Pathologists aren't pigeons: exploring the neural basis of visual recognition and perceptual expertise in pathology.

Visual (perceptual) reasoning is a critical skill in many medical specialties, including pathology, diagnostic imaging, and dermatology. However, in an ever-compressed medical curriculum, learning and practicing this skill can be challenging. Previous studies (including work with pigeons) have suggested that using reward-feedback-based activities, novices can gain expert levels of visual diagnostic accuracy in shortened training times. But is this level of diagnostic accuracy a result of image recognition (categorization) or is it the acquisition of diagnostic expertise? To answer this, the authors measured electroencephalographic data (EEG) and two components of the human event-related brain potential (reward positivity and N170) to explore the nature of visual expertise in a novice-expert study in pathology visual diagnosis. It was found that the amplitude of the reward positivity decreased with learning in novices (suggesting a decrease in reliance on feedback, as in other studies). However, this signal remained significantly different from the experts whose reward positivity signal did not change over the course of the experiment. There were no changes in the amplitude of the N170 (a reported neural marker of visual expertise) in novices over time. Novice N170 signals remained statistically and significantly lower in amplitude compared to experts throughout task performance. These data suggest that, while novices gained the ability to recognize (categorize) pathologies through reinforcement learning as quantified by the change in reward positivity, increased accuracy, and decreased time for responses, there was little change in the neural marker associated with visual expertise (N170). This is consistent with the multi-dimensional and complex nature of visual expertise and provides insight into future training programs for novices to bridge the expertise gap.

ERP evidence of heightened attentional response to visual stimuli in migraine headache disorders.

New findings from migraine studies have indicated that this common headache disorder is associated with anomalies in attentional processing. In tandem with the previous explorations, this study will provide evidence to show that visual attention is impacted by migraine headache disorders. 43 individuals were initially recruited in the migraine group and 33 people with non-migraine headache disorders were in the control group. The event-related potentials (ERP) of the participants were calculated using data from a visual oddball paradigm task. By analyzing the N200 and P300 ERP components, migraineurs, as compared to controls, had an exaggerated oddball response showing increased amplitude in N200 and P300 difference scores for the oddball vs. standard, while the latencies of the two components remained the same in the migraine and control groups. We then looked at two classifications of migraine with and without aura compared to non-migraine controls. One-Way ANOVA analysis of the two migraine groups and the non-migraine control group showed that the different level of N200 and P300 amplitude mean scores was greater between migraineurs without aura and the control group while these components' latency remained the same relatively in the three groups. Our results give more neurophysiological support that people with migraine headaches have altered processing of visual attention.

A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?

Social species rely on the ability to modulate feedback-monitoring in social contexts to adjust one's actions and obtain desired outcomes. When being awarded positive outcomes during a gambling task, feedback-monitoring is attenuated when strangers are rewarded, as less value is assigned to the awarded outcome. This difference in feedback-monitoring can be indexed by an event-related potential (ERP) component known as the Reward Positivity (RewP), whose amplitude is enhanced when receiving positive feedback. While the degree of familiarity influences the RewP, little is known about how the RewP and reinforcement learning are affected when gambling on behalf of familiar versus nonfamiliar agents, such as robots. This question becomes increasingly important given that robots may be used as teachers and/or social companions in the near future, with whom children and adults will interact with for short or long periods of time. In the present study, we examined whether feedback-monitoring when gambling on behalf of oneself compared with a robot is impacted by whether participants have familiarized themselves with the robot before the task. We expected enhanced RewP amplitude for self versus other for those who did not familiarize with the robot and that self-other differences in the RewP would be attenuated for those who familiarized with the robot. Instead, we observed that the RewP was larger when familiarization with the robot occurred, which corresponded to overall worse learning outcomes. We additionally observed an enhanced P3 effect for the high-familiarity condition, which suggests an increased motivation to reward. These findings suggest that familiarization with robots may cause a positive motivational effect, which positively affects RewP amplitudes, but interferes with learning.

Hemispheric activation differences in novice and expert clinicians during clinical decision making.

Clinical decision making requires knowledge, experience and analytical/non-analytical types of decision processes. As clinicians progress from novice to expert, research indicates decision-making becomes less reliant on foundational biomedical knowledge and more on previous experience. In this study, we investigated how knowledge and experience were reflected in terms of differences in neural areas of activation. Novice and expert clinicians diagnosed simple or complex (easy, hard) cases while functional magnetic resonance imaging (fMRI) data were collected. Our results highlight key differences in the neural areas activated in novices and experts during the clinical decision-making process. fMRI data were collected from ten second year medical students (novices) and ten practicing gastroenterologists (experts) while they diagnosed sixteen (eight easy and eight hard) clinical cases via multiple-choice questions. Behavioral data were collected for diagnostic accuracy (correct/incorrect diagnosis) and time taken to assign a clinical diagnosis. Two analyses were performed with the fMRI data. First, data from easy and hard cases were compared within respective groups (easy > hard, hard > easy). Second, neural differences between novices and experts (novice > expert, expert > novice) were assessed. Experts correctly diagnosed more cases than novices and made their diagnoses faster than novices on both easy and hard cases (all p's < 0.05). Time taken to diagnose hard cases took significantly longer for both novices and experts. While similar neural areas were activated in both novices and experts during the decision making process, we identified significant hemispheric activation differences between novice and expert clinicians when diagnosing hard clinical cases. Specifically, novice clinicians had greater activations in the left anterior temporal cortex and left ventral lateral prefrontal cortex whereas expert clinicians had greater activations in the right dorsal lateral, right ventral lateral, and right parietal cortex. Hemispheric differences in activation were not observed between novices and experts while diagnosing easy clinical cases. While clinical decision-making engaged the prefrontal cortex (PFC) in both novices and experts, interestingly we observed expertise related differences in the regions and hemispheres of PFC activation between these groups for hard clinical cases. Specifically, in novices we observed activations in left hemisphere neural regions associated with factual rule-based knowledge, whereas in experts we observed right hemisphere activation in neural regions associated with experiential knowledge. Importantly, at the neural level, our data highlight differences in so called type 2 clinical decision-making processes related to prior knowledge and experience.

Working memory, reasoning, and expertise in medicine-insights into their relationship using functional neuroimaging.

Clinical reasoning is dependent upon working memory (WM). More precisely, during the clinical reasoning process stored information within long-term memory is brought into WM to facilitate the internal deliberation that affords a clinician the ability to reason through a case. In the present study, we examined the relationship between clinical reasoning and WM while participants read clinical cases with functional magnetic resonance imaging (fMRI). More specifically, we examined the impact of clinical case difficulty (easy, hard) and clinician level of expertise (2nd year medical students, senior gastroenterologists) on neural activity within regions of cortex associated with WM (i.e., the prefrontal cortex) during the reasoning process. fMRI was used to scan ten second-year medical students and ten practicing gastroenterologists while they reasoned through sixteen clinical cases [eight straight forward (easy) and eight complex (hard)] during a single 1-h scanning session. Within-group analyses contrasted the easy and hard cases which were then subsequently utilized for a between-group analysis to examine effects of expertise (novice > expert, expert > novice). Reading clinical cases evoked multiple neural activations in occipital, prefrontal, parietal, and temporal cortical regions in both groups. Importantly, increased activation in the prefrontal cortex in novices for both easy and hard clinical cases suggests novices utilize WM more so than experts during clinical reasoning. We found that clinician level of expertise elicited differential activation of regions of the human prefrontal cortex associated with WM during clinical reasoning. This suggests there is an important relationship between clinical reasoning and human WM. As such, we suggest future models of clinical reasoning take into account that the use of WM is not consistent throughout all clinical reasoning tasks, and that memory structure may be utilized differently based on level of expertise.

Running as Interoceptive Exposure for Decreasing Anxiety Sensitivity: Replication and Extension.

A brief, group cognitive behavioural therapy with running as the interoceptive exposure (IE; exposure to physiological sensations) component was effective in decreasing anxiety sensitivity (AS; fear of arousal sensations) levels in female undergraduates (Watt et al., Anxiety and Substance Use Disorders: The Vicious Cycle of Comorbidity, 201-219, 2008). Additionally, repeated exposure to running resulted in decreases in cognitive (i.e., catastrophic thoughts) and affective (i.e., feelings of anxiety) reactions to running over time for high AS, but not low AS, participants (Sabourin et al., "Physical exercise as interoceptive exposure within a brief cognitive-behavioral treatment for anxiety-sensitive women", Journal of Cognitive Psychotherapy, 22:302-320, 2008). A follow-up study including the above-mentioned intervention with an expanded IE component also resulted in decreases in AS levels (Sabourin et al., under review). The goals of the present process study were (1) to replicate the original process study, with the expanded IE component, and (2) to determine whether decreases in cognitive, affective, and/or somatic (physiological sensations) reactions to running would be related to decreases in AS. Eighteen high AS and 10 low AS participants completed 20 IE running trials following the 3-day group intervention. As predicted, high AS participants, but not low AS participants, experienced decreases in cognitive, affective, and somatic reactions to running over time. Furthermore, decreases in cognitive and affective, but not in somatic, reactions to running were related to decreases in AS levels. These results suggest that the therapeutic effects of repeated exposure to running in decreasing sensitivity to anxiety-related sensations are not related to decreasing the experience of somatic sensations themselves. Rather, they are related to altering the cognitive and affective reactions to these sensations.

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

Relationship between reward-related brain activity and opportunities to sit.

The present study tested whether energy-minimizing behaviors evoke reward-related brain activity that promotes the repetition of these behaviors via reinforcement learning processes. Fifty-eight healthy young adults in a standing position performed a task where they could earn a reward either by sitting down or squatting while undergoing electroencephalographic (EEG) recording. Reward-prediction errors were quantified as the amplitude of the EEG-derived reward positivity. Results showed that reward positivity was larger on reward versus no reward trials, confirming the validity of our paradigm to measure evoked reward-related brain activity. However, results showed no evidence that sitting (versus standing and squatting) trials led to larger reward positivity. Moreover, we found no evidence suggesting that this effect was moderated by typical physical activity, physical activity on the day of the study, or energy expenditure during the experiment. However, at the behavioral level, results showed that the probability of choosing the stimulus more likely to lead to sitting than standing increased as the number of trials increased. In addition, results revealed that the probability of changing the selected stimulus was higher when the previous trial was a stand trial relative to a sit trial. In sum, neural results showed no evidence supporting the theory that opportunities to minimize energy expenditure are rewarding. However, behavioral findings suggested participants tend to choose the less effortful behavioral alternative and were therefore consistent with the theory of effort minimization (TEMPA).

Electroencephalographic evidence of vector inversion in antipointing.

Mirror-symmetrical reaching movements (i.e., antipointing) produce a visual-field-specific pattern of endpoint bias consistent with a perceptual representation of visual space (Heath et al. in Exp Brain Res 192:275-286, 2009a; J Mot Behav 41:383-392 2009b). The goal of the present investigation was to examine the concurrent behavioural and event-related brain potentials (ERP) of pro- and antipointing to determine whether endpoint bias in the latter task is related to a remapping of the environmental parameters of a target (i.e., vector inversion hypothesis) or a shift of visual attention from a veridical to a cognitively represented target location (i.e., reallocation of attention hypothesis). As expected, results for antipointing-but not propointing-yielded a visual-field-specific pattern of endpoint bias. In terms of the ERP findings, an early component (i.e., the N100) related to the orienting of visuospatial attention was comparable across pro- and antipointing. In contrast, a later occurring component (i.e., the P300) demonstrated a reliable between-task difference in amplitude. Notably, the P300 has been linked to the revision of a 'mental model' when a mismatch is noted between a stimulus and a required task goal (so-called context-updating). Thus, we propose that the between-task difference in the P300 indicates that antipointing is associated with a remapping of a target's veridical location in mirror-symmetrical space (i.e., vector inversion). Moreover, our combined behavioural and ERP findings provide evidence that vector inversion is mediated via perception-based visual networks.

When "it" becomes "mine": attentional biases triggered by object ownership.

Previous research has demonstrated that higher-order cognitive processes associated with the allocation of selective attention are engaged when highly familiar self-relevant items are encountered, such as one's name, face, personal possessions and the like. The goal of our study was to determine whether these effects on attentional processing are triggered on-line at the moment self-relevance is established. In a pair of experiments, we recorded ERPs as participants viewed common objects (e.g., apple, socks, and ketchup) in the context of an "ownership" paradigm, where the presentation of each object was followed by a cue indicating whether the object nominally belonged either to the participant (a "self" cue) or the experimenter (an "other" cue). In Experiment 1, we found that "self" ownership cues were associated with increased attentional processing, as measured via the P300 component. In Experiment 2, we replicated this effect while demonstrating that at a visual-perceptual level, spatial attention became more narrowly focused on objects owned by self, as measured via the lateral occipital P1 ERP component. Taken together, our findings indicate that self-relevant attention effects are triggered by the act of taking ownership of objects associated with both perceptual and postperceptual processing in cortex.

Bootstrap analysis of the single subject with event related potentials.

Neural correlates of cognitive states in event-related potentials (ERPs) serve as markers for related cerebral processes. Although these are usually evaluated in subject groups, the ability to evaluate such markers statistically in single subjects is essential for case studies in neuropsychology. Here we investigated the use of a simple test based on nonparametric bootstrap confidence intervals for this purpose, by evaluating three different ERP phenomena: the face-selectivity of the N170, error-related negativity, and the P3 component in a Posner cueing paradigm. In each case, we compare single-subject analysis with statistical significance determined using bootstrap to conventional group analysis using analysis of variance (ANOVA). We found that the proportion of subjects who show a significant effect at the individual level based on bootstrap varied, being greatest for the N170 and least for the P3. Furthermore, it correlated with significance at the group level. We conclude that the bootstrap methodology can be a viable option for interpreting single-case ERP amplitude effects in the right setting, probably with well-defined stereotyped peaks that show robust differences at the group level, which may be more characteristic of early sensory components than late cognitive effects.

Observational practice benefits are limited to perceptual improvements in the acquisition of a novel coordination skill.

There is disagreement about the effectiveness of observational practice for the acquisition of novel coordination skills and the type of processes involved in observation of novel movements. In this study, we examined learning of a bimanual 90 degrees phase offset through comparisons of three groups; physical practice, observational practice and no practice (n = 12/group). Groups were compared before and after practice on perception and production scans of the practised pattern. The observation group was yoked to the physical group such that observers watched repeated demonstrations of a learning model. Although there were no positive effects of observational practice for physical performance measures, the observation group did not differ from the physical practice group and was more accurate than controls on perceptual discrimination measures after practice. We concluded that observation of a novel bimanual movement can aid perception but that physical practice is necessary for immediate physical performance benefits. These results are discussed in terms of cognitive mediation models of motor skill learning.

Electroencephalographic correlates of target and outcome errors.

Different neural systems underlie the evaluation of different types of errors. Recent electroencephalographic evidence suggests that outcome errors -- errors indicating the failure to achieve a movement goal -- are evaluated within medial-frontal cortex (Krigolson and Holroyd 2006, 2007a, b). Conversely, evidence from a variety of manual aiming studies has demonstrated that target errors -- discrepancies between the actual and desired motor command brought about by an unexpected change in the movement environment -- are mediated within posterior parietal cortex (e.g., Desmurget et al. 1999, 2001; Diedrichsen et al. 2005). Here, event-related brain potentials (ERP) were recorded to assess medial-frontal and parietal ERP components associated with the evaluation of outcome and target errors during performance of a manual aiming task. In line with previous results (Krigolson and Holroyd 2007a), we found that target perturbations elicited an ERP component with a parietal scalp distribution, the P300. However, the timing of kinematic changes associated with accommodation of the target perturbations relative to the timing of the P300 suggests that the P300 component was not related to the online control of movement. Instead, we believe that the P300 evoked by target perturbations reflects the updating of an internal model of the movement environment. Our results also revealed that an error-related negativity, an ERP component typically associated with the evaluation of speeded response errors and error feedback, was elicited when participants missed the movement target. Importantly, this result suggests that a reinforcement learning system within medial-frontal cortex may play a role in improving subsequent motor output.

Hierarchical error processing: different errors, different systems.

Error processing during motor control involves the evaluation of "high-level" errors (i.e., failures to meet a system goal) by a frontal system involving anterior cingulate cortex and the evaluation of "low-level" errors (i.e., discrepancies between actual and desired motor commands) by a posterior system involving posterior parietal cortex. We have recently demonstrated that high-level errors committed within the context of a continuous tracking task elicited an error-related negativity (ERN) -- a component of the event-related brain potential (ERP) generated within medial-frontal cortex that is sensitive to error commission. The purpose of the present study was to demonstrate that low-level motor errors do not elicit an ERN, but may instead evoke other ERP components associated with visual processing and online motor control. Participants performed a computer aiming task in which they manipulated a joystick to move a cursor from a start to a target position. On a random subset of trials the target jumped to a new position at movement onset, requiring the participants to modify their current motor command. Further, on one half of these "target perturbation" trials the cursor did not respond to corrective movements of the joystick. Consistent with our previous findings, we found that the uncorrectable errors elicited an ERN. We also found that the target perturbations on both correctable and uncorrectable trials did not elicit an ERN, but rather evoked two other ERP components, the N100 and P300. These results suggest that medial-frontal cortex is insensitive to low-level motor errors, and are in line with a recent theory that holds that the P300 reflects stimulus-response optimization by the impact of locus coeruleus activity on posterior cortex.

Two Interventions Decrease Anxiety Sensitivity Among High Anxiety Sensitive Women: Could Physical Exercise Be the Key?

A brief group-based cognitive behavioral therapy (CBT), with running as an interoceptive exposure (IE) component, was effective in reducing anxiety sensitivity (AS) levels in undergraduate women (Watt, Stewart, Lefaivre, & Uman, 2006). This study investigated whether the CBT/IE intervention would result in decreases in AS and emotional distress that would be maintained over 14 weeks. Female undergraduates, high (n = 81) or low (n = 73) in AS, were randomized to 3-day CBT plus forty-two 10-min running IE trials (n = 83) or 3-day health education control (HEC) with interactive discussions and problem solving on exercise, nutrition, and sleep (n = 71). The CBT/IE intervention led to decreases in AS, depression, and stress symptoms for high AS participants, which were maintained at 14 weeks. Unexpectedly, HEC participants experienced similar and lasting decreases in AS, depression, and anxiety symptoms. Furthermore, there were no post-intervention differences between CBT/IE and HEC participants in any of the outcomes. Low AS participants experienced few sustained changes. Clinical implications and the possible role of aerobic exercise in explaining outcomes of both interventions are discussed.

A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO.

Biological systems are capable of learning that certain stimuli are valuable while ignoring the many that are not, and thus perform feature selection. In machine learning, one effective feature selection approach is the least absolute shrinkage and selection operator (LASSO) form of regularization, which is equivalent to assuming a Laplacian prior distribution on the parameters. We review how such Bayesian priors can be implemented in gradient descent as a form of weight decay, which is a biologically plausible mechanism for Bayesian feature selection. In particular, we describe a new prior that offsets or "raises" the Laplacian prior distribution. We evaluate this alongside the Gaussian and Cauchy priors in gradient descent using a generic regression task where there are few relevant and many irrelevant features. We find that raising the Laplacian leads to less prediction error because it is a better model of the underlying distribution. We also consider two biologically relevant online learning tasks, one synthetic and one modeled after the perceptual expertise task of Krigolson et al. (2009). Here, raising the Laplacian prior avoids the fast erosion of relevant parameters over the period following training because it only allows small weights to decay. This better matches the limited loss of association seen between days in the human data of the perceptual expertise task. Raising the Laplacian prior thus results in a biologically plausible form of Bayesian feature selection that is effective in biologically relevant contexts.

The role of visual processing in motor learning and control: Insights from electroencephalography.

Traditionally our understanding of goal-directed action been derived from either behavioral findings or neuroanatomically derived imaging (i.e., fMRI). While both of these approaches have proven valuable, they lack the ability to determine a direct locus of function while concurrently having the necessary temporal precision needed to understand millisecond scale neural interactions respectively. In this review we summarize some seminal behavioral findings across three broad areas (target perturbation, feed-forward control, and feedback processing) and for each discuss the application of electroencephalography (EEG) to the understanding of the temporal nature of visual cue utilization during movement planning, control, and learning using four existing scalp potentials. Specifically, we examine the appropriateness of using the N100 potential as an indicator of corrective behaviors in response to target perturbation, the N200 as an index of movement planning, the P300 potential as a metric of feed-forward processes, and the feedback-related negativity as an index of motor learning. Although these existing components have potential for insight into cognitive contributions and the timing of the neural processes that contribute to motor control further research is needed to expand the control-related potentials and to develop methods to permit their accurate characterization across a wide range of behavioral tasks.