The Next Generation of Fear Generalization

Abstract

Generalizing from past experience is a fundamental feature of cognition. Although generalization has been of long-standing interest to diverse domains in psychological science, the past decade has seen a rapid acceleration of fear generalization research in particular. The field of human fear generalization is at a pivotal juncture where it is now possible to synthesize across extensive behavioral work, competing theoretical models, animal and human neuroscience, and clinical and individual differences research, to address enduring gaps. Here, we provide an updated account of where the field of human fear generalization currently resides and provide concrete avenues for new research emphasizing tighter integration between animal and human neuroscience, prioritizing cross-disciplinary perspectives, and updating conceptual frameworks to align with rapidly advancing methods.

The Complexity of Human Fear Generalization

Franklin D. Roosevelt famously declared that “the only thing we have to fear is fear itself” [1], yet from the perspective of behavioral and neural sciences, the picture is more complicated. Fear is not inherently maladaptive. It is an evolutionarily adaptive process that has guided survival across species for millennia, and extending fear to new stimuli that resemble past threats—fear generalization (see Glossary)—can be profoundly functional. The challenge is not whether to fear, but what and when to fear. Core to navigating everyday life is evaluating whether new experiences carry genuine danger or whether apparent similarities to past threats are incidental. Generalizing too broadly wastes resources and opportunities; generalizing too narrowly risks harm. In modern life these tradeoffs are rarely life-or-death, yet the bias towards generalization of fear persists and is elevated in anxiety-related psychopathology [2]. A childhood car crash, for example, can produce lifelong avoidance of all motor vehicles—a response that identifies a genuine risk yet discounts accident rarity and the social, occupational, and health costs of forgoing transportation. Fear generalization is thus emblematic of a fundamental tension: the very process that once ensured survival can, when poorly calibrated, become the “fear itself” that Roosevelt warned against.

The study of fear generalization is rooted in Pavlovian conditioning. Some of Pavlov’s earliest investigations quantified generalization gradients as a function of similarity to the conditioned stimulus (CS) [3–5]. For most of the past century, human fear generalization research relied primarily on stimulus generalization approaches translated from non-human animal conditioning work [6], demonstrating orderly gradients of conditioned responses (CR) that generalize in proportion to similarity to the original threat. More recently, the field has borrowed from concept formation, episodic memory, machine learning, and psychopathology to capture the complexity of how humans generalize knowledge to draw novel inferences [2,7]. Rapid innovation expands our methodological toolkit, while highlighting the challenges of characterizing such a complex capability. In this Review, we synthesize recent advances spanning behavioral and cognitive neuroscience, experimental psychology, and clinical science, and highlight promising future directions. We begin with paradigmatic shifts in defining and measuring fear generalization in humans and discuss progress and obstacles in identifying neural substrates. We next consider sources of individual variability, including clinical differences. Human fear generalization research is at a pivotal juncture as new data and methods rapidly emerge. Now is the time to re-evaluate our core models and assumptions. The next generation of this research depends less on any single discipline than on their integration. Only by bridging across disciplines can we precisely map fear generalization and deliver relevant, clinically impactful progress.

Expanding the Conceptual Scope of Fear Generalization

Understanding how fear generalizes beyond an initial encounter first requires understanding how basic threat associations are formed. Insights into this process are derived almost exclusively from work on Pavlovian fear conditioning. In its basic form, a neutral CS that predicts an aversive unconditioned stimulus (US) comes to elicit CRs, (e.g., sympathetic arousal changes in humans, freezing in rodents). Virtually all human fear conditioning research employs differential fear conditioning: one CS is paired with the US (CS+), and another distinct CS is never paired with the US (CS−) [6]. To test generalization, novel stimuli—termed generalization stimuli (GSs)—that vary from high to low perceptual similarity to the CS+/CS− are presented without US reinforcement (Figs. 1A and 1B). GSs resembling the CS+ typically elicit similar CRs.

Figure 1. Fear generalization in the laboratory and real-world.

Human fear conditioning tasks often include a preconditioning phase for measuring baseline responses without aversive stimuli. In some designs, neutral associations are formed between cues. The fear conditioning/acquisition phase endows threat association to the CS+ via CS-US pairings, while the CS− remains unpaired. (A) Basic first-order fear generalization procedures test how responses generalize to stimuli with shared features of the original CS+, which was directly associated with the US. Perceptual first-order generalization involves ‘generalization stimuli’ (GS) that parametrically vary in perceptual similarity to the CS+ and CS−. Conceptual first-order generalization uses trial-unique exemplars from a common knowledge semantic category (e.g., animals, tools), with fear generalizing across individual exemplars within the CS+ category. (B) In higher-order tasks, the US is indirectly related to a neutral stimulus or category via a neutral association with the CS+ (denoted as CS1+ here). Here, sensory preconditioning facilitates the spread of higher-order fear to the cue (perceptual) or category (conceptual) that is never associated with the US, termed the preconditioned stimulus (denoted as CS2 here). The CS2+ is compared to a control CS2− during a generalization test to test the degree of higher-order fear generalization. Thus, higher-order generalization can involve retroactive and proactive spread of fear to otherwise neutral events, via secondary associations with the CS+. (C) Real-world generalization contains aspects of both first-order and higher-order fear and safety generalization along perceptual and conceptual pathways. Abbreviations: CS+, conditioned threat stimulus; CS−, conditioned safety stimulus; CS1, conditioned stimulus 1; CS2, conditioned stimulus 2; GS, generalization stimulus; US, unconditioned stimulus.

Prior to the last decade, human fear generalization work primarily relied on simple sensory stimuli translated from animal research (e.g., basic shapes, auditory tones) [8]. This practice allowed researchers to precisely modulate stimulus properties and organize GSs into quantifiable gradients of similarity. For humans, perceptual overlap efficiently promotes generalization across many contexts and helps explain why disorders (e.g., posttraumatic stress disorder [PTSD], specific phobias) are characterized by heightened fear and avoidance toward stimuli resembling a known threat.

Although perceptual generalization paradigms remain an important laboratory tool and explanatory model, humans also generalize along non-perceptual pathways. Initial work raised important questions about aspects of the external validity of perceptual generalization tasks, as life is far more complex than variability in singular perceptual features [7]. Crucially, humans rely on induction to conceptually link dissimilar stimuli (i.e., categorization [9]). Here, we assess progress in expanding beyond perceptual fear generalization models and discuss newer empirical and theoretical developments (for quantitative and measurement considerations, see Box 1).

Box 1: Quantifying Fear Generalization.

Psychometrics of fear generalization

Generalization paradigms typically require multiple stimulus types, variations, and conditions, leading to both higher degrees of dimensionality than standard conditioning tasks and greater quantitative demands for yielding reliable output metrics [92,93]. A recent comprehensive effort surveyed and tested commonly used techniques for quantifying fear generalization [83]. Techniques were split into group-level statistical approaches (e.g., linear and quadratic trend contrasts) and individual-level arithmetic (e.g., weighted composite score) or model-fitting methods. Concerningly, all tested approaches showed relatively low robustness to noisy data (measurement error), and those with stronger psychometric properties presented trade-offs, including weaker relations to individual differences. Additionally, all showed weak-to-null relationships with anxiety-related traits. Here, we amplify calls for more intentional quantitative methods research and promote selecting techniques based on psychometric properties and alignment with formalized conceptual frameworks, rather than intuition or study-specific idiosyncrasies.

What is “overgeneralization”?

The term overgeneralization has roots in cognitive distortion models [37]. Following initial use in experimental generalization studies in anxiety-related disorders, the term is now widely used in the literature. Although evocative and with some clinical utility, there are several arguments against its use in scientific contexts. First, “over” can be arbitrary and difficult to separate from any definition of normative generalization. It also implies a standard threshold of normative generalization, against which individuals with higher responses are “overreacting”. This is a potentially derogatory framing that also imposes an oversimplified categorical distinction on an inherently dimensional concept and its quantitative indices. Not only does this overly simplify the generalization process, but it implies an undefined process of “undergeneralization” and lumps together all “overgeneralizers” while ignoring important variability. Second, any degree of fear response to a stimulus never paired with threat is technically overgeneralized and the defensive system is likely operating under “better safe than sorry” assumptions. Third, the term lacks portability: it is readily applied to perceptual generalization tasks but less suited to conceptual or higher-order tasks. Further, what is quantified as overgeneralization can vary substantially across paradigms, even among perceptual tasks. Finally, the pathological connotations of overgeneralization emerged from cross-sectional studies of anxiety-related disorders. This evidence is fundamentally correlational: co-occurrence with a disorder does not inherently render an observed pattern as pathological. Here, we advocate for dimensional descriptors such as heightened or increased fear generalization, which emphasize relative magnitude without imposing arbitrary thresholds or assumptions about the (mal)adaptiveness of the response.

Higher-Order and Category Conditioning Paradigms

Real-world environments frequently involve challenges that are largely absent from standard generalization tasks. Higher-order fear conditioning encompasses associative learning processes in which a neutral stimulus acquires threat value indirectly, via its relationship with a CS that has been directly paired with the US (Fig. 1B). Two prominent paradigms—sensory preconditioning and second-order conditioning (Box 2)—vary in the temporal ordering of stimulus-stimulus and stimulus-outcome associations. In sensory preconditioning, two neutral stimuli (CS1 and CS2) are first paired, after which one undergoes fear conditioning (i.e., the CS1, now a CS1+); in second-order conditioning, this order is reversed [10,11]. In both cases, the unpaired neutral stimulus (CS2) can evoke a CR at test despite the CS+ being the only stimulus paired with the US. This demonstrates that fear can generalize across stimuli sharing an association but no physical resemblance.

Box 2: Second-Order Conditioning and Neurocircuitry Comparisons with Sensory Preconditioning.

Although aversive second-order conditioning is a popular explanation for the spread of fear in PTSD [94,95] and has received considerable interest in animal neuroscience [96,97], translation to human research has proven surprisingly difficult [98]. Human second-order conditioning research is extremely sparse, likely due to both methodological and theoretical challenges. Procedurally, it is difficult to induce second-order conditioning via laboratory tasks in humans [98] as it can produce both excitatory or inhibitory outcomes based on subtle task parameters [99]. That said, current parameters may still be far from optimal. Human research participants are inherently aware that they are not in a state of actual danger, which necessarily dilutes the extent of generalized responding. Indeed, the only successful second-order conditioning in humans is when they are explicitly instructed to encode the stimulus-stimulus association [11,98]. Accordingly, sensory preconditioning is a more tractable paradigm for human research which more readily maps onto inference-based accounts of learning, memory, and decision making (see Fig. I for paradigm depictions). However, human research has yet to translate the full set of task parameters that can strengthen second-order conditioning in rodents, potentially indicating that this comparison is an unfair one at this time.

Rodent studies identify partially overlapping neural circuits for both higher-order paradigms, primarily involving medial temporal lobe structures. In both, BLA encodes aversive CS-US relationship akin to standard fear conditioning [10,100,101]. In sensory preconditioning, the initial CS-neutral stimulus association recruits the PRC, which encodes the neutral association [102], along with the entorhinal cortex, and cortical regions such as the retrosplenial cortex, involved in coordinating sensory and subcortical interactions [103,104]. After conditioning, the PRC mediates the link between the prior neutral stimulus and CS representation and the newly-formed aversive CS+-US association, with both the PRC and BLA driving generalized threat responses at test. In contrast, second-order conditioning does not recruit these cortical and medial temporal regions. Instead, the BLA is already engaged by initial fear conditioning (during CS+-US learning) and directly encodes the CS+-neutral stimulus threat association, bypassing the need for PRC-mediated retrieval [10,105] (see Fig. I).

These circuit-level distinctions between second-order conditioning and sensory preconditioning underscore the need to carefully evaluate which neural structures are considered “core” to different types of higher-order fear generalization. Also important is considering which systems are most relevant for human translation, particularly given limited human fear generalization research involving parahippocampal structures. Since their human applications have progressed at different speeds, there is currently insufficient data to compare second-order conditioning and sensory preconditioning neurocircuitry in humans rigorously.

Figure I, Box 2. Procedural and neural differences between aversive sensory preconditioning and second-order conditioning.

Both paradigms involve three experimental stages, but differ in the order of the initial two stages. Below each paradigm schematic we summarize patterns reported across the literature by depicting stylized neural activation magnitudes in BLA (red) and parahippocampal (PRC/ERC) and/or cortical structures (blue). These regions are non-exhaustive, and this visualization is conceptual rather than quantitative. Relative magnitudes are approximate and for illustration purposes only. Abbreviations: BLA, basolateral amygdala; CS1, conditioned stimulus 1; CS2, conditioned stimulus 2; ERC, entorhinal cortex; PRC, perirhinal cortex; US, unconditioned stimulus.

Renewed interest in higher-order conditioning reflects that many real-world fears are acquired indirectly rather than through direct experience, closely paralleling laboratory models [12,13]. Fear can propagate through temporal chains of association, retroactively contaminating previously neutral memories and prospectively biasing new associations. For instance, fear acquired from an assault by a friend might transfer to the broader friend group via the shared semantic link, even with no one else present during the assault. In principle, this capacity to integrate learning across discrete episodes is adaptive. However, in the context of threat, it enables fear to infiltrate associative networks far removed from the original danger.

A central question is when fear transfers to the indirectly paired benign stimuli: during conditioning itself (online integration) or after, at the generalization test (retrieval-based chaining) [14,15]. One recent human neuroimaging study found evidence for online integration during sensory preconditioning. Decoded fMRI representations of preconditioned stimuli were reactivated during initial fear conditioning in hippocampus and medial prefrontal cortex (PFC), with the degree of reactivation predicting subsequent basolateral amygdala (BLA) activity at test [16]. Evidence for retrieval-based chaining was also observed in the amygdala during the test phase. Complementary findings from a similar study showed hippocampal and PFC reactivation predicting retroactive memory enhancements [17], while a behavioral study found that higher-order fear generalization occurred regardless of declarative memory for the preconditioned associations [18]. Both studies further support online integration as a primary mechanism. Important future directions include clarifying when each mechanism (online vs. chaining integration) predominates, the degree to which these mechanisms reflect fear generalization (versus broader inferential processes distinct from generalization), and linking these mechanisms directly to trauma-related processes (Box 2).

Higher-order paradigms have direct clinical relevance. Pre-, peri-, and post-trauma periods each have distinct memory encoding properties [19,20]. Sensory preconditioning maps onto this temporal structure: preconditioning corresponds to pre-existing associations, fear conditioning to the traumatic event, and the transfer phase to post-trauma encounters where fear might generalize. Critically, if online integration is a primary mechanism, then fear spreads during the trauma itself and leaves limited opportunity for intervention. If retrieval-based generalization predominates, optimizing exposure protocols might reduce the spread of fear to associated stimuli.

Category Conditioning

A notable variation to fear conditioning involves replacing a repeated CS with trial-unique stimuli from the same category (i.e., category conditioning [21]) (Figs. 1A and 1B). Semantic categories, such as animals, tools, or food, encompass items that share strong conceptual overlap despite widespread physical dissimilarity (e.g., starfish, moose). In this paradigm, fear conditioning itself is predominantly predicated on generalizing to each novel stimulus. Superordinate category-outcome relationships provide a scaffold for generalization and are facilitated by complementary, but not interchangeable, inferential processes (e.g., induction). Multiple studies demonstrate reliable category-wide fear conditioning, extinction, and episodic memory effects [21,22].

Asymmetry Between Fear and Safety Generalization

Generalization of learned fear is only one side of a broader adaptive challenge. Organisms must also learn what is safe and efficiently generalize that safety. Exposure therapy’s goal is not merely to reduce fear to a singular stimulus but reduce it across stimuli and situations. Safety generalization is therefore a critical, if historically neglected, boundary condition on the spread of fear [23].

In the lab, exposure therapies are modeled via fear extinction: a threat-conditioned CS+ is presented without further reinforcement, gradually reducing CRs. Extinction does not erase the original fear association. It instead generates a competing safety memory that coexists with the fear memory rather than replacing it [24]. Accordingly, extinction is a context-dependent form of learning that struggles to outcompete generalized fear. Fear memories are resilient and generalized, being easily retrieved across time and novel contexts [25,26]. Conversely, safety memories remain fragile and context-bound, tethered to their original encoding context [27]. As such, retrieval competition can become chronically skewed toward fear by virtue of asymmetrical fear-safety generalization [28].

Recent theoretical advancements from episodic memory research highlight how reinstatement of neural contexts (i.e., both external and internal states) fundamentally underlie memory retrieval [29], and might explain impaired extinction generalization. For example, contexts associated with safety learning (e.g., therapist’s office) might be less accessible outside in novel environments [30]. Future work should consider the role of neural contexts in the persistence of fear generalization.

Generalization asymmetry provides theoretical scaffolding for major forms of post-extinction fear return—spontaneous recovery, reinstatement, renewal, and rapid reacquisition—and limits standard exposure therapy. Augmenting safety generalization is thus a key translational target. Studies administering dopamine-promoting drugs post-extinction [31], or pairing CS+ with rewarding or novel outcomes during extinction [32,33], report stronger fear inhibition and memory generalization after 24hrs. This suggests these strategies promote safety memories to generalize more competitively against fear memories.

A recent review comparing extinction of the original CS+, a single GS, or multiple GSs found that across studies only direct CS+ extinction consistently generalizes, and even this effect fades over 24hr delays [23]. This is a translational problem: CS+ exposure is frequently untenable and potentially unethical, and extinguishing a range of GSs better approximates real-world exposure therapy [6,12]. Unfortunately, safety poorly generalizes to GSs and fear returns in response to original and novel stimuli [34,35] unless the GS closely matches CS+ aversiveness [36]. Interestingly, GSs that are perceptually more intense than CS+ (e.g., a higher-pitched tone) elicit even stronger safety generalization (i.e., peak shift effect). Overall, instilling transferable safety memories under realistic conditions remains a central challenge.

Together, these lines of evidence support a view of fear recovery arising from the disparity between fear and safety memory generalization. Rather than solely attempting to diminish fear generalization directly, it might be more effective to augment safety memories that work to constrain and inhibit the spread of fear.

Increasing Complexity is a Constant

The fear generalization researcher has a wealth of experimental options, each with strengths and weaknesses. Further research within specific areas, particularly neuroimaging, will refine knowledge and techniques. However, merging the discussed frameworks to produce more comprehensive tasks is necessary to truly approximate the real-world complexity of fear generalization in the lab. We anticipate one future challenge in task development is balancing experimental control with the complexity of simultaneously measuring perceptual and conceptual dimensions of fear and safety generalization.

Neural Models, Advances, and Challenges

Rapid proliferation of noninvasive neuroimaging tools in humans has led to exciting advances in understanding fear generalization while highlighting the limitations of established working models and experimental paradigms. Here, we take stock of recent animal and human data guiding updated neurobiological accounts of fear generalization. We highlight where overly narrow focus on specific circuits and processes warrant reexamination. We also discuss what human neuroimaging likely can and cannot tell us about fear generalization, and caution against overinterpretation or mistranslation of causal mechanistic investigations in rodent.

Current State of Human Neuroimaging of Fear Generalization

Earlier neural models of human fear generalization and its perturbations focused on connecting rodent neurobiology and emerging human neuroimaging research [37,38]. The hippocampus emerged as the bottleneck for adjudicating similarity of novel stimuli with the CS+ representation. Projections from amygdala and other threat regions to the CA1 and CA3 hippocampal subfields bias towards pattern completion, resulting in failure to differentiate from the CS+ and prompting generalized responding. In contrast, the hippocampus supports pattern separation when sensory cortices and threat circuitry input does not reach a sufficient completion threshold, prompting the dentate gyrus (DG) subregion to recruit PFC safety circuitry and inhibit generalized responding.

Human studies provide inconsistent support for this model. An fMRI meta-analysis found robust cortical generalization, but only moderately stronger fear generalization activity in the hippocampus and inhibitory generalization in the amygdala (i.e., activity decreases as stimuli become more dissimilar to the CS+) [39] (Fig. 2). Possible explanations include mismatches between the mechanistic specificity of the model and the limitations of human fMRI, particularly regarding inconsistent amygdala activation findings [39–41].

Figure 2. Neurobehavioral approaches to quantifying human fear generalization.

Univariate and multivariate fMRI approaches are the among most common neuroimaging techniques employed in human fear generalization research. Univariate analyses capture generalization magnitude, but averaging across voxels results in information loss. Thus, these analyses cannot resolve representational structure. Multivariate approaches address representational geometry and provide more precise mappings of generalization routes. Robustly linking neural activity to behavior remains a priority for this area of research. Neural regions are highlighted for demonstration purposes and relevance in fear conditioning meta-analyses and are non-exhaustive. Abbreviations: fMRI, functional magnetic resonance imaging.

The magnetic field strength typically used in human fMRI (3T) limits the image resolution available to researchers, resulting in poor spatial resolution for smaller subcortical structures. This limits efforts to disentangle hippocampal subdivisions purportedly underlying fear generalization. However, a 7T fMRI study tested the neural substrates of fear generalization at the subregion level for the first time in the human brain [42]. Consistent with safety processing and pattern separation, DG activity scaled with increased similarity to the CS− and was strongest for the CS− itself. Nonetheless, a similar pattern was also found for CA1 and CA3, contradicting the pattern separation/completion model. Additional higher-resolution MRI work is needed to disentangle subfield contributions to fear generalization. Given the breadth of cortical-subcortical interactions in human memory [43], we posit that the study of hippocampal processes in humans are unlikely to yield a direct one-to-one translation of rodent subfield findings.

Regarding the amygdala, a recent study measured neural activity related to aversive generalization via electrodes planted deep in the brains of patients with epilepsy [44]. This rare ultra-high-resolution look into the human brain assessed activations in individual subcortical neurons. Amygdala neurons fired at significantly higher rates to GSs and correlated with generalized behavioral responses; other regions did not show this pattern. Machine learning applied to individual amygdala neuron activity predicted generalized responding ~1 second before the response itself. Although methodologically different from most generalization work (primarily due to the aversiveness of the US emerging from loss of reward, not direct threat or harm, and the nature of data acquisition) amygdala-specific results reinforce this region’s central role in aversive generalization. This is a promising sign for future investigations and relevant for debates regarding the amygdala in fear conditioning [41,45].

Multivariate Pattern Analysis of Fear Generalization in the Human Brain

In rodent models, the precision of threat memories is partially governed by the fidelity of memory engrams—neuronal ensembles of varying density that are activated during learning and reactivated at retrieval [46]. In some circumstances, denser engrams produce less discriminative memory traces. As such, stimuli only partially overlapping with the original threat can reactive the trace, causally promoting generalization of threat representations across amygdala, hippocampal, PFC, and related circuitry [47,48]. The relative sparseness of memory traces might then naturally regulate the tendency to generalize, as a sparsely coded (i.e., more specific) aversive memory might be less likely to prompt retrieval from partial cues [49].

Although precision animal neuroscience tools used in engram research are not feasible in humans, multivariate pattern analysis (MVPA) of fMRI data offers a framework for examining similar questions. MVPA can test how distributed patterns of neural representations support the encoding, discrimination, and generalization of threat and safety (Fig. 2), but at the macroscopic scale and without causal explanatory power [50]. Rather than collapsing activity across voxels into a single average, as done in conventional univariate analyses, MVPA techniques preserve the informational content present in spatial patterns of activity across voxels [51,52].

Two MVPA variants are most relevant here. Representational similarity analysis (RSA) quantifies similarity between multi-voxel activity patterns evoked by individual stimuli, with stronger similarity indicating more neural overlap. For instance, within-category pattern similarity in category-sensitive cortex is selectively boosted when that category undergoes fear conditioning [53]. This indicates tighter integration of items within a threat-associated representation. Classification or decoding methods assess whether a brain region contains sufficient information to distinguish neural states, providing the ability to interrogate whether previously encoded representational content is reinstated during generalization tests. Both approaches have revealed representational dynamics in fear conditioning and extinction that would otherwise remain undetectable via univariate analyses [40].

MVPA has strong theoretical coherence with fear generalization. Generalization is fundamentally a problem of representational overlap: does a novel stimulus evoke a pattern resembling the acquired threat representation? Are neural traces of fear or safety learning recapitulated when partially similar events are later encountered? Several recent studies using RSA found that CS+ category items demonstrate stronger neural similarity than CS− items in hippocampus [17], insula, and PFC [54,55], establishing that pattern-based fear generalization is widespread throughout canonical threat circuitry. In the context of higher-order generalization, RSA applied to a sensory preconditioning paradigm showed that conditioning indirectly tightened within-category similarity for the preconditioned threat category across perirhinal cortex (PRC), hippocampus, amygdala, and medial PFC [16]. These representational changes persisted in hippocampus and PRC at 24-hours but not amygdala and mPFC, suggesting dissociable timescales across structures. Moving forward, increasingly sophisticated experimental designs can help refine neural models of how threat representations spread, persist, and ultimately constrain or promote fear generalization.

Translation is Still Key

Human neuroimaging of fear generalization continues to grow in sophistication and scope. Yet it is not ready to flourish independently from non-human animal research, nor should this be a goal. MVPA is an appreciable improvement on univariate analyses but is a rough approximation of tools used in cutting-edge, but still evolving, animal research. As such, we caution against human neuroscience committing too strongly to a single neural model of fear generalization. We encourage continued translation in lockstep with emerging animal research and increased incorporation of causal techniques (e.g., transcranial magnetic stimulation [56]) to better approximate animal neuroscience.

Individual Differences and Clinical Significance

The uncertain nature of new and ambiguous experiences precludes an optimal response appropriate across people and situations. Accordingly, fear generalization is not uniform and is largely informed by individual differences in psychological and biological traits that lead to different outcomes based on how these traits interact with the demands of the current situation [57].

Comprehensively mapping traits that enhance or ameliorate (un)helpful forms of fear generalization is a growing priority in psychology and related fields, particularly in relation to clinical anxiety. Earlier clinical accounts [58] theorized that the magnitude and pattern of fear generalization would align with specific categorical anxiety-related disorder (including PTSD and OCD [59]). For example, PTSD was predicted to involve generalization to a wider range of stimuli, whereas social anxiety disorder or specific phobia would be limited to circumscribed stimuli. Initial psychopathology studies of fear generalization were organized around these disorders and partially supported this hypothesis, particularly in PTSD [40,60], although sample sizes were modest relative to current standards. Recent meta-analytic findings [61,62] challenge categorical disorders as an explanatory model for individual differences in fear generalization. Instead, fear generalization is associated with most forms of anxiety-related disorders, and group-level gradients of fear generalization have limited utility as clinical phenotypes (Figs. 3A and 3B).

Figure 3. Progress in understanding anxiety-related individual differences in human fear generalization.

(A). Categorical and dimensional psychopathology associations with fear generalization in behavioral studies. Left subpanel organizes anxiety-related DSM disorders by HiTOP Internalizing subfactors (Distress and Fear, relabeled as Broad and Circumscribed Anxiety to align with fear conditioning models). Pie charts show number of studies with ≥1 significant heightened-generalization finding in anxiety vs. control groups. Right subpanel shows higher-order HiTOP spectra; pie charts show number of studies with ≥1 significant positive generalization-dimensional psychopathology association. Spectra are annotated with associated constructs examined in ≥1 study. (C) Meta-analytic effect sizes of anxiety-related psychopathology associations with fear generalization (Cooper et al., 2022; Sep et al., 2019) and other fear conditioning indices (Kausche et al., 2025) in behavioral studies. Values >0 indicate heightened generalization. Fear generalization shows strongest effects; dimensional measures yield larger effects than categorical. (D) Neural regions differentiating anxiety-disorder from comparison participants. Points are at peak MNI coordinates; bicolor points indicate two studies with identical coordinates. Adapted from Cooper et al. 2022 (supplement). Abbreviations: BOLD, blood-oxygen-level-dependent level; CS+, conditioned threat stimulus; CS−, conditioned safety stimulus; dACC, dorsal anterior cingulate cortex; DSM, Diagnostic and Statistical Manual of Mental Disorders; GAD, generalized anxiety disorder; HiTOP, Hierarchical Taxonomy of Psychopathology; OCD, obsessive-compulsive disorder; PD, panic disorder; PTSD, posttraumatic stress disorder; SAD, social anxiety disorder; SP, specific phobia; vmPFC, ventromedial prefrontal cortex; ΔCS, CS+/CS− differential.

Recent Research on Anxiety-Related Psychopathology and Fear Generalization

Recent studies linking psychopathology to fear generalization both echo and diverge from earlier work. Some corroborate heightened fear generalization in PTSD. Newer evidence includes elevated behavioral risk ratings to GSs resembling the CS− [63,64] and more generalized network connectivity in networked frontoparietal regions involved in higher-order cognition and working memory [63] (Fig. 3C). Additionally, behavioral fear generalization across socially-relevant stimuli was associated with social anxiety disorder [65], further supporting disorder-specific stimuli as preferentially evoking generalization in this psychopathology [66].

Other recent studies do not support categorical diagnoses as consistently associated with heightened fear generalization. A larger study comprised of multiple anxiety-related diagnoses (OCD, social anxiety disorder, and specific phobia) found no evidence for heightened generalization in any specific disorder [67]. Another study testing children and adolescents with various anxiety-related disorders also did not find heightened generalization [68]. Two recent studies have tested elevated symptom (i.e., sub-clinical) groups as determined by disorder-specific symptom scales. Neither elevated panic [69] nor social anxiety [70] symptoms were associated with heightened fear generalization.

Modeling psychopathology using empirical dimensions, as opposed to clinician-adjudicated categorical diagnoses [71], is gaining traction in the fear conditioning field [72,73]. Dimensional psychopathology approaches help reduce arbitrary distinctions between health and disorder, statistically account for substantial heterogeneity inherent to categorial disorders, and eliminate issues posed by diagnostic comorbidity [74]. One drawback is that these approaches require larger sample sizes and thus are infrequently combined with neuroimaging.

Multiple behavioral studies find that continuous internalizing (negative affect) measures are associated with heightened fear generalization [64,67,68,75–79] or decreased safety generalization [80]. This is a robust pattern: meta-analysis [81] and different scales and constructs, including trait anxiety [75,77,78,80,82], neuroticism/negative emotionality [64,76], or specific symptoms [67,68], all demonstrated small-to-moderate associations with fear generalization (Figs. 3A and 3B). There are also notable null results from larger studies [83]. This could reflect the true effect size trends towards small. Yet it also suggests a need for further refinement to psychopathology operationalization, including expanding to domains not within the internalizing spectrum (Box 3).

Box 3: Beyond Cross-Sectional Fear and Anxiety States, Traits, and Mechanisms.

Fear generalization outside of anxiety-related psychopathology

Fear dysfunction extends beyond anxiety to other areas of psychopathology. Recent work finds that anhedonia statistically outperforms anxiety- or fear-related factors in predicting heightened fear generalization [76], a pattern also observed in extinction studies [106]. In schizophrenia, reduced generalization in salience network regions and increased generalization in default mode network regions are linked to negative symptoms [107], supporting the relevance of broader psychopathological dimensions.

Predicting clinical outcomes

Most clinically-relevant fear generalization work is cross-sectional, limiting its explanatory contribution. Early longitudinal studies found that stronger fear generalization predicted later increases in trait [108], generalized, and social anxiety symptoms [109] weeks-to-months later. A recent study tracked PTSD symptoms in firefighters over two years; baseline generalization predicted peak symptoms at follow-up, offering preliminary evidence that fear generalization contributes to PTSD development [64]. Another study measured fear generalization in specific phobia before VR exposure therapy [110]. Heightened fear generalization and reduced safety generalization—both behavioral and dorsolateral PFC activity indices—predicted non-response, and a machine learning model trained on pre-treatment data distinguished responders from non-responders. Both studies accounted for baseline differences in symptoms. Together, these results cautiously support fear generalization’s role in mechanistic models of anxiety-related psychopathology development and its treatment.

Do other aversive emotions generalize differently?

Threat-related generalization dominates the literature due to its applicability to animal models and centrality in theories of anxiety-related psychopathology. Whether other negative emotions generalize similarly is unclear. Initial work on disgust confirms comparable generalization patterns to fear, but with stronger subjective and avoidance yet weaker physiological generalization [111,112]. Individual differences in disgust-related traits consistently accounts for variance in generalized responding, possibly reflecting the more idiosyncratic nature of disgust relative to fear. Additional work has examined generalization of loss [44], (mis)trust [113], and negative-affect related impulsiveness [114]. Other clinically-relevant emotions (e.g., sadness) have yet to be studied in generalization frameworks.

Generalization as a broad scientific principle

The broader scientific principle of generalization spans many domains, from ecology and education to perception and cognition, and recently to machine learning and large language models [2,5,115,116]. Fear generalization, therefore, represents one instance of a much larger phenomenon. Although the concepts reviewed here are particularly relevant to psychology and neuroscience, they extend beyond these domains and intersect with work across many disciplines. The vast generalization literature across these domains cannot be comprehensively reviewed here, but creative cross-disciplinary perspectives from outside the traditional fear generalization disciplines will be essential for driving conceptual and methodological advances.

Overall, the promise of understanding fear generalization as a pathogenic mechanism lags behind the data. Evidence suggests fear generalization is a transdiagnostic correlate with broad relations to internalizing variance. Empirical shortcomings of categorical diagnoses limit more precise explanations and yield inconsistent results. We contend it is misguided for future generalization work to continue prioritizing categorical approaches to conceptualizing and operationalizing psychopathology. Dimensional psychopathology has strong empirical support, yet inferences regarding fear generalization are currently limited to behavioral data. A critical priority is to build on, not discard, initial neuroimaging work in categorical disorders to guide well-powered dimensional psychopathology studies.

Intra- and Inter-Individual Differences in Cognitive Processes and Latent Mechanisms

Clinical variance in fear generalization receives substantial empirical attention, yet many dimensions of cognition and behavior are relevant for understanding why some people generalize more than others (see Box 3). Newer research employing computational latent mechanism methods offers compelling cross-species evidence for cognitive processes explaining both between- and within-person variance in generalized responding [84,85]

Accumulating research suggests that accounting only for emotional mechanisms of generalization—responding similarly to distinct stimuli because both are identified as threats—underestimates variance explained by how people (mis)perceive and remember stimuli. Observed generalization effects can be partially explained by individual differences in perceptual errors that confuse safe stimuli for threats, rather than emotional processes [84,86,87]. Perceptual accuracy also varies dynamically within individuals across trials, whereas retrospective memory stabilizes, constituting an additional source of variance [88].

Whether perceptual variation renders emotional or higher-level cognitive sources statistically less relevant is a matter of ongoing debate [84,86,87]. Recent work directly contrasted perceptual and value-based (emotional) mechanisms, with perceptual discriminability individually calibrated to account for baseline acuity differences [82]. Value-based generalization accounted for the majority of response variance and was selectively related to trait anxiety. This finding suggests that emotional mechanisms retain explanatory primacy, though perceptual discriminability was also associated with widespread generalization.

Taken together, this is a promising but growing area with many unsettled conclusions. Already clear is the need to challenge long-standing assumptions regarding mental representations of GSs and its operationalization using observed behavioral responses [84,89]. Further latent mechanism modeling that considers multiple sources of cognitive variability, be it perceptual, attentional or otherwise [70,90], is vital for explaining fear generalization and its relation to personality and clinical traits [72].

Optimizing for Individual Differences is Not Optional

Mechanistic accounts of fear generalization are incomplete without considering individual differences, clinical or not. We contend that individual differences need to be a central consideration for fear generalization paradigms. Some peripheral choices (e.g., including multidimensional psychopathology assessments or perceptual sensitivity tests) will not disrupt fundamental design decisions. However, peripheral changes are insufficient if fear generalization tasks are only designed to maximize within-subjects differences (e.g., CS+ category vs CS− category), decreasing the between-subjects variability needed for individual differences research (i.e., reliability paradox [91]). Synthesizing the strengths of experimental and individual differences approaches is an indispensable step for advancing models of fear generalization.

Concluding Remarks

If generalizing about the science of fear generalization, a reasonable conclusion is that simplicity is the exception, not the rule. Shepard famously proposed that generalization should be psychology’s first universal law [5], yet decades of research have revealed a landscape far more complex than any single law can capture. As detailed here, recent research underscores how humans generalize fear through interacting perceptual, conceptual, and contextual mechanisms from across associative learning theory that resist reduction to a unitary perceptual gradient. Several emerging themes carry implications for future work (see Outstanding Questions). First, the translational bridge between human and non-human neuroscience remains central and bidirectional. Advances in multivariate analyses and computational modeling might eventually be “backwards translated” to generate targeted hypotheses for circuit-level investigation in animals, just as animal models continue to motivate new human paradigms. Second, explaining meaningful variability in fear generalization demands improved systematic measurement of inter- and intra-individual differences, spanning traits, states, and task contexts. These variables then require integration within computational frameworks that can accommodate their interactions. Third, while fear generalization has clear transdiagnostic relevance, mechanistic work linking generalization dynamics to the onset, maintenance, or treatment of anxiety-related psychopathology remains nascent. Longitudinal designs, incorporation of operant conditioning (Box 4), and integration with treatment research can bridge this gap.

Outstanding Questions.

• How can we use newer and emerging technologies and techniques, such as more naturalistic paradigms, to improve ecological validity and empirically verify the relevance of fear generalization tasks to the real-world while maintaining internal validity in complex designs?

• Is there unique or preferentially recruited neural circuitry underlying fear generalization, relative to fear acquisition and extinction? How can we leverage information-sensitive multivariate methods to identify neural regions which support fear generalization across different conditions, tasks, and individuals?

• What are the boundary conditions limiting the retention and durability of fear generalization memories? Relatedly, which contextual factors or individual traits will best predict the strength and intensity of fear generalization measured at remote timepoints?

• Is fear generalization a viable target for explaining the etiology of anxiety-related psychopathology?

• Is it possible to protect or psychologically inoculate against maladaptive forms of fear generalization? Similarly, how can we translate the wealth of experimental fear generalization research into actionable augmentations to or innovations in psychological and biological treatment protocols?

• What are the next steps in mapping the development of fear generalization across the lifespan? Specifically, how does the development of defensive and higher-order neural circuitry and behavioral repertoires in the earlier stages of life contribute to (mal)adaptive fear generalization in later life, and is it causally linked to the development of psychopathology?

• Threat and reward systems heavily overlap, yet how do they interact to produce or modify fear generalization? Is there a latent mechanism sensitive to generalization that differentially responds to the valence of the situation and stimuli? If so, can we leverage reward generalization to buffer against fear generalization in a clinically meaningful way?

Box 4: Behavioral Consequences of Fear Generalization: Avoidance Generalization.

Theories of fear generalization have focused predominantly on biological and subjective threat responses. The true cost, however, extends to the (mal)adaptive behaviors fear motivates. Earlier learning accounts placed equal emphasis on Pavlovian and operant processes [117]: avoidance of a feared CS+ negatively reinforces future avoidance and prevents extinction, maintaining elevated fear and anxiety. Stronger fear generalization should therefore lead to more widespread avoidance. Although avoidance paradigms saw reduced attention following these earlier theories [118], recent work drawing on decision neuroscience and approach-avoidance theory has revitalized interest by considering the cost of avoidance beyond threat evasion—specifically, how often reward is unnecessarily forfeited when avoidance becomes pervasive [119].

The past decade has seen a wave of research on this question. In typical paradigms, participants undergo fear conditioning, then learn to avoid the US at some cost (e.g., forfeiting monetary reward). In a subsequent phase, they choose whether to avoid the CS+, CS−, or perceptually similar GSs. Avoidance generalization mirrors Pavlovian gradients, with the most pronounced avoidance to stimuli resembling the CS+ and parametric decreases with dissimilarity. Individual differences research has identified both risk factors (anxiety-related traits [120–122]) and protective factors (acceptance and reward-related motivation [123–125]) moderating the fear–avoidance relationship, including recent multivariate fMRI evidence [126]. Generalized avoidance also spreads across semantic categories via category conditioning [34,127]. Earlier work relied on dichotomous avoidance decisions, but real-world contexts afford graded responses. More recent paradigms address this through dimensional avoidance scaling with possible reward [128] and effort-based responding that adds cost beyond reward forfeiture [129]. Along with ongoing developments in understanding nonhuman animal avoidance neurobiology [130], avoidance generalization methodology is well positioned to contextualize the behavioral costs of fear generalization and strengthen links between laboratory research and real-world pathology.

In closing, we propose that the field is at an inflection point and generating more complete accounts of how fear spreads beyond their origins—and how this process can be constrained. Sustaining this momentum will require revisiting foundational assumptions as new evidence accumulates. Overall, the next generation of fear generalization must engage with the reality that both depth and breadth across commonly siloed fields is required for meaningful progress. Even light-year advances in human neuroimaging would not compensate for failures at the cross-species and clinical translation levels. Likewise, a perfect understanding of human individual differences in one isolated domain of generalization could still leave substantial gaps in other critical areas. True progress cannot be achieved without deliberate and sustained cross-disciplinary integration.

Highlights.

• Fear generalization research now extends beyond perceptual similarity to higher-order conditioning, category-level learning, avoidance behavior, and the asymmetry between broad fear generalization and context-specific safety learning.

• Cutting-edge animal studies inform evolving neural models and multivariate neuroimaging methods that clarify how threat and safety representations are encoded, distinguished, and overlap in the human brain.

• Inconsistent links between psychiatric diagnoses and fear generalization coincide with growing adoption of dimensional models of psychopathology.

• Dimensional and computational approaches to psychopathology and latent cognitive mechanisms are reshaping how fear generalization is measured, modeled, and linked to clinical traits.

Glossary

Basolateral amygdala (BLA)

A collection of nuclei in the amygdala critical for the encoding and retrieval of the aversive associations which drive both conditioned and generalized threat responses.

CA1 and CA3 (Cornu Ammonis 1 and 3)

Hippocampal subfields implicated in pattern completion, where partial cues reactivate stored representations.

Conditioned response (CR)

A learned defensive response to a CS after pairing with a US. Common measures in humans include skin conductance, startle electromyography, and explicit threat expectancy ratings.

Conditioned stimulus (CS)

A neutral stimulus that elicits defensive responses after pairing with a US. The CS+ is paired with the US; the CS− is unpaired. Differential responses between the CS+ and CS− are used to index the degree of learned fear associations.

Dentate gyrus (DG)

A hippocampal subregion implicated in pattern separation.

Engram

A neuronal ensemble activated during learning that undergoes lasting changes that affect later retrieval. Engram density (proportion of encoding neurons) influences memory specificity.

Extinction

Repeated CS presentation without the US, generating a competing safety memory that coexists with rather than erases the original fear association.

Fear generalization

Extension of conditioned defensive responses to stimuli never directly paired with threat, typically as a function of similarity to the CS+.

Generalization gradient

A function plotting conditioned responding across stimuli varying in similarity to the CS+, typically peaking at the CS+ and decreasing parametrically with dissimilarity.

Generalization stimulus (GS)

A novel stimulus sharing varying similarity with the CS+, used to assess generalized responding.

Higher-order fear conditioning

Learning in which conditioned responses transfer to stimuli indirectly or abstractly linked to the US, including sensory preconditioning and second-order conditioning. Aversive forms of higher-order learning involve both fear generalization and complementary inferential and associative processes (e.g., conditioned inhibition).

Multivariate pattern analysis (MVPA)

Neuroimaging analysis techniques leveraging the information contained in distributed spatial patterns of activity, including representational similarity analysis and decoding analyses.

Overgeneralization

A common but contested term for heightened fear generalization, typically describing purportedly pathological gradients in perceptual fear generalization paradigms.

Pattern completion and pattern separation

Complementary memory operations in which cues either reactivate a stored representation (completion) or are distinguished as distinct (separation).

Perirhinal cortex (PRC)

A parahippocampal cortical region in the medial temporal lobe involved in the encoding and retrieval of neutral stimulus associations.

Prefrontal cortex (PFC)

Frontal lobe cortical regions regulating threat responses and safety learning. Relevant subdivisions include the ventromedial PFC (extinction/safety) and dorsolateral PFC (higher-order cognition).

Second-order conditioning

A learning process in which a CS (CS1) is first paired with a US, then paired with a new neutral stimulus (CS2) in the absence of the US. At a subsequent test the CS2 elicits generalized conditioned responses.

Sensory preconditioning

A learning process in which a CS (CS1) and a neutral stimulus (CS2) are first associated, the CS1 is then paired with a US, and conditioned responses generalize to the non-reinforced CS2 at a subsequent test.

Unconditioned stimulus (US)

An inherently aversive stimulus (e.g., mild shock) that elicits defensive responses without prior learning.