Javier Ortiz-Tudela

Sudden perceptual learning refers to the abrupt disambiguation of an initially ambiguous stimulus. Despite the limited encoding time, these brief perceptual experiences transform into enduring memory engrams. Still, a comprehensive framework of why these memories are so long-lasting and their neural underpinnings, in particular the role of the hippocampus, is currently lacking. Here, we build on the apparent connection of sudden perceptual learning with long-term memory to outline how a representational perspective may help to overcome current limitations, similar to other areas of memory research. In short, we claim that sudden perceptual learning triggers initial prediction errors across the hierarchy, including strong higher-level (semantic) ones. Once a solution is found, the formerly ambiguous image can be connected to previous schemas and a disambiguation cascade of prediction error minimization is triggered, starting with semantic prediction errors. As a consequence, these events lead to low-dimensional representations that show signs of early semanticization, thereby favouring semantics over perceptual details. In sum, we propose that multiple memory traces are simultaneously encoded during sudden perceptual learning, and while low-dimensional representations engage neocortical structures, high-dimensional, episodic representations require the hippocampus. This perspective offers a promising framework to advance research on sudden perceptual learning and related phenomena. This article is part of the theme issue 'The role of hippocampal predictions in cognition: bridging perception and memory'.

Misinformed and polarized beliefs often persist despite repeated fact-checking efforts. This resistance to evidence is amplified by contextual features of political information environments and underlies political motives that distort judgments about which policy information is accurate and which media source is reliable. Across two preregistered experiments (N = 735), participants evaluated the accuracy of political news headlines from a reliable and an unreliable source. After each initial evaluation, fact-checking feedback was provided followed by a second evaluation. We modelled inference using a dual reinforcement learning framework which captured within-trial belief updating and between-trial updating of trust in sources. We assessed the influence of four contextual factors: politically charged content, partisan source cues, political congruence of fact-checking evidence, and the type of news engagement (accuracy assessment vs news sharing). Results revealed that those who tended to incorporate fact-checking evidence into their beliefs were also likely to update their trust in sources. Despite this positive link, belief updating and trust updating were influenced by different factors. Belief updating followed near Bayesian dynamics and was compromised by politically charged content but improved when individuals engaged in news sharing. In contrast, trust updating was more resistant to fact-checking and was undermined by partisan source cues, politically-incongruent fact-checks, and news sharing. These findings suggest that different features of political information environments may enhance misinformation susceptibility and belief polarization through distinct effects on belief updating and trust in news sources. Recognizing these divergent pathways can inform targeted interventions to curb political misinformation and belief polarization.

Visual inputs during natural perception are highly ambiguous: objects are frequently occluded, lighting conditions vary, and object identification depends significantly on prior experiences. However, why do certain images remain unidentifiable while others can be recognized immediately, and what visual features drive subjective clarification? To address these critical questions, we developed a unique dataset of 1854 ambiguous images and collected more than 100,000 ratings (from a total of 947 participants) evaluating their identifiability before and after seeing undistorted versions of the images. Relating the representations of a brain-inspired neural network model in response to our images with human ratings, we show that subjective identification depends largely on the extent to which higher-level visual features from the original images are preserved in their ambiguous counterparts. In line with these results, an image-level regression analysis showed that the subjective identification of ambiguous images was best explained by high-level visual dimensions. Notably, the predominance of higher-level features over lower-level ones softens after participants disambiguate the images, suggesting that the visual system flexibly shifts between top-down guessing to bottom-up matching after disambiguation. Moreover, we found that the process of ambiguity resolution was accompanied by a notable decrease in semantic distance and a greater consistency in object naming among participants. However, the relationship between information gained after disambiguation and subjective identification was non-linear, indicating that acquiring more information does not necessarily enhance subjective clarity. Instead, we observed a U-shaped relationship, suggesting that subjective identification improves when the acquired information either strongly matches or mismatches prior predictions. Collectively, these findings advance our understanding on how we resolve ambiguity and extract meaning from incomplete visual information.

Sudden perceptual learning refers to the abrupt disambiguation of an initially ambiguous stimulus. Despite the limited encoding time, these brief perceptual experiences transform into enduring memory engrams. Still, a comprehensive framework of why these memories are so long-lasting and their neural underpinnings, in particular the role of the hippocampus, is currently lacking. Here, we build on the apparent connection of sudden perceptual learning with long-term memory to outline how a representational perspective may help to overcome current limitations, similar to other areas of memory research. In short, we claim that sudden perceptual learning triggers initial prediction errors across the hierarchy, including strong higher-level (semantic) ones. Once a solution is found, the formerly ambiguous image can be connected to previous schemas and a disambiguation cascade of prediction error minimization is triggered, starting with semantic prediction errors. As a consequence, these events lead to low-dimensional representations that show signs of early semanticization, thereby favouring semantics over perceptual details. In sum, we propose that multiple memory traces are simultaneously encoded during sudden perceptual learning, and while low-dimensional representations engage neocortical structures, high-dimensional, episodic representations require the hippocampus. This perspective offers a promising framework to advance research on sudden perceptual learning and related phenomena.

Visual inputs during natural perception are highly ambiguous: objects are frequently occluded, lighting conditions vary, and object identification depends significantly on prior experiences. However, why do certain images remain unidentifiable while others can be recognized immediately, and what visual features drive subjective clarification? To address these critical questions, we developed a unique dataset of 1,854 ambiguous images and collected more than 100,000 ratings (from a total of 947 participants) evaluating their identifiability before and after seeing undistorted versions of the images. Relating the representations of a brain-inspired neural network model in response to our images with human ratings, we show that subjective identification depends largely on the extent to which higher-level visual features from the original images are preserved in their ambiguous counterparts. In line with these results, an image-level regression analysis showed that the subjective identification of ambiguous images was best explained by high-level visual dimensions. Notably, the predominance of higher-level features over lower-level ones softens after participants disambiguate the images, suggesting that the visual system flexibly shifts between top-down guessing to bottom-up matching after disambiguation. Moreover, we found that the process of ambiguity resolution was accompanied by a notable decrease in semantic distance and a greater consistency in object naming among participants. However, the relationship between information gained after disambiguation and subjective identification was non-linear, indicating that acquiring more information does not necessarily enhance subjective clarity. Instead, we observed a U-shaped relationship, suggesting that subjective identification improves when the acquired information either strongly matches or mismatches prior predictions. Collectively, these findings advance our understanding on how we resolve ambiguity and extract meaning from incomplete visual information.

The predictive processing framework posits that one of the main functions of the brain is to anticipate the incoming information. Internal models facilitate interactions with the world by predicting future states against which actual evidence is compared. The difference between predicted and actual states, the prediction error (PE), signals novel information. However, how PE affects cognitive processing downstream is not fully understood: one such aspect pertains to how PE influences episodic memories, and whether those effect on memory differ across the lifespan. We examine the relationship between PE and episodic memory in children, young and older adults. We use a novel paradigm whereby rich visual narratives are used to build action schemas that enable probing different mnemonic aspects. To create different levels of PE, we manipulate the story endings to be either expected, neutral or unexpected with respect to the unfolded action. We show that (i) expected endings are better encoded than neutral endings and (ii) unexpected endings improve the encoding of mismatching events and other aspects of the narrative. These effects are differentially modulated across the lifespan with PE-driven encoding being more prominent in children and young adults and with schema integration playing a larger role on memory encoding in older adults. These results highlight the role of predictions by enriching past experiences and informing future anticipations. This article is part of the theme issue ‘Elements of episodic memory: lessons from 40 years of research’.

Perception is an intricate interplay between feedforward visual input and internally generated feedback signals that comprise concurrent contextual and time-distant mnemonic (episodic and semantic) information. Yet, an unresolved question is how the composition of feedback signals changes across the lifespan and to what extent feedback signals undergo age-related dedifferentiation, i.e., a decline in neural specificity. Previous research on this topic has focused on feedforward perceptual representation and episodic memory reinstatement, suggesting reduced fidelity of neural representations at the item and category levels. In this fMRI study, we combined an occlusion paradigm that filters feedforward input to the visual cortex and multivariate analysis techniques to investigate the information content in cortical feedback, focusing on age-related differences in its composition. We further asked to what extent differentiation in feedback signals (in the occluded region) is correlated to differentiation in feedforward signals. Comparing younger (18 - 30 years) and older female and male adults (65 -75 years), we found that contextual but not mnemonic feedback was prone to age-related dedifferentiation. Semantic feedback signals were even better differentiated in older adults, highlighting the growing importance of generalized knowledge across age. We also found that differentiation in feedforward signals was correlated with differentiation in episodic but not semantic feedback signals. Our results provide evidence for age-related adjustments in the composition of feedback signals and underscore the importance of examining dedifferentiation in aging for both feedforward and feedback processing.Significance StatementCognitive decline in aging is related to reduced neural specificity (dedifferentiation) in the brain, which has mainly been examined in feedforward processing. Using an occlusion paradigm, we tested whether there is dedifferentiation in contextual and mnemonic feedback signals internally generated in the early visual cortex to aid perception. Older adults’ contextual but not mnemonic feedback signals suffered from dedifferentiation, with semantic mnemonic representations being even better differentiated in older age. Neural differentiation between feedforward and episodic feedback signals was positively correlated in both age groups. In sum, these results highlight the growing importance of semantic knowledge across the lifespan and imply that the impact of dedifferentiation on cognition highly depends on the nature of the recruited information.

The predictive processing framework posits that brain function is essentially predictive. Internal models facilitate mental time travel by predicting future states against which incoming information is compared. The difference between predicted and actual states, the prediction error (PE), signals novel information. However, the constrains of the further use of PE are not fully understood: In particular, with respect to how PE influence episodic memories across the lifespan. We examine the relationship between PE and episodic memory in children, young and older adults. We use a novel paradigm that uses rich visual narratives to build action schemas and enables measuring different mnemonic aspects. Critically, we manipulate the endings to be either expected, neutral, or unexpected based on the unfolded action. We show that 1) expected endings are better encoded than neutral ones and 2) unexpected endings upregulate encoding of the mismatching event but also of other aspects of the narrative. Moreover, these effects are modulated across the lifespan with PE-driven-encoding being more prominent in children and young adults and with schema integration playing a larger role on memory encoding in older adults. These results highlight the role of predictions in mental time travel by enriching past experiences and informing future anticipations.

The characterization of the relationship between predictions and one-shot episodic encoding poses an important challenge for memory research. On the one hand, events that are compatible with our previous knowledge are thought to be remembered better than incompatible ones. On the other hand, unexpected situations, by virtue of their novelty, are known to cause enhanced learning. Several theoretical accounts try to solve this apparent paradox by conceptualizing prediction error (PE) as a continuum ranging from low PE (for expectation-matching events) to high PE (for expectation-mismatching ones). Under such a framework, the relationship between PE and memory encoding would be described by a U-shape function with higher memory performance for extreme levels of PE and lower memory for middle levels of PE. In this study, we tested the framework by using a gradual manipulation of the strength of association between scenes and objects to render different levels of PE and then tested for item memory of the (mis)matching events. In two experiments, in contrast to what was anticipated, recognition memory for object identity followed an inverted U-shape as a function of PE, with higher performance for intermediate levels of PE. Furthermore, in two additional experiments, we showed the relevance of explicit predictions at encoding to reveal such an inverted U pattern, thus providing the boundary conditions of the effect. We discussed our findings in light of existing literature relating PE and episodic memory, pointing out the potential roles of uncertainty in the environment, and the importance of the cognitive operations underlying encoding tasks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).