Acute otitis externa: Consensus definition, diagnostic criteria and core outcome set development.

OBJECTIVE: Evidence for the management of acute otitis externa (AOE) is limited, with unclear diagnostic criteria and variably reported outcome measures that may not reflect key stakeholder priorities. We aimed to develop 1) a definition, 2) diagnostic criteria and 3) a core outcome set (COS) for AOE. STUDY DESIGN: COS development according to Core Outcome Measures in Effectiveness Trials (COMET) methodology and parallel consensus selection of diagnostic criteria/definition. SETTING: Stakeholders from the United Kingdom. SUBJECTS AND METHODS: Comprehensive literature review identified candidate items for the COS, definition and diagnostic criteria. Nine individuals with past AOE generated further patient-centred candidate items. Candidate items were rated for importance by patient and professional (ENT doctors, general practitioners, microbiologists, nurses, audiologists) stakeholders in a three-round online Delphi exercise. Consensus items were grouped to form the COS, diagnostic criteria, and definition. RESULTS: Candidate COS items from patients (n = 28) and literature (n = 25) were deduplicated and amalgamated to a final candidate list (n = 46). Patients emphasised quality-of-life and the impact on daily activities/work. Via the Delphi process, stakeholders agreed on 31 candidate items. The final COS covered six outcomes: pain; disease severity; impact on quality-of-life and daily activities; patient satisfaction; treatment-related outcome; and microbiology. 14 candidate diagnostic criteria were identified, 8 reaching inclusion consensus. The final definition for AOE was 'diffuse inflammation of the ear canal skin of less than 6 weeks duration'. CONCLUSION: The development and adoption of a consensus definition, diagnostic criteria and a COS will help to standardise future research in AOE, facilitating meta-analysis. Consulting former patients throughout development highlighted deficiencies in the outcomes adopted previously, in particular concerning the impact of AOE on daily life

Reward Prediction Errors Shape Memory during Reinforcement Learning

In this dissertation, I characterize the role or reward prediction errors (RPEs) in shaping episodic memory across three series of behavioral experiments and computational modeling of learning and memory behavior. In Chapter 1, I show that large unsigned RPEs increase learning for those outcomes (i.e., learning rate) as well as memory for those outcome events. However, I do not find these effects to be correlated, suggesting distinct underlying mechanisms. In Chapter 2, I further test whether depressive symptoms modulate unsigned-RPE effects on learning and memory. I do not find depressive symptoms to lead to overall differences in learning and memory. Instead, I find that symptom group predicts opposite biases in the unsigned-RPE modulation of memory: in depressive participants, unsigned RPEs increased memory more for negative- versus positive-RPE events, whereas in non-depressive participants, unsigned RPEs increased memory more for positive- versus negative-RPE events. In Chapter 3, I dissociate the effects of RPEs experienced at reward cue from those at outcome on learning and memory for those events. I show, in line with classic associative models of attention, that signed RPEs at reward cue and unsigned RPEs at reward outcome modulate a dynamic learning rate in reinforcement learning models fit to behavior. When characterizing RPE effects on memory, I replicate previous results and find that unsigned RPEs at outcome enhance memory throughout learning, especially for outcome events. In addition to this, memory for cue events increases as a function of learning wherein a signed RPE at cue boosts memory for events associated with more valued reward categories. Finally, in Chapter 4, I investigate the computational mechanism supporting better memory for large unsigned-RPE events by testing whether they create event boundaries in memory. Large-RPE events are more strongly encoded and show intact associative links with their predecessors; nevertheless, they consistently disrupt the integration of events that occur across them, thereby creating event boundaries in memory. I capture these effects in a computational model of memory modified to incorporate RPEs into the encoding process. To conclude, I link my findings to interactions between reinforcement learning and memory systems, offering targets for future neuroscientific research

Negative emotional events retroactively disrupt semantic scaffolding of temporal memory

Emotional responses pervade everyday life and exert temporally extended effects on cognition. Prior work shows that these modulatory effects of emotion on memory are highly selective, with semantic overlap helping to determine which nearby neutral details are prioritized in long-term memory. Although this has been demonstrated in item recognition, less is known about how emotion interacts with semantic information to influence temporal memory. Here, we developed an emotional oddball task in which participants encoded lists of neutral words that were either semantically related to or unrelated to a perceptually deviant emotional or neutral oddball word. We hypothesized that an adaptive memory system should selectively enhance temporal order and recall memory for information that precedes or follows a conceptually related emotional stimulus. We found that order memory was enhanced for word pairs that preceded a semantically related neutral oddball, suggesting that semantics helps to scaffold temporal encoding processes. By contrast, emotional oddballs retroactively disrupted this mnemonic benefit of semantic overlap on temporal memory. Emotional oddballs also led to proactive impairments in order memory irrespective of semantic relatedness. After a 24-hr delay, emotion enhanced recall of preceding, semantically unrelated words. Encountering an emotional oddball also enhanced recall for subsequent words irrespective of semantic relatedness. Our findings suggest that emotion bidirectionally and selectively disrupts the temporal organization of memory, while also enhancing memory for individualized, unrelated elements of an emotional episode

Multiple routes to enhanced memory for emotionally relevant events

Events associated with aversive or rewarding outcomes are prioritized in memory. This memory boost is commonly attributed to the elicited affective response, closely linked to noradrenergic and dopaminergic modulation of hippocampal plasticity. Here, we review and compare this ‘affect’ mechanism to an additional, recently discovered, ‘prediction’ mechanism whereby memories are strengthened by the extent to which outcomes deviate from expectations, that is, by prediction errors. The mnemonic impact of prediction errors is separate from the affective outcome itself and has a distinct neural signature. While both routes enhance memory, these mechanisms are linked to different, and sometimes opposing, predictions for memory integration. We discuss new findings that highlight mechanisms by which emotional events strengthen, integrate, and segment memory

Decisions on a platter: Food biases and arousal alter reward learning

Food seeking and avoidance are powerful drivers of decision-making in healthy and clinical populations. The urge to eat engages primary reward systems in the brain, thought to bias preference for energy-dense or high-calorie food more likely to sate hunger. This process is however interrupted in eating disorders where high-calorie food is avoided. It is nevertheless unclear how innate or learned food biases may interact with general reward processing to predict learning and decisions. We developed a novel paradigm to investigate whether and how biases for high- and low-calorie food alter decision-making in a large sample of participants with typical eating (‘TE’) and disordered eating (‘DE’) behavior. Importantly, food characteristics (high- vs. low-calorie) were completely incidental to the task goal of maximizing monetary reward. An arousal manipulation involving a large monetary win or loss, examined whether heightened arousal, thought to influence goal-directed behavior, enhances the impact of food biases on reward learning. Consistent with prior notions of food-relevant biases, the TE group learned better (i.e., made more correct choices) when high-calorie foods were rewarding while the DE group learned better when low-calorie foods were rewarding. The arousal manipulation boosted this group-dependent bias. Fitting behavior to reinforcement learning models enabled us to identify distinct cognitive components underlying the effect. Specifically, the impact of food-related biases on learning was best explained when accounting for group differences in the initial values and learning rates (for positive prediction errors) for high- and low-calorie foods. In other words, typical and disordered eaters showed differences in their pre-experimental preference for a food type (high- or low-calorie food, respectively), as well as the extent to which they learned from positive outcomes associated with that preference. These findings provide a mechanistic account of how food biases alter reward-based choice, especially under heightened arousal. Our results suggest that interventions altering innate or learned food preference should target habit-directed mechanisms to help mitigate maladaptive eating behavior

Reward prediction errors create event boundaries in memory

We remember when things change. Particularly salient are experiences where there is a change in rewards, eliciting reward prediction errors (RPEs). How do RPEs influence our memory of those experiences? One idea is that this signal directly enhances the encoding of memory. Another, not mutually exclusive, idea is that the RPE signals a deeper change in the environment, leading to the mnemonic separation of subsequent experiences from what came before, thereby creating a new latent context and a more separate memory trace. We tested this in four experiments where participants learned to predict rewards associated with a series of trial-unique images. High-magnitude RPEs indicated a change in the underlying distribution of rewards. To test whether these large RPEs created a new latent context, we first assessed recognition priming for sequential pairs that included a high-RPE event or not (Exp. 1: n = 27 & Exp. 2: n = 83). We found evidence of recognition priming for the high-RPE event, indicating that the high-RPE event is bound to its predecessor in memory. Given that high-RPE events are themselves preferentially remembered (Rouhani, Norman, & Niv, 2018), we next tested whether there was an event boundary across a high-RPE event (i.e., excluding the high-RPE event itself; Exp. 3: n = 85). Here, sequential pairs across a high RPE no longer showed recognition priming whereas pairs within the same latent reward state did, providing initial evidence for an RPE-modulated event boundary. We then investigated whether RPE event boundaries disrupt temporal memory by asking participants to order and estimate the distance between two events that had either included a high-RPE event between them or not (Exp. 4). We found (n = 49) and replicated (n = 77) worse sequence memory for events across a high RPE. In line with our recognition priming results, we did not find sequence memory to be impaired between the high-RPE event and its predecessor, but instead found worse sequence memory for pairs across a high-RPE event. Moreover, greater distance between events at encoding led to better sequence memory for events across a low-RPE event, but not a high-RPE event, suggesting separate mechanisms for the temporal ordering of events within versus across a latent reward context. Altogether, these findings demonstrate that high-RPE events are both more strongly encoded, show intact links with their predecessor, and act as event boundaries that interrupt the sequential integration of events. We captured these effects in a variant of the Context Maintenance and Retrieval model (CMR; Polyn, Norman, & Kahana, 2009), modified to incorporate RPEs into the encoding process

Value restructures the organization of free recall

A large body of research illustrates the prioritization of goal-relevant information in memory; however, it is unclear how reward-related memories are organized. Using a rewarded free recall paradigm, we investigated how reward motivation structures the organization of memory around temporal and higher-order contexts. To better understand these processes, we simulated our findings using a reward-modulated variant of the Context Maintenance and Retrieval Model (CMR; Polyn et al., 2009). In the first study, we found that reward did not influence temporal clustering, but instead organized memory based on reward category. Further, we showed that a reward-modulated learning rate and source features of CMR most accurately depict reward’s enhancement of memory and clustering by value. In a second study, we showed that reward-memory effects can exist in both extended periods of sustained motivation and frequent changes in motivation, by showing equivocal reward effects using mixed- and pure-list motivation manipulations. However, we showed that a reward-modulated learning rate in isolation most accurately depicts reward’s enhancement of memory using a pure-list manipulation. Overall, we conclude that reward-related memories are adaptively organized by higher-order value information, and contextual binding to value contexts may only be necessary when rewards are intermittent versus sustained