In certain specialty areas of medical diagnostics, such as pathology, diagnostic imaging, and dermatology, the information needed to form a diagnosis is predominantly visual, requiring that experts have a high level of perceptual expertise–termed “visual diagnostic reasoning” (Warren et al., 2018).
In experts, the processes by which they make decisions (diagnoses) are mostly internalized and still poorly understood, and for specialties where most of the reasoning is visual, this is even further pronounced (ten Cate and Durning, 2017). Exacerbating this, in pathology as an exemplar, the number of lesions that students need to visually learn in medical or veterinary curricula is enormous (10,000s of such visual exemplars of disease in veterinary pathology databases (Davis-Thompson Foundation, 2021)), such that for many diseases, students only experience one exemplar of a lesion during their training. They are then expected to link these lesions to pathogenesis, treatment options, and disease outcome, and transfer this knowledge into clinical situations (Warren and Donnon, 2013). Taking the internalized nature of visual reasoning into account, along with the plethora of visual lesions students need to learn, it begs the question–how do we best educate health professionals to acquire expertise in this area?
With competing and ongoing demands in health professions curricula, the time students have to develop visual reasoning skills is increasingly compressed (Rockarts et al., 2020). This compression has prompted work exploring the use of reinforcement-based learning methods where students iteratively learn to identify anatomic structures or formulate diagnoses from clinical data, through the repetitive presentation of data, selecting an appropriate answer from a list, and providing immediate feedback (Anderson et al., 2018; Anderson et al., 2019). There is even work with pigeons that uses reinforcement-learning to train pigeons to distinguish benign vs. malignant breast masses through histopathology and diagnostic imaging (Levenson et al., 2015). In both these paradigms, it was demonstrated that in short periods, the behavioural results of time to respond and accuracy of novices (and pigeons) reached similar levels to experts–a skill that otherwise takes years to accrue. Initially, this may seem an attractive teaching methodology to a compressed curriculum, however, this is premised on the concept that perceptual recognition, equates to perceptual expertise, not accounting for other aspects of experience, knowledge, context, and metacognition that are commonly cited components of clinical reasoning expertise (Jensen, Resnick and Haddad, 2008). Put another way, we contend that while pigeons and novices may become proficient in image recognition in reinforcement-learning activities, pathologists are not pigeons, and perceptual expertise is multifaceted that involves other components, including but not limited to: deliberate practice, clinical exposure, learning from misdiagnoses, and further learning (Ericsson and Harwell, 2019; Pusic et al. 2011). This statement appears intuitive, almost laughable, but what evidence, specifically biological evidence, exists to support this assumption?
To address this, we studied visual/perceptual recognition and expertise within a reinforcement-based learning paradigm in both novice and expert populations using behavioural metrics–response time and accuracy–and electroencephalographic (EEG) event-related potentials (ERPs; measured through EEG) to tease apart object recognition and visual expertise in pathology. ERPs are evoked responses to visual, auditory, or somatosensory stimuli and are posited to reflect various cognitive processes. For example, two ERP components associated with decision making have informed our understanding of expertise: the visual N170 ERP component and the feedback evoked reward positivity ERP component (Anderson et al., 2018; Krigolson et al., 2009; Krigolson, Hassall, and Handy, 2014). The N170 is an ERP component related to object recognition that occurs approximately 170ms after visual stimulus onset over the occipitotemporal regions (Scott, 2011). N170 has also been used as a marker of expertise in multiple different areas including dog, bird, and car identification (Scott, 2011; Scott et al., 2006). In a health profession context, Rourke et al. demonstrated increased N170 amplitude in experts when interpreting visual data specific to their expertise using chest radiographs and electrocardiogram (EKG) tracings (Rourke et al., 2016). Considering the large repertoires of pathology images pathologists are trained to categorize and recognize during their training, it is likely that similar visual processing and expertise patterns exist for expert pathologists as they do for other areas of visual expertise, evoking similar N170 patterns.
The reward positivity ERP component has been used to track visual learning in novices and learning in general, in other domains. This component is produced by feedback provided during trial and error reinforcement learning paradigms (Krigolson, Hassall, and Handy, 2014). In a medical training context, reinforcement learning occurs after a decision (for example, a diagnosis) is followed by feedback as to whether the decision is correct or incorrect (also referred to as reward-punishment feedback) (Anderson et al., 2018). For instance, in previous work reinforcement learning has been used in medical education to teach students neuroanatomy (Anderson et al., 2018, Anderson et al., 2019). In Anderson et al. (2018), students were provided with a neuro-anatomical image and a correct or incorrect label that they then needed to indicate whether there was a match between image and label, after which, feedback was given as to whether they were able to correctly match image and label (Anderson et al., 2018). EEG tracing from such experiments evokes a reward positivity ERP component (a neural correlate of a reinforcement learning prediction error) that is initially large as learners anticipate the outcome of the feedback and decreases as students become more proficient in visual (or diagnostic) categorization (Anderson et al., 2018; Williams et al., 2017).
Here, for the first time in a medical training context, we sought to examine the reward positivity and N170 ERP components in both novices and experts to take a two-pronged approach to further dissect visual neural processing in these groups–specifically to identify if there is a difference between image categorization and visual expertise. During a reinforcement-learning paradigm, we expect experts would not rely on feedback to correct or corroborate their results (and thus we would see a reduced reward positivity) while novices would rely more on feedback initially (a have a larger amplitude reward positivity) and this reliance would decrease over time. In other words, we would expect that with learning in a visual categorization task, initial differences in reward positivity amplitude would converge by the end of the reinforcement-based learning task, indicating that novices had become proficient at the visual categorization/recognition task. Indeed, our studies in neuroanatomy and medical reasoning learning have shown similar changes in reward positivity amplitude in novices (Anderson et al., 2018; Williams et al., 2017). Concurrently, we hypothesized that N170 amplitude, a marker of visual expertise, would be significantly different between novice and expert over the entire course of the learning paradigm, suggesting that categorization does not equate to expertise. Previous studies suggest that N170 does not change within novice participants over learning task time (Anderson et al., 2018) and N170 amplitude in the domain of expertise remains constant over the course of a visual categorization task (Rourke et al., 2016; Tanaka and Curran, 2001). In sum and based on previous findings in the perceptual expertise domain, we were interested in whether we could tease out the components of visual expertise by combining the use of reinforcement learning paradigm (training to recognize objects) with novices and experts in pathology.