Different areas of psychology operate at different levels of analysis. For example, neuroscience is based in biology (e.g., molecules, neurons, blood flow) while social psychology is based on group-level phenomena (e.g., attitudes, prejudice, leadership). Clearly, some levels of analysis are more appropriate for addressing particular challenges than others. As cognitive scientists, we think about thinking. We examine mental processes such as learning, memory, perception, attention, insight, language, reasoning, bias, problem-solving, and decision-making. We investigate these cognitive phenomena using experiments, simulations, and behavioural measures, and rely on a variety of analytical methods and statistical techniques to understand how the mind works without needing to directly observe mental processes. The cognitive scientific approach allows us to distinguish between what people ‘say’ they remember and what they remember, whether people ‘think’ they are biased and whether they are biased, or whether (mis)information that ‘feels’ genuine is in fact true. Below are a few general themes that have emerged in our research over the years.
“CSI”-style TV shows give the impression that fingerprint identification is fully automated. In reality, when a fingerprint is found at a crime scene, it is a human examiner who is faced with the task of identifying the person who left the print. We conducted one of the first empirical tests of fingerprint identification, finding that examiners possess genuine expertise in matching fingerprints (Tangen, Thompson & McCarthy, 2011; Thompson, Tangen & McCarthy, 2013). Since these early experiments, we have conducted dozens of lab- and field-based experiments on fingerprint expert, trainee, and novice participants. We provided evidence that expertise in forensic identification is characteristically fast (Thompson, Tangen & McCarthy, 2014; Thompson, Tangen & Searston, 2014), is affected by the similarity (Searston, Tangen, & Eva, 2016), that the visual structure of forensic evidence is distributed across prior instances (Searston & Tangen, 2016), and is domain specific (Searston & Tangen, 2017). We have examined how experts constrain their attention to relevant features that will enable them to make decisions quickly and accurately (Robson, Tangen, & Searston, 2020), and extract important features more efficiently than novices (Robson, Searston, Edmond, McCarthy, & Tangen, 2020). We have also demonstrated that pooling the decisions of small, independent groups of analysts can substantially boost the performance of these crowds and reduce the influence of errors (Tangen, Kent, & Searston, 2020).
As John Allen Paulos once said, “Uncertainty is the only certainty there is.” Indeed, we all try to articulate or even quantify this sense of uncertainty everyday in predicting future events or deciding what to do. In science, we tend to convey our level of uncertainty by comparing our observations to “chance” or control groups to infer something meaningful about reality. But if uncertainty is not communicated clearly and effectively, then mistakes will happen, even in high-stakes situations where people’s lives are at stake in areas such as diagnostic medicine, legal decision making, climate change, or intelligence analysis. We have explored perceptions of error and involvement of human judgment in each stage of forensic analysis (Ribeiro, Tangen, McKimmie, 2019), and demonstrated that people struggle to understand and evaluating probabilistic information (Ribeiro, Tangen, McKimmie, 2020) compared to conveying the same information using a diagnostic information approach that we developed (Edmond, Thompson, & Tangen, 2014), which provides decision-makers with the tools they need to make inferences about the current case based on information about how examiners perform in previous, similar situations. We have translated our findings for use in the legal system to support law reform by publishing dozens of review and commentary papers in leading law reviews, and discipline-focused journals (e.g., Edmond et al, 2014, 2015, 2017).
An “Aha!” or insight experience occurs when a solution to a problem presents itself suddenly and without warning. For example, while waiting to go to a concert, mathematician Yitang Zhang discovered the solution to the twin prime problem. He said that he “...immediately knew that it would work,” and then it took several months to verify his solution. The mathematician Jacques Hadamard said that, “on being very abruptly awakened by an external noise, a solution long searched for appeared to me at once without the slightest instant of reflection on my part.” Given the myriad thoughts that appear in our minds at any given moment, it’s interesting to know why some ideas are dismissed as meaningless distractions while others are grasped as significant or profound. These insight moments make an idea feel more true or valuable in order to aid quick and efficient decision-making — akin to a heuristic — which we can now detect and measure reliably (Laukkonen & Tangen, 2018). But we have argued that these feelings of insight have a dark side: they can make misinformation feel true and we discuss circumstances where they may inspire false beliefs and delusions (Laukkonen, Kaveladze, Tangen, & Schooler, 2020). Indeed, these so-called false insights have been difficult to investigate, but we have developed a new paradigm to reliably induce false insights in order to explore their origins. Our broader goal is to better understand how to calibrate these experiences so we can help people to better distinguish between true and false information.
In 2010, Sean Murphy — an honours student in the lab — was “eye aligning” hundreds of faces for a memory experiment we were about to run. He noticed that when he quickly flicked through the faces on the screen one-by-one, they began to appear highly distorted and even monstrous. For example, if a person had a large jaw, it looked particularly large, almost ogre-like. If a person had a slender nose, then it looked remarkably thin. The faces appeared to be almost like caricatures. When Sean stopped, the faces appeared normal again. We described this basic finding as a flashed face distortion effect (Tangen, Murphy, & Thompson, 2011), and uploaded a simple demonstration to YouTube, which attracted a lot of attention so we posted another video shortly after using celebrity faces. We have conducted dozens of experiments since then to figure out how to optimise the effect; we developed an elegant way to quantify its strength, and used multidimensional scaling to predict which faces would appear most distorted. Unfortunately, these experiments were fairly basic in order to fit within the scope of a one-year honours project, so we haven’t yet published the results. We’re hoping to find an enthusiastic PhD student who’s willing to spend a few years working on this project so we can properly investigate this interesting effect.
Caricature by Eugeni Llopart
Humans and non-humans are remarkably sensitive to style. We recognise the works of artists and composers, a sense of what does and does not belong to a particular genre of writing, what drivers in the right lane are likely to do that drivers in the left are not, or what a normal interaction with a teller at our bank is like. Often this sensitivity develops effortlessly and without any intention to learn them. Many animals, such as chimpanzees, rats, pigeons, and fish can demonstrate similar sensitivities to style in art, music, and even handwriting. For example, we demonstrated that even honeybees can learn to distinguish paintings by Monet from those by Picasso (Wu, Moreno, Tangen, & Reinhard, 2013). The visual style of a category refers to the features and visual cues that covary across images. For example, Monet was certainly fond of waterlilies, but this feature certainly doesn’t define his artistic style. It’s the same notion as Wittgenstein’s description of various “games” — board games and ball games have some commonalities, but also many differences. It is only when you look across several instances that a resemblance emerges. For example, we demonstrated that people are able to remember images that have been downscaled to a single pixel and can distinguish between categories well above chance with images that are only 2x2 pixels (Searston, Thompson, Vokey, French, & Tangen, 2019). Much of the research in our lab is based on this notion of style since this sensitivity influences our performance in virtually every task that we undertake.
|Laukkonen, R., Dr., Webb, M. E., Salvi, C., Tangen, J. M., & Schooler, J. (2018, February 24). Eureka Heuristics: How feelings of insight signal the quality of a new idea. https://doi.org/10.31234/osf.io/zq3vd|
|Robson, S. G., Baum, M. A., Beaudry, J. L., Beitner, J., Brohmer, H., Chin, J. M., Jasko, K., Kouros, C. D., Laukkonen, R. E., Moreau, D., Searston, R. A., Slagter, H. A., Steffens, N. K., & Tangen, J. M. (2021, April 1). Nudging Open Science. https://doi.org/10.31234/osf.io/zn7vt|
|Grimmer, H. J., Laukkonen, R. E., Tangen, J. M., & von Hippel, W. (2021, April 2). Eliciting false insights with semantic priming. https://doi.org/10.31234/osf.io/kwhe9|
|Lee, H. D. H., McKimmie, B. M., Masser, B. M., & Tangen, J. M. (2021). Guided by the rape schema: The influence of event order on how jurors evaluate the victim’s testimony in cases of rape. Psychology, Crime, & Law. https://doi.org/10.1080/1068316X.2021.1984483|
|Laukkonen, R. E., Ingledew, D., Grimmer, H. J., Schooler, J. W., & Tangen, J. M. (2021). Getting a grip on insight: Real-time and embodied Aha experiences predict correct solutions. Cognition and Emotion, 35(5), 918–935. https://doi.org/10.1080/02699931.2021.1908230|
|Robson, S. G., Tangen, J. M. & Searston, R. A. (2021). The effect of expertise, target usefulness and image structure on visual search. Cognitive Research: Principles and Implications, 6(16), 1–19. https://doi.org/10.1186/s41235-021-00282-5|
|Ribeiro, G., Tangen, J., & McKimmie, B. (2020). Does DNA evidence in the form of a likelihood ratio affect perceivers’ sensitivity to the strength of a suspect’s alibi? Psychonomic Bulletin & Review, 27(6), 1325–1332. https://doi.org/10.3758/s13423-020-01784-x|
|Tangen, J. M., Kent, K. M., & Searston, R. A. (2020). Collective intelligence in fingerprint analysis. Cognitive Research: Principles and Implications, 5(23), 1–7. https://doi.org/10.1186/s41235-020-00223-8|
|Laukkonen, R. E., Kaveladze, B. T., Tangen, J. M., & Schooler, J. W. (2020). The dark side of Eureka: Artificially induced Aha moments make facts feel true. Cognition, 196, 104122. https://doi.org/10.1016/j.cognition.2019.104122|
|Robson, S. G., Searston, R. A., Edmond, G., McCarthy, D. J., & Tangen, J. M. (2020). An expert–novice comparison of feature choice. Applied Cognitive Psychology, 34(5), 984–995. https://doi.org/10.1002/acp.3676|
|Searston, R. A., Thompson, M. B., Robson, S. G., Corbett, B. J., Ribeiro, G., Edmond, G., & Tangen, J. M. (2019). Truth and transparency in expertise research. Journal of Expertise, 2(4), 199–209.|
|Searston, R. A., Thompson, M. B., Vokey, J. R., French, L. A., & Tangen, J. M. (2019). How low can you go? Detecting style in extremely low resolution images. Journal of Experimental Psychology: Human Perception and Performance, 45(5), 573–584. https://doi.org/10.1037/xhp0000628|
|Ribeiro, G., Tangen, J. M., & McKimmie, B. M. (2019). Beliefs about error rates and human judgment in forensic science. Forensic Science International, 297(1), 138–147. https://doi.org/10.1016/j.forsciint.2019.01.034|
|Vokey, J. R., Jamieson, R. K., Tangen, J. M., Searston, R. A., & Allen, S. W. (2018). A visual familiarity account of evidence for orthographic processing in pigeons (Columbia livia): A reply to Scarf, Corballis, Güntürkün, and Colombo (2017), Animal Cognition, 21(3), 425-431. https://doi.org/10.1007/s10071-018-1166-2|
|Searston, R. A., & Tangen, J. M. (2017). The emergence of perceptual expertise with fingerprints over time. Journal of Applied Research in Memory and Cognition, 6(4), 442–451. https://doi.org/10.1016/j.jarmac.2017.08.006|
|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. https://doi.org/10.3758/s13423-016-1211-6|
|Searston, R. A., & Tangen, J. M. (2017). Expertise with unfamiliar objects is flexible to changes in task but not changes in class. PLoS ONE, 12(6), e0178403. https://doi.org/10.1371/journal.pone.0178403|
|Searston, R. A., & Tangen, J. M. (2017). Training perceptual experts: Feedback, labels, and contrasts. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 71(1), 32–39. https://doi.org/10.1037/cep0000124|
|Edmond, G., Towler, A., Growns, B, Ribeiro, G., Found, B., White, D., Ballantyne, K., Searston, R. A., Thompson, M. B., Tangen, J. M., Kemp, R. I., & Martire, K. (2017). Thinking forensics: Cognitive science for forensic practitioners. Science & Justice, 57(2), 144–154. https://doi.org/10.1016/j.scijus.2016.11.005|
|Laukkonen, R. E., & Tangen, J. M. (2017). Can observing a Necker cube make you more insightful? Consciousness and Cognition, 48, 198–211. https://doi.org/10.1016/j.concog.2016.11.011|
|Edmond, G., Found, B., Martire, K., Ballantyne, K., Hamer, D., Searston, R. A., Thompson, M. B., Cunliffe, E., Kemp, R., San Roque, M., Tangen, J. M., Dioso-Villa, R., Ligertwood, A., Hibbert, B., White, D., Ribeiro, G., Porter, G., Towler, A., & Roberts, A. (2016). Model forensic science. Australian Journal of Forensic Sciences, 48(5), 496-537. https://doi.org/10.1080/00450618.2015.1128969|
|Searston, R. A., Tangen, J. M., & Eva, K. W. (2015). Putting bias into context: The role of familiarity in identification. Law and Human Behavior, 40(1), 50-64. https://doi.org/10.1037/lhb0000154|
|Thompson, M. B. & Tangen, J. M. (2014). The nature of expertise in fingerprint matching: Experts can do a lot with a little. PLoS ONE, 9(12), e114759. https://doi.org/10.1371/journal.pone.0114759|
|Edmond, G., Martire, K., Found, B., Kemp, R., Hamer, D., Hibbert, B., Ligertwood, A., Porter, G., San Roque, M., Searston, R., Tangen, J., Thompson, M., White, D. (2014). How to cross-examine forensic scientists: A guide for lawyers. Australian Bar Review, 39, 174-197.|
|Edmond, G., Tangen, J. M., Searston, R. A., & Dror, I. E. (2014). Contextual bias and cross-contamination in the forensic sciences: The corrosive implications for investigations, plea bargains, trials and appeals. Law, Probability & Risk, 14(1), 1-25. https://doi.org/10.1093/lpr/mgu018|
|Thompson, M. B. & Tangen, J. M. (2014). Generalization in fingerprint matching experiments. Science & Justice, 54(5), 391-392. https://doi.org/10.1016/j.scijus.2014.06.008|
|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. https://doi.org/10.3389/fpsyg.2014.00737|
|Edmond, G., Thompson, M. B., & Tangen, J. M. (2014). A guide to interpreting forensic testimony: Scientific approaches to fingerprint evidence. Law, Probability and Risk, 13(1), 1-25. https://doi.org/10.1093/lpr/mgt011|
|Thompson, M. B., Tangen, J. M., & McCarthy, D. J. (2014). Human matching performance of genuine crime scene latent fingerprints. Law and Human Behavior, 38(1), 84–93. https://doi.org/10.1037/lhb0000051|
|Thompson, M. B., Tangen, J. M., & McCarthy D. J. (2013). Expertise in fingerprint identification. Journal of Forensic Sciences, 58(6), 1519-1530. https://doi.org/10.1111/1556-4029.12203|
|Tangen, J. M. (2013). Identification personified. Australian Journal of Forensic Sciences, 45(3), 315-322. https://doi.org/10.1080/00450618.2013.782339|
|Wu, W., Moreno, A. M., Tangen, J. M., & Reinhard, J. (2013). Honeybees can discriminate between Monet and Picasso paintings. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 199(1), 45-55. https://doi.org/10.1007/s00359-012-0767-5|
|Tangen, J. M., Thompson, M. B., & McCarthy, D. J. (2011). Identifying fingerprint expertise. Psychological Science, 22(8), 995-997. https://doi.org/10.1177/0956797611414729|
|Tangen, J. M., Murphy, S. C., & Thompson, M. B. (2011). Flashed face distortion effect: Grotesque faces from relative spaces. Perception, 40(5), 628-630. https://doi.org/10.1068/p6968|
|Tangen, J. M., Constable, M. D., Durrant, E., Teeter, C., Beston, B. R., & Kim, J. A. (2011). The role of interest and images in slideware presentations. Computers & Education, 56(3), 865-872. https://doi.org/10.1016/j.compedu.2010.10.028|
|Humphreys, M. S., Tangen, J. M., Cornwell, T. B., Quinn, E. A., & Murray, K. L. (2010). Unintended effects of memory on decision making: A breakdown in access control. Journal of Memory and Language, 63(3), 400-415. https://doi.org/10.1016/j.jml.2010.06.006|