In recent years, educational research has faced mounting scrutiny, reflecting a concern about its future direction and impact. These critiques converge around two central themes. First, ongoing debate questions the significance [71], methodological soundness [15] and sustainability [2] of the field. Researchers have voiced concerns about insufficient attention to critical issues, such as curricular content, instructional methods, and the translation of research into practice [2,30,35,62,80]. In response, disciplinary voices increasingly call for stronger engagement with social and systemic problems, greater epistemological openness, and heightened methodological rigor. As Muis and Schutz [65] argued in the Handbook of Educational Psychology, "we must take more inter-, multi-, and transdisciplinary approaches, methodologies, and perspectives" (p. 726). They further emphasized the necessity of "innovative perspectives and new voices" ([88], p. 5) to revitalize the field and address its foundational shortcomings.

Second, numerous researchers advocate teaching learners to critically evaluate information to distinguish between fact and fiction—an essential skill in an era of rampant misinformation (American Psychological Association [[4], 2024]. This objective centers on achieving information literacy and often focuses on science misconceptions in domains such as climate change [99], vaccination safety (Fridman et al., 2020), and eliminating neuromyths [58]. Belief change research, which addresses how individuals revise or retain beliefs, is central to this work. Beliefs unsupported by empirical evidence can lead to systematic reasoning errors, negatively influencing personal decision-making and public discourse (Sinatra & Hofer, 2020). To counteract false beliefs, researchers frequently employ refutational strategies—interventions designed to challenge and replace faulty mental models with more accurate representations. While some belief change efforts succeed and endure [54], many produce only temporary revision or shallow change. These mixed outcomes have led to speculation about the cognitive and motivational mechanisms underpinning belief persistence and correction failure [105].

One approach to address these themes is multi-disciplinary evidence from neuroscience. Over the last three decades, no other strand of psychology has shown greater growth than neuroscience [112]. Yet, it remains underleveraged in educational research and is rarely used to inform instructional or motivational strategies [56]. This persists despite repeated calls from researchers (see [49,78,79]) who advocated using neuroscience perspectives to enhance academic motivation and learning. As Martin and Burns [60] argued, “until there is greater uptake of biopsychological perspectives and data, there will be persistent gaps in knowledge about the explanatory mechanisms underlying motivation theories” (p. 383). Similarly, renowned scholar Suzanne Hidi stated, “Interestingly, only recently have limited efforts been made to link neuroscience and motivational research. With few exceptions, the importance of neuroscientific data has been underestimated in social, educational, and psychological literature” (2016, p. 3).

These omissions are surprising given the robust evidence revealing how information is processed by the brain. While neuroscience investigates relationships between neural functioning, brain structures, and behavior [113], neuropsychology specifically addresses the relationship between the brain and cognitive, motivational, and emotional functioning...key variables in belief change. Neuropsychology research examines which brain structures, neural circuits, neuromodulators, and hormones impact thinking, motivation, learning, and emotion and how those factors influence attention, belief formation and resistance, and decision-making. The omissions are obvious, especially given neuroimaging advances that monitor brain activity during behavioral tasks, which have significantly improved our understanding of brain function and localization. These methods (e.g., electroencephalography (EEG) and magnetoencephalography (MEG)) allow researchers to monitor brain activity during various tasks, providing precise information about how the brain processes, stores, and retrieves information.

Given this backdrop, and to provide more reliable explanations as to why some belief change efforts are transitory, fragmented, and superficial, we suggest that neuropsychology can supplement behavioral evidence. The findings presented may prompt an ontological shift in practitioners’ beliefs about why some belief change initiatives fail, while others succeed. First, we summarize relevant neuroscience findings illuminating the biological roots of belief change, including how the brain processes both instrumental information and emotionally valenced content. Next, we outline the neural mechanisms of belief updating and describe neuromodulation. Last, we highlight how current instructional strategies may inadvertently contradict neuropsychological principles and propose targeted mediation strategies rooted in neuroscience to promote durable and meaningful belief change.

Adopting neuropsychological perspectives on belief change offers four key benefits. First, neuropsychological research enhances understanding of cognition through direct measurements of brain activity, offering insights that behavioral data alone cannot capture. These measurements allow researchers to reliably distinguish between different cognitive and emotional processes, using techniques like functional magnetic resonance imaging (fMRI) that quantify neural representations of the subjective value of information — a key factor in determining how individuals evaluate belief-relevant content [46]. Additionally, neuropsychological measurements, like EEG or MEG, offer high temporal precision revealing the exact timing and execution of cognitive processes [59], information unobtainable through behavioral measures alone.

Second, neuropsychological methods offer unique perspectives concerning individual differences during information processing, focusing on brain-behavior interactions [31]. This approach determines how variation in neural structures or brain areas influence cognitive processes such as reaction time, attentional focus, or learning efficiency. For example, if imaging revealed that concept learning (or unlearning) was maximized when processing occurred in the prefrontal cortex (PFC), a change message could be restructured to encourage PFC activation. However, such applications should be interpreted cautiously, as these correlations do not imply causation without interventional studies (e.g., TMS or lesion studies). Even so, lesion data must be interpreted carefully, as atypical brain states or diffuse damage can obscure typical cognition [98].

Third, neuropsychological evidence reduces threats to internal validity by providing direct measures of underlying reasoning processes. One criticism of behavioral belief change research is over-reliance on unreliable self-report responses [32], which increase false inferences by incorrectly attributing a cause-and-effect relationship between independent and dependent variables. Conversely, brain localization data is less susceptible to conscious manipulation than self-reported behavior or beliefs. Neuroimaging can detect brain activity operating below conscious awareness, while self-report is limited to conscious experiences. Neural measures can capture implicit biases or attitudes that individuals may not recognize or disclose. Additionally, neuroimaging can detect automatic processing of stimuli that occurs too quickly for conscious registration, including emotional responses that haven't yet reached awareness [47]. Neuropsychology measures thus reduce confounding variables and strengthen causal inferences [24] in ways impossible with behavioral research alone.

Fourth, behavioral and neuropsychological evidence evaluate context and behavior interactions differently. Behavioral research identifies functional relationships between environmental stimuli and behavioral responses by manipulating contextual variables to observe resulting behavior. While explanatory, behavioral approaches cannot accurately make claims about internal cognitive or neural mechanisms. In contrast, neuropsychological evidence examines gene expression—the process revealing how genes and environment interact to influence behavior and functioning. Research spanning forty years from Sapolsky [84,85] and colleagues [27,74] demonstrates that gene expression can alter neurological system development, which neuroimaging can detect [36]. These alterations manifest as measurable changes in the brain's emotional regulation and stress response systems, both known to influence the receptivity and resistance to belief change. Although still emerging, connecting gene expression to belief change mechanisms suggests a promising avenue for understanding the biology of individual differences. Knowledge of gene expression ultimately contributes to identifying how individual differences in behavior and psychological functioning develop and persist, evidence unobservable by traditional behavioral inquiries.

Despite these advantages neuropsychological measurement faces criticism. Challenges include low statistical power and inflated effects sizes due to small sample sizes, causing some studies to falsely assume reliability [18]. Second, over-reliance on functional localization can assume brain functions are rigidly compartmentalized, overlooking distributed neural processing [63]. Some researchers may incorrectly assume that if a brain area is activated during a task, activation indicates engagement of a specific cognitive process. Third, variability in neuroimaging data preprocessing can affect study outcomes as different software packages and parameter choices can lead to different outcomes even with identical raw data [14].

While these limitations warrant caution, they do not undermine the overall value of neuropsychological data when interpreted rigorously and contextually. The four advantages highlight that neuropsychological evidence is an important supplement to behavioral measures. As Mayer [62] advocated, it is not about replacing confluent behavioral findings but, "it would be useful to build better connections between neuroscience and psychology, with the goal of developing a theory of learning that is relevant to education" (p. 176). Following Martin [60], Lee and Reeve (2019a), Hidi [38], and others, we believe that supplementing behavioral findings with neuropsychological evidence is essential for successful belief change interventions.