Strong mnemonic prediction errors increase cognitive control, attention, and arousal

Internal models of the world are constructed using memories of past experiences. Knowledge retained from events in the past enables predictions about ongoing and future events, allowing for adaptive behavior. As events unfold, alignment between what is experienced and what is predicted provides validation for the current internal model and signals that continuing to retrieve information from memory may be useful for guiding behavior. On the other hand, divergence of ongoing experience from mnemonic predictions signals that the environment has potentially changed and that past experiences may not be informative or relevant for current and future behavior. Such mnemonic prediction errors (MPEs) may elicit increased attention and arousal (Nolden et al., 2024) and signal that more cognitive control – the collection of cognitive and neural mechanisms that effortfully influence information processing to be aligned with one’s goals (Cohen, 2017) – is needed.

Insights into the effects of MPEs on attention and arousal have come from studies of event segmentation. According to Event Segmentation Theory (Zacks and Swallow, 2007; Kurby and Zacks, 2008), the mismatch between one’s internal model and ongoing events can elicit a prediction error, leading to event segmentation (i.e., the psychological perception of an event boundary along with accompanying neural computations), which discretizes current experience into ‘episodes’ in memory (Radvansky and Zacks, 2017; Ezzyat and Davachi, 2011). Extant data indicate that increases in pupil size are observed at event boundaries (Clewett et al., 2020; Clewett et al., 2025) and in response to prediction errors of varying content, signs, and modalities (den Ouden et al., 2012; Frank and Kafkas, 2021; Brod et al., 2022). Such changes in pupil size reflect increases in attention and arousal (Kjaerby et al., 2022; Strauch et al., 2022; Joshi and Gold, 2020) and may be linked to noradrenergic activity in the locus coeruleus (LC) (Joshi and Gold, 2020; Joshi et al., 2016). The LC has been associated with signaling strong prediction errors (Jordan, 2024; Yu and Dayan, 2005), also characterized as ‘global model failure signals’ (Jordan, 2024) that initiate a network reset (Bouret and Sara, 2005). The LC may further support adaptive behavior in the face of MPEs by enhancing memory encoding to facilitate model updating (Duszkiewicz et al., 2019). New learning following MPEs may depend on increases in attention, by direct modulation of hippocampal learning mechanisms (Duszkiewicz et al., 2019; Wahlheim and Zacks, 2025), or both. While MPE responses in the hippocampus appear to vary with prediction strength (Chen et al., 2015), it is unclear whether effects on attention and arousal are similarly modulated. Here, we hypothesize that pupil size will show a greater increase in response to strong compared to weak MPEs, that MPE-induced increases in attention and arousal will support learning, and that learning from MPEs will differ as a function of prediction strength (Brod et al., 2022).

Complementing evidence of MPE-related increases in pupil size (Clewett et al., 2020; Clewett et al., 2025; Brod et al., 2022), time-resolved electrophysiological measures of visuospatial attention – specifically, electroencephalography (EEG) posterior alpha power suppression (Klimesch, 2012; Peylo et al., 2021) – may also show that attention increases following MPEs, evidenced by greater alpha suppression (Carp and Compton, 2009). The underlying sources of these measures of attention may be partially shared, given that pupil size and posterior alpha are coupled in certain contexts (Pfeffer et al., 2022; Podvalny et al., 2021; Hong et al., 2014). Moreover, LC lesions in monkeys reduce the amplitude of the P3 event-related potential (ERP), another electrophysiological measure of attention/alertness (Foote et al., 1991). However, it remains unclear whether LC activity additionally modulates posterior alpha suppression. Posterior alpha and pupil size could also be differentially influenced by other mechanisms of MPE detection, such as those subserved by prefrontal cortex (PFC) (Helfrich et al., 2017).

PFC theta, observed using magnetoencephalography (MEG) (Garrido et al., 2015; Rhodes et al., 2023) and scalp EEG (Cavanagh et al., 2010; Cavanagh et al., 2012; Mas-Herrero and Marco-Pallarés, 2014), is known canonically as frontal theta (Cavanagh and Frank, 2014) or frontal midline theta (Cavanagh and Cohen, 2022) and is considered a marker of cognitive control (Cavanagh and Frank, 2014; Hsieh and Ranganath, 2014). Frontal theta increases following reward prediction errors (Cavanagh et al., 2010; Mas-Herrero and Marco-Pallarés, 2014; Cavanagh and Frank, 2014), scales with negative prediction errors (Cavanagh et al., 2010), and fosters behavioral adaptation (Cavanagh et al., 2010). Behavioral adaptation in response to increases in frontal theta may be mediated by changes in information processing (Cavanagh and Frank, 2014) by way of increased attention. Control may directly increase attention to and prioritization of bottom-up sensory information from the external environment and the overcoming of memory-guided, but now irrelevant, representations and behavioral responses (Badre, 2025). In rodents, PFC activity can modulate LC activity (Totah et al., 2021), raising the possibility that the behavioral adaptation effects associated with increased frontal theta could be mediated in part by changes in LC activity and thus pupil-linked attention and arousal. Whether MPEs elicit (a) an increase in cognitive control that (b) consequently results in increases in attention and arousal to facilitate adaptive behavior remains an open question. A primary aim of the current study was to test whether there are interactions among cognitive control, attention, and arousal upon detection of MPEs.

Characterization of potential process interactions may additionally provide critical insights into mechanisms by which MPEs facilitate learning in service of future behavior. Higher learning rates following MPEs may be mediated, in part, by increases in attention and arousal (Nolden et al., 2024), which facilitate memory encoding (Brod et al., 2022; Schwartz et al., 2025; Chun and Turk-Browne, 2007). In addition, increases in pupil-linked attention and arousal following MPEs could reflect direct involvement of LC activity in fostering learning, given that optogenetic activation of LC neurons in mice enhances memory encoding (Takeuchi et al., 2016) and higher LC integrity among older adults is associated with better episodic memory performance (Dahl et al., 2023). LC effects on learning may also be mediated by the hippocampus, which receives profuse projections from LC neurons (Takeuchi et al., 2016). Increases in attention and arousal following MPEs may require the initial detection that cognitive control, as instantiated via frontal theta (Cavanagh and Frank, 2014), is needed. Higher frontal theta prior to (Cohen et al., 2015) and during (Hsieh and Ranganath, 2014; White et al., 2013) encoding has independently been associated with better subsequent memory, leaving open the additional possibility that MPE-related learning effects stem from changes in frontal theta. Finally, MPE-driven increases in frontal theta may also enhance learning by driving hippocampal theta (Garrido et al., 2015), which is also associated with better subsequent memory (Lin et al., 2017).

There are thus multiple possible pathways by which MPEs impact cognition. Here, we focus on two mechanisms: (1) increases in cognitive control (assayed via frontal theta) and (2) increases in attention and arousal (assayed via posterior alpha and pupil size). To elicit MPEs, we used an associative memory task wherein participants were tested on their memory for verb-picture pairings they had studied four times (strong memories) or once (weak memories). Associative memory of a paired picture was cued by the presentation of a studied verb. On a subset of trials, verb presentation was followed by the presentation of a mismatched picture. Onset of a mismatch would indicate a violation of a mnemonic prediction and the elicitation of an MPE (Chen et al., 2011). Using time-resolved scalp EEG (Experiment 1) and pupillometry (Experiments 1 and 2) measured as participants completed the associative memory task, we document the effects of strong vs. weak MPEs on cognitive control, attention, and arousal. Then, we characterize how control signals interact with attention and arousal signals, and how EEG and pupil assays of attention/arousal relate to each other. Finally, we examine whether MPE-related changes in each of these physiological assays relate to the encoding of prediction-violating stimuli, as evidenced by subsequent memory for mismatch probes.

Collectively, our findings (Figure 1) show that strong MPEs, relative to weak MPEs, evoke greater increases in cognitive control (frontal theta), attention (posterior alpha suppression and pupil), and arousal (pupil). Exploratory mediation analyses did not provide strong support for the hypothesis that increases in attention and/or arousal induced by MPEs can be explained in part by increases in cognitive control. Finally, increases in frontal theta, but not posterior alpha or pupil size, are linked to enhanced learning of strong prediction-violating (i.e., mismatch) stimuli. As such, strong MPEs trigger increases in multiple neurocognitive modulatory processes, with error-driven increases in cognitive control fostering new learning.

Figure 1

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We conducted and, for most analyses, pooled data across two experiments that used a common associative memory task paradigm with pupillometry. Experiments 1 and 2 differed in: participant recruitment pool; the duration of the delay between the Associative Memory Task and a final subsequent Recognition Test on the mismatch probes; day of administration of the gradual continuous performance task (gradCPT); inclusion of a Localizer Task (only administered in Experiment 1); and concurrent recording of EEG (only collected in Experiment 1). Images of animate and inanimate objects were taken from the BOSS database (Brodeur and Guérard, 2014), an Animacy x Size stimulus set (Konkle and Caramazza, 2013), and the internet. Objects were isolated from their white or complex backgrounds and were then overlaid on a gray background for stimulus presentation. Scene stimuli in the Localizer Task in Experiment 1 were taken from the ‘Massive Memory’ Scene Categories stimulus set (Konkle et al., 2010). Luminance of image stimuli in all tasks, with the exception of the gradCPT, was controlled using the SHINE Toolbox (Willenbockel et al., 2010).

Experimental design

Associative memory task

Participants performed an associative memory task, which consisted of an associative encoding phase and an associative retrieval test. The associative memory task was administered in four study-test cycles, with encoding and retrieval phases separated by a 3 min distractor task wherein participants reported the parity of sequentially presented integers ranging from 1 to 100. Each integer was presented immediately following a response. The purpose of the distractor task was to prevent participants from maintaining the studied pairs in working memory prior to the associative retrieval test.

In the associative encoding phase, participants were instructed to learn verb-picture pairs by forming a mental image involving the verb and the image in each pair (Figure 2a). On each trial, an action verb was presented at the center of the screen for 1.5 s. The subsequent inter-stimulus interval (ISI) ranged from 0.5 to 1.5 s after which a picture of an animate or inanimate object was then displayed for 2.5 s. During the presentation of the object, participants generated a mental image and reported (using a button press) whether their mental image of the verb-picture pairing was ‘very vivid’ or ‘not very vivid.’ The inter-trial interval (ITI) ranged from 1.25 to 1.5 s. Participants were shown 52 pairs in each encoding phase; 26 were presented four times over the course of an encoding phase cycle (‘Strong’ pairings) and 26 were presented once (‘Weak’ pairings). Repeated exposures to strong pairings were distributed across the encoding phase cycle, such that each strong pairing was viewed once during each quartile of trials. As such, each cycle of encoding included 130 trials (26 pairs presented four times and 26 pairs presented once). A black fixation dot was presented at the center of the screen during all trials and participants were instructed to fixate throughout. Verb-picture pairings were randomly assigned per participant, as was the assignment of pairs to the Strong and Weak conditions.

Figure 2 with 5 supplements see all

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After the distractor phase, an associative retrieval test examined memory for the verb-picture pairings (Figure 2b). On each trial, a verb cue was presented for 1 s. Participants were instructed to try to bring to mind the associated picture as soon as the cue appeared on the screen. Following a 1 s delay, during which only the central fixation dot was presented on the screen, a picture was then presented for 0.75 s. Participants were instructed to respond as quickly as possible, using a button press to indicate whether or not they thought they had studied the picture with that verb. Memory for all 52 pairs in the encoding phase of a given cycle was tested. The brief (0.75 s) presentation duration of the test probe and instruction to respond rapidly were designed to encourage participants to retrieve the associate upon presentation of the verb cue, setting up a situation in which memory of the associate and perception of the probe either match or mismatch (Chen et al., 2011). To focus on trials on which retrieval likely preceded probe onset, retrieval task trials were retained for analysis if responses were made within 1 s after probe onset (Chen et al., 2011). In each study-test cycle, 36 trials were ‘Match’ trials, wherein the picture after the verb matched that studied in the encoding phase, and 16 were ‘Mismatch’ trials, wherein the picture did not match what was studied. On Mismatch trials, the probe picture was from a different category as the correct associate (i.e., if the picture associated with a verb cue was animate, the mismatch probe was inanimate, and vice versa). To control for stimulus novelty during retrieval, mismatch stimuli were initially introduced to participants at the outset of the experiment in a pre-exposure 1-back task (see below). ITIs ranged from 1.25 to 1.5 s. A black fixation dot was presented at the center of the screen at all times and participants were instructed to fixate throughout. The test order of pairings was randomized.

Each study-test cycle included unique verbs and pictures. The assignment of each picture to each verb for Match and Mismatch pairings was randomized across participants.

Pre-exposure task

At the outset of the experiment, prior to the associative memory task, participants performed a 1-back task in which they were shown a sequence of images (each presented for 2 s) separated by variable-length ITIs (1.0–1.2 s). For each stimulus, participants were instructed to report, using a button press, whether the current stimulus was the same as or different from the previous stimulus. To maximize the duration with which participants attended to each stimulus, they were instructed to provide their response when each stimulus had left the screen. There were 96 unique stimuli in total (48 animate and 48 inanimate objects); 64 of the stimuli were later presented as mismatch probes during the retrieval phase of the associative memory task (see above), and all stimuli were later presented in the subsequent recognition test; the order of the stimuli was randomized. Of the 32 items not presented in the retrieval phase of the associative memory task, nine were repeated in the 1-back task. A black fixation dot was presented at the center of the screen at all times during all trials and participants were instructed to fixate on the central dot throughout the task. The purpose of this pre-exposure task was to familiarize participants with the stimuli that would later serve as mismatch probes during the associative memory task. By using mismatch probes that were familiar rather than novel, item novelty could not be a primary source of memory evidence guiding decisions in the retrieval phase of the associative memory task.

GradCPT

To obtain a subject-level assay of attention, we collected 5 min of gradCPT (Rosenberg et al., 2013) data in each experiment with modified stimulus presentation timings. In Experiment 1, participants performed the gradCPT after four cycles of the associative memory task to introduce a delay before the surprise recognition test (see below) and prevent maintenance of material studied in the associative memory task in working memory. In Experiment 2, where the surprise recognition test took place 48 hrs after the associative memory test, the gradCPT was performed after the recognition test in Session 2. In the gradCPT, a centrally presented picture gradually transitioned between different mountain and city images (90% of images were of cities and 10% were of mountains). Each image was presented for 0.05 s and transitioned to the next image over 0.8 s. Participants were instructed to press the spacebar when they saw a city scene and to withhold responding for mountain scenes. Performance in the gradCPT was not central to the core hypotheses and is not reported in the current manuscript.

Recognition test

To assess the effects of MPEs on learning, a recognition test was administered. In this surprise recognition memory test, participants were tested on their memory for the 64 mismatch probes shown during the associative retrieval test. Non-mismatch foils on the recognition test consisted of the 32 stimuli from the pre-exposure 1-back that were not repeated in that task nor used as mismatch probes during the associative retrieval test; as such, they were old but not associated with MPEs. An additional 64 novel foils were included in the recognition test. On each recognition trial, participants reported whether they had seen the image at any point during the experiment, by pressing one button for ‘old’ and another for ‘new.’ Each image was displayed for a minimum of 1.2 s and a maximum of 2 s, such that if participants failed to respond within 1.2 s, the image remained on the screen for an additional 0.8 s. ITIs varied from 0.5 to 1.5 s and the order of the stimuli was randomized. A black fixation dot was presented at the center of the screen at all times and participants were instructed to fixate. In Experiment 1, the recognition test was administered after the gradCPT. In Experiment 2, the recognition test was the first task administered in Session 2. The recognition test examined subsequent memory for mismatch probes either ~5–10 min (Expt 1) or ~48 hrs (Expt 2) after the associative memory task.

Localizer task

At the end of Experiment 1, participants performed a second 1-back task designed to enable training of an EEG classifier that discriminates between evoked electrophysiological activity patterns associated with animate and inanimate objects. The localizer task was included in Experiment 1 to address other research questions and results from this task are not reported in the current manuscript. For completeness, the task is described here. On each trial, an image of an animate object, an inanimate object, or a scene (Konkle et al., 2010) was displayed on the screen for 2 s. Images were separated by a 1.0–1.2 s ITI. The instructions were the same as for the pre-exposure 1-back task, such that participants were instructed to report whether a given image was the same as or different from the previous image, and to respond after the image had left the screen. The stimuli were novel (i.e., had not appeared in any other experimental phase), with 270 unique stimuli presented in total (90 per category); the order of the stimuli was randomized. Ten percent of the stimuli were repeated once (as 1-backs) over the course of the task.

Features of present experiences often overlap with those of the past. Shared features allow for aspects of current experience to cue memories and enable knowledge about how similar events unfolded in the past to inform predictions about ongoing experience. When ongoing experience unexpectedly diverges from mnemonic predictions, multiple neurocognitive processes may be elicited, fostering adaptation to novel circumstances and facilitating more accurate predictions in the future. Drawing on behavioral, electrophysiological, and pupillary assays, the current study revealed that strong MPEs elicit increases in cognitive control, attention, and arousal, with potential impacts on later cognition and new learning (for a summary, see Figure 1).

Data and code for this manuscript are accessible at https://github.com/alicexue/mpe-attention-cog-ctrl (copy archived at Xue, 2026).

1. 1. Anderson AK
   2. Wais PE
   3. Gabrieli JDE

   (2006) Emotion enhances remembrance of neutral events past

   PNAS 103:1599–1604.

   https://doi.org/10.1073/pnas.0506308103

   • Google Scholar
2. 1. Anderson KL
   2. Rajagovindan R
   3. Ghacibeh GA
   4. Meador KJ
   5. Ding M

   (2010) Theta oscillations mediate interaction between prefrontal cortex and medial temporal lobe in human memory

   Cerebral Cortex 20:1604–1612.

   https://doi.org/10.1093/cercor/bhp223

   • PubMed
   • Google Scholar
3. 1. Arnsten AF
   2. Goldman-Rakic PS

   (1984) Selective prefrontal cortical projections to the region of the locus coeruleus and raphe nuclei in the rhesus monkey

   Brain Research 306:9–18.

   https://doi.org/10.1016/0006-8993(84)90351-2

   • PubMed
   • Google Scholar
4. 1. Badre D

   (2025) Cognitive control

   Annual Review of Psychology 76:167–195.

   https://doi.org/10.1146/annurev-psych-022024-103901

   • PubMed
   • Google Scholar
5. 1. Barr DJ

   (2013) Random effects structure for testing interactions in linear mixed-effects models

   Frontiers in Psychology 4:328.

   https://doi.org/10.3389/fpsyg.2013.00328

   • PubMed
   • Google Scholar
6. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear mixed-effects models using lme4

   Journal of Statistical Software 67:1–48.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
7. 1. Bein O
   2. Duncan K
   3. Davachi L

   (2020) Mnemonic prediction errors bias hippocampal states

   Nature Communications 11:3451.

   https://doi.org/10.1038/s41467-020-17287-1

   • Google Scholar
8. 1. Bouret S
   2. Sara SJ

   (2005) Network reset: a simplified overarching theory of locus coeruleus noradrenaline function

   Trends in Neurosciences 28:574–582.

   https://doi.org/10.1016/j.tins.2005.09.002

   • PubMed
   • Google Scholar
9. 1. Brockmann MD
   2. Pöschel B
   3. Cichon N
   4. Hanganu-Opatz IL

   (2011) Coupled oscillations mediate directed interactions between prefrontal cortex and hippocampus of the neonatal rat

   Neuron 71:332–347.

   https://doi.org/10.1016/j.neuron.2011.05.041

   • PubMed
   • Google Scholar
10. 1. Brod G
    2. Greve A
    3. Jolles D
    4. Theobald M
    5. Galeano-Keiner EM

    (2022) Explicitly predicting outcomes enhances learning of expectancy-violating information

    Psychonomic Bulletin & Review 29:2192–2201.

    https://doi.org/10.3758/s13423-022-02124-x

    • Google Scholar
11. 1. Brodeur MB
    2. Guérard K

    (2014) 930 new normative photos

    PLOS ONE 9:e106953.

    https://doi.org/10.1371/journal.pone.0106953

    • Google Scholar
12. 1. Bürkner PC

    (2017) Brms: an r package for bayesian multilevel models using stan

    Journal of Statistical Software 80:1–28.

    https://doi.org/10.18637/jss.v080.i01

    • Google Scholar
13. 1. Carp J
    2. Compton RJ

    (2009) Alpha power is influenced by performance errors

    Psychophysiology 46:336–343.

    https://doi.org/10.1111/j.1469-8986.2008.00773.x

    • PubMed
    • Google Scholar
14. 1. Cavanagh JF
    2. Frank MJ
    3. Klein TJ
    4. Allen JJB

    (2010) Frontal theta links prediction errors to behavioral adaptation in reinforcement learning

    NeuroImage 49:3198–3209.

    https://doi.org/10.1016/j.neuroimage.2009.11.080

    • PubMed
    • Google Scholar
15. 1. Cavanagh JF
    2. Figueroa CM
    3. Cohen MX
    4. Frank MJ

    (2012) Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation

    Cerebral Cortex 22:2575–2586.

    https://doi.org/10.1093/cercor/bhr332

    • Google Scholar
16. 1. Cavanagh JF
    2. Frank MJ

    (2014) Frontal theta as a mechanism for cognitive control

    Trends in Cognitive Sciences 18:414–421.

    https://doi.org/10.1016/j.tics.2014.04.012

    • PubMed
    • Google Scholar
17. Book

    1. Cavanagh JF
    2. Cohen MX

    (2022) Frontal midline theta as a model specimen of cortical theta

    In: Gable PA, Miller MW, Bernat EM, editors. The Oxford Handbook of EEG Frequency. Oxford University Press. pp. 178–201.

    https://doi.org/10.1093/oxfordhb/9780192898340.013.9

    • Google Scholar
18. 1. Chen J
    2. Olsen RK
    3. Preston AR
    4. Glover GH
    5. Wagner AD

    (2011) Associative retrieval processes in the human medial temporal lobe: Hippocampal retrieval success and CA1 mismatch detection

    Learning & Memory 18:523–528.

    https://doi.org/10.1101/lm.2135211

    • Google Scholar
19. 1. Chen J
    2. Dastjerdi M
    3. Foster BL
    4. LaRocque KF
    5. Rauschecker AM
    6. Parvizi J
    7. Wagner AD

    (2013) Human hippocampal increases in low-frequency power during associative prediction violations

    Neuropsychologia 51:2344–2351.

    https://doi.org/10.1016/j.neuropsychologia.2013.03.019

    • PubMed
    • Google Scholar
20. 1. Chen J
    2. Cook PA
    3. Wagner AD

    (2015) Prediction strength modulates responses in human area CA1 to sequence violations

    Journal of Neurophysiology 114:1227–1238.

    https://doi.org/10.1152/jn.00149.2015

    • Google Scholar
21. 1. Chun MM
    2. Turk-Browne NB

    (2007) Interactions between attention and memory

    Current Opinion in Neurobiology 17:177–184.

    https://doi.org/10.1016/j.conb.2007.03.005

    • PubMed
    • Google Scholar
22. 1. Clewett D
    2. Gasser C
    3. Davachi L

    (2020) Pupil-linked arousal signals track the temporal organization of events in memory

    Nature Communications 11:4007.

    https://doi.org/10.1038/s41467-020-17851-9

    • PubMed
    • Google Scholar
23. 1. Clewett D
    2. Huang R
    3. Davachi L

    (2025) Locus coeruleus activation “resets” hippocampal event representations and separates adjacent memories

    Neuron 113:2521–2535.

    https://doi.org/10.1016/j.neuron.2025.05.013

    • PubMed
    • Google Scholar
24. 1. Cohen N
    2. Pell L
    3. Edelson MG
    4. Ben-Yakov A
    5. Pine A
    6. Dudai Y

    (2015) Peri-encoding predictors of memory encoding and consolidation

    Neuroscience and Biobehavioral Reviews 50:128–142.

    https://doi.org/10.1016/j.neubiorev.2014.11.002

    • PubMed
    • Google Scholar
25. Book

    1. Cohen JD

    (2017) Cognitive control: core constructs and current considerations

    In: Egner T, editors. The Wiley Handbook of Cognitive Control 1–28. John Wiley & Sons. pp. 1–28.

    https://doi.org/10.1002/9781118920497.ch1

    • Google Scholar
26. 1. Craik FIM
    2. Govoni R
    3. Naveh-Benjamin M
    4. Anderson ND

    (1996) The effects of divided attention on encoding and retrieval processes in human memory

    Journal of Experimental Psychology 125:159–180.

    https://doi.org/10.1037/0096-3445.125.2.159

    • Google Scholar
27. 1. Dahl MJ
    2. Bachman SL
    3. Dutt S
    4. Düzel S
    5. Bodammer NC
    6. Lindenberger U
    7. Kühn S
    8. Werkle-Bergner M
    9. Mather M

    (2023) The integrity of dopaminergic and noradrenergic brain regions is associated with different aspects of late-life memory performance

    Nature Aging 3:1128–1143.

    https://doi.org/10.1038/s43587-023-00469-z

    • PubMed
    • Google Scholar
28. 1. den Ouden HEM
    2. Kok P
    3. de Lange FP

    (2012) How prediction errors shape perception, attention, and motivation

    Frontiers in Psychology 3:548.

    https://doi.org/10.3389/fpsyg.2012.00548

    • PubMed
    • Google Scholar
29. 1. Devoto P
    2. Flore G
    3. Saba P
    4. Fà M
    5. Gessa GL

    (2005) Stimulation of the locus coeruleus elicits noradrenaline and dopamine release in the medial prefrontal and parietal cortex

    Journal of Neurochemistry 92:368–374.

    https://doi.org/10.1111/j.1471-4159.2004.02866.x

    • PubMed
    • Google Scholar
30. 1. Duncan K
    2. Ketz N
    3. Inati SJ
    4. Davachi L

    (2012) Evidence for area CA1 as a match/mismatch detector: a high‐resolution fMRI study of the human hippocampus

    Hippocampus 22:389–398.

    https://doi.org/10.1002/hipo.20933

    • Google Scholar
31. 1. Duszkiewicz AJ
    2. McNamara CG
    3. Takeuchi T
    4. Genzel L

    (2019) Novelty and dopaminergic modulation of memory persistence: a tale of two systems

    Trends in Neurosciences 42:102–114.

    https://doi.org/10.1016/j.tins.2018.10.002

    • PubMed
    • Google Scholar
32. 1. Ezzyat Y
    2. Davachi L

    (2011) What constitutes an episode in episodic memory?

    Psychological Science 22:243–252.

    https://doi.org/10.1177/0956797610393742

    • PubMed
    • Google Scholar
33. 1. Florin-Lechner SM
    2. Druhan JP
    3. Aston-Jones G
    4. Valentino RJ

    (1996) Enhanced norepinephrine release in prefrontal cortex with burst stimulation of the locus coeruleus

    Brain Research 742:89–97.

    https://doi.org/10.1016/s0006-8993(96)00967-5

    • PubMed
    • Google Scholar
34. 1. Foote SL
    2. Berridge CW
    3. Adams LM
    4. Pineda JA

    (1991) Electrophysiological evidence for the involvement of the locus coeruleus in alerting, orienting, and attending

    Progress in Brain Research 88:521–532.

    https://doi.org/10.1016/s0079-6123(08)63831-5

    • PubMed
    • Google Scholar
35. 1. Frank D
    2. Kafkas A

    (2021) Expectation-driven novelty effects in episodic memory

    Neurobiology of Learning and Memory 183:107466.

    https://doi.org/10.1016/j.nlm.2021.107466

    • PubMed
    • Google Scholar
36. 1. Garrido MI
    2. Barnes GR
    3. Kumaran D
    4. Maguire EA
    5. Dolan RJ

    (2015) Ventromedial prefrontal cortex drives hippocampal theta oscillations induced by mismatch computations

    NeuroImage 120:362–370.

    https://doi.org/10.1016/j.neuroimage.2015.07.016

    • Google Scholar
37. 1. Gramfort A
    2. Luessi M
    3. Larson E
    4. Engemann DA
    5. Strohmeier D
    6. Brodbeck C
    7. Goj R
    8. Jas M
    9. Brooks T
    10. Parkkonen L
    11. Hämäläinen M

    (2013) MEG and EEG data analysis with MNE-Python

    Frontiers in Neuroscience 7:267.

    https://doi.org/10.3389/fnins.2013.00267

    • PubMed
    • Google Scholar
38. 1. Hasselmo ME
    2. Wyble BP

    (1997) Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function

    Behavioural Brain Research 89:1–34.

    https://doi.org/10.1016/S0166-4328(97)00048-X

    • Google Scholar
39. 1. Hasselmo ME
    2. Bodelón C
    3. Wyble BP

    (2002) A proposed function for hippocampal theta rhythm: separate phases of encoding and retrieval enhance reversal of prior learning

    Neural Computation 14:793–817.

    https://doi.org/10.1162/089976602317318965

    • Google Scholar
40. 1. Helfrich RF
    2. Huang M
    3. Wilson G
    4. Knight RT

    (2017) Prefrontal cortex modulates posterior alpha oscillations during top-down guided visual perception

    PNAS 114:9457–9462.

    https://doi.org/10.1073/pnas.1705965114

    • PubMed
    • Google Scholar
41. 1. Hong L
    2. Walz JM
    3. Sajda P

    (2014) Your eyes give you away: prestimulus changes in pupil diameter correlate with poststimulus task-related EEG dynamics

    PLOS ONE 9:e91321.

    https://doi.org/10.1371/journal.pone.0091321

    • Google Scholar
42. 1. Hsieh LT
    2. Ranganath C

    (2014) Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval

    NeuroImage 85 Pt 2:721–729.

    https://doi.org/10.1016/j.neuroimage.2013.08.003

    • PubMed
    • Google Scholar
43. 1. Jodo E
    2. Chiang C
    3. Aston-Jones G

    (1998) Potent excitatory influence of prefrontal cortex activity on noradrenergic locus coeruleus neurons

    Neuroscience 83:63–79.

    https://doi.org/10.1016/s0306-4522(97)00372-2

    • PubMed
    • Google Scholar
44. 1. Jordan R

    (2024) The locus coeruleus as a global model failure system

    Trends in Neurosciences 47:92–105.

    https://doi.org/10.1016/j.tins.2023.11.006

    • PubMed
    • Google Scholar
45. 1. Joshi S
    2. Li Y
    3. Kalwani RM
    4. Gold JI

    (2016) Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex

    Neuron 89:221–234.

    https://doi.org/10.1016/j.neuron.2015.11.028

    • PubMed
    • Google Scholar
46. 1. Joshi S
    2. Gold JI

    (2020) Pupil size as a window on neural substrates of cognition

    Trends in Cognitive Sciences 24:466–480.

    https://doi.org/10.1016/j.tics.2020.03.005

    • PubMed
    • Google Scholar
47. 1. Kamiński J
    2. Mamelak AN
    3. Birch K
    4. Mosher CP
    5. Tagliati M
    6. Rutishauser U

    (2018) Novelty-sensitive dopaminergic neurons in the human substantia nigra predict success of declarative memory formation

    Current Biology 28:1333–1343.

    https://doi.org/10.1016/j.cub.2018.03.024

    • PubMed
    • Google Scholar
48. 1. Kensinger EA

    (2009) Remembering the details: effects of emotion

    Emotion Review 1:99–113.

    https://doi.org/10.1177/1754073908100432

    • PubMed
    • Google Scholar
49. 1. Kjaerby C
    2. Andersen M
    3. Hauglund N
    4. Untiet V
    5. Dall C
    6. Sigurdsson B
    7. Ding F
    8. Feng J
    9. Li Y
    10. Weikop P
    11. Hirase H
    12. Nedergaard M

    (2022) Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine

    Nature Neuroscience 25:1059–1070.

    https://doi.org/10.1038/s41593-022-01102-9

    • PubMed
    • Google Scholar
50. 1. Klimesch W

    (2012) α-band oscillations, attention, and controlled access to stored information

    Trends in Cognitive Sciences 16:606–617.

    https://doi.org/10.1016/j.tics.2012.10.007

    • PubMed
    • Google Scholar
51. 1. Konkle T
    2. Brady TF
    3. Alvarez GA
    4. Oliva A

    (2010) Scene memory is more detailed than you think: the role of categories in visual long-term memory

    Psychological Science 21:1551–1556.

    https://doi.org/10.1177/0956797610385359

    • Google Scholar
52. 1. Konkle T
    2. Caramazza A

    (2013) Tripartite organization of the ventral stream by animacy and object size

    The Journal of Neuroscience 33:10235–10242.

    https://doi.org/10.1523/JNEUROSCI.0983-13.2013

    • PubMed
    • Google Scholar
53. 1. Kret ME
    2. Sjak-Shie EE

    (2019) Preprocessing pupil size data: guidelines and code

    Behavior Research Methods 51:1336–1342.

    https://doi.org/10.3758/s13428-018-1075-y

    • PubMed
    • Google Scholar
54. 1. Kumaran D
    2. Maguire EA

    (2006) An unexpected sequence of events: mismatch detection in the human hippocampus

    PLOS Biology 4:e424.

    https://doi.org/10.1371/journal.pbio.0040424

    • PubMed
    • Google Scholar
55. 1. Kumaran D
    2. Maguire EA

    (2007) Which computational mechanisms operate in the hippocampus during novelty detection?

    Hippocampus 17:735–748.

    https://doi.org/10.1002/hipo.20326

    • Google Scholar
56. 1. Kumaran D
    2. Maguire EA

    (2009) Novelty signals: a window into hippocampal information processing

    Trends in Cognitive Sciences 13:47–54.

    https://doi.org/10.1016/j.tics.2008.11.004

    • PubMed
    • Google Scholar
57. 1. Kurby CA
    2. Zacks JM

    (2008) Segmentation in the perception and memory of events

    Trends in Cognitive Sciences 12:72–79.

    https://doi.org/10.1016/j.tics.2007.11.004

    • PubMed
    • Google Scholar
58. 1. Lepage M
    2. Ghaffar O
    3. Nyberg L
    4. Tulving E

    (2000) Prefrontal cortex and episodic memory retrieval mode

    PNAS 97:506–511.

    https://doi.org/10.1073/pnas.97.1.506

    • PubMed
    • Google Scholar
59. 1. Lin J-J
    2. Rugg MD
    3. Das S
    4. Stein J
    5. Rizzuto DS
    6. Kahana MJ
    7. Lega BC

    (2017) Theta band power increases in the posterior hippocampus predict successful episodic memory encoding in humans

    Hippocampus 27:1040–1053.

    https://doi.org/10.1002/hipo.22751

    • PubMed
    • Google Scholar
60. 1. Lisman JE
    2. Grace AA

    (2005) The hippocampal-VTA loop: controlling the entry of information into long-term memory

    Neuron 46:703–713.

    https://doi.org/10.1016/j.neuron.2005.05.002

    • PubMed
    • Google Scholar
61. 1. Madore KP
    2. Khazenzon AM
    3. Backes CW
    4. Jiang J
    5. Uncapher MR
    6. Norcia AM
    7. Wagner AD

    (2020) Memory failure predicted by attention lapsing and media multitasking

    Nature 587:87–91.

    https://doi.org/10.1038/s41586-020-2870-z

    • PubMed
    • Google Scholar
62. 1. Mas-Herrero E
    2. Marco-Pallarés J

    (2014) Frontal theta oscillatory activity is a common mechanism for the computation of unexpected outcomes and learning rate

    Journal of Cognitive Neuroscience 26:447–458.

    https://doi.org/10.1162/jocn_a_00516

    • PubMed
    • Google Scholar
63. 1. Mitchell DJ
    2. McNaughton N
    3. Flanagan D
    4. Kirk IJ

    (2008) Frontal-midline theta from the perspective of hippocampal “theta”

    Progress in Neurobiology 86:156–185.

    https://doi.org/10.1016/j.pneurobio.2008.09.005

    • PubMed
    • Google Scholar
64. 1. Mulligan NW

    (1998) The role of attention during encoding in implicit and explicit memory

    Journal of Experimental Psychology 24:27–47.

    https://doi.org/10.1037/0278-7393.24.1.27

    • Google Scholar
65. 1. Neves RM
    2. van Keulen S
    3. Yang M
    4. Logothetis NK
    5. Eschenko O

    (2018) Locus coeruleus phasic discharge is essential for stimulus-induced gamma oscillations in the prefrontal cortex

    Journal of Neurophysiology 119:904–920.

    https://doi.org/10.1152/jn.00552.2017

    • PubMed
    • Google Scholar
66. 1. Nolden S
    2. Turan G
    3. Güler B
    4. Günseli E

    (2024) Prediction error and event segmentation in episodic memory

    Neuroscience and Biobehavioral Reviews 157:105533.

    https://doi.org/10.1016/j.neubiorev.2024.105533

    • PubMed
    • Google Scholar
67. 1. Numan R

    (2021) The Prefrontal-Hippocampal comparator: volition and episodic memory

    Perceptual and Motor Skills 128:2421–2447.

    https://doi.org/10.1177/00315125211041341

    • PubMed
    • Google Scholar
68. 1. Peylo C
    2. Hilla Y
    3. Sauseng P

    (2021) Cause or consequence? Alpha oscillations in visuospatial attention

    Trends in Neurosciences 44:705–713.

    https://doi.org/10.1016/j.tins.2021.05.004

    • PubMed
    • Google Scholar
69. 1. Pfeffer T
    2. Keitel C
    3. Kluger DS
    4. Keitel A
    5. Russmann A
    6. Thut G
    7. Donner TH
    8. Gross J

    (2022) Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

    eLife 11:e71890.

    https://doi.org/10.7554/eLife.71890

    • PubMed
    • Google Scholar
70. 1. Podvalny E
    2. King LE
    3. He BJ

    (2021) Spectral signature and behavioral consequence of spontaneous shifts of pupil-linked arousal in human

    eLife 10:e68265.

    https://doi.org/10.7554/eLife.68265

    • PubMed
    • Google Scholar
71. 1. Radvansky GA
    2. Zacks JM

    (2017) Event boundaries in memory and cognition

    Current Opinion in Behavioral Sciences 17:133–140.

    https://doi.org/10.1016/j.cobeha.2017.08.006

    • PubMed
    • Google Scholar
72. 1. Rajasethupathy P
    2. Sankaran S
    3. Marshel JH
    4. Kim CK
    5. Ferenczi E
    6. Lee SY
    7. Berndt A
    8. Ramakrishnan C
    9. Jaffe A
    10. Lo M
    11. Liston C
    12. Deisseroth K

    (2015) Projections from neocortex mediate top-down control of memory retrieval

    Nature 526:653–659.

    https://doi.org/10.1038/nature15389

    • PubMed
    • Google Scholar
73. Software

    1. R Development Core Team

    (2024) R: a language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
74. 1. Rhodes N
    2. Rea M
    3. Boto E
    4. Rier L
    5. Shah V
    6. Hill RM
    7. Osborne J
    8. Doyle C
    9. Holmes N
    10. Coleman SC
    11. Mullinger K
    12. Bowtell R
    13. Brookes MJ

    (2023) Measurement of frontal midline theta oscillations using OPM-MEG

    NeuroImage 271:120024.

    https://doi.org/10.1016/j.neuroimage.2023.120024

    • PubMed
    • Google Scholar
75. 1. Rosenberg M
    2. Noonan S
    3. DeGutis J
    4. Esterman M

    (2013) Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task

    Attention, Perception & Psychophysics 75:426–439.

    https://doi.org/10.3758/s13414-012-0413-x

    • PubMed
    • Google Scholar
76. 1. Rouhani N
    2. Niv Y

    (2021) Signed and unsigned reward prediction errors dynamically enhance learning and memory

    eLife 10:e61077.

    https://doi.org/10.7554/eLife.61077

    • Google Scholar
77. 1. Sara SJ
    2. Hervé-Minvielle A

    (1995) Inhibitory influence of frontal cortex on locus coeruleus neurons

    PNAS 92:6032–6036.

    https://doi.org/10.1073/pnas.92.13.6032

    • PubMed
    • Google Scholar
78. 1. Schwartz ST
    2. Yang H
    3. Xue AM
    4. Wagner AD

    (2025) Attending to remember: recent advances in methods and theory

    Current Directions in Psychological Science 34:330–341.

    https://doi.org/10.1177/09637214251339452

    • Google Scholar
79. 1. Siapas AG
    2. Lubenov EV
    3. Wilson MA

    (2005) Prefrontal phase locking to hippocampal theta oscillations

    Neuron 46:141–151.

    https://doi.org/10.1016/j.neuron.2005.02.028

    • PubMed
    • Google Scholar
80. 1. Sinclair AH
    2. Manalili GM
    3. Brunec IK
    4. Adcock RA
    5. Barense MD

    (2021) Prediction errors disrupt hippocampal representations and update episodic memories

    PNAS 118:e2117625118.

    https://doi.org/10.1073/pnas.2117625118

    • PubMed
    • Google Scholar
81. 1. Strauch C
    2. Wang C-A
    3. Einhäuser W
    4. Van der Stigchel S
    5. Naber M

    (2022) Pupillometry as an integrated readout of distinct attentional networks

    Trends in Neurosciences 45:635–647.

    https://doi.org/10.1016/j.tins.2022.05.003

    • PubMed
    • Google Scholar
82. 1. Takeuchi T
    2. Duszkiewicz AJ
    3. Sonneborn A
    4. Spooner PA
    5. Yamasaki M
    6. Watanabe M
    7. Smith CC
    8. Fernández G
    9. Deisseroth K
    10. Greene RW
    11. Morris RGM

    (2016) Locus coeruleus and dopaminergic consolidation of everyday memory

    Nature 537:357–362.

    https://doi.org/10.1038/nature19325

    • PubMed
    • Google Scholar
83. 1. Tomita H
    2. Ohbayashi M
    3. Nakahara K
    4. Hasegawa I
    5. Miyashita Y

    (1999) Top-down signal from prefrontal cortex in executive control of memory retrieval

    Nature 401:699–703.

    https://doi.org/10.1038/44372

    • PubMed
    • Google Scholar
84. 1. Totah NK
    2. Logothetis NK
    3. Eschenko O

    (2021) Synchronous spiking associated with prefrontal high γ oscillations evokes a 5-Hz rhythmic modulation of spiking in locus coeruleus

    Journal of Neurophysiology 125:1191–1201.

    https://doi.org/10.1152/jn.00677.2020

    • PubMed
    • Google Scholar
85. 1. Tulving E

    (2002) Episodic memory: from mind to brain

    Annual Review of Psychology 53:1–25.

    https://doi.org/10.1146/annurev.psych.53.100901.135114

    • PubMed
    • Google Scholar
86. 1. Urai AE
    2. Braun A
    3. Donner TH

    (2017) Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias

    Nature Communications 8:14637.

    https://doi.org/10.1038/ncomms14637

    • PubMed
    • Google Scholar
87. 1. Varga DK
    2. Raykov PP
    3. Jefferies E
    4. Ben-Yakov A
    5. Bird CM

    (2025) Hippocampal mismatch signals are based on episodic memories and not schematic knowledge

    PNAS 122:e2503535122.

    https://doi.org/10.1073/pnas.2503535122

    • PubMed
    • Google Scholar
88. 1. Vinogradova OS

    (2001) Hippocampus as comparator: role of the two input and two output systems of the hippocampus in selection and registration of information

    Hippocampus 11:578–598.

    https://doi.org/10.1002/hipo.1073

    • Google Scholar
89. 1. Vuorre M
    2. Bolger N

    (2018) Within-subject mediation analysis for experimental data in cognitive psychology and neuroscience

    Behavior Research Methods 50:2125–2143.

    https://doi.org/10.3758/s13428-017-0980-9

    • PubMed
    • Google Scholar
90. 1. Wahlheim CN
    2. Zacks JM

    (2025) Memory updating and the structure of event representations

    Trends in Cognitive Sciences 29:380–392.

    https://doi.org/10.1016/j.tics.2024.11.008

    • PubMed
    • Google Scholar
91. 1. Waskom ML
    2. Frank MC
    3. Wagner AD

    (2017) Adaptive engagement of cognitive control in context-dependent decision making

    Cerebral Cortex 27:1270–1284.

    https://doi.org/10.1093/cercor/bhv333

    • Google Scholar
92. 1. White TP
    2. Jansen M
    3. Doege K
    4. Mullinger KJ
    5. Park SB
    6. Liddle EB
    7. Gowland PA
    8. Francis ST
    9. Bowtell R
    10. Liddle PF

    (2013) Theta power during encoding predicts subsequent-memory performance and default mode network deactivation

    Human Brain Mapping 34:2929–2943.

    https://doi.org/10.1002/hbm.22114

    • PubMed
    • Google Scholar
93. 1. Willenbockel V
    2. Sadr J
    3. Fiset D
    4. Horne GO
    5. Gosselin F
    6. Tanaka JW

    (2010) Controlling low-level image properties: the SHINE toolbox

    Behavior Research Methods 42:671–684.

    https://doi.org/10.3758/BRM.42.3.671

    • PubMed
    • Google Scholar
94. Software

    1. Xue AM

    (2026) Mpe-attention-cog-ctrl, version swh:1:rev:d1443971f1b402b0abcfa740f67bedc99daf88db

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:3ac439122caa72e6e4eb447ebc0a4a48d80f1dec;origin=https://github.com/alicexue/mpe-attention-cog-ctrl;visit=swh:1:snp:2844cfd3acb0b267fb1b1d067b68c486ffa4fa86;anchor=swh:1:rev:d1443971f1b402b0abcfa740f67bedc99daf88db
95. 1. Yu AJ
    2. Dayan P

    (2005) Uncertainty, neuromodulation, and attention

    Neuron 46:681–692.

    https://doi.org/10.1016/j.neuron.2005.04.026

    • PubMed
    • Google Scholar
96. 1. Zacks JM
    2. Swallow KM

    (2007) Event segmentation

    Current Directions in Psychological Science 16:80–84.

    https://doi.org/10.1111/j.1467-8721.2007.00480.x

    • PubMed
    • Google Scholar

Author details

1. Alice M Xue

   1. Department of Psychology, Stanford University, Stanford, United States
   2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   alicexue@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0006-3176-8786

2. Jenna Jokhani

   1. Department of Psychology, Stanford University, Stanford, United States
   2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

3. Anthony M Norcia

   1. Department of Psychology, Stanford University, Stanford, United States
   2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

4. Anthony D Wagner

   1. Department of Psychology, Stanford University, Stanford, United States
   2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0624-4543

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