R3 3.3 March 21, 2025: AI Resource Roundup; Neuroscience of Critical Thinking

What happens in the brain when people resist the temptation to fall back on their pre-existing assumptions – and what might that tell us about the way that critical reasoning works?

Followers of R3 know that this year, I’m paying special attention to a couple of timely topics: AI, and also critical thinking. In this issue, I want to first share with you a time-sensitive announcement on an AI-related professional development opportunity that happens to be free and open to all: AAC&U’s upcoming AI Week Webinars, which run from March 24 to March 27. I do apologize for the late notice on this one, but some of you may still want to check this offering out as it features topics of broad interest, presented by nationally-recognized experts. You can register for as many or as few sessions as you like; signing up is easy – and did I mention free?

For those interested in deep dives on AI, I also want to put in a word for particularly valuable resource, the Future Trends Forum weekly webinar series led by futurist and author Bryan Alexander, one of my very favorite thinkers in the higher education space. Future Trends Forum isn’t exclusively devoted to AI-related topics but has featured them frequently over the past two years. If you haven’t experienced its unique format before, do note that it’s not the traditional slides-plus-questions webinar, but rather, a truly authentic and totally unscripted conversation between experts, the host, and the audience. This week, I was particularly glad to attend a session featuring Anna Mills and Nate Angell speaking about their new article Are We Tripping? The Mirage of AI Hallucinations, which takes a critical look at the metaphors that shape – for better or for worse – approaches to AI literacy in education.

Lastly, I want to alert you to a new resource I contributed to: an episode of the consistently-stellar podcast Tea for Teaching. The episode, slated to be released on March 26, focuses on both critical thinking and AI, and I have to say, I’m really happy with how it turned out. Subscribe to the podcast if you don’t already, and check out the conversation I had with hosts John Kane and Rebecca Mushtare when that comes out.

For our in-depth article analysis this issue, we’ll be going back to the topic of critical thinking, with a look at a neuroscience study that reveals some completely new perspectives on what goes on in the brain when a person sidesteps a common cognitive error known as belief bias. This particular form of bias happens when people judge the validity of an argument based on whether its conclusion aligns with their preexisting beliefs, rather than on logical structure alone – leading them to accept conclusions that "feel true" even when those conclusions are logically invalid.

Citation:

Lv, X., Jia, Y., Brinthaupt, T. M., & Ren, X. (2024). Event-related potentials of belief-bias reasoning predict critical thinking. Journal of Educational Psychology, 116(6), 982–996.

DOI:

https://doi.org/10.1037/edu0000845

Paywall or Open:

Paywall

Summary:

This study explores the neural mechanisms underlying belief-bias reasoning and its relationship to critical thinking. Researchers measured event-related potentials (ERPs), meaning electrical activity generated in response to a given stimulus, recorded during a belief-bias syllogistic reasoning task. Research questions focused on correlations between patterns of activity known to reflect different types of cognitive processing and individual differences in critical thinking ability. The results suggest that individuals with stronger critical thinking skills, as measured by established tests, show distinct neural activity patterns during syllogistic reasoning, particularly in response to conflicts between belief and logical validity.

Research Questions:

- What are the neural correlates (i.e., measurable patterns of brain activity) associated with belief-bias reasoning, and how do these patterns differ according to critical thinking ability?

- Do event-related potentials (ERPs) observed during belief-bias reasoning predict individual differences in critical thinking skills?

- Are there qualitative differences in the ways that individuals with greater and lesser critical thinking abilities process responses to belief-bias conflicts in syllogistic reasoning?

Sample:

72 students recruited at universities in China, mean age 19.44, 24 female and 48 male.

Method/Design:

First, here’s a short introduction to EEG, and the related technique known as ERP, for those unfamiliar with it or who could use a refresher. (I also talk about EEG in this previous R3 post.) EEG involves recording tiny fluctuations in electrical activity going on in groups of neurons throughout the brain via electrodes placed externally on the scalp and face. For practical purposes, ERP (which stands for event-related potentials) is simply a sub-type of EEG study, one in which the measurements are time-locked to specific stimuli so that researchers can track neural responses that were produced by those stimuli.

EEG has been an important research technique in behavioral science for around 100 years, and this remarkable longevity reflects the unique advantages of this approach. It is cheap, relatively low-tech, and portable, especially when compared to other neuroscience imaging technologies such as functional MRI (fMRI), positron emission tomography (PET), or magnetoelectroencephalography (MEG). It’s noninvasive, safe, and fairly easy to learn to administer.

These advantages come with some limitations, which are important to keep in mind when interpreting ERP/EEG data. Chief among these is that it’s not great at localizing where activity is happening in the brain, lacking the ability to pinpoint specific structures the way that MEG, MRI, and PET scanning can. But what it does do very well is provide down-to-the-millisecond measurements of electrical activity that can be distilled into patterns, including ones that we can now reliably link to particular kinds of cognitive processing. Several such known patterns are highlighted in this study, such as semantic processing (essentially, pondering meaning), syntactic processing (analyzing grammar), detecting and dealing with conflicting information, and executive function/cognitive control (essentially the combination of focusing attention while holding information in working memory). Researchers use a shorthand for labeling these patterns; for example, they usually reference the positive or negative direction of a waveform (which isn’t all that meaningful in terms of interpretation, just a description of the way the waveform looks), and how long after the stimulus it tends to show up. So for example, the N400 ERP is a negative-drifting trend that shows up around 400 milliseconds after the event that triggered it.

Researchers in this study examined how these telltale patterns arose at different points in a logic problem involving syllogistic reasoning, in which premises are presented and conclusions evaluated according to whether they logically follow from those premises. Belief bias comes in when the premises lead to a conclusion that goes against common sense – what the article terms “incongruous” syllogisms. Here are a few that I excerpted from their supplemental information document (which you can find here if you’d like to look over all the details yourself):

Major premise: All mammals can walk.

Minor premise: All whales are mammals.

Conclusion: All whales can walk.

(incongruous because it doesn’t fit with common-sense beliefs, but is actually logical)

Major premise: All flowers need water.

Minor premise: All roses need water.

Conclusion: All roses are flowers.

(incongruous because it fits with common-sense beliefs, but isn’t logical)

Researchers used ERP to detect engagement of cognitive mechanisms for detecting conflicting information, executive function, syntactic processing, and semantic processing. Then, they divided participants into higher and lower critical thinking ability (as measured by an established test that was adapted for Chinese-speaking students in a Chinese cultural context), and contrasted the ERP data across groups.

Key Findings:

There were qualitatively different patterns of brain activity for the higher-and lower-critical thinking participants, especially when they were grappling with the incongruous syllogism problems. High-scoring participants showed stronger ERP components associated with analyzing syntax and integrating relationships. Notably, this group also showed stronger activity associated specifically with noticing and processing incongruent information, which may reflect a greater engagement with resolving the conflict between beliefs and logic while reasoning.

The high-scoring group also showed enhanced activity in frontal brain regions overall – a finding that tracks with the long-standing associations between frontal lobe structures and being able to inhibit irrelevant information (in this case, pre-existing beliefs) and monitoring one’s own cognitive processes in real time (e.g., through metacognition). Lastly, this group showed reduced ERP components associated with routine interpretation of meaning.

Choice Quote from the Article:

Our findings revealed distinct patterns of neural activity related to critical thinking. Specifically, individuals with high-critical thinking exhibited larger amplitudes of the P600 and LPC, indicating enhanced syntactic analysis and relation integration. Moreover, high-critical thinkers displayed smaller N400 amplitudes, suggesting reduced reliance on automatic semantic processing. Notably, high critical thinkers demonstrated larger LNC amplitudes specifically in response to incongruent syllogisms, highlighting their heightened capacity to address conflicts between belief and logic. These results suggest that individuals with high-critical thinking skills possess the ability to control and adapt their cognitive resource allocation strategies based on task demands. Additionally, our study provides novel evidence supporting the involvement of both conflict resolution (indexed by the LNC) and logic-based reasoning (indexed by the P3 family components) in high-critical thinking abilities. Overall, our findings offer a direct neural explanation for the theoretically postulated link between belief-bias reasoning and critical thinking.

Why it Matters:

Of course, ERP doesn’t offer a way to read minds, nor does it precisely measure (or even ballpark) how different structures within the brain collaborate to produce critical thinking. However, it does reveal what the authors describe as "neurophysiological markers" of critical thinking—distinct patterns in brain activity that correlate with reasoning processes. These markers could, in the future, help us better understand how people engage with logical reasoning and why some individuals are more adept at overcoming belief bias.

The findings suggest that stronger critical thinkers are particularly attuned to the tension between logical reasoning and their preexisting beliefs, showing a heightened neural response when confronted with such contradictions. It’s exciting to think that teaching people to notice and deal with biases might produce transformative change, maybe even down to the level of neural processing.

This study also drives home just how varied students can be in their reasoning abilities, even within a seemingly homogenous group. If students are at vastly different cognitive and neural stages in their development as critical thinkers, it’s no wonder that one-size-fits-all approaches to teaching critical thinking often fail to work.

Finally, in an easy-to-miss but important point that crops up late in the article, the results establish a direct connection between belief bias and critical thinking, providing needed empirical support for theoretical models of what critical thinking is and how it works.

Most Relevant For:

Faculty seeking to emphasize critical thinking in their teaching; researchers interested in the neural basis for reasoning; leaders advocating for new approaches to addressing critical thinking; anyone interested in metacognition, misinformation, and media literacy

Limitations, Caveats, and Nagging Questions:

I wondered how the cultural and educational background of the participants might come into play. While I can’t see any obvious reason why the findings wouldn’t generalize to a Western cultural setting, there are some possible disparities to keep in mind. For example, if Chinese students typically receive more formal training in logic as part of their education, that could affect how readily they detect and resolve conflicts between belief and the kind of syllogistic reasoning featured in the study. It would be useful to see similar studies conducted with participants from different educational systems to determine how much prior training influences these neural markers of reasoning.

Another question involves the potential for practical application. This study offers valuable insights into the neural processes that drive critical thinking, but it is not itself an educational intervention. The authors lightly speculate about how their discoveries might inform better methods for assessing and teaching reasoning skills, but for now, these remain theoretical possibilities rather than actionable strategies. It’s difficult to imagine a future in which students undergo EEG screening to personalize their critical thinking education—though in principle, such a program could allow for more targeted instruction that strengthens the specific reasoning steps where individual students struggle.

If you liked this article, you might also appreciate:

Abrami, P. C., Bernard, R. M., Borokhovski, E.,Waddington, D. I., Wade, C. A., & Persson, T. (2015). Strategies for teaching students to think critically: A meta-analysis. Review of Educational Research, 85(2), 275– 314. https://doi.org/10.3102/0034654314551063

Butler, H. A. (2012). Halpern Critical Thinking Assessment predicts real-world outcomes of critical thinking. Applied Cognitive Psychology, 26(June), 721–729. https://doi.org/10.1002/acp.2851

Dwyer, C. P. (2023). An evaluative review of barriers to critical thinking in educational and real-world settings. Journal of Intelligence, 11(6). https://doi.org/10.3390/jintelligence11060105

Halpern, D. F. (1998). Teaching critical thinking for transfer across domains: Disposition, skills, structure training, and metacognitive monitoring. American Psychologist, 53(4), 449–455. https://doi.org/10.1037/0003- 066X.53.4.449

Halpern, D. F., & Duan, S. D. (2021). Critical thinking: A model of intelligence for solving real-world problems. Journal of Intelligence, 9(2), Article 22. https://doi.org/10.3390/jintelligence9020022

Innes, J. M., & Morrison, B. W. (2024). Teaching psychology that does not exist: Counterfactuals as catalysts for critical thinking in psychology education. Scholarship of Teaching and Learning in Psychology. https://doi.org/10.1037/stl0000402

Niu, L., Behar-horenstein, L. S., Niu, L., Behar-horenstein, L. S., & Garvan, C. W. (2014). Do instructional interventions influence college students’ critical thinking skills? A meta-analysis. Educational Research Review, 9, 114–128. https://doi.org/10.1016/j.edurev.2012.12.002

Toplak, M. E., West, R. F., & Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks. Memory and Cognition, 39(7), 1275–1289. https://doi.org/10.3758/s13421-011-0104-1

West, R. F., Toplak, M. E., & Stanovich, K. E. (2008). Heuristics and biases as measures of critical thinking: Associations with cognitive ability and thinking dispositions. Journal of Educational Psychology, 100(4), 930– 941. https://doi.org/10.1037/a0012842

Zembylas, M. (2024). Revisiting the notion of critical thinking in higher education: Theorizing the thinking-feeling entanglement using affect theory. Teaching in Higher Education, 29(6), 1606–1620. https://doi.org/10.1080/13562517.2022.2078961

File under: critical thinking, reasoning, cognitive biases, neuroscience, individual differences