Publishing fast and slow: A path toward generalizability in psychology and AI.

Artificial intelligence (AI) shares many generalizability challenges with psychology. But the fields publish differently. AI publishes fast, through rapid preprint sharing and conference publications. Psychology publishes more slowly, but creates integrative reviews and meta-analyses. We discuss the complementary advantages of each strategy, and suggest that incorporating both types of strategies could lead to more generalizable research in both fields.

A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex.

UNLABELLED: The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes. SIGNIFICANCE STATEMENT: Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or "belief distribution") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true "state" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or "schema"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas.

Review of a medical student-run surgery lecture series and skills lab curriculum.

Evidence suggests that early exposure to surgical techniques, surgical knowledge and mentors strongly correlates with students' interest, knowledge and confidence in general surgery as a postgraduate career choice. Preclerkship exposure to surgery and implementation of a formal surgical curriculum is often restricted owing to attending surgeon time commitments and cost limitations. To promote earlier exposure to surgery, a group of senior medical students at McMaster University, Hamilton, Ont., developed and implemented a novel pilot program with a surgical lecture series and a surgical skills laboratory for preclerkship students. This commentary discusses the effectiveness of these initiatives.

Language models, like humans, show content effects on reasoning tasks.

reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable "content effects"; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models-whose prior expectations capture some aspects of human knowledge-similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks-like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently-particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.

Orbitofrontal cortex and learning predictions of state transitions.

The orbitofrontal cortex (OFC) has been implicated in goal-directed planning and model-based decision-making. One key prerequisite for model-based decision-making is learning the transition structure of the environment-the probabilities of transitioning from one environmental state to another. In this work, we investigated how the OFC might be involved in learning this transition structure, by using fMRI to assess OFC activity while humans experienced probabilistic cue-outcome transitions. We found that OFC activity was indeed correlated with behavioral measures of learning about transition structure. On a trial-by-trial basis, OFC activity was associated with subsequently increased expectation of the more probable outcome; that is, with subsequently more optimal cue-outcome predictions. Interestingly, this relationship was observed no matter what outcome occurred at the time of the OFC activity, and thus is inconsistent with an interpretation of the OFC activity as representing a "state prediction error" that would facilitate learning transitions via error-correcting mechanisms. Finally, OFC activity was related to more optimal predictions only for subsequent trials involving the same cue that was observed at the time of OFC activity-this relationship was not observed for subsequent trials involving a different cue. All together, these results indicate that the OFC is involved in updating or reinforcing a learned transition model on a trial-by-trial basis, specifically for the currently observed cue-outcome associations. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Methotrexate for the treatment of noninfectious scleritis.

OBJECTIVE: To assess corticosteroid-sparing and inflammation control in patients with noninfectious scleritis treated with methotrexate. DESIGN: Retrospective review. PARTICIPANTS: Patients who received methotrexate treatment for noninfectious scleritis and who had 12 months of follow-up after treatment initiation were included in this review. METHODS: The clinical records of noninfectious scleritis patients presenting at the University of Ottawa Eye Institute between September 1, 2010 and December 31, 2014 treated with methotrexate were retrospectively reviewed. Seventeen patients (21 eyes) were included in the study. Main outcome included inflammation control and corticosteroid-sparing success. Secondary outcomes were reduction of immunosuppression load and best-corrected visual acuity. RESULTS: The proportion of eyes with corticosteroid-sparing success was 69.2% at 3 months and 92.3% at 12 months. The proportion of eyes that achieved inflammation control was 61.9% at 3 months and 90.5% at 12 months. The corticosteroid immunosuppression load at treatment start was 1.9 ± 2.07 and at 12 months was 0.48 ± 1.03 (p < 0.01). There was no statistically significant difference in best-corrected visual acuity. CONCLUSIONS: The treatment of noninfectious scleritis with methotrexate appears to be effective at both achieving steroid-sparing success and controlling inflammation during 12 months of therapy. Immunosuppression load decreased significantly over 12 months of therapy while best corrected visual acuity was stable.

Lingering representations of stimuli influence recall organization.

Several prominent theories posit that information about recent experiences lingers in the brain and organizes memories for current experiences, by forming a temporal context that is linked to those memories at encoding. According to these theories, if the thoughts preceding an experience X resemble the thoughts preceding an experience Y, then X and Y should show an elevated probability of being recalled together. We tested this prediction by using multi-voxel pattern analysis (MVPA) of fMRI data to measure neural evidence for lingering processing of preceding stimuli. As predicted, memories encoded with similar lingering thoughts about the category of preceding stimuli were more likely to be recalled together. Our results demonstrate that the "fading embers" of previous stimuli help to organize recall, confirming a key prediction of computational models of episodic memory.