To make good decisions we use the time machine that is our brain to cast ourselves into the future, consider the likely outcomes of our choices, and evaluate which one is currently most desirable. This time machine is programed by our memories. To know what is in the future, we often rely on the past. Previously learned associative relationships (e.g. stimulus-outcome) support decision making by enabling us to mentally simulate likely future outcomes Balleine and Dickinson, 1998a; Delamater, 2012; Fanselow and Wassum, 2015. These memories support understanding of the predictive ‘states’ that signal available or forthcoming outcomes. Such states are fundamental components of the internal model of environmental relationships, aka cognitive map Tolman, 1948, we use to generate the predictions and inferences needed for flexible, advantageous decision making Delamater, 2012; Fanselow and Wassum, 2015; Dayan and Daw, 2008; Balleine, 2019. For example, during the 2020 quarantine many of us learned that the stimuli (e.g. restaurant logos) embedded in food-delivery apps signal the availability of specific types of food (e.g. tacos, sushi, pizza). These cues allow us to mentally represent each predicted food, consider its value, and decide if it is a suitable dinner option. To ensure flexible behavior, these representations must be detailed. To choose the best dinner option, it is not sufficient to know that each leads to something ‘good’ or to ‘food’. Rather, the identifying, sensory features of each food (e.g., flavor, texture, nutritional content) must be represented. You might have just had Mexican for lunch, rending tacos undesirable. If you develop gluten intolerance, you will know to avoid pizza. After your doctor suggests increasing your Omega-3 intake, you may consider sushi a better option. Rich, outcome-specific, appetitive, associative memories enable expectations, ensure rapid behavioral adjustments to internal and environmental changes, and allow one to infer the most advantageous option in novel situations Balleine and Dickinson, 1998a; Delamater, 2012; Fanselow and Wassum, 2015; Delamater and Oakeshott, 2007. Failure to properly encode or use such memories can lead to absent or inaccurate reward expectations and, thus, ill-informed motivations and decisions. This is characteristic of the cognitive symptoms underlying substance use disorder and many other psychiatric conditions, including obsessive-compulsive disorder, compulsive overeating, schizophrenia, depression, anxiety, autism, and even aspects of neurodegenerative disease Hogarth et al., 2013; Morris et al., 2015; Seymour and Dolan, 2008; Alvares et al., 2014; Gleichgerrcht et al., 2010; Hogarth et al., 2013; Dayan, 2009; Voon et al., 2015; Heller et al., 2018; Chen et al., 2015; Huys et al., 2015; Culbreth et al., 2016. Thus, my broad goal here is to discuss recent findings on the neuronal systems that support outcome-specific, appetitive, associative memory and its influence on decision making.

In recent years, our understanding of the neuronal circuits of appetitive associative learning and decision making has grown dramatically. There has been considerable work on the bidirectional connections between the basolateral amygdala and orbitofrontal cortex. I review recent discoveries made about the function of this circuit using sophisticated behavioral approaches to diagnose the content of appetitive memory in combination with modern circuit dissection tools. Table 1 summarizes key findings. I focus on work in experimental rodents in which these tools have been most commonly applied, but provide some functional comparison to primates, including humans. I finish with emergent conclusions, hypotheses, and future directions.

The BLA is widely known as a processing hub for emotionally significant events. Such events are major contributors to learning and decision making and, thus, the BLA is a good entry point to understanding the neuronal circuits of such processes. The BLA’s function in aversive emotional learning has been well demonstrated. BLA lesion or inactivation severely disrupts the acquisition and expression of conditional fear and active avoidance Davis and Smith, 1992; Fanselow and LeDoux, 1999; Killcross et al., 1997; Lázaro-Muñoz et al., 2010. By contrast, such manipulations have little or no effect on general measures of appetitive Pavlovian (e.g. goal- or cue approach responses to reward-predictive stimuli) or instrumental (e.g. pressing a lever that earns reward) behavior Wassum and Izquierdo, 2015. This has led to the notion that the BLA is a brain locus for fear.

But the BLA does way more than fear. Null effects of BLA manipulations can arise because behavior can be guided by multiple different control systems. Humans and other animals can encode the relationship between a Pavlovian cue and the specific outcome it predicts (stimulus-outcome), as well as an instrumental action and the outcome it earns (action-outcome). These associative memories contribute to an internal model of the structure of an environment that enables predictions and inferences for flexible, advantageous decision making Delamater, 2012; Fanselow and Wassum, 2015; Dayan and Daw, 2008; Balleine, 2019; Doll et al., 2012, for example, considering which dinner option to choose based on current circumstances. However, this is not the only type of memory we form. For example, we and other animals also form habits Balleine, 2019; Sutton and Barto, 2022; Malvaez and Wassum, 2018, response policies performed relatively automatically based on their past success without forethought of their consequences, e.g., always order pizza on Fridays. Specific predicted outcomes are not encoded in this memory system Balleine, 2019; Sutton and Barto, 2022; Malvaez and Wassum, 2018. General Pavlovian or instrumental behaviors do not typically require any consideration of their specific outcome, so they can be controlled by either system. Thus, BLA lesion or inactivation could shift behavioral control strategy without any ostensible effect on behavior.

Using tests that reveal the content of associative memory and, thus, behavioral control system guiding behavior, the BLA has been shown to play a fundamental role in encoding, updating, and retrieving detailed, outcome-specific reward memories critical for the predictions and inferences that support flexible decision making Wassum and Izquierdo, 2015; Chesworth and Corbit, 2017; Balleine and Killcross, 2006. The most canonical of these tests is outcome-selective devaluation. When making a decision, we consider the current value of the potential outcome. If using a stimulus-outcome or action-outcome memory, we will reduce performance of a behavior when its outcome has been devalued by selective satiation or pairing with illness. This will occur even without the opportunity to learn that the particular behavior leads to a devalued outcome. Memories of the predicted reward allow inferences about how advantageous it would be to pursue. For example, you can infer Mexican might not be great for dinner if you just had tacos for lunch (sensory-specific satiety) or you will avoid ordering sushi from a particular restaurant if you became ill the last time you had it (conditioned taste aversion). Similarly, animals will press less on a lever that earns a devalued outcome relative to a valuable reward, or will show fewer food-port approach responses to a cue signaling a devalued outcome relative to a valuable one. Although BLA lesion or inactivation does not disrupt general Pavlovian or instrumental behavior, it does render these behaviors insensitive to post-training devaluation of the predicted outcome Parkes and Balleine, 2013; Hatfield et al., 1996; Johnson et al., 2009; Murray and Izquierdo, 2007; Málková et al., 1997; West et al., 2012; Balleine et al., 2003; Coutureau et al., 2009; Pickens et al., 2003. Thus, the BLA is important for stimulus-outcome and action-outcome memory.

The BLA also helps to learn the value of a reward and adapt decisions accordingly. A reward’s value as an incentive is dependent on current motivational state. For example, a food item has a high value and incentivizes robust pursuit when hungry, but low value supporting less pursuit when sated. This incentive information is encoded during experience in a relevant motivational state (i.e. incentive learning; Wassum et al., 2011; Dickinson and Balleine, 1994; Dickinson and Balleine, 1990; Balleine et al., 1995). For example, if a friend serves you pizza when you are hungry, you will learn that pizza is delicious and satisfying (i.e. valuable) when you are hungry and will be more likely to order it yourself when hungry again in the future. Likewise, after being trained sated to lever press for a particular food reward, non-continent experience with that food while hungry will cause animals to increase pressing when they are hungry subsequently. The converse is also true; after experiencing a particular food when sated, animals will decrease actions that earn that food when they are sated again in the future. The BLA mediates such incentive learning Malvaez et al., 2019; Parkes and Balleine, 2013; Wassum et al., 2009; Wassum et al., 2011.

In both these cases, the value manipulation is outcome specific. For example, having tacos for lunch will make you less inclined to select them for dinner, but will not affect the desirability of pizza or sushi. What you learn about the pizza at your friend’s house is unlikely to change your decisions for sushi or tacos. Likewise, changes to the value of one food reward (e.g. sucrose) by feeding to satiety, pairing with malaise, or experiencing it while hungry, will primarily affect behaviors for that specific and not other foods (e.g. pellets; Dickinson and Balleine, 1994). Thus, the BLA is critical for detailed, outcome-specific reward memory.

Further supporting BLA function in outcome-specific reward memory is evidence that the BLA is required for outcome-specific Pavlovian-to-instrumental transfer (PIT) Ostlund and Balleine, 2008; Corbit and Balleine, 2005; Hatfield et al., 1996; Blundell et al., 2001; Malvaez et al., 2015. Subjects first learn that two different cues each predict a unique food reward (e.g., pellets or sucrose) and, separately, that they can press on one lever to earn one of the foods and another lever to earn the other. The PIT test assesses the ability to use the cues to mentally represent which specific reward is predicted and use this to motivate choice of the action known to earn that same unique reward Kruse et al., 1983; Colwill and Motzkin, 1994; Gilroy et al., 2014; Corbit and Balleine, 2016. This is consistent with the notion that the subjects use the cue to infer which reward is more likely to be available and, thus, which action is most advantageous. For example, a billboard advertising an appetizing pizza on your way home may make you think about pizza and order it for dinner instead of tacos or sushi. Pre- or post-training BLA lesions will disrupt the expression of outcome-specific PIT Ostlund and Balleine, 2008; Corbit and Balleine, 2005; Hatfield et al., 1996; Blundell et al., 2001; Malvaez et al., 2015. BLA lesion will not, however, prevent cues from motivating behavior more broadly. For example, the BLA is not needed for general Pavlovian-to-instrumental transfer in which, absent the opportunity to seek out the specific predicted reward, a cue will invigorate performance of an action that earns a different reward (although typically one of the same class, e.g. food) Corbit and Balleine, 2005. Thus, the BLA is critical when adaptive appetitive behavior requires a detailed representation of a specific predicted outcome Janak and Tye, 2015; Wassum and Izquierdo, 2015; Balleine and Killcross, 2006.

Recent evidence indicates that the BLA contributes to both forming and using outcome-specific reward memories. During appetitive Pavlovian conditioning, BLA principle neurons are robustly activated at the time of stimulus-outcome pairing (reward delivery during the cue) Sias et al., 2021; Crouse et al., 2020. This activity is necessary for outcome-specific, appetitive associative memories to be formed, so that they can later influence decision making Sias et al., 2021. Similarly, BLA glutamate activity tracks the encoding of a reward’s value Malvaez et al., 2019. BLA NMDA Malvaez et al., 2019; Parkes and Balleine, 2013 and mu opioid receptors Wassum et al., 2009; Wassum et al., 2011 support such incentive learning. Thus, the BLA is activated by rewarding events and this is necessary to link the specific reward to the associated cue and to encode its incentive value. Following conditioning, the BLA is activated by reward-predictive cues Sias et al., 2021; Malvaez et al., 2015; Lutas et al., 2019; Crouse et al., 2020; Schoenbaum et al., 1998; Tye and Janak, 2007; Paton et al., 2006; Belova et al., 2008; Sugase-Miyamoto and Richmond, 2005; Beyeler et al., 2016; Schoenbaum et al., 1999; Muramoto et al., 1993; Tye et al., 2008; Beyeler et al., 2018. During the cue, transient outcome-specific BLA glutamate signals selectively precede and predict choice of the action that earns the predicted reward Malvaez et al., 2015. Correspondingly, the BLA is required to use outcome-specific stimulus-outcome memories to guide adaptive behavior and choice (e.g. express PIT) Ostlund and Balleine, 2008; Malvaez et al., 2015; Johnson et al., 2009; Lichtenberg and Wassum, 2017. BLA glutamate activity prior to bouts of reward seeking Wassum et al., 2012 also reflects the learned value of the predicted reward Malvaez et al., 2019; Wassum et al., 2012 and activation of both BLA NMDA and AMPA receptors is necessary for value-guided reward-seeking Malvaez et al., 2019. Thus, the BLA is activated by cues and during decision making and this activity is critical for using information about the predicted reward to guide choice.

These data indicate that the BLA mediates both the formation of outcome-specific reward memories and the use of these memories to inform decision making. They also suggest the BLA is important for using states to predict information about associated rewards. Stimulus-outcome memories are state-dependent: the external cue sets the state predicting a specific rewarding outcome. Incentive value is gated by motivational state. Internal physiological conditions dictate the incentive value of a particular reward. Thus, BLA activity is critical for linking specific rewarding events to the states, defined by both by external cues and internal physiological signals, with which they are associated and for using those memories to guide adaptive reward pursuit choices.

Other recent evidence also supports a role for the BLA in appetitive learning and decision making. For example, optical inhibition of BLA neurons disrupts risky decision making Orsini et al., 2017. When applied prior to choice, BLA inhibition will decrease choices of the larger risky reward Orsini et al., 2017, likely by preventing the subject from retrieving the incentive value of that large reward. This can also occur with less temporally-specific BLA inactivation Ghods-Sharifi et al., 2009. When applied during outcome experience, BLA inhibition will promote risky decision making, perhaps by preventing encoding of the punishing outcome Orsini et al., 2017 or by forcing learning to occur via another, less punishment-sensitive system. Indeed, post-training BLA lesions will also increase risky choice Zeeb and Winstanley, 2011; Orsini et al., 2015 and chemogenetic BLA inhibition prevents learning from positive or negative outcomes to update cue-response strategies Stolyarova et al., 2019.

The BLA also encodes information relevant for learning and using state-dependent, outcome-specific reward memories. BLA neurons can signal the unsigned Roesch et al., 2010; Esber et al., 2012, positive, or negative Esber and Holland, 2014 prediction errors that support learning. Populations of BLA neurons can reflect taste-specific gustatory information Fontanini et al., 2009 and respond selectively to unique food rewards Liu et al., 2018; Courtin et al., 2022, which could support the generation of outcome-specific reward memories. In both rodents and primates, BLA neuronal responses to predictive cues can encode the value of the predicted reward Schoenbaum et al., 1998; Paton et al., 2006; Belova et al., 2008; Jenison et al., 2022; Saddoris et al., 2005; Belova et al., 2007, inferences about reward magnitude Lucantonio et al., 2015, prospectively reflect goal plans Hernádi et al., 2015, and predict behavioral choices Grabenhorst et al., 2012. BLA neurons also encode state-dependent exploratory behaviors in distinct neuronal ensembles Fustiñana et al., 2021. Thus, during decision making BLA activity reflects critical state-dependent decision variables. The extent to which BLA neuronal ensembles encode outcome-specific predictions during decision making is an exciting open question.

Both reward learning and expectation signals have also been detected in human amygdala Elliott et al., 2004; Hampton et al., 2007; Yacubian et al., 2006, with some evidence that these occur in BLA in particular Prévost et al., 2011. BLA activity in humans also relates to the ability to use an internal model of environmental structure to guide decision making Prévost et al., 2013, including the ability to use cues to generate the outcome-specific reward expectations that influence PIT Prévost et al., 2012. Thus, BLA function in learning and using outcome-specific reward memories is conserved in humans.

Projections back to the OFC are likely candidates for the BLA output pathways responsible for using state-dependent, outcome-specific appetitive memories to guide decision making. Indeed, the OFC-BLA circuit is bidirectional and the OFC has been implicated using knowledge of the associative relationships within an environment to inform the predictions and inferences necessary for flexible decision making Wilson et al., 2014; Schuck et al., 2016; Bradfield and Hart, 2020; Shields and Gremel, 2020; Sharpe et al., 2019; Wikenheiser and Schoenbaum, 2016; Rudebeck and Rich, 2018; Gardner and Schoenbaum, 2020. Pathway-specific BLA→OFC manipulations indicate these functions are facilitated, in part, via input from the BLA and are distinct between the BLA→lOFC and BLA→mOFC pathways.

Although the boundary conditions of OFC-BLA function remain to be fully delineated, emerging evidence suggests the OFC-BLA circuit may specialize in learning about and using states to make predictions about available rewards and their value, information that supports flexible decision making.

The OFC-BLA circuit is not necessary for the acquisition or expression of general conditional response policies. Inactivation of neither OFC→BLA, nor BLA→OFC pathways prevents subjects from approaching the goal location (e.g. food-delivery port) during a cue Lichtenberg et al., 2021; Lichtenberg et al., 2017. This is consistent with evidence that neither the BLA, lOFC, nor mOFC is needed for this behavior Corbit and Balleine, 2005; Hatfield et al., 1996; Malvaez et al., 2015; Bradfield et al., 2015; Bradfield et al., 2018; Everitt et al., 2000; Parkinson et al., 2000; Morse et al., 2020. Although influenced by positive outcome valence, such general cue responses do not require an outcome expectation and can be executed via a previously learned response policy that relies instead on past success. The BLA-OFC circuit is not necessary for stamping in or expressing such a response policy and, therefore, is not simply necessary for assigning valence to predictive events. Rather the BLA-OFC circuit is critical when one must use cues to access a representation of the predicted reward to support reward pursuit or decision making.

Thus far, the OFC-BLA circuit has not been found to be important for accessing knowledge of the specific consequences of an instrumental action (i.e. action-outcome memories). OFC-BLA pathway manipulations do not to affect general instrumental activity, consistent with evidence from BLA and OFC lesions Ostlund and Balleine, 2008; Corbit and Balleine, 2005; Murray and Izquierdo, 2007; Balleine et al., 2003; Ostlund and Balleine, 2007 and BLA-OFC disconnection Fiuzat et al., 2017; Baxter et al., 2000; Zeeb and Winstanley, 2013. BLA→lOFC, BLA→mOFC, or mOFC→BLA pathway inactivation also does not disrupt sensitivity of instrumental choice to devaluation of one of the predicted rewards Lichtenberg et al., 2021; Lichtenberg et al., 2017. Thus, these pathways are not needed to retrieve or use simple action-outcome memories. Both BLA and mOFC are required for this Ostlund and Balleine, 2008; Johnson et al., 2009; Balleine et al., 2003; Bradfield et al., 2015; Bradfield et al., 2018; Gourley et al., 2016. They likely achieve this function via alternate projections, perhaps those to the striatum Corbit et al., 2013; Gremel and Costa, 2018; Gremel et al., 2016; Morse et al., 2020; van Holstein et al., 2020, a region heavily implicated in action-outcome memory Malvaez and Wassum, 2018; Malvaez et al., 2018; Malvaez, 2020; Yin et al., 2005. It remains unknown whether lOFC→BLA projections are important for sensitivity of instrumental choice to devaluation. This is unlikely because lOFC→BLA projections are not needed for other tasks that require action-outcome and outcome value information Malvaez et al., 2019; Lichtenberg et al., 2017 and this pathway has generally been found to be primarily important for learning, rather than using, reward memories. The lOFC is also itself not required for sensitivity of instrumental choice to devaluation Parkes et al., 2018; Ostlund and Balleine, 2007. The lOFC is, however, involved in action-outcome memory. It becomes needed for sensitivity of instrumental choice to devaluation after action-outcome contingencies have been switched Parkes et al., 2018. This nuanced function in action-outcome memory may rely on lOFC function in state-dependent memory. After the contingencies change, one must use the latent state to know which set of action-outcome contingencies are at play. This may also explain why lOFC-BLA disconnection will disrupt choice behavior following a degradation of one action-outcome contingency Zimmermann et al., 2017. Thus, a critical open question is whether components of the OFC-BLA circuit contribute to action-outcome memory by facilitating the use of states to retrieve current action-outcome relationships.

That OFC-BLA circuitry is not necessary for the sensitivity of instrumental choice to outcome devaluation (at least in its simple form) ostensibly contradicts evidence from BLA-OFC disconnections Fiuzat et al., 2017; Baxter et al., 2000; Zeeb and Winstanley, 2013. Using cross lesions to disconnect OFC and BLA, these studies demonstrate OFC-BLA connectivity is critical for adapting choices following post-training devaluation of the predicted reward. There are three ways to reconcile these findings. First, cross lesions will disconnect both direct and multisynaptic OFC-BLA connections. The broader effects of OFC-BLA disconnection could be via the multisynaptic connections. Second, cross lesions disrupt the devaluation learning process, which is spared with more temporally-restricted manipulations. This may account for their effects on later choice. Indeed, lOFC→BLA projections mediate reward value learning Malvaez et al., 2019. Third, although involving instrumental choices, the disconnection tasks included cues (e.g. objects, visual stimuli) associated with the actions and outcomes, such that OFC-BLA disconnection could have impacted the ability to use those cues to guide instrumental performance, similar to pathway-specific OFC circuit function Lichtenberg et al., 2021; Lichtenberg et al., 2017.

That the mOFC→BLA pathway is required for adjusting instrumental reward seeking based on the hunger-state-dependent incentive value of the predicted reward Malvaez et al., 2019 but not for sensitivity of instrumental choice to sensory-specific satiety devaluation Lichtenberg et al., 2021 is another seemingly contradictory set of results. This discrepancy may be explained by differences in the type of value learning. Incentive value is a long-term, consolidated, motivational state-dependent memory Dickinson and Balleine, 1994. Subjects learn the value of the reward in a particular state (e.g. hunger) and then 24 hr or more later are tested for their ability to use that information to guide their reward seeking. By contrast, the influence of sensory-specific satiety devaluation is typically tested immediately, with no opportunity for sleep or consolidation. The mOFC→BLA pathway is, therefore, important for using consolidated memories of the relationship between an internal physiological state and an expected outcome’s value to guide reward-pursuit decisions. This interpretation is consistent with mOFC→BLA function in the expression of outcome-specific PIT and sensitivity of Pavlovian conditional responses to devaluation, both of which require the use of consolidated external cue state memories to know which specific rewards are predicted. Thus, the mOFC→BLA pathway is important when previously learned states, whether internal or external, are needed to generate reward predictions. This implies that the mOFC→BLA pathway is recruited to support decision making with memory consolidation. This could be further tested by comparing mOFC→BLA pathway activity and necessity in instrumental choice following sensory-specific satiety devaluation with Balleine and Dickinson, 1998b and without the opportunity for memory consolidation.

The OFC-BLA circuit is critical for learning and memory processes that support decision making. There is a tendency to think BLA is primarily important for assigning general valence to predictive cues Pignatelli and Beyeler, 2019; Smith and Torregrossa, 2021; O’Neill et al., 2018; Correia and Goosens, 2016; Tye, 2018. It is. But, the above data reveal that the BLA, with support from OFC, helps to link information beyond valence, sensory-specific features of rewarding events to the external and internal states with which they are associated. And then, via its outputs to OFC to use that information to enable the predictions and inferences needed for flexible decision making. Thus, the BLA, via its connections with the OFC, is a critical contributor to decision making. The OFC has long been thought to support adaptive decision making. The data above reveal that many of these functions are supported via direct connections with BLA.

Each pathway in the OFC-BLA circuit makes a unique contribution to its overall function in forming state-dependent, outcome-specific reward memories and using this information to inform the predictions and inferences that guide reward-seeking decisions (Figure 1). When a rewarding event is experienced, activity in the lOFC→BLA pathway helps to link that specific reward to predictive states. For example, while eating the pizza you ordered via delivery, the lOFC→BLA pathway helps you link that specific pizza to the associated logos in the food-delivery app and to learn that meal is desirable when you are hungry. Later, activity in the mOFC→BLA pathway facilitates the ability to use these memories to guide decision making Sias et al., 2021; Malvaez et al., 2019; Lichtenberg et al., 2021. When you are hungry and see those logos in the future, the mOFC→BLA pathway helps you know pizza might be a good dinner option. Activity in BLA neurons projecting to the OFC enable state-dependent reward memories to guide decision making. BLA→lOFC projections contribute to using detailed representations of expected rewards to support decision making Sias et al., 2021; Lichtenberg et al., 2017. This pathway helps you to know what specific food is predicted by the restaurant logos (e.g. New York style pepperoni pizza). BLA→mOFC projections mediate the ability to adapt behavior based on the value of the predicted upcoming event Lichtenberg et al., 2021. This pathway helps you to know how desirable that pizza is, making you less likely to order it if you just had pizza for lunch. Together this circuit helps to form the associative memories we need to build an internal model of the world that we can later use to generate predictions about forthcoming events and inferences about how advantageous a certain course of action might be.

Figure 1

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Recent work on the OFC-BLA circuit has opened many questions critical for understanding the function of this circuit and the neuronal substrates of appetitive associative memory and decision making more broadly.

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Author details

1. Kate M Wassum

   1. Department of Psychology, University of California, Los Angeles, Los Angeles, United States
   2. Brain Research Institute, University of California, Los Angeles, Los Angeles, United States
   3. Integrative Center for Learning and Memory, University of California, Los Angeles, Los Angeles, United States
   4. Integrative Center for Addictive Disorders, University of California, Los Angeles, Los Angeles, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Writing – original draft, Writing – review and editing

   For correspondence

   kwassum@ucla.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2635-7433

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