts even when doing so would aid performance (e.g., Farah, Wilson, Drain & Tanaka, 1998; Richler, Tanaka, Brown, & Gauthier, 2008). The evidence indicates that holistic processing is a marker of expertise in a perceptual domain (Wong et al., 2012). Many experiments that investigate inattentional blindness in domains of perceptual expertise focus on unexpected objects in a small, local regions within the image. However, if a key difference between experts and novices is one of global versus feature-based processing, then we ought to consider this distinction when investigating the effect. Experts may be less susceptible to inattentional blindness than novices because their attentional capacity is less occupied by the primary task, as noted earlier. But the ability to process a familiar object holistically may provide them an even greater advantage over novices in spotting unexpected anomalies spanning the entirety of an image. That said, experts are good at what they do because they can see through noise and irrelevant detail. If an anomaly represents nondistinctive or irrelevant information, experts may be even less likely to spot it. ## The present experiment In the present experiment, we aim to compare experts to novices in an inattentional blindness experiment using fingerprint examination as a testbed. In the same vein as previous demonstrations, we will embed gorillas into fingerprints and then measure whether experts and novices spot these unexpected gorillas while completing a match/no-match fingerprint comparison task. In one condition, we will place a small gorilla in a nondistinctive area (based on results from a study about distinctive and nondistinctive features, preregistered here: https://osf.io/rxe25/). In another condition, we will embed a gorilla into the prints such that it is blended over the entire fingerprint to represent added pressure — a source of variability that is not indicative of identity (Vandervolk, 2011). ## Hypotheses We will test competing hypotheses in this experiment. **Hypothesis 1.** It is likely that experts will find the primary task much easier than the novices. Expert fingerprint examiners perform better than novices at matching prints (e.g. Tangen, Thompson & McCarthy, 2011). They also show less physiological arousal when making these decisions (Laukkonen, 2012), indicating less cognitive load, and perform well despite very little information or time when making decisions (Thompson, Tangen & Searston, 2014). If experts generally have more efficient processing when comparing fingerprints, then we expect lower rates of inattentional blindness among experts than among novices in both conditions. Expert efficiency and reduced cognitive load means they will be more receptive to an unexpected object wherever it is located or however it is placed. And, under this hypothesis, we expect the difference between experts and novices to be greater if experts process fingerprints more holistically. We also expect the fingerprints examiners to perform better at matching prints throughout the experiment, indicating that the task is in fact easier for them than for novices. **Hypothesis 2.** Although experts may be more efficient than novices, this may be because their visual search patterns are more efficient. There is evidence that expert fingerprint examiners view very constrained areas or features relative to novices (Busey & Parada, 2010; Roads, Mozer & Busey, 2016). This 'Tunnel vision' for certain features may cause experts to make a decision before searching within other less-informative areas of the print (Dror, 2011). Thus, if expert performance is driven by a careful comparison of only highly distinctive or task-relevant features, then we would expect higher rates of inattentinal blindness among experts than novices in the local condition. **Hypothesis 3.** There is evidence that expert fingerprint examiners process fingerprints more holistically than novices (Busey & Vandervolk, 2005; Searston & Tangen, 2016; Vogelsang, Palemeri & Busey, 2017). Experts may be more sensitive to variation in important global features, but they may filter and ignore irrelevant information like changes in pressure. Novices, on the other hand, may pay more attention to salient but task-irrelevant global detail like added pressure. In turn, we may see higher rates of inattentional blindness among experts in the global condition than among novices. ## Method ### Participants We will aim to test at least 40 experts (including trainees with at least 1 year of experience) from various forensic bureaus across Australia. We will begin testing in November 2019. However, this number is subject to time and availability, with a stopping rule to finish testing experts by the April 2020. Each expert participant will be presented with a unique pre-generated event sequence of images. Once we have collected data from the experts, we will collect data from an equal number of novice participants from the UQ course credit or paid pools. Each novice participant will see the same event sequence as one of the expert participants. Power analyses showed that, to detect a medium-large effect size (ω = .44) with .80 power, 40 participants would be sufficient. No data has been collected yet. ### Design A between-subjects design will be used. Each participant will be allocated to one of two conditions (local or global). Each participants will complete 6 fingerprint matching trials. On the final trial, one of the fingerprints will contain either a small gorilla (local) or a gorilla blended over the entirety of the print (global). We measured whether the participants could detect these unexpected gorillas. ### Materials We sourced 60 fingerprint pairs ranging from easy to difficult in how easy they seemed to be to tell apart. Half matched and half did not. We also sourced 10 fully rolled (tenprint) images and inserted either a small gorilla or a large gorilla into the image. We then paired each fingerprint with either a matching print or a non-matching print such that there were 40 pairs in total. #### Local gorilla images We superimposed a gorilla silhouette over each fingerprint in Photoshop, placing it in a ‘nondistinctive’ location (based on the results of Robson, Searston, Edmond, McCarthy & Tangen, under review). We used the stroke blending option to create a 2pt white outline around the gorilla. We then reduced its size to a height of 0.3cm and an opacity of 60%. We based these properties on a pilot study in which the gorilla increased in size and opacity with each successive trial as novices matched fingerprints (https://osf.io/t49qr/). The results are displayed below. Half (6/12) of the novices were able to spot the gorilla at this level of concealment. ![enter image description here][2] #### Global gorilla images In Photoshop, we inserted the same gorilla silhouette used above over each of the rolled fingerprints. We altered its size so that it fit within the boundaries of the fingerprint. Next, we blended it using ‘Color burn’ blending option and reduced the fill opacity to 80%. We based these properties on the same pilot test above, which showed that 4/13 novices were able to spot the gorilla at this level of concealment (https://osf.io/t49qr/). ![enter image description here][3] ![enter image description here][1] ### Measures and Procedure The procedure is based on a protocol analysis, preregistered here: https://osf.io/kz475. Participants will first be randomly assigned to one of two conditions (local or global). They input their age, gender and years of experience as a fingerprint examiner, before watching a set of video instructions and running through a series of example think aloud problems. For instance, they may be prompted to describe out loud as they think through how many windows are in their parents' house. The prompt then disappears and they will then be asked to remember what they found distinctive or memorable while solving the previous problem. The fingerprint segment of the experiment then begins. Participants are presented with two fingerprints on each trial and asked to think aloud as they make a decision about whether those two prints match or not. Once they are ready, they then make a decision about whether the prints match or not. Next, they will be asked to explain what they found most distinctive about the fingerprints and how it helped them make a decision. Participants will first see two easy trials, followed by two moderate trials, followed by 1 difficult trial. On the sixth and final trial, participants will be presented with a fully-rolled tenprint (which always contains a gorilla) and a latent (crime-scene print) and proceed just as they did in all of the other trials. Once finished, if they spotted the gorilla during the trial, the experiment will end. If they did not mention the gorilla, they will be asked three follow-up questions in case they spotted it but did not say so: “did the final trial seem different to any of the other trials?”, “Did you notice anything unusual in the final trial?” and “Did you notice a gorilla in the final trial?”. ### Detection rate We will measure the rate at which participants spot the gorilla on the sixth and final trial. We will have three different criteria of detection: * **Strict criterion**: the participant mentions a gorilla, monkey, man, bigfoot, or something similar in the fingerprint, and they point to it on the screen either when prompted or unprompted. Or they mention it when recollecting about what was most distinctive. * **Medium criterion**: they mention a gorilla, monkey, man or something similar after either of the first two post-experiment follow-up questions. * **Liberal criterion**: they say they spotted a gorilla when directly asked if they noticed a gorilla in the third follow-up question. At each stage, participants will be coded as either having detected the gorilla (hit) or not (miss). ## Analyses We will run chi-square analysis between experts and novices for each condition, comparing how many participants in each group detected the gorilla (yes) and how many did not (no). We will do so for each of the different criteria mentioned above. We will also assess the matching performance throughout the experiment for each group and compare the rates of true positives and false positives. ## Ethics Ethics clearance has been given from human research ethics committees at The University of Queensland (Approval Numbers: 16-PSYCH-PHD-25-AH and 2018001369), The University of Adelaide: (33115), and Murdoch University (2018/149). ## References Bangert, D., Fabian, D., Schubert, E., & Yeadon, D. (2014). Performing solo Bach: A case study of musical decision-making. *Musicae Scientiae*, 18(1), 35-52. Beilock, S. L., Carr, T. H., MacMahon, C., & Starkes, J. L. (2002). When paying attention becomes counterproductive: impact of divided versus skill-focused attention on novice and experienced performance of sensorimotor skills. *Journal of Experimental Psychology: Applied*, 8(1), 6. Berbaum, K. S., Franken Jr, E. A., Dorfman, D. D., Miller, E. M., Caldwell, R. T., Kuehn, D. M., & Berbaum, M. L. (1998). Role of faulty visual search in the satisfaction of search effect in chest radiography. *Academic Radiology*, 5(1), 9-19. Burton, A. M., Kramer, R. S., Ritchie, K. L., & Jenkins, R. (2016). Identity from variation: Representations of faces derived from multiple instances. *Cognitive Science*, 40(1), 202-223. Busey, T. A., & Parada, F. J. (2010). The nature of expertise in fingerprint examiners. *Psychonomic Bulletin & Review*, 17(2), 155-160. Busey, T. A., & Vanderkolk, J. R. (2005). Behavioral and electrophysiological evidence for configural processing in fingerprint experts. *Vision research*, 45(4), 431-448. Buttle, H., & Raymond, J. E. (2003). High familiarity enhances visual change detection for face stimuli. *Perception & psychophysics*, 65(8), 1296-1306. Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. *Cognitive science*, 5(2), 121-152. Curby, K. M., Glazek, K., & Gauthier, I. (2009). A visual short-term memory advantage for objects of expertise. *Journal of Experimental Psychology: Human Perception and Performance*, 35(1), 94. Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: Connecting findings and theories of transfer of learning. *Educational Psychologist*, 47(3), 153-176. Diamond, R., & Carey, S. (1986). Why faces are and are not special: an effect of expertise. *Journal of Experimental Psychology: General*, 115(2), 107. Drew, T., Võ, M. L. H., & Wolfe, J. M. (2013). The invisible gorilla strikes again: Sustained inattentional blindness in expert observers. *Psychological science*, 24(9), 1848-1853. Dror, I. E. (2011). The paradox of human expertise: why experts get it wrong. The paradoxical brain (9), 177-188. Farah, M. J., Tanaka, J. W., & Drain, H. M. (1995). What causes the face inversion effect?. *Journal of Experimental Psychology: Human perception and performance*, 21(3), 628. Farah, M. J., Wilson, K. D., Drain, M., & Tanaka, J. N. (1998). What is" special" about face perception?. *Psychological review*, 105(3), 482. Feltovich, P. J., Prietula, M. J. & Ericsson, K. A. (2006) in Ericsson, Charness, Feltovich, Hoffman (Eds.), Cambridge Handbook of Expertise and Expert Performance. New York, NY: Cambridge University Press. Freire, A., Lee, K., & Symons, L. A. (2000). The face-inversion effect as a deficit in the encoding of configural information: Direct evidence. *Perception*, 29(2), 159-170. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. *Cognitive science*, 7(2), 155-170. Gibson, J. J., & Gibson, E. J. (1955). Perceptual learning: Differentiation or enrichment?. *Psychological review*, 62(1), 32. Goodrich, R. I., & Yonelinas, A. P. (2019). The effects of face inversion on perceiving-and sensing-based change detection. *Journal of Experimental Psychology: General*. Advance online publication. Haider, H., & Frensch, P. A. (1999). Eye movement during skill acquisition: More evidence for the information-reduction hypothesis. *Journal of Experimental Psychology: Learning, Memory, and Cognition*, 25(1), 172. Hoffman, A. B., & Rehder, B. (2010). The costs of supervised classification: The effect of learning task on conceptual flexibility. *Journal of Experimental Psychology: General*, 139(2), 319. James, W. (1890). The principles of psychology, 2. NY, US: Henry Holt and Company. Kahneman, D. (2011). Thinking, fast and slow. New York, NY, US: Farrar, Straus and Giroux. Kellman, P. J., & Garrigan, P. (2009). Perceptual learning and human expertise. *Physics of life reviews*, 6(2), 53-84. Laukkonen, R. (2012). Pupil dilation as a physiological indicator of perceptual expertise [Honours thesis]. Leder, H., & Carbon, C. C. (2006). Face‐specific configural processing of relational information. *British Journal of Psychology*, 97(1), 19-29. Lum, T. E., Fairbanks, R. J., Pennington, E. C., & Zwemer, F. L. (2005). Profiles in patient safety: Misplaced femoral line guidewire and multiple failures to detect the foreign body on chest radiography. *Academic Emergency Medicine*, 12(7), 658-662. Maurer, D., Le Grand, R., & Mondloch, C. J. (2002). The many faces of configural processing. *Trends in cognitive sciences*, 6(6), 255-260. Most, S. B. (2010). What’s “inattentional” about inattentional blindness?. *Consciousness and Cognition*, 19(4), 1102-1104. Memmert, D. (2006). The effects of eye movements, age, and expertise on inattentional blindness. *Consciousness and cognition*, 15(3), 620-627. Myles-Worsley, M., Johnston, W. A., & Simons, M. A. (1988). The influence of expertise on X-ray image processing. *Journal of Experimental Psychology: Learning, Memory, and Cognition*, 14(3), 553. Näsholm, E., Rohlfing, S., & Sauer, J. D. (2014). Pirate stealth or inattentional blindness? The effects of target relevance and sustained attention on security monitoring for experienced and naïve operators. *PloS one*, 9(1), e86157. Potchen, E. J. (2006). Measuring observer performance in chest radiology: some experiences. *Journal of the American College of Radiology*, 3(6), 423-432. Rhodes, G., Brake, S., & Atkinson, A. P. (1993). What's lost in inverted faces?. *Cognition*, 47(1), 25-57. Richards, A., Hannon, E. M., & Derakshan, N. (2010). Predicting and manipulating the incidence of inattentional blindness. *Psychological Research*, 74(6), 513-523. Richler, J. J., Bukach, C. M., & Gauthier, I. (2009). Context influences holistic processing of nonface objects in the composite task. *Attention, Perception, & Psychophysics*, 71(3), 530-540. Richler, J., Palmeri, T. J., & Gauthier, I. (2012). Meanings, mechanisms, and measures of holistic processing. *Frontiers in Psychology*, 3, 553. Richler, J. J., Tanaka, J. W., Brown, D. D., & Gauthier, I. (2008). Why does selective attention to parts fail in face processing?. *Journal of Experimental Psychology: Learning, Memory, and Cognition*, 34(6), 1356. Roads, B., Mozer, M. C., & Busey, T. A. (2016). Using highlighting to train attentional expertise. *PloS one*, 11(1), e0146266. Robson, Searston, Edmond, McCarthy & Tangen (under review) *Expert-novice differences in feature choice.* Schneider, W. & Shiffrin, R. M. (1977). Controlled and automatic human information processing: Perceptual learning, automatic attending and a general theory. *Psychological review*, 84(2), 127. Searston, R. A., & Tangen, J. M. (2017). The style of a stranger: Identification expertise generalizes to coarser level categories. *Psychonomic bulletin & review*, 24(4), 1324-1329. Sheridan, H., & Reingold, E. M. (2014). Expert vs. novice differences in the detection of relevant information during a chess game: evidence from eye movements. *Frontiers in psychology*, 5, 941. Sheridan, H., & Reingold, E. M. (2017). The holistic processing account of visual expertise in medical image perception: a review. *Frontiers in psychology*, 8, 1620. Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. *Perception*, 28(9), 1059-1074. Simons, D. J., & Jensen, M. S. (2009). The effects of individual differences and task difficulty on inattentional blindness. *Psychonomic Bulletin & Review*, 16(2), 398-403. Tangen, J. M., Thompson, M. B., & McCarthy, D. J. (2011). Identifying fingerprint expertise. *Psychological science*, 22(8), 995-997. Tarr, M. J., & Cheng, Y. D. (2003). Learning to see faces and objects. *Trends in cognitive sciences*, 7(1), 23-30. Thompson, M. B., Tangen, J. M., & Searston, R. A. (2014). Understanding expertise and non-analytic cognition in fingerprint discriminations made by humans. *Frontiers in psychology*, 5, 737. Vanderkolk, J. R. (2011). Examination process. The Fingerprint Sourcebook, 9-12. Vogelsang, M. D., Palmeri, T. J., & Busey, T. A. (2017). Holistic processing of fingerprints by expert forensic examiners. *Cognitive Research: Principles and Implications*, 2(1), 15. Wang, Q., Cavanagh, P., & Green, M. (1994). Familiarity and pop-out in visual search. *Perception & psychophysics*, 56(5), 495-500. Weiser, M., & Shertz, J. (1983). Programming problem representation in novice and expert programmers. *International Journal of Man-Machine Studies*, 19(4), 391-398. Werner, S., & Thies, B. (2000). Is" change blindness" attenuated by domain-specific expertise? An expert-novices comparison of change detection in football images. *Visual Cognition*, 7(1-3), 163-173. Wolpert, R. S. (2000). Attention to key in a nondirected music listening task: Musicians vs. nonmusicians. *Music Perception: An Interdisciplinary Journal*, 18(2), 225-230. Wong, A. C. N., Bukach, C. M., Hsiao, J., Greenspon, E., Ahern, E., Duan, Y., & Lui, K. F. (2012). Holistic processing as a hallmark of perceptual expertise for nonface categories including Chinese characters. *Journal of Vision*, 12(13), 7-7. [1]: https://files.osf.io/v1/resources/rtm58/providers/osfstorage/5dbf874fbc32f4000f184260?mode=render [2]: https://files.osf.io/v1/resources/rtm58/providers/osfstorage/5dbbc2d9af84c3000be86c0b?mode=render [3]: https://files.osf.io/v1/resources/rtm58/providers/osfstorage/5dbf8700bc32f4000e14e65a?mode=render