Communications Psychology

Confirmation bias impacts judgments and decisions across a range of domains including finance, policy and science. Here we examine whether explicitly labelling information as true or false disrupts a core underlying computational mechanism that can generate this pervasive bias - asymmetric learning. Human participants (Study 1: N=47; Study 2: N=57) completed a 2 alternative forced choice (2AFC) task previously used to test for the presence of confirmation bias. Participants made choices between pairs of options that could win or lose money and received either factual or counterfactual feedback after each choice. We introduced a key novel feature into the task - providing explicit cues that signalled to participants whether feedback they had seen was true (verified) or false (debunked). Learning in response to feedback was attenuated under false compared to true labels but was present under both. Fitting participants choices to computational models enabled us to examine how sensitivity to the feedback varied as a function of both the label (true/false) and confirmation (confirmatory/disconfirmatory). This revealed a distinct pattern of learning rates typical of confirmation bias (enhanced learning from positive prediction errors for chosen options and from negative prediction errors for unchosen options) in response to both true and false labels. The findings highlight how confirmation bias plays an important role in the effectiveness of interventions designed to verify true and/or debunk false claims. Verification is less likely to succeed when information disconfirms prior beliefs. Conversely, debunking false claims is unlikely to succeed when the information confirms ones prior beliefs.

Memory generalization allows individuals to extract and apply information from prior experiences to novel situations, supporting flexible learning and efficient decision-making. Theoretical models suggest that sleep should facilitate generalization, yet the literature examining its role in promoting generalization is mixed. We recruited 137 participants via Prolific to complete an image-location memory task over two sessions spaced 12 hours apart. Participants were randomly assigned to the Wake group (learning in the morning) or the Sleep group (learning in the evening). In Session 1, participants learned the location of stimuli on the screen and were tested on their memory five minutes later. Twelve hours later, in Session 2, they were tested on their memory again. Stimuli consisted of 160 images from eight semantic categories and were strategically positioned on-screen to test the effects of generalization on retrieval (i.e., category-based memory distortions and biases). After the delay, retrieval was less accurate and demonstrated more generalization. However, these effects were mostly independent of Group, with some evidence for enhanced generalization following a period of wakefulness over sleep. Generalization was also driven by time of day, with more generalization in the evening relative to the morning. Taken together, our results, based on a large online sample, do not support a role for sleep in promoting memory generalization. Significance StatementBehavior is often guided by memories of previous experiences. However, for behavior to be adaptive and flexible (e.g., when encountering never-before-seen stimuli), regularities about the world must be extracted from these memories. This process, termed memory generalization, has been hypothesized to rely on sleep. We used a large online sample to test sleeps role in generalization and found no support for this hypothesis. Our results suggest that sleep and wakefulness contribute to generalization equally, with the latter potentially having a larger contribution.

Confidence judgments play a critical role in guiding behavior by shaping information-seeking, learning, and decision strategies. These functions are most effective when confidence is well calibrated, that is, when subjective uncertainty aligns with the objective uncertainty in the presented evidence. Motivated by this, we investigated how people form confidence judgments from noisy samples of information, and whether they use statistically grounded strategies or rely on heuristics. Participants performed two categorization tasks, one with visual orientation stimuli and one with number stimuli. In each task, participants saw sequentially presented observations and made a decision about the generating category and simultaneously reported their confidence in that decision. We independently manipulated the number of observations and standard deviation of the sample to assess whether confidence reflected an integrated estimate of both sources of statistical uncertainty. Behaviorally, confidence and accuracy both increased with larger sample sizes and lower variability. Furthermore, confidence and accuracy were equivalent in samples matched for standard error, suggesting that participants relied on a statistically grounded strategy. Computational modeling further supported this interpretation: a model that scaled confidence according to the standard error of the sample mean provided the best fit to the data, outperforming more heuristic and Bayesian alternatives. This pattern generalized across the orientation and number tasks, suggesting a domain-general strategy for uncertainty estimation. Together, these findings demonstrate that people use structured, statistically grounded strategies to compute their confidence, supporting well-calibrated decision-making even in the absence of full Bayesian inference.

The benefits of routines for daily functioning are widely acknowledged, yet, despite their apparent importance, methods for quantifying routine maintenance and the causes of their disruption remain lacking. Here, we propose a novel means of defining and quantifying routines (transition entropy). Using the transition entropy, we show that routines can be robustly elicited on tasks that require searching through a grid of squares for a hidden target. Over two experiments (N=100 each), we show that use of routines--as quantified by transition entropy--is robustly perturbed by frequent switches between search grids, as locations specific to the currently irrelevant grid become competitive for selection. Using a normative model that tracks task dynamics, we show that disruption to routines can be attributed to reduced sensitivity to the odds of success for completing a task. This suggests that routine maintenance may be disrupted by over-sensitivity to a lack of reward early in routine performance, or increased expectations regarding the utility of pursuing other tasks.

Predictive coding has influenced many conceptual accounts of delusions, the bizarre and distressing beliefs that accompany a range of neuropsychiatric conditions. However, these explanations remain incomplete and have rarely been tested directly using formal modelling. Here, we present a formal account of delusional beliefs based on hybrid predictive coding, which sheds light on the computational mechanisms underpinning the core features of delusions: thematic recurrence and imperviousness to contradictory evidence. In simulation experiments, we demonstrate that a combination of contextually inadequate initialisation of beliefs and excessive certainty (a hallmark of psychosis), triggers a reorganisation of the generative model relating observed events to hidden causes. This reorganisation enables the maintenance of delusional beliefs that are thematically stable, internally consistent with external inputs, and impervious to contradictory evidence, all without an increase in prediction error. Overall, our results suggest that delusions may arise not from faulty inference, as previously argued, but as an adaptive consequence of generative models learned under atypical conditions. These findings provide mechanistic insights into the computations underpinning delusions and have important implications for a novel therapeutic strategy in terms of re-training generative models.

Despite several age-related processes impacting motor performance, older adults often retain the ability to implicitly adapt to sensory prediction errors. Here, we leverage the fact that implicit adaptation is not attenuated by aging to study the impact of aging on responses to motor errors. In other domains, such as reinforcement learning, aging has been shown to influence how task outcomes or rewards are processed and used to guide subsequent actions, with some studies emphasizing that older adults react more strongly to a miss than to a hit. We aimed to extend these reinforcement learning findings to the motor domain with two preregistered experiments testing whether missing the target leads to larger implicit adaptation in young and older adults to the same extent. In addition, we compared these results to one reinforcement learning task in the motor domain (Boolean feedback after reaching in the absence of visual feedback) and one in the cognitive domain (reward-based decision making). While we found age-related effects in the cognitive domain, we did not observe a consistent effect of age on the modulation of reaching direction or motor adaptation by task outcomes. These results suggest a domain-specific nature of age-related changes in sensitivity to task outcomes.

We readily detect structure in our environments, which in turn guides future learning. Disentangling this structure from the superficial features of a specific learning environment provides an especially strong basis for future generalization, but it remains unclear when and how this kind of abstraction occurs. Memory reactivation during sleep has been hypothesized to support such abstraction, but this has yet to be directly tested. Here we examined this hypothesis by teaching participants novel categories in which patterns of feature covariation were governed by different graph structures. Participants then learned a new category, defined by entirely different features, whose structure was either congruent or incongruent with a previously learned category. If structural knowledge is abstracted away from superficial features, it should facilitate transfer when structures are congruent. In Experiment 1, when two categories were learned in immediate succession, participants showed no transfer benefit, suggesting that structure understanding remained tied to the original features. In Experiment 2, we tested whether offline processing promotes abstraction. Participants either remained awake between learning phases spaced 3 hours apart, or took a nap during which a previously learned category was reactivated using targeted memory reactivation (TMR). Transfer benefits emerged only when the reactivated and target categories shared the same structure, and these benefits increased with the number of cues presented during slow-wave sleep. These findings provide the first direct evidence that memory reactivation during sleep promotes the abstraction of structure, enabling knowledge to transfer across learning episodes with no overlap in features.

Formal thought disorder (FTD) is a core psychosis feature. Disentangling its dimensions requires tasks simple enough for formal modeling yet sensitive enough to capture individual variation across the psychosis spectrum. The semantic verbal fluency task offers precisely this: a structured behavioral trace of semantic memory sampling, amenable to computational analysis using distributed word embeddings. We hypothesized that this sampling process is governed by two dissociable mechanisms mapping onto FTD dimensions: initial retrieval drive (d0), quantifying the motivational resource sustaining production, and semantic search precision (), quantifying how strongly similarity to the preceding word constrains each retrieval step from near-random to highly structured. We hypothesized that reduced d0 would track negative psychosis symptoms and alogia, while degraded would track language disorganization and left inferior longitudinal fasciculus (ILF) fractional anisotropy. We tested these predictions in a primary (N = 120) and an independent replication sample (N = 249) of German-speaking individuals across the psychosis spectrum. Both parameters decreased with greater psychosis severity and, in the primary sample, they dissociated regarding their clinical correlates. d0 correlated negatively with negative symptoms, general psychopathology, and poverty of speech, consistent with a computational signature of alogia. correlated negatively with positive symptoms and cognitive flexibility, and, in individuals with psychosis, positively with left ILF fractional anisotropy. The association between d0 and negative symptoms was replicated in the independent sample. These findings pave the way for mechanistic, automatically derived FTD markers capturing subclinical variation across the psychosis spectrum and mapping onto underlying cognitive and neural processes.

How do we make sense of something weve never seen before? Classifying objects into superordinate classes like animal is a key step in interpreting novel experiences, but is challenging because radically different items (e.g., octopus, rabbit) must somehow be grouped together. In general, no single feature is shared by all members. We reasoned that to classify or imagine novel items from outside the distribution of previous experiences, observers parse objects into meaningful component features that they can mentally recombine ( compositionality). To test this, we asked participants to draw familiar and novel members of nine superordinate object classes. We then asked other participants to classify the drawings, and mark and label their defining parts. We find that human classification performance is well predicted by a Bayesian classifier that optimally combines the part labels, suggesting humans can create and classify out-of-distribution experiences through a compositional generative representation of object features.

Engineering controllers solve musculoskeletal reaching but typically violate the kinematic invariants of human reach: bell-shaped speed profiles, near-straight paths, and a peak-velocity time at 40-50 % of movement duration. For the MyoSuite myoArm (20-DoF, 34 Hill-type muscles) we implement a biologically motivated controller combining (i) Feldmans {lambda}-equilibrium-point hypothesis, (ii) a minimum-jerk virtual trajectory{lambda} (t), (iii) a 200 ms visuomotor correction, and (iv) {gamma}-compatible spinal reflexes (Ia, Ib, reciprocal inhibition). Across n = 50 randomised targets the full controller is practically equivalent to an endpoint-PD + spinal baseline (Cartesian PD descending command paired with the same spinal reflex layer; see [&#167;]2.6) on minimum tip error (Cohens d = +0.03; paired Wilcoxon detects only a +10.6 mm residual against a {approx} 100 mm absolute error, well within a pre-defined {+/-}20 mm equivalence margin) while halving peak speed (1.78 vs 3.90 m s-1, d = -7.39, p < 10-15) and reducing jerk by 40 % (d = -1.74). Only the variant with stretch reflexes brings the velocity-peak ratio into the canonical human range (0.40-0.50). Straightness stays below the human reference, so we frame the result as a partial reproduction of the bell-shape and smoothness invariants, not full human-like reach. A factorial ablation (n = 20) decomposes the contributions: virtual trajectory primarily controls smoothness, visuomotor feedback primarily controls accuracy, and reflexes primarily control velocity-peak timing, with two quantifiable secondary effects reported explicitly. An attempted online cerebellar correction in joint or {lambda} space did not improve performance, consistent with -- but not by itself demonstrating -- the cerebellum as a slow inverse-model learner rather than a within-trial steering controller. We release a deterministic_reset patch for a seeding bug in the MyoSuite reach environments (in the versions tested). The result is mechanistic rather than task-optimal: it attributes separable kinematic axes to distinct biological control layers in a 34-muscle arm.

Some images are consistently remembered better than others, suggesting that memorability reflects intrinsic image properties. We tested whether within-category distinctiveness underlies this effect. Across three experiments (N = 477), participants categorized indoor scenes previously rated for subjective typicality and then completed recognition memory tests. Typical scenes were categorized faster and more accurately, but were remembered worse and showed a more liberal response bias than atypical scenes. These opposing effects were robust across categories. To link subjective typicality to visual representations, we quantified image distinctiveness using a convolutional neural network (CNN). Across layers, CNN-derived distinctiveness closely tracked human typicality judgments and predicted both categorization speed and memorability, with strongest effects in higher, semantic layers. Critically, the memory advantage for atypical scenes persisted even when most images were atypical, ruling out rarity within the experimental context. Together, the results show that intrinsic scene memorability reflects an images position within a category-specific representational space.

Significance StatementAdaptive behavior depends on knowing when to persist and when to let go; even when letting go appears as avoidance. While classical accounts of avoidance emphasize reward-effort trade-offs, we show that these decisions are critically guided by meta-control and inferences about outcome controllability and agency. Using a novel paradigm, we dissociate drivers of avoidance and demonstrate that threat does not uniformly promote disengagement. When outcome control is preserved, threat instead increases persistence, particularly following experiences that build agency in failure-safe contexts. We formalize these dynamics in the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which captures how experience-dependent beliefs about agency guide learning and choice across contexts. Our results show that similar avoidance behaviors can arise from distinct computational pathways. This shifts the focus from global avoidance biases to the dynamic regulation of agency as a core principle of adaptive behavior, with implications for neuroscience, psychiatry, and adaptive artificial intelligence. Adaptive behavior requires deciding when to persist and when to disengage under uncertainty and partial outcome control. Avoidance has often been studied as a response to threat or cost, yet existing paradigms cannot disentangle whether disengagement reflects threat sensitivity, expected failure, or reduced perceived control. We introduce a persistence-escape paradigm that independently manipulates incentive structures, effort demands, and outcome controllability. In a large online sample (N = 457), we show that avoidance is context-dependent rather than a stable, global trait. When outcome control was preserved under threat, the typical avoidance response reversed, promoting persistence rather than withdrawal. At the individual level, high-performing individuals were not uniformly more persistent, but more selective, disengaging when control was low. Moreover, higher anxiety symptoms were linked to cost-dominant evaluation and reduced use of accumulated competence. Conversely, higher depressive symptoms were linked to diminished sensitivity to effort and higher expected failure. To explain these behavioral patterns, we developed the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which embeds value learning and affective evaluation within a meta-control architecture. Critically, we formalize agency as a dynamically inferred learning gate, distinct from self-efficacy, that determines whether outcomes are treated as informative based on controllability and feedback reliability. The model explains context-specific avoidance and reveals that similar behaviors can arise from distinct computational pathways. It further shows how experience in failure-safe contexts guides subsequent behavior in adverse contexts. Our findings show that avoidance is guided by the dynamic regulation of engagement based on inferred controllability and competence. By combining a novel paradigm with a computational model, we provide a formal account of agency and a unifying framework in which meta-control regulates adaptive and maladaptive engagement across contexts, with implications for neuroscience, psychiatry, and adaptive artificial intelligence.

Previous studies suggest that visual mental imagery (VMI) acts as a weaker form of top-down visual perception (VP), with the two becoming more similar as VMI vividness increases. However, this relationship remains ill-defined, and it is unclear precisely how much weaker VMI is relative to VP. Here, we introduce an original probabilistic deep learning approach to quantify vividness at the neural level. Thirty-four participants either imagined or perceived stimuli presented at varying levels of vividness and provided trial-by-trial, picture-based vividness ratings. EEG activity recorded during VP was used to train a convolutional neural network (EEGNet) to predict perceived vividness from eight posterior electrodes located around early visual areas. A leave-one-subject-out cross-validation procedure showed that the model generalised across participants with above-chance accuracy during VP. On VP trials, predictions tracked vividness labels, with reliable interpolation to new vivid labels not included during training. Applied to VMI trials, mean expected VMI vividness remained substantially lower than expected vividness for seen stimuli but slightly higher than baseline, supporting a barely rather than quasi depictive imagery. For 91% of participants, mean expected VMI vividness was also lower than, yet scaled with, mean reported VMI vividness. This framework provides a principled way to quantify and compare VMI and VP on a shared neural-behavioural scale, with implications for studying individual differences and aphantasia.

The ability to store abstract mental representations underlies generalization across virtually every domain of human cognition, from vision and language to concept learning. Yet whether the motor system generates such abstractions and whether they causally contribute to skill learning remain open questions. Here, we introduce a paradigm in which human participants learned to refine novel movement patterns by learning to precisely copy unfamiliar handwritten characters. To examine the role of motor abstractions in this form of motor learning, participants were trained on markedly rotated versions of the characters, which recruited vastly different muscle commands while still maintaining the relevant abstract movement trajectory. Across eight experiments, abstraction training drove robust skill improvements that were comparable to having repetitive practice on the canonical form of each novel character. Moreover, this learning was motoric in nature: it required neither visual feedback nor visual mental imagery and was sensitive to the sequential structure of the abstract movement trajectory. These findings establish a causal role for abstract representations in motor learning, revealing that the motor system likely deploys abstractions in the earliest stages of skill acquisition.

Adaptive learning requires distinguishing environmental volatility from observation stochasticity, two sources of uncertainty that demand opposite adjustments to the learning rate but inflate experienced variance similarly. Disentangling them is computationally difficult with no tractable closed-form solution. Particle-filter methods are the natural tool for this kind of joint inference, but their stochastic likelihoods and non-differentiable objectives force derivative-free fitting protocols and discourage the individual-difference analyses central to cognitive modeling, where small effect sizes leave little room for additional estimator noise. We introduce the Categorical Bayes Filter (CBF), a deterministic alternative that preserves the conditional structure of recent particle-filter accounts but replaces the stochastic outer layer with a categorical distribution on a quantile grid parameterized through differentiable Beta quantile functions. The procedure performs evidence maximization with an exact, deterministic marginal likelihood that is fully differentiable in the grid parameters. In a volatility-stochasticity task with N = 643 participants, fitted CBF dispersion parameters reveal a cross-over phenotyping pattern between volatility-blind and stochasticity-blind subjects that is not recoverable from particle-filter parameters fit to the same data under a state-of-the-art protocol. The deterministic structure also yields a trial-by-trial ambiguity signal that predicts response times not used in fitting. More broadly, the approach opens individual-level analyses in cognitive modeling and computational psychiatry that stochastic methods have effectively foreclosed.

Digital mental health applications enable high-frequency behavioral monitoring and scalable interventions. Journaling provides a therapeutically grounded and intrinsically engaging activity for many users. AI-based text analysis enables privacy-preserving phenotyping of clinically relevant patterns in naturalistic writing, including emotional distress and behavioral risk (e.g., indicators of intent, planning, or preparatory actions for harm to self or others). We evaluated a mobile journaling platform in an 8-week randomized controlled trial (N = 507) of young adults with mild-to-moderate anxiety and depression symptoms. Journaling produced modest reductions in anxiety relative to controls at the 8-week endpoint and 1-month follow-up (d = 0.16-0.19). Effects were small and did not remain significant after correction for multiple comparisons; complementary Bayesian models nonetheless provided moderate-to-strong directional evidence (90-97%) supporting a modest anxiety reduction. In parallel, behavioral phenotyping analyses showed that high-risk journal entries were more common among younger users (OR = 0.77 per year of age, p = 0.007). Text-based risk signals and self-reported energy exhibited significant circadian variation (e.g., risk probability was highest during late-night and overnight hours). Within-person analyses demonstrated strong short-term persistence in mood and risk states, with calm/relaxed showing the highest persistence and anxious/agitated exhibiting the lowest persistence. High-risk journal entries clustered temporally and were preceded by sustained low valence and energy. Although affective volatility was associated with acute declines within the same affective dimension (pleasantness or energy), it was not associated with escalation to high-risk states. Key behavioral dynamics observed in the trial were replicated in an independent general population dataset (N = 16,630). Collectively, these findings demonstrate that privacy-preserving digital journaling can support scalable longitudinal behavioral phenotyping and real-time risk monitoring while providing modest clinical benefit for anxiety symptoms.

Each individual person looks at natural scenes in their own unique way, resulting in a distinct perceptual experience of the world. However, little is known about why such differences in gaze emerge. Here, we test the hypothesis that idiosyncrasies in gaze behavior are predicted by inter-subject variations in internal models--expectations about how scenes typically look. In two experiments, we first characterized participants personal internal models by asking them to draw typical bathroom and kitchen scenes. Individual differences in these drawings were quantified using an objective deep learning pipeline and, in turn, related to individual differences in gaze behavior. In Experiment 1, where participants freely viewed a set of kitchen and bathroom photographs, inter-subject similarities in internal models did not predict inter-subject similarities in gaze. In Experiment 2, we encouraged strategic exploration through gaze-contingent viewing and a memory task. Here, inter-subject similarities in internal models predicted similarities in fixation frequency and the sequence in which different object categories were inspected. These findings suggest that the influence of internal models on visual exploration is stronger under increased sensory uncertainty and when expectation-guided sampling of the environment is encouraged. Together, our results provide new insights into how individual expectations shape gaze behavior and help explain why people differ in how they explore the visual world.

Incomplete letter recognition tasks are frequently used to detect visual deficits arising from neurodegenerative syndromes, including Posterior Cortical Atrophy (PCA; visual-variant Alzheimers disease). A recent development of this approach is the Graded Incomplete Letters Test (GILT), which measures recognition thresholds for letters degraded by removing pixelated sections (decreasing completeness). Although GILT thresholds are strongly elevated in PCA relative to typical adults, the precise cortical visual impairments underlying these deficits are unclear, as is the potential contribution from age-related optical limitations. We compared candidate cortical factors (crowding and global integration) with optical limitations (blur and low contrast) by simulating these factors in typical adults (n=6) viewing incomplete letter stimuli. Participants identified foveally presented letters (12 alternatives), with completeness varied using QUEST. At baseline, thresholds averaged [~]5% completeness. Optical factors were simulated by separately applying blur and lowered contrast. These factors had minimal effect on thresholds, except where blur/contrast levels approached visibility limits, where thresholds rose modestly but remained far below clinical levels in PCA. Cortical factors were simulated by increasing crowding (disruptions from clutter) through peripheral presentation, with global-integration impairments simulated by varying pixel size to alter the distribution of degradation (limiting spatial integration) or degrading letters dynamically with limited-lifetime pixels (limiting temporal integration). These manipulations substantially elevated thresholds, with combined crowding and global-integration impairments increasing thresholds to levels comparable with PCA. We conclude that impaired incomplete letter recognition is driven primarily by cortical rather than optical factors, and that neurodegenerative deficits may reflect the combined impact of multiple cortical limitations.

How do humans store sequences that far exceed working memory capacity? Using visuo-spatial and binary auditory sequences, we previously showed that a Language of Thought (LoT) architecture -- in which simple primitives are recursively combined into hierarchical programs -- enables efficient storage of structured sequences. Here we ask whether this principle extends to purely ordinal structure: sequences defined by how items repeat and in what order, as in AABBCCAABBCC, independently of their spatial content. Across three experiments, participants reproduced 12-item sequences of spatial locations with various ordinal structures. The minimal description length derived from the LoT model predicted recall accuracy with remarkable precision (r = .96), substantially outperforming Shannon entropy, Lempel-Ziv complexity, chunking models and subjective complexity ratings. Critically, fine-grained analyses of participants inter-click intervals during reproduction revealed systematic slowdowns at the hierarchical boundaries predicted by the LoT programs, providing a behavioral signature of the underlying mental syntax. These results identify a compact vocabulary of mental primitives -- repetition, mirroring, and interleaving -- whose composition accounts for the symbolic compression of ordinal structures. For ordinal regularities, human sequence memory operates as a form of program induction, leveraging a domain-general capacity for hierarchical compression to encode complex structured information. Author SummaryHuman short-term memory is heavily limited, holding no more than a few items at once. Yet humans routinely memorize complex sequences that far exceed this capacity. How is this possible? We propose that the brain acts like a programmer: rather than storing each element individually, it compresses sequences into short mental "programs." Just as a programmer writes "repeat ABC four times" instead of typing ABCABCABCABC, the brain leverages regularities such as repetitions (ABC-ABC) or mirror patterns (ABC-CBA) to encode sequences efficiently. We tested this idea across three experiments: two in which participants memorized and reproduced sequences of spatial positions on a screen, one where they only rated their perceived complexity. Sequences described by shorter programs were remembered far better and judged as simpler -- even when they were the same length as less structured sequences. When reproducing sequences, participants paused longer at structural boundaries, revealing the internal organization of their mental programs. Strikingly, program length predicted memory performance better than participants own complexity ratings, suggesting that these mental representations are not fully accessible to conscious awareness. Finally, we identified key new patterns -- including temporal inversion and interleaving -- that extend the Language of Thought framework. Together, these findings suggest that a compositional Language of Thought is a fundamental aspect of how the human brain efficiently store and represent structured information.

The Mnemonic Similarity Task (MST) is highly sensitive to age-related cognitive decline in humans and has been adapted for rodents using 3D objects, where aged animals show deficits in discriminating similar lures. To improve translational alignment with human testing and increase automation, we developed a touchscreen-based rat analog using a morphed Object-Cued Spatial Choice (OCSC) task with 2D image stimuli. Young (4-month) and aged (21-month) male and female Fischer 344 x Brown Norway hybrid rats were trained in Bussey-Saksida touchscreen chambers and tested on discrimination performance using image pairs that varied parametrically in feature overlap. We also assessed perirhinal cortical engagement in a subset of animals using Arc expression as a readout of activity-related principal cell firing following low-and high-overlap task epochs. Across shaping and procedural training, aged rats required more errors to reach criterion on one stimulus set, but both age groups successfully acquired the task. During morph testing, performance declined systematically as stimulus similarity increased, confirming that the task manipulated discrimination difficulty. However, contrary to expectations, young and aged rats performed similarly across overlap conditions, with no significant age-related impairment. In the Arc experiment, discrimination accuracy was again reduced by greater stimulus overlap, but Arc expression in perirhinal cortex did not differ reliably by age or overlap condition, although expression was associated with behavioral accuracy and deep layers showed higher ensemble similarity than superficial layers. These findings indicate that, while the touchscreen morph OCSC task is sensitive to stimulus similarity, it does not detect the robust age-related mnemonic discrimination deficits previously observed with 3D object-based rodent MST paradigms, underscoring the importance of considering ethological relevance when designing translational cognitive assays.