Mitra Taghizadeh, MS, MT(ASCP)

• Former Assistant Professor
• Department of Medical and Research Technology
• University of Maryland School of Medicine
• Baltimore, Maryland

Given the tendency of most neuroscientific approaches to assume crosslinguistic unity (or only examine a small range of languages) anxiety symptoms early pregnancy buy nortriptyline uk, this chapter aims to highlight how the neurobiology of language may be shaped by crosslinguistic diversity anxiety blanket 25 mg nortriptyline. This should anxiety symptoms of menopause order 25 mg nortriptyline amex, however anxiety chest tightness buy nortriptyline 25 mg with mastercard, not be taken to suggest that brain mechanisms of language processing show no crosslinguistic generalizations at all anxiety symptoms muscle twitching discount nortriptyline 25 mg free shipping. Rather, we assume that many basic assumptions laid out in the other chapters of this section hold across the languages of the world. There is no evidence to date to suggest that the basic networks underlying language processing (see chapter 75) differ across languages. Likewise, all languages must draw on basic combinatory mechanisms (see chapter 74) and complex, distributed conceptual representations (see chapter 76). Further compelling evidence for crosslinguistic similarities stems from sign languages, which show a wide range of neurocognitive- processing parallels to spoken languages (see chapter 73). Building on these basic observations, we will examine how the brain attunes its linguistic information processing to crosslinguistic diversity. While recent years have seen a sharp increase in examinations of language production in understudied and typologically diverse languages (cf. However, in view of ongoing research we are hopeful that the next decade will see the emergence of a more complete picture of the crosslinguistic neuroscience of language that integrates production and comprehension. We believe that this complex area requires separate treatment, particularly since the implications of crosslinguistic similarities and differences in multilingual language acquisition and processing have not yet been studied systematically. Finally, the chapter aims to present a framework for the crosslinguistic neuroscience of language based on the evidence currently available. To this end, it draws primarily on domains/phenomena for which systematic crosslinguistic comparisons exist. We first review the effects of crosslinguistic diversity on the processing of linguistic categories before addressing information-processing mechanisms and concluding with a discussion of future directions. Throughout the chapter, we focus primarily on mechanisms and, where possible, aim to link observations to neurobiologically plausible explanations (cf. Different languages offer different categorization systems at multiple linguistic levels, including sounds, prosody (speech melody), words, and possibly even higher- order combinations of words into phrases and sentences. Sounds Categorization is crucial for speech-sound processing, as it defines the perception of phonemes: the smallest units that differentiate meaning. For example, the contrast between l and r is phonemic in English-lap and rap have distinguishable meanings-but not Japanese. Thus, English speakers perceive a categorical contrast between the syllables la and ra that transcends acoustic variability (cf. Language-specific features for phoneme categorization are learned during the first year of life (Werker & Hensch, 2015) as the brain learns to group input from the same category in a given language, thereby allowing for effective communication in spite of massive acoustic variability. Phoneme categorization emerges at the cortical level-namely, in early auditory areas (Bidelman & Lee, 2015). It relies on feature detectors attuned to relevant (language- specific) cues (Chang et al. While this basic neural architecture appears to be shared across languages, cortical responses attune to language- specific phonemic properties. Plausibly, this occurs via an integration of topdown predictions with bottom-up input (predictive coding; Garrido, Kilner, Stephan, & Friston, 2009), including both the adjustment of the current predictive model (of the sensory memory trace) to the deviant (Näätänen & Winkler, 1999; Winkler, Karmos, & Näätänen, 1996) and the adaptation of auditory cortex activity to the standard (Jääskeläinen et al. These findings have been replicated using a range of other language comparisons and have been shown to persist even for acoustically variable standards and deviants (see the review in Näätänen et al. Similar observations hold for pitch in tone languages- that is, languages in which pitch has a phonemic status. While phonemes and tones show similar cortical patterns of linguistic attunement (see also Bidelman and Lee [2015] for evidence regarding the categorical cortical encoding of tone contrasts), tones appear to differ from phonemes in that they also shape responses at the subcortical level. This shapes feature detectors at the cortical level, is evident in preattentive sound processing, and feeds into categorical perception. Subcortical responses within the ascending auditory system, by contrast, are more closely tied to the (continuous) acoustic structure of the input. However, when language experience engenders increasing sensitivity to additional features such as pitch, such features may already be tracked at the level of the brain stem. In language typology, by contrast, the crosslinguistic validity of word categories is controversial. An extreme stance posits that some languages lack word category distinctions altogether (for critical discussion, see Evans & Osada, 2005). While this may be too extreme, many languages show a higher category fluidity than most familiar European languages-that is, have many words with multiple functions (akin to the noun/verb ambiguity of English words such as cut). But even assuming that all languages have word categories, it remains controversial whether these categories are, in fact, comparable across languages (Croft, 2001). On the basis of their findings, both groups of authors argued for a more rapid use of semantic information, vis-à-vis word category information in Mandarin as opposed to Western European languages. At first glance, this would appear to suggest that the rigidity (or lack thereof) of category information in a particular language changes the time course of information processing during sentence comprehension. It has now been demonstrated repeatedly that there is no one-toone mapping between components such as the N400 and P600 and particular linguistic domains (for a recent overview, see Bornkessel- Schlesewsky, Staub, & Schlesewsky, 2016; Bornkessel-Schlesewsky & Schlesewsky, 2019). In addition, recent predictive coding­based perspectives on the neurocognition of sentence processing. They highlight the need to consider both the potential specificity of a prediction from the sentence context and the type of evidence in the input that leads to a prediction match or prediction error (see BornkesselSchlesewsky, Staub, & Schlesewsky, 2016 for a detailed discussion). It would be illuminating to reexamine the effects of category fluidity on sentence-level combinatorics from the perspective of these approaches. Concepts Beyond sounds and word categories, languages also provide power ful classification systems for concepts, as revealed by certain concepts receiving similar grammatical treatment, as opposed to others. The For a comprehensive review of research on the differences between tone and nontone languages, including neuroanatomical differences, see Gandour and Krishnan (2016). Kemmerer (2017) presents a comprehensive and compelling overview of possible synergies between semantic typology and concept representation, arguing that "the ways in which categories of object concepts are organised and represented in the brain reflect not only universal tendencies but also language-particular idiosyncrasies" (p. Drawing on results regarding the distributed, categorical representation of objects in ventral temporal cortex, he suggests that crosslinguistic categorization differences along particular semantic parameters. Conversely, the way in which these properties cluster to form categories in object recognition and conceptualization may depend on language-specific categories. Information-Processing Strategies Of course, language is more than just categorization. Indeed, one of its most fascinating properties is its vast combinatory power: words flexibly combine to form sentences and discourses, thus allowing for the expression of ever-new meanings (see chapter 74). The weighting of individual cues differs from language to language and is governed by cue validity. Thus, as for linguistic categorization, the language-processing system attunes to those cues that are the most relevant for sentence interpretation in a given language. Crosslinguistic diversity in combinatorial strategies In the neuroscience of language, this idea has been generalized to differing combinatorial strategies. Specifically, the human brain appears to apply distinct information-processing strategies in languages that rely primarily on word order (sequence- dependent combinatorics) compared to languages that rely more strongly on other cues, such as case marking or animacy (sequenceindependent combinatorics). The common denominator distinguishing English and Dutch from German, Turkish, and Mandarin is that the former rely heavily on word order for sentence interpretation (sequence-dependent languages), while the latter weigh other sequence-independent cues, such as case marking (German, Turkish) and animacy (Mandarin) more strongly (Bornkessel- Schlesewsky et al. In primarily sequence-based languages, word- order regularities permit top- down predictions regarding upcoming categories. In English, for example, the majority of sentences can be processed via a sequential agent- action- object template (Bever, 1970), and sentences not adhering to this template are more likely to be misunderstood (Ferreira, 2003). However, these bottom-up features are considerably more important in sequence-independent 844 Language languages. The crosslinguistic presence or absence of N400 effects for sentence-level interpretation can thus be explained by differences in the treatment of prediction errors induced by bottom-up features. Feedforward error signals propagated up the cortical hierarchy are weighted by precision (Bastos et al. Neurobiologically, this can be modeled by changes in the postsynaptic gain of the pyramidal cells in superficial cortical layers that encode prediction errors and propagate these to higher cortical areas (Bastos et al. Neuroanatomically, sequence- dependent and sequence-independent sentence interpretation strategies may be more closely tied to the dorsal and ventral auditory streams, respectively (Bornkessel- Schlesewsky & Schlesewsky, 2013; Bornkessel- Schlesewsky et al. These include crosslinguistically applicable interpretation strategies related to linguistic actors. Across languages, comprehenders prefer (1) actor-initial word orders and (2) sentences with prototypical. Sentences deviating from these preferences engender model updating (N400) responses (Bornkessel- Schlesewsky & Schlesewsky, 2009). The actor-first preference holds even in languages in which an actor interpretation of the initial argument is not the most frequent option. Thus, the classifications discussed here are language- specific defaults rather than absolutes. Both preferences can be derived from more general information-processing strategies employed by the brain: the tendency to preferentially attend to potential causers (typically animates) over entities that are less likely to cause events (typically inanimates; New, Cosmides, & Tooby, 2007) and the association between agency and properties related to animacy, such as biological motion (Frith & Frith, 2010). Converging evidence for this view stems from overlapping neuroanatomical correlates for nonlinguistic agency detection (Frith & Frith, 2010) and actor-related language processing (Grewe et al. The preference for actor-first word orders and actors as a uniform category is further supported by crosslinguistic distributions (Bickel et al. Actor-related preferences in processing and grammar are thus clear sentence- level candidates for which linguistic distributions accord with neurocognitive-processing mechanisms. Note, however, that this assumption needs to be tested more rigorously in languages that constitute clear exceptions to these crosslinguistic generalizations (for a first attempt, see Yasunaga, Yano, Yasugi, & Koizumi, 2015). Conclusions and Future Directions We have outlined a framework for how crosslinguistic diversity affects neurobiological mechanisms of language processing. While the underlying neurobiological processing architecture appears similar across languages, it attunes to relevant language- specific features in both categorization and information processing, thus giving rise to diverse processing signatures that may manifest themselves in apparent qualitative differences. Crucially, the neurobiological architecture explicitly permits variability as to how its processing goals. Crosslinguistically variable properties can thus be viewed as Our discussion of word- order processing only touches on simple sentences rather than more complex cases involving embeddings. The rich literature on crosslinguistic differences in the processing of relative clauses has hitherto focused exclusively on possible cognitive effects and mechanisms-. In some cases, certain solutions may be preferred over others-for example, because, like the actor strategy, they align with processing in other nonlinguistic domains- and this is reflected in skewed linguistic distributions. We have already outlined how this can be envisaged within the context of a hierarchically organized cortical predictive- coding architecture, in which top- down predictions are integrated with bottom-up input via feedback and feedforward connections, respectively, and in which the brain draws active inferences about the causes of its sensorium (Bastos et al. Crosslinguistic diversity (via linguistic experience) shapes this process both in terms of how continuous and ambiguous input is mapped onto linguistic categories and in regard to the dynamics of information processing. Initial evidence suggests that this mechanism holds across typologically diverse languages. However, potential crosslinguistic differences in speech tracking remain to be explored-for example, in languages in which there is little evidence for syllables. We thus suggest that future examinations of the assumed relationship between a shared neurobiological information-processing architecture and crosslinguistic diversity will need to test predictions derived from neurobiological models, particularly for those exceptional languages that do not fit crosslinguistic generalizations. Speech comprehension is correlated with temporal response patterns recorded from auditory cortex. Distributional typology: Statistical inquiries into the dynamics of linguistic diversity. The neurophysiology of language processing shapes the evolution of grammar: Evidence from case marking. Effects of language experience and stimulus context on the neural organization and categorical perception of speech. Tracing the emergence of categorical speech perception in the human auditory system. Precategoriality and syntax- based parts of speech: the case of late archaic Chinese. Think globally: Cross-linguistic variation in electrophysiological activity during sentence comprehension. The role of prominence information in the real-time comprehension of transitive constructions: A cross- linguistic approach. Reconciling time, space and function: A new dorsal ventral stream model of sentence comprehension. Towards a neurobiologically plausible model of languagerelated, negative event-related potentials. Neurobiological roots of language in primate audition: Common computational properties. Syntactic complexity and ambiguity resolution in a free word order language: Behavioral and electrophysiological evidences from Basque. The myth of language universals: Language diversity and its importance for cognitive science. A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 815­836. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 165­176. Cortical oscillations and speech processing: Emerging computational principles and operations. The role of the posterior superior temporal sulcus in the processing of unmarked transitivity. Categories of object concepts across languages and brains: the relevance of nominal classification systems to cognitive neuroscience. Structure and limited capacity in verbal working memory: A study with event-related potentials. Electrophysiological distinctions in processing conceptual relationships within simple sentences. Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex. An effect of linguistic experience: the discrimination of [r] and [l] by native speakers of Japanese and English.

Increasingly anxiety blog proven 25 mg nortriptyline, views of how the adult attentive brain operates have been modified to incorporate influences on attention by the contents of working goals or long-term memories (Chun anxiety symptoms to get xanax 25 mg nortriptyline otc, Golomb anxiety symptoms depression nortriptyline 25 mg order with visa, & Turk-Browne anxiety symptoms hives purchase nortriptyline us, 2011; Gazzaley & Nobre anxiety klonopin discount 25 mg nortriptyline, 2012). It is, in particular, the interface between attention and these internally held representations that will be the focus of the current chapter. In the first section, I detail the role of attention in shaping short- and long-term memory from infancy into childhood, with a focus on both changing and stable mechanisms, whereas the second section highlights growing evidence of how the contents of short-term and longer-term representations influence attention deployment across development. Attentional Influences on Short-Term and Long-Term Memory over Development Before delving into attentional influences on memory, it is worth describing, briefly, the amazing changes that characterize attention mechanisms from infancy into adulthood. From the first months of life, changes in attention are indexed by the way in which infants increasingly control their eye movements. While referring the interested reader to fuller reviews on the neural basis of attention development in infancy. Second, even though attention orienting can dissociate from eye movements (covert attention), even in adults there is a 301 high degree of overlap in neural correlates supporting overt and covert orienting. However, and finally, it is very difficult to study covert attention in infants, as this normally requires observers to follow explicit instructions. Indeed, many aspects of oculomotor control show dramatic improvements between birth and 4 months (Johnson, 1994). The engagement and efficiency of these circuits improves staggeringly and steadily from infancy into adulthood. For example, the ability to inhibit overt orienting toward salient peripheral stimuli emerges from 3 or 4 months of age (Johnson, 1995), but it continues to develop over early childhood and well into adulthood, as indexed by the increasing accuracy in producing antisaccades (Luna, Velanova, & Geier, 2008). Alongside the control of overt eye movements, infants between 4 and 6 months of age become increasingly able to orient covert attention to stimuli in the environment, as indexed by the benefits that peripheral visual cues accrue to their orienting (Hood, 1993; Johnson, Posner, & Rothbart, 1994). In neural terms these gradual changes in the control of the overt and covert orienting of attention have long been accounted for by increasing frontoparietal control on subcortical mechanisms. Early electrophysiological evidence pertaining to eye movements indicated that the infant brain before 1 year of age deploys frontoparietal mechanisms when preparing eye movements. Developments in methods such as near infrared spectroscopy have more recently also pinpointed a role for classic control nodes in frontal and parietal cortex from early during the first year of life, when young infants direct attention to higher-level representations that might guide their actions (Werchan, Collins, Frank, & Amso, 2016). Later in childhood and into adolescence, attentional mechanisms continue to develop, with increasing control over the orienting of attention in space, over the temporal alerting of attention, and over competing responses (Amso & Scerif, 2015; Rueda et al. These changes are supported by the maturation of the cognitive control regions and, most importantly, by strengthened effective connectivity across the frontoparietal areas and their partners across the brain (Fair et al. Of note, initial neurocognitive models of infant and childhood attention development treated attentional processes as relatively independent from other developing processes, as they were keenly focused on tracing the onset and maturation of attention in and of itself. In contrast, recent work has investigated how attention influences short-term and long-term memory in differentiable ways that distinguish infants, children, and adults, to which we now turn. Influences of attention development on short-term memory Given the protracted changes in attentional circuitry described above, it is not surprising that the effects of attentional cues on memory also show protracted change over infancy and into childhood. Although traditions differ in whether they use the term working memory interchangeably with short-term memory or distinguish between the two (see Cowan, 2017 for a recent review), perhaps one of the most robust findings in developmental science is the fact that in both infants (Ross- Sheehy, Oakes, & Luck, 2003) and young children (visual but also auditory), short-term memory spans index lower capacity than those of older children and adults (Cowan et al. Adapting this change- detection paradigm, Ross- Sheehy, Oakes, and Luck (2011) investigated the role of attentional cues on memory for 5and 10-month- old infants, who experienced changes in arrays composed of three differently colored squares. Older infants detected changes for the cued item when the cue was spatial (a peripheral flash preceding the onset of the item at its location), but even younger infants could exhibit this enhanced memory, although the necessary cue here was motion. Indeed, attention may influence how well children and adults remember in dif ferent ways: by dynamically preparing to encode information better or by refreshing it while it is held in memory. As the attentional networks that support adaptive cognitive control are slow to develop, their 302 Attention and Working Memory maturation may also constrain the efficiency with which memories are encoded and maintained. Let us take, for example, a very simple memory task, such as being presented with four items that then disappear and then asked if a memory probe item was part of the initial array. Using a version of this task with both 9- to 12-year- olds and adults, Astle et al. In addition to the general involvement of attentional control networks at encoding, spatially selective attention mechanisms seem to play an even more specific role in the maintenance of visual information. B, Activity in frontoparietal network (slow frequency theta 4­7 Hz) oscillations predicted accuracy of memory at the end of the trial in children and similarly, but not significantly so, in adults. The map shows the spatial extent of the component networks (in terms of the absolute Pearson correlation values between each brain location and this component). C, the time course of the regressor (black line) shows that accuracy is predicted by oscillations for this network at the time of encoding of the memoranda. Exploiting the retro- cueing paradigm, Shimi, Nobre, Astle, and Scerif (2014) asked whether the interactions between spatial attentional cues and memory show agerelated dissociations. They found that although children as young as 7 years of age are as capable as adults at drawing benefits from spatial attentional precues to better remember information encoded into short-term memory, their ability to use retro- cues is less well developed. Extending this work to younger children, Guillory, Gliga, and Kaldy (2018) found an increasing refinement in short-term memory capacity in 4- to 7-year- olds such that precues were more effective than retro- cues in benefiting their short-term memory capacity. Adults exhibited a set of neural markers that were broadly similar in preparation for encoding and maintenance. In children, as suggested for adults, these attentional refreshment mechanisms may operate by reactivating and strengthening the signal of visual representations associated with memoranda (Astle et al. Other key contributing factors (such as memory load itself, decay of information over time, and the nature of the memoranda) also deserve further investigation by developmental cognitive neuroscientists, as they have, in the main, been studied only through behavioral indices by developmental psychologists (see Shimi and Scerif [2017] for a review and integrative proposal). Evidence that not all attentional mechanisms play equivalent roles in the interaction between attention and memory over development comes from other recent electroencephalographic evidence. This was not the case for the high- capacity adults and, intriguingly, the children: the response to memory arrays containing two target items and two distracters was equivalent to the response elicited by arrays containing only two target items. Importantly, despite their obvious differences in capacity, children were not specifically impaired at filtering out distracters, a characteristic of low- capacity adults. Indeed, these findings are consistent with cognitive work by Cowan and colleagues, especially when the number of items to be encoded into memory is small. This study measured brain activity with functional magnetic resonance imaging in adults and 13-year- olds using a paradigm in which participants were provided information to maintain in memory. During the delay period, they were 304 Attention and Working Memory also presented with irrelevant distracter stimuli. Distraction during the delay evoked activation in the parietal and occipital cortices in both adults and children, whereas it activated frontal cortex only in children, suggesting overlapping and yet distinct cortical recruitment while suppressing competing distracter information. Resistance to distracters competing for attentional resources seems to recruit overlapping but also differing networks over development, with neural signatures that deserve further investigation, as they have been studied in the context of attentional influences on longer-term memory, to which we now turn. Attention development and its influence on long-term memory A parallel body of work suggests that basic attentional mechanisms influence long-term memory from infancy onward. For example, Markant and Amso (2013) found that visual selection mechanisms limit distracter interference during item encoding for infants, a process they found to be key to successfully retaining information in long-term memory. In a modified spatial cueing task, 9-month- old infants encoded multiple objects following orienting cues that required them to inhibit distracter information, as opposed to a condition that did not. When their memory was tested, infants in the distracter- suppression condition retrieved item- specific information from memory (by discriminating items that were old from new). These data suggested that developing selective attention (and, more precisely, the suppression of distracting information) enhances the efficacy of memory encoding for subsequent retrieval. The effects of these attentional biases on the encoding of information in long-term memory span beyond infancy and into childhood and adolescence. Markant and Amso (2014) used a similar spatialcueing paradigm geared to engage distracter suppression, while also incidentally presenting participants with unique line drawings of objects, across a large sample spanning 6 to 16 years of age. Across the full sample, distracter suppression resulted in longterm benefits for a surprise memory recognition test that followed the cueing phase of the study. Functionalimaging evidence in adults indeed also suggests that engaging distracter- suppression mechanisms may result in better long-term memory encoding. The mechanisms underpinning the role of attentional cueing and distracter-processing effects on long-term memory relate to the growing literature on memoryguided attention (Stokes, Atherton, Patai, & Nobre, 2012; Summerfield, Lepsien, Gitelman, Mesulam, & Nobre, 2006). As reviewed in depth in this section (see chapter 25), memory-guided attention paradigms ask participants to search repeatedly for unique targets in scenes. Repeated searching engenders learning, after which long-term memory for target locations is assessed. In a final memory-guided attention- orienting phase, the speed of target detection is assessed for targets that are presented at locations consistent with their locations in memory, as opposed to locations inconsistent with memory. Attention allocation is faster at locations consistent with memory and recruits both frontoparietal and hippocampal circuits (Summerfield et al. Like the cueing paradigms by Amso and colleagues above, memory- guided attention paradigms therefore offer the opportunity to test both the effects of attentional allocation during learning and the role of distracters competing for attention while encoding information in long-term memory, in both adults and children. First, in adults, Doherty, Patai, Duta, Nobre, and Scerif (2017) asked participants to search for targets in scenes containing social or nonsocial distracters. Eye tracking revealed significantly more attentional capture to social compared to nonsocial distracters matched for low-level visual salience. Critically, memory precision for target locations was poorer for social scenes, suggesting a role for differential attentional allocation to competing distracters on long-term memory. The power ful effects of social distracters alert us to the fact that attentional biases influencing later memory do not operate equivalently across stimuli of all types but that preexisting preferences for certain stimuli also guide attention. Attentional influences on long- term memory are robust from infancy and into childhood. Distracter effects, albeit far from fully understood, also suggest that the nature of the items to which attention is directed. B, Subsequent memory precision was lower for social compared to nonsocial distracters for both children and adults. A possible interpretation is that slower and less efficient attentional orienting may paradoxically result in a longer or qualitatively different exploration of complex natural scenes in children compared to adults and therefore, in the longer run, better encoding of the context and location at which targets were places. We therefore now turn to how developmental studies can begin to investigate the mechanisms by which these preexisting representations influence attention. Influences of Short-Term and Long-Term Memory Representations on Attention Deployment In this section I overview developmental data suggesting that the contents of memory have a power ful influence on attention. Starting from the realm of short-term memory representations, an open question is how attentional biases interact with the nature of the internal memory codes on which they operate. Later in childhood, the influence of short-term memory representations on attentional deployment has also been studied. The memoranda contained either highly familiar items or unfamiliar abstract shapes. Replicating earlier findings, all participants benefited from cues during maintenance, although benefits were smaller for 7-year- olds than for older participants. These data suggest that attentional biases during maintenance operate more efficiently on memory representations that are more familiar and can therefore be retrieved more easily, pointing to the need to consider the influence of memory representations themselves on attention orienting. Work investigating memory- guided attention orienting most directly tackles the influence of memory traces onto attention. Nussenbaum, Scerif, and Nobre (forthcoming) pitted against each other the effects of salient visual cues and of memory- guided cues on attention orienting in children and in adults. Over three complementary experiments, children demonstrated faster reaction times to targets both when they were cued by sudden visual events and by memories (see figure 26. These findings suggest that memories may be a particularly robust source of influence on attention in children. Returning to the critical role of the nature of memory traces themselves, Doherty, van Ede et al. Poorer memory per for mance for scenes with social distracters was marked by reduced anticipatory dynamics of spatially lateralized 8­12 Hz alpha-band oscillations during the orienting phase. But do the effects of distracters influence memory-guided attention differently in children compared to adults Intriguingly, although both children and adults were less precise in remembering targets that had appeared in social versus nonsocial scenes, children demonstrated overall better memory precision than adults. Furthermore, when participants detected previously learned targets within visual scenes, adults were slower for targets appearing at unexpected (invalid) locations within social scenes compared to nonsocial scenes, whereas children did not show this cost, suggesting that social memory traces may play a different role for them than for adults. In summary, therefore, the contents of short- and long-term memory guide attention across development. Conclusion and Future Directions-Attention and Memory Interactions over Development A growing body of evidence suggests that developmental changes in attentional control constrain cooccurring changes in short-term memory and long-term memory skills from infancy and into childhood. The efficiency of a frontoparietal network engaged in attentional control seems critical to these increasingly adultlike interactions. I have also described how early goal- and memory-related activity bias attention from very early on in infancy and therefore how the interactions between attention, memory, and learning are the target of much recent work in the developmental cognitive neuroscience of this area. After learning about the specific locations of objects within scenes over repeated learning blocks, participants were presented with an orienting task in which they had to respond as quickly as possible to targets that appeared either at the location cued by their memory, at a location that was inconsistent with that memory, at a location cued by the sudden presentation of a visual event (a flash), or at a location inconsistent with the visual event. However, only children benefited significantly in response to memories, demonstrating faster reaction times when the memory cued the target location. Electrophysiological mea sures of fronto-parietal networks in typically developing children using magnetoencephalography. On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Seven-year- olds allocate attention like adults unless working memory is overloaded. Of note, interactions between attention and short-term and longer-term memory over developmental time have only recently been tackled with methods that are complementary to behavioral data: eye tracking and electro- and magnetoencephalography, as well as functional neuroimaging methods, are increasingly being used in this field and will yield many needed insights. Complementary methodologies in developmental cognitive neuroscience will be needed to shed further light on the mechanisms through which attention and memory interact over development. Acknowledgments I am very grateful to too many colleagues and students to acknowledge all in full as I should, but I dedicate this chapter to Annette Karmiloff- Smith and Jon Driver, two scientists and mentors who influenced me a great deal and who are sorely missed. The functional consequences of social attention on memory precision and on memory- guided orienting in development. The functional consequences of social distraction: Attention and memory for complex scenes. The functional consequences of social attention for memory- guided attention orienting and anticipatory neural dynamics. Proceedings of the National Academy of Sciences of the United States of America, 104(33), 13507­13512. Quantifying attentional effects on the fidelity and biases of visual working memory in young children. Inhibition of return produced by covert shifts of visual-attention in 6-month- old infants.

Because the appetitive properties of environmental cues directly influence how they are encoded anxiety symptoms concentration discount nortriptyline 25 mg overnight delivery, sensitivity to reinforcement may account for a great deal of observable differences in adolescent behav ior (figure 21 anxiety quitting smoking purchase nortriptyline with a visa. In humans anxiety symptoms physical 25 mg nortriptyline order mastercard, adolescents have been shown to exhibit hypersensitivity to primary reinforcers (Fareri anxiety symptoms lasting all day buy 25 mg nortriptyline free shipping, Martin anxiety symptoms during exercise order nortriptyline 25 mg otc, & Delgado, 2008; Steinberg, 2008), with similar patterns observed in mice (Adriani, Chiarotti, & Laviola, 1998). Research in rats has also indicated that the appetitive qualities of drugs and alcohol are elevated during adolescence (Pautassi, Myers, Spear, Molina, & Spear, 2008; Vastola, Douglas, Varlinskaya, & Spear, 2002). Notably, both human and rodent adolescents display increased responsiveness to environmental cues signaling a potential reinforcer (Hare et al. In line with this, the presence of an appetitive stimulus produces a drastically different pattern of per formance in inhibitory control tasks relative to tests with neutral stimuli, with both humans and rodents specifically exhibiting difficulty suppressing responses to appetitive cues during adolescence compared to younger or older ages (Galván, 2013; Hare et al. Furthermore, across species, appetitive memories appear to be more resistant to updating with new information during adolescence (Levin et al. Particularly notable examples of this effect have been shown in studies considering the extinction of either a Pavlovian appetitive cue or an instrumental reinforcer- eliciting response in rats (Andrzejewski et al. Perseveration on the reinforcing properties of appetitive cues, even in the absence of the expected reinforcer, has been taken to indicate increased strength of the appetitive cue memory specifically during adolescence. Furthermore, activity remained higher in adolescence even when behavioral measures of extinction learning. Thus, persistent appetitive cue-related activity may contribute to an increased susceptibility to both generalization and spontaneous recovery of the original appetitive memory, despite subsequent learning about the decreased likelihood of reinforcement. Interestingly, age differences in reinforcement processing may also be attributable to altered signaling in dorsal striatum. Dorsal striatal circuitry is recruited both earlier and to a greater degree in adolescents relative to adults during the retrieval of a reinforcer (Sturman & Moghaddam, 2012). Interactions between the mesolimbic system and the nigrostriatal system, extending between substantia nigra and dorsal striatum, are of great importance for mediating the interface between motivation and action (Mogenson, Jones, & Yim, 1980; Nauta, Smith, Faull, & Domesick, 1978), indicating a possible mechanism underlying the heightened approach of appetitive cues observed during adolescence. Here, we have outlined examples of how the individual stimulus representations composing the memory of an environment can have great impact on subsequently manifesting behav iors. We have discussed a range of dynamic neurobiological changes in circuitries for both aversive and appetitive learning and memory that offers context for understanding how individuals at varying developmental stages utilize alternative processes in the generation of behavioral goals and the influence of memories on overt behav ior. Importantly, many of the age- specific features of emotional memory we have discussed promote behavioral patterns that are adaptive for the developmental period during which they manifest, highlighting evolutionary biases in the context of brain development that allow one to meet the environmental demands of each stage of life and acquire the skills necessary to progress through subsequent stages. Moreover, striking parallels in the developmental features of both aversive and appetitive memory systems indicate that despite differences in underlying circuitry these memory systems are coordinated in their ability to recognize the most salient features of an environment and subsequently use this information in the ser vice of goal- directed behav ior targeted to discrete developmental stages. For example, both fear and appetitive memory during infancy are biased toward forming an attachment to a caregiver, which maximizes the chances of survival (Brown, 1986). Interactions between the oxytocin and dopamine systems allow an infant to distinguish social from nonsocial cues and promote reinforcement learning specifically for the caregiver (Nelson & Panksepp, 1996). Moreover, a maternal presence serves as a buffer, modifying cued fear learning in rodents (Moriceau & Sullivan, 2006). In addition, emotional memory processes apparent during adolescence can facilitate the acquisition of the skills and experiences necessary for the maturation to adulthood (Spear, 2010). Adolescence (especially in rodents) is a time when heightened exploratory behaviors facilitate the transition away from parental dependence to relative independence. This is reflected in fear response patterns that promote not only the exploration of new environments but the generalization of fear toward cues that predict a threat (Fanselow, 1994). Decreased exploration as a result of contextual fear could result in the depletion of food in the home environment and a failure to mate. Similarly, heightened sensitivity to cues of threat in novel environments contributes to vigilance to threats and is similarly adaptive as an evolutionary measure. Thus, heightened cued fear expression combined with attenuated contextual fear expression during adolescence (McCallum, Kim, & Richardson, 2010; Pattwell et al. Likewise, characteristics of appetitive memory during adolescence are well suited for forms of learning that occur in uncertain or changing environments (Johnson & Wilbrecht, 2011; Qin et al. Indeed, the contingencies defining when and how much of an appetitive outcome will be available can be highly variable in different environments. Thus, during the transition to independence, as an adolescent is likely to experience increased exposure to new environments, hypersensitivity to reinforcers and the perseveration on reinforcer-associated behav iors may actually increase the likelihood of attaining reinforcement, until such a time when sufficient information about contingencies in discrete environments can be established. Despite these evolutionarily advantageous developmental changes in emotional memory, a multitude of psychiatric conditions emerge during development as the brain is undergoing complex and dynamic changes. Unfortunately, the earlier emergence of emotional disorders has in some cases been associated with an increased severity of symptoms as well as comorbidities (Andersen & Teicher, 2008; Gutman & Nemeroff, 2003). Thus, there is significant interest in understanding the Meyer and Pattwell: Memory across Development 249 interplay between the specific neurobiological and behavioral factors that characterize developmental stages and in identifying why particular individuals are susceptible to negative outcomes. While this chapter provides an overview of the behavioral, neural, and molecular properties of both aversive and appetitive learning as a function of age, various factors, including but not limited to gender, early life stress, the environment, and genetic differences, may also influence the properties outlined here and should be considered in the developmental landscape of learning and memory (Pattwell & Bath, 2017). As more is uncovered about the brain through the modern technologies associated with basic neuroscience research, the field of developmental memory is on the verge of great advances. A body of literature has begun to probe these questions for various types of emotional memory, investigating whether memories depend on the age at which they are encoded or the age at which they are retrieved (Barnet & Hunt, 2006; Richardson & Fan, 2002; Simcock & Hayne, 2002). How retrieval processes, such as those outlined in chapter 23 on reconsolidation, may strengthen or weaken memories across development is also of great interest for understanding just how the brain forms, maintains, and alters aversive and appetitive memories across the formative years of childhood and adolescence and how this sets the stage for the adult memory processing of similar or related experiences. Elevated novelty seeking and peculiar d- amphetamine sensitization in periadolescent mice compared with adult mice. Total expenses and percent distribution for selected conditions by type of ser vice. Ontogeny of contextual fear memory formation, specificity, and persistence in mice. A comparison of adult and adolescent rat behav ior in operant learning, extinction, and behavioral inhibition paradigms. The expression of fearpotentiated startle during development: Integration of learning and response systems. Incentive- elicited brain activation in adolescents: Similarities and differences from young adults. Early-life stress affects extinction during critical periods of development: An analysis of the effects of maternal separation on extinction in adolescent rats. Changes in the categorization of appetitive and aversive events during postnatal development of the rat. Neural and psychological mechanisms underlying appetitive learning: Links to drug addiction. Treating the developing versus developed brain: Translating preclinical mouse and human studies. The storm and 250 Memory stress of adolescence: Insights from human imaging and mouse genetics. Developmental neurocircuitry of motivation in adolescence: A critical period of addiction vulnerability. Early developments in the ability to understand the relation between stimulus and reward. Fear and anxiety from principle to practice: Implications for when to treat youth with anxiety disorders. Reward sensitivity for a palatable food reward peaks during pubertal developmental in rats. Early developmental emergence of human amygdala-prefrontal connectivity after maternal deprivation. Proceedings of the National Academy of Sciences of the United States of America, 110(39), 15638­15643. Social anxiety in children with anxiety disorders: Relation with social and emotional functioning. Persistent central nervous system effects of an adverse early environment: Clinical and preclinical studies. Biological substrates of emotional reactivity and regulation in adolescence during an emotional go- nogo task. Juvenile mice show greater flexibility in multiple choice reversal learning than adults. Fear extinction across development: the involvement of the medial prefrontal cortex as assessed by temporary inactivation and immunohistochemistry. Immunohistochemical analyses of long-term extinction of conditioned fear in adolescent rats. Reward anticipation is encoded differently by adolescent Meyer and Pattwell: Memory across Development 251 ventral tegmental area neurons. Prior juvenile diagnoses in adults with mental disorder: Developmental follow-back of a prospective-longitudinal cohort. The ventral striatopallidal pathway mediates the effect of predictive learning on choice between goal- directed actions. Developmental changes in per for mance on tests of purported frontal lobe functioning. Developmental timing and critical windows for the treatment of psychiatric disorders. Adaptive adolescent flexibility: Neurodevelopment of decision-making and learning in a risky context. Journal of the American Academy of Child and Adolescent Psychiatry, 49(10), 980­989. From motivation to action: Functional interface between the limbic system and the motor system. Adolescent immaturity in attention-related brain engagement to emotional facial expressions. Regulating critical period plasticity: Insight from the visual system to fear circuitry for therapeutic interventions. Efferent connections and nigral afferents of the nucleus accumbens septi in the rat. Oxytocin mediates acquisition of maternally associated odor preferences in preweanling rat pups. Juvenile female rats, but not male rats, show renewal, reinstatement, and spontaneous recovery following extinction of conditioned fear. Emotional learning, stress, and development: An ever- changing landscape shaped by early-life experience. Selective early-acquired fear memories undergo temporary suppression during adolescence. Proceedings of the National Academy of Sciences of the United States of America, 108(3), 1182­1187. Proceedings of the National Academy of Sciences of the United States of America, 109(40), 16318­16323. Dynamic changes in neural circuitry during adolescence are associated with persistent 252 Memory attenuation of fear memories. Adolescent but not adult rats exhibit ethanol- mediated appetitive second- order conditioning. Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex (G. Memory for fearful faces across development: Specialization of amygdala nuclei and medial temporal lobe structures. Relationship of childhood anxiety to adult panic disorder: Correlates and influence on course. The change of the brain activation patterns as children learn algebra equation solving. Proceedings of the National Academy of Sciences of the United States of Amer ica, 101(15), 5686­5691. Behavioral expression of learned fear in rats is appropriate to their age at training, not their age at testing. Contextual conditioning and auditory cue conditioning dissociate during development. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents. Distinct patterns of outcome valuation and amygdala-prefrontal cortex synaptic remodeling in adolescence and adulthood. Adolescents exhibit behavioral differences from adults during instrumental learning and extinction. Reduced neuronal inhibition and coordination of adolescent prefrontal cortex during motivated behav ior. Proceedings of the National Academy of Sciences of the United States of America, 109(5), 1719­1724. One-trial olfactory learning enhances olfactory bulb responses to an appetitive conditioned odor in 7- day- old rats. Developmental emergence of fear learning corresponds with changes in amygdala synaptic plasticity. In this article we review evidence from two parallel literatures revealing the influence of emotion and reward motivation on episodic memory processes, mediated by the amygdala and the dopaminergic system, respectively. Taking an adaptive-memory perspective, we argue that emotion- and reward-related information is prioritized in memory from the earliest stages of encoding, leading to targeted effects on memory for salient information as well as spillover effects that affect memory for other information encoded around the same time. We distinguish these effects at encoding from the modulation of consolidation processes, which may serve to further prioritize memory for emotion- and reward-related information. Importantly, across the different stages of memory formation, emotionand reward-related memories appear to share several key principles. These parallels shed light on the similar adaptive impact of two distinct neuromodulatory systems on memory. The selectivity of memory has been an enduring puzzle: Why do we easily remember some information for years but quickly forget most information that we encounter In this article we review evidence that memory systems are adaptive, protecting memories for information that could be useful in the future, such as events that signal potential threats or rewards, while discarding the rest. We focus on the effects of emotionally negative and rewarding events on encoding and consolidation processes that shape episodic memory.

Aversive Learning and Memory Under normal circumstances anxiety symptoms for hiv discount nortriptyline 25 mg mastercard, fear learning is an adaptive anxiety symptoms questionnaire buy cheap nortriptyline 25 mg, evolutionarily conserved process that allows one to respond appropriately to cues predictive of danger anxiety journal 25 mg nortriptyline purchase with mastercard. In the case of psychiatric disorders anxiety xanax dosage generic nortriptyline 25 mg buy on-line, however anxiety wrap discount nortriptyline online amex, fear may persist long after a threat has passed. Experimental methods for studying aversive memory Behavioral paradigms relying on Pavlovian principles have become standard for studying fear in both humans and nonhuman animals (Pavlov, 1927). Through associative learning techniques based on these classicalconditioning principles, long-lasting, aversive memories can be formed in the rodent (Maren, 2001), and animal the understanding of learning and memory remains one of the central goals of modern neuroscience. The study of emotional memory, in particular, has garnered significant interest in recent years for its inherent role in various psychiatric disorders. Specifically, alterations in memory processing for aversive or traumatic experiences lie at the heart of many clinical psychiatric disorders, which often trace their roots to the early childhood and adolescent years. Reinforcementprocessing abnormalities have also been implicated in a variety of psychiatric disorders and are linked to drastic and long-term effects on behav ior. Indeed, blunted signaling in reinforcement-related brain regions is apparent in major depression. By studying the neural circuitry of emotional memory, insight can be gained into not only how these 243 models of fear learning are frequently relied on and held in high regard due to their ease and experimental control. Developmental influences on fear learning and memory While existing therapies and medications offer significant benefit to adult patients, a comparative knowledge gap surrounding the dynamic fear neural circuitry across early development may prohibit similarly successful treatment outcomes in children and adolescents (Liberman, Lipp, Spence, & March, 2006). Infant and juvenile fear memories Studies investigating aversive learning in infants and juveniles have uncovered key developmental windows involving both critical and sensitive periods (Marin, 2016). For clarity, a critical period is associated with molecular or genetic brakes/accelerators and is defined as a time of extreme interdependence between experience and development, after which there is a decrease in neural plasticity. These resultant behavioral changes are typically irreversible, as is seen with amblyopia of the visual system (Nabel & Morishita, 2013). Conversely, a sensitive period is a window during which a functional process and its underlying brain circuit temporarily experience heightened plasticity. Neural development is especially receptive to particular types of experience during this time (Nabel & Morishita, 2013). Fear learning in rodents emerges very early in postnatal development and coincides with amygdala maturation. During this early developmental window (within 10 postnatal days [P10]), rodents develop a seemingly paradoxical Pavlovian fear response to odor/tone shock pairings (Camp & Rudy, 1988; Sullivan, Landers, Yeaman, & Wilson, 2000) during a sensitive period for attachment learning, in which maternal presence serves to block the acquisition of fear (Landers & Sullivan, 2012). Coinciding with the onset of learning-induced synaptic plasticity in the amygdala after P10, rodents begin to exhibit more traditional cued fear learning to odor- shock pairs (Thompson, Sullivan, & Wilson, 2008), yet this can be modified by maternal presence up until about P15 (Moriceau & Sullivan, 2006). Fear memories acquired prior to P10 are not as robust or as persistent as those acquired later in life and remain susceptible to forgetting through a process known as infantile amnesia, which is highly influenced by exposure to early life stress, such as maternal deprivation (Alberini & Travaglia, 2017; Callaghan & Richardson, 2012; Campbell & Spear, 1972; Kim & Richardson, 2007; Pattwell & Bath, 2017). Contextual fear conditioning in rodents emerges later (P23 in rats) than cued fear learning (P18 in rats) (Akers, Arruda- Carvalho, Josselyn, & Frankland, 2012; Rudy, 1993), which may reflect the maturation of hippocampal-amygdala connectivity or hippocampal activity (Raineki et al. In addition to new learning associated with fear conditioning, the capacity for fear extinction learning also changes across early juvenile periods. Prior to P24 (circa weaning age), rodent pups display a normal decrease in fear expression when undergoing classical extinction paradigms, yet this learning differs from that of the adult, as the fear neither reemerges with reinstatement or renewal nor exhibits a spontaneous recovery, which is potentially indicative of infantile amnesia (Gogolla, Caroni, Luthi, & Herry, 2009; Kim, Hamlin, & Richardson, 2009; Yap & Richardson, 2007), although notable differences in female rats have been observed (Park, Ganella, & Kim, 2017). Adolescent fear memories Adolescence, in particular, is a period of increased prevalence of emotional psychopathology (Monk et al. Adolescence also coincides with a period of significant cortical rearrangement that is normatively accompanied by drastic cognitive and behavioral changes (Spear, 2000). Longitudinal studies of brain maturation illustrate a nonlinear process that is not complete until early adulthood (Giedd et al. Prefrontal cortical regions, 244 Memory such as those implicated in top- down control, response inhibition, executive function, and fear extinction learning, undergo protracted development relative to subcortical structures, including the amygdala (Casey, Jones, & Somerville, 2011; Casey, Glatt, & Lee, 2015; Casey, Tottenham, Liston, & Durston, 2005). During tasks involving self-regulation and reappraisal, children show a greater and more diffuse activation of prefrontal loci compared to adults, suggestive of regional immaturity (Galvan et al. Converging evidence from human and rodent studies suggests that insufficient top- down regulation of subcortical structures, such as the amygdala, may coincide with impairments in prototypical extinction learning. In addition, recent work highlights distinct patterns of amygdaloid and medial temporal lobe activation between children and adolescents when learning about neutral versus fearful faces (Pinabiaux et al. Sensitive periods and critical periods have been the focus of infant and juvenile models for some time, yet throughout the past decade, rodent models have started incorporating the older, more intermediate adolescent ages between P23 and P42 (Hefner & Holmes, 2007; J. Kim, Li, & Richardson, 2011; McCallum, Kim, & Richardson, 2010; Pattwell, Bath, Casey, Ninan, & Lee, 2011; Pattwell et al. By examining fear conditioning as mice transitioned through adolescence, recent research has uncovered an aspect of fear learning in which contextual fear expression is suppressed during adolescence (Pattwell et al. This lack of contextual fear expression did not result from global impairments in fear memory acquisition or consolidation, as amygdala- dependent cued fear remained intact at all developmental ages examined and correlated with electrophysiological recordings in their amygdalae. Interestingly, despite a suppression of contextual fear expression and corresponding blunted synaptic activity in the basal amygdala and hippocampus during adolescence, mice were able to retrieve and express the contextual fear memory as they transitioned out of adolescence and into adulthood. Despite a lack of contextual fear expression, mice given contextual extinction during this adolescent window did not exhibit the fear later as adults, suggesting prophylactic extinction-when behav ior was other wise absent-may prevent fear memory expression in adulthood (Pattwell et al. Despite the suppression of contextual fear expression in adolescent mice, cued fear expression appeared to be not only enhanced but also highly resistant to extinction in both adolescent rodents and humans (Drysdale et al. Converging evidence from human and rodent studies suggests that insufficient top- down regulation of subcortical structures (Casey et al. A, A schematic of the neural circuitry of adolescent cued fear as simplified from retrograde tracer studies (Pattwell et al. Left, An adolescent rodent perseverates on the delivery of a reinforcer by spending more time in the reinforcer receptacle. Right, An adolescent rodent perseverates on a visual stimulus associated with reinforcer delivery. Of particular importance for the vulnerable adolescent age group are the deleterious effects that psychiatric disorders can have on social and academic contexts (Ginsburg, La Greca, & Silverman, 1998), when peer relationships are paramount, as well as the enhanced potential for persisting disorders in adulthood (Foulkes & Blakemore, 2018). As adolescence is also a time associated with prototypical increases in risky behav ior, stress, thrill seeking, impulsivity, and heightened reward sensitivity, seeking more effective treatments for anxiety and affective disorders in this population may also indirectly lead to reductions in substance abuse and the other maladaptive behav iors often employed as forms of anxiolytic self-medication. Appetitive Learning and Memory the core purpose of fear learning and memory is to facilitate the avoidance of aversive outcomes. In contrast, appetitive learning and memory provide information about the reinforcement-predictive properties of a cue, as well as the circumstances that modulate these properties. In turn, this facilitates the fine-tuning of behavioral patterns that will maximize the opportunity for an appetitive outcome. Early in development, appetitive memory is critical for the ability to establish beneficial social networks, initially with caregivers and later with peers. Subsequently, an elevated focus on appetitive stimuli and outcomes can contribute to enhanced learning and flexibility (McCormick & Telzer, 2017). Unfortunately, the pursuit of appetitive outcomes can in some cases lead to risky and impulsive behaviors that increase the possibility of harm or even premature death. Moreover, the altered processing of reinforcement has been implicated in a variety of clinical psychiatric disorders, many of which emerge during development, and has been associated with an increased vulnerability to substance use and abuse (Cardinal & Everitt, 2004; Chambers, Taylor, & Potenza, 2003). Thus, an understanding of how appetitive memories are encoded will inform the under pinnings of goal- directed behav ior, reveal how a disruption of this process can manifest in psychiatric disorders, and further advise psychiatric treatments as well as interventions for pathological reinforcer- seeking behav iors. Experimental methods for studying appetitive memory In the laboratory, appetitive conditioning, not unlike fear conditioning, trained through repeated pairings of an initially neutral stimulus with an appetitive outcome will provide value to an initially neutral cue in the environment, thus increasing the salience of the cue (figure 21. In turn, the salience of a cue is included in the information encoded about the cue and upon subsequent recall can be used to guide behav ior. Quantifiable mea sures of the strength of the reinforcing properties include the number of head entries during the cue. The strength of the appetitive memory can also be measured by how long it takes to update the memory once the cue is no longer paired with reinforcement. Appetitiveconditioning processes can also be applied to diffuse contexts, rather than discrete cues, when the presence of a reinforcer in a given context results in a preference for that context relative to a similar context in which no reinforcer has been presented. Developmental influences on appetitive memory in infancy One of the earliest examples of appetitive memory in development is the attachment to a caregiver. This attachment promotes the survival of an infant by facilitating access to resources and protection (Bowlby, 1969). Neonatal mice as young as P3 can form an appetitive memory for an odor predicting access to the mother (Armstrong, DeVito, & Cleland, 2006). Similarly, rat pups exhibit learned preferences for odors paired with tactile stimulation comparable to that received from the dam (Sullivan & Leon, 1987). To date, no cortical regions for attachment have been found in the neonatal mammalian brain. In humans, infants are capable of encoding appetitive memories that underlie the subsequent expectation of reinforcement. Indeed, in the mobile conjugate reinforcement paradigm (Rovee & Rovee, 1969), infants learn the contingency between the instrumental response of kicking their legs and the movement of a mobile hanging above their crib. A high specificity of a cue necessary for the associative recall of the appetitive memory is apparent until three months (Rovee- Collier & Hayne, 1987), diminishing thereafter alongside increases in the ability to generalize across stimuli and experiences. The retention for the appetitive association also shows a gradual increase across infancy. Notably, the ability to learn that a cue itself is representative Meyer and Pattwell: Memory across Development 247 of an appetitive outcome is limited in early infancy, the first year of life, despite intact cue recognition memory (Diamond, Churchland, Cruess, & Kirkham, 1999). Childhood and adolescence Reinforcement learning and appetitive memory formation during childhood in humans occur similarly to that observed in adulthood (Galvan et al. Strikingly, subsequent changes in components of the appetitive memory circuitry during adolescence in both humans and animals have been shown to greatly influence the utilization of appetitive memory in ser vice of guiding behav ior (figure 21. Negative emotions and rewards are thought to influence episodic memory through separable neural circuits. Enhancements in memory for emotional experiences have been linked to noradrenergic activity in the amygdala (reviewed by LaBar & Cabeza, 2006; McGaugh, 2004). The amygdala is strongly interconnected with the authors contributed equally to this work. Amygdala activity and concomitant changes in stress hormone levels are thought to modulate the consolidation of new memories, thereby protecting memories for arousing experiences. The amygdala is also positioned to influence the quality of memory encoding through its connections with the multiple brain systems involved in attention and perception (Price, 2006). In contrast, reward-based memories are thought to depend on the mesolimbic dopaminergic circuit (for current reviews, see Miendlarzewska, Bavelier, & Schwartz, 2016; Murty & Dickerson, 2017). The models suggest that the three regions are thought to be key to forming a functional loop that prioritizes learning and memory for rewarded information by enhancing plasticity (Lisman & Grace, 2005; Lisman, Grace, & Düzel, 2011). Targeted Effects of Emotion and Reward on Encoding Encoding processes supporting memory for emotional content From the earliest stages of neural processing, emotionally evocative stimuli compete for prioritized neural representation (Dolan & Vuilleumier, 2006; Mather & Sutherland, 2011). Emotional content influences perceptual processes as early as 100 to 200 ms after stimulus onset (Pizzagalli et al. Emotional information is also more likely to reach conscious awareness under conditions of reduced attentional resources (Anderson & Phelps, 2001). Such effects have been shown to depend on the integrity of the amygdala (Anderson & Phelps, 2001; Vuilleumier, Richardson, Armony, Driver, & Dolan, 2004), which has direct projections back to primary sensory cortex (Amaral, Behniea, & Kelly, 2003). The early biasing of perception and attention has direct implications for the quality of memory encoding. For instance, divided attention has a smaller effect on emotional memory encoding compared to neutral (Kensinger & Corkin, 2004; Talmi, Schimmack, Paterson, & Moscovitch, 2007), suggesting that reflexive orienting toward arousing information facilitates memory encoding. Although arousal appears to drive these effects (Kensinger & Corkin, 2004), emotional valence may influence which features are attended and encoded. It has been suggested that negative memories include more perceptual details, whereas positive memories include more semantic details. Negative objects are remembered with greater visual detail, and negative memory encoding elicits greater activity in visual cortex than neutral encoding (Kensinger, Garoff-Eaton, & Schacter, 2007). Positive memory encoding, on the other hand, elicits greater activity in lateral prefrontal areas (Mickley & Kensinger, 2008) and stronger prefrontal-hippocampal interactions supporting encoding (Ritchey, LaBar, & Cabeza, 2011). Compared to neutral item encoding, negative item encoding is associated with greater activity in the amygdala and perirhinal cortex. Enhancements in emotional item recollection have not necessarily been tied to improvements in memory for source context (Yonelinas & Ritchey, 2015). Some studies have suggested that emotional arousal might actually interfere with associative memory encoding, leading to diminished hippocampal activity and worse memory for associations including emotional items (Bisby, Horner, Hørlyck, & Burgess, 2016; Madan, Fujiwara, Caplan, & Sommer, 2017). Encoding processes supporting memory for rewarding information Similar to emotional material, cues that signal a future reward have been shown to enhance early perceptual and attentional processes (Bunzeck, GuitartMasip, Dolan, & Düzel, 2011; Gruber & Otten, 2010; Yeung & Sanfey, 2004). In the last decade, evidence has accumulated of how reward anticipation facilitates encoding via the mesolimbic dopaminergic circuit. In one study, activity elicited by high-reward cues- but not low-reward cues-was predictive of whether the upcoming image would be remembered later (Adcock, Thangavel, Whitfield- Gabrieli, Knutson, & Gabrieli, 2006). Furthermore, findings on multivoxel activity patterns suggest that the hippocampus codes the value of information, thereby leading to enhanced memory for high-value information (Gruber, Ritchey, Wang, Doss, & Ranganath, 2016; Wolosin, Zeithamova, & Preston, 2013). In another seminal study, participants incidentally encoded scene images that served as reward cues (Wittmann et al. Consistent with prominent theories on dopamine and hippocampus- dependent consolidation (Lisman & Grace, 2005), a reward effect on memory emerged for high-reward compared to low-reward scene cues in a three-week delayed memory test. In summary, although there is increasing evidence of how reward enhances incidental and intentional 256 Memory hippocampus- dependent learning via the mesolimbic dopaminergic circuit, more research is needed to better understand how reward affects memory. For example, future research would need to delineate the neural effects of reward-related anticipation compared to the effects of reward feedback and outcome (Mather & Schoeke, 2011). In addition to the dopaminergic modulation on memory, reward/value motivation can also lead to the strategic engagement of semantic processes supported by a frontotemporal network (Cohen, Rissman, Suthana, Castel, & Knowlton, 2014). Future research would need to address how interactions between reward- and semantic-related processes. Spillover Effects of Emotion and Reward during Encoding Spillover effects of emotion during encoding Studies of emotional memory have primarily focused on enhancements for the emotional information itself. However, a growing literature has documented the existence of emotional spillover effects: changes in memory for intrinsically neutral information that is encoded around the same time as an emotional stimulus or while in a state of arousal. For instance, enhancements in memory for emotional items tend to be accompanied by impairments in memory for their neutral background scenes (Waring & Kensinger, 2009, 2011). This effect has been associated with enhanced activity in temporoparietal regions associated with attention (Waring & Kensinger, 2011). It has been argued that this apparent discrepancy can be explained by differences in prioritization during encoding-that is, emotional arousal gives way to memory enhancements for prioritized information and memory impairments for everything else, due to arousal-biased competition for encoding resources (Mather & Sutherland, 2011).

These results showed a close association between premotor neuronal activity and behav ior anxiety 25 mg zoloft buy cheap nortriptyline 25 mg on line, supporting the idea that frontal lobe neurons do not code the stimulus parameter but rather convey information about perceptual judgments (stimulus-presence or stimulus-absence) anxiety symptoms from work order nortriptyline 25 mg with visa. The results described above raise the question of whether the neural correlate of perceptual judgments emerges abruptly in a particular cortical area or gradually builds up as information is transmitted and transformed across areas between S1 and the premotor cortex anxiety symptoms 8 dpo cheap 25 mg nortriptyline free shipping. To quantify the role of each area anxiety symptoms in women order nortriptyline with a mastercard, the relationship between stimulus amplitude and firing rate was calculated (figure 35 anxiety depression order 25 mg nortriptyline. The authors performed a linear regression on the normalized firing rate as a function of the logarithm of the stimulus amplitude. The semilogarithm slopes approximate increasingly to zero in neurons downstream to S1 (areas 3b and 1), areas 2 and 5, and second somatosensory cortex (S2). As a consequence, responses from downstream areas to somatosensory areas do not modulate their activity as functions of the stimulus amplitude, as early somatosensory areas do. Therefore, the stimulus encoding was transformed from a stimulus parametric code to an abstract representation. Thus, frontal lobe circuits that employ this abstract coding do not modulate their activity as a function of stimulus amplitude. This means that frontal neurons exhibit allor-none responses, depending on whether the subject 412 Neuroscience, Cognition, and Computation: Linking Hypotheses A Pre-stimulus kd (1. A trial began when the mechanical probe indented the glabrous skin of one fingertip of the right restrained hand, and the monkey reacted by placing its left free hand on an immovable key (key down [kd]). Then the stimulator moved up after a fixed delay period (3 s), cueing the monkey to communicate its decision about stimulus-presence or stimulus- absence by pressing one of two push-buttons (yesbutton; no-button). B, Left panel, the psychometric detection curve resulting from plotting the proportion of yes-button responses as a function of stimulus amplitude. Lower panel, Mean normalized firing rate in stimulus-present trials across all the recorded cortical areas. Lines correspond to linear fitting of the firing rate as a function of the stimulus- amplitude logarithm. D, Timing and the ability to predict the behavioral response across cortical areas. Ellipses are the 1 contour for a two- dimensional Gaussian fit to the neurons from each recorded area. Grayscale vertical markers above the abscissa- axis indicate the mean response latency for each cortical region. The top left inset plot illustrates the increase of the mean choice probability as a function of the mean response latency (r2 = 0. Rossi-Pool, Vergara, and Romo: Constructing Perceptual Decision- Making 413 felt or missed the stimulus. This evidence suggests that this task involves the conjoined activity of many brain areas. Hence, the vibrotactile stimulus evoked a distributed activity from S1 to premotor and motor areas. Although neurons could respond during the detection task, they may or may not be part of the perceptual construction. To understand how the sensory percept emerges, it is necessary to define proper measures to quantify how neural responses covary with the perceptual behav ior. As explained above, a near-threshold stimulus may (hit) or may not be detected (miss). This evidence suggests that premotor areas seem more involved in perceptual judgments than in the motor responses during the detection task. A notable feature is the response latency to the stimulus for each cortical area during the detection task. Indeed, de Lafuente and Romo (2005) sought to relate the response latency with the hierarchy of each area in sensory processing. Recently, timescales of intrinsic fluctuations in spiking activity across areas were related to an analogous hierarchical ordering (Murray et al. These intrinsic timescales, measured with the autocorrelation function, revealed areal specialization for task-relevant computations. In particular, frontal areas exhibit much longer timescales (~200 ms) than somatosensory areas (~65 ms). Future studies could help understand what underlying mechanisms contribute to the cortical areal hierarchy of these intrinsic timescales. However, the construction of a perceptual decisionmaking process may involve circuits outside the cerebral cortex. Interestingly, de Lafuente and Romo (2011) sought to determine other types of neurons not directly related to somatosensory processing during the detection task. For the subject in this task, it is impossible to differentiate between subthreshold stimulus-present trials and stimulus-absent trials. Constructing Decision-Making across Cortex during Sensory Discrimination Two important perceptual processes are impossible to study in the sensory- detection task. The first is the mechanism to store in working memory a previously transformed and encoded sensory input. This mnemonic process (Rossi-Pool, Vergara, and Romo, 2018), associated with an internal representation of the stimulus, cannot be addressed with the detection task. Another important missing step is the comparison of the current sensory input to a sensory referent, which could have been stored in working memory or in longterm memory. To understand the value of sensory transformation, working memory, and comparison in the generation of perceptual decision-making, Romo and colleagues (Hernández et al. Monkeys had to indicate whether the frequency of the comparison stimulus (f2) was lower or higher (f2 < f1 or f2 > f1) than the frequency of a base stimulus (f1) that was stored in working memory during a fixed delay period (figure 35. Furthermore, the key condition for a real discrimination is to vary the first stimulus frequency (f1) in each trial, such that each f1 value is followed by a higher or a lower comparison frequency (f2). Notice that these are scalar analog quantities on which the discrimination per for mance must be based. Neurons in S1 respond with a fine temporal structure of spike trains, representing f1 and f2. In general, mean firing rate responses increase monotonically as a function of the increasing stimulus frequency. Thus, the S1 responses could be described reasonably well as a linear function of the stimulus frequency. In this model, coefficient a1 is the slope of the activity frequency function and is a measure of how strongly a neuron is driven by changes of f1 frequency (top, formula, figure 35. Notably, S1 neurons exhibit only positive slope values (a1 > 0; green dots, figure 35. Analogously, during f2, S1 neurons are also modulated as a function of f2, with positive linear functions (a2 > 0; red dots, figure 35. This means that during the delay period between f1 and f2, no stimulusmodulation responses are found (figure 35. Hence, S1 neurons code the stimulus quantities, f1 and f2, only during the stimulus periods in this task. In addition to heterogeneity among responses, single neurons by themselves display complex dynamics often attributable to more than one task component (mixed selectivity; Rigotti et al. In particular, neurons from S2 exhibit in their activity a disappearance of the phase lock responses observed in S1 neurons. Even if there is strong evidence that S2 is directly driven by S1, a clear sensory transformation was recognized between these two areas (Salinas et al. Another important difference is that neurons in S2 have both positive and negative slopes during f1 (green dots, figure 35. In negative neurons, the firing rate decreases as a function of the increasing stimulus frequency in an approximately linear manner. Importantly, analogous complementary populations of positive and negative tuning have also been observed in another task (Rossi- Pool et al. Such equalization may be useful for subtracting common noise and increasing coding efficiency (Carnevale et al. Importantly, several S2 neurons display f1- dependent responses that continue for some hundreds of Rossi-Pool, Vergara, and Romo: Constructing Perceptual Decision- Making 415 A Pre-stimulus (1. At the end of the second stimulus, the monkey releases the key (key up) and presses either the medial or lateral push-buttons to indicate whether the comparison frequency was lower (f2 < f1) or higher (f2 > f1) than the base frequency. For each neuron, responses were fitted to the equation: firing rate = a1 × f1 + a2 × f2 + b, where f1 is the base stimulus frequency, f2 is the comparison stimulus frequency, and a1, a2, and b are coefficients. Each data point corresponds to one neuron with at least one significant coefficient (a1 0, a2 0, or both are dif ferent from zero, p < 0. Each panel shows the highest coefficients from each significant neuron coding during three dif ferent epochs: the first stimulus period (f1, 0. Green and red circles correspond to those neurons 416 Neuroscience, Cognition, and Computation: Linking Hypotheses milliseconds immediately after the end of f1, into the working memory delay between f1 and f2 (green dots, figure 35. Some neurons convey information during the early part, others only during the late part, and still others persistently throughout the entire delay period. This means that the mnemonic representation of f1 is not static, in the sense that the intensity of the coding activity varies across the delay. A comparison across areas shows a considerable overlap between the working memory coding, possibly reflecting interconnectivity between them. Upon the presentation of f2, neuronal responses in areas downstream from S1 are no longer defined by one variable (f1) but by two (both f1 and f2). Therefore, the potential repertoire of responses increases greatly, and analysis of the neural data should take this into account. To quantify the simultaneous dependence of the firing rate on f1 and f2, a first- order approximation to a bilinear function of f1 and f2 was used (Romo et al. That is, neuronal firing rates were modeled as linear functions of both f1 and f2: firing rate = a1. Over the course of the comparison period, a1 and a2 might change, indicating mixed selectivity. Except for S1, all the other cortical areas contain neurons with four dif ferent types of coding. Green dots correspond to neurons that had only significant f1 dependence, and red points correspond to neurons that have a significant f2 coding. Additionally, blue dots correspond to point cluster along the diagonal a2 = -a1, meaning that during that period the neurons respond as functions of the difference between f2 and f1. The neurons that encode this difference indicate the discrimination result, and they are interpreted as categorical decision coding. Additionally, gray dots indicate sensory differential encoding (intermediate decision coding), with significant but not equal values for a1 and a2. Notably, during the first 100 ms of f2, the activity of several neurons across cortical areas (except S1) was mainly a function of f1 frequency (green dots). This finding is consistent with a memory recall of the base stimulus frequency (f1). Further, some neurons initially code f1 or f2 frequencies and later code whether f2 is greater than f1 or f2 is less than f1 (blue and gray dots, figure 35. Actually, just as in the neural representation of the sensory stimuli, decision- coding neurons were represented by two complementary (positive and negative) populations. In brief, the decision of which of two stimuli has the higher vibration frequency engages multiple cortical areas on the parietal and frontal lobes (figure 35. The vibrotactile information arrives to S1, assuming in this model that this is the initial representation of whose responses depend on f1 only (a1 0, a2 = 0; dots on the abscissa axis) or on f2 only (a1 = 0, a2 0; red dots on the ordinate axis), respectively. Gray circles correspond to neurons with both significant coefficients of opposite signs (a1 > 0 and a2 < 0; a1 < 0 and a2 > 0) but significantly dif ferent magnitudes (a1 -a2); these responses are classified as partially differential neurons (dots between the diagonal and the ordinate or abscissa axis). Blue circles correspond to neurons with both significant coefficients (a1 0 and a2 0) but opposite signs and statistically equal magnitudes (a1 =-a2); these responses encode f2-f1 in a categorical or fully differential manner (dots on the diagonal). The macaque brain diagrams depict the activated cortical areas during the three main periods of the task: f1, working memory delay, and f2 (bottom traces, not drawn to scale). The primary somatosensory cortex (S1) encodes f1 only through positive monotonic responses (see also panel B). Importantly, at the end of the second stimulus, neurons with decision-related activity in frontal and parietal cortical areas reflected the difference in frequency between the two stimuli frequencies (f2­ f1). This decision-related activity could arise by subtracting the firing rates of neurons encoding f1 and f2 with opposite tuning. Note that somatosensory neurons encode the stimulus frequencies (f1 and f2) only through positive monotonic responses (red spot, figure 35. These findings suggest that S2 may be involved in this sensory transformation to further distribute this processed information to downstream frontal areas. Importantly, during the delay between the two stimuli (f1 and f2), information of f1 is retained (mnemonic coding) by the sustained activity of frontal lobe areas. Even if S2 neurons encode during the delay between f1 and f2, they are only engaged for some hundreds of milliseconds immediately after the end of f1. Notably, after the delay period the information of f2 is encoded in all recorded areas of the frontal and parietal lobes, including M1. Remarkably, some M1 neurons encoded sensory information on which the decision is based (f1 and f2). Importantly, neurons with decision-related activity, reflecting the comparison of f1 against f2, emerge in all cortical areas except S1. This decision activity is coded as the difference in frequency between the two stimuli (sensory differential encoding), and they are dif ferent from the categorical, all- or-none motor signal. Notoriously, decision- encoding latency appeared almost simulta neously in all these cortical areas (Hernández et al. These results are evidence that decision-making is a distributed process that arises not from serial processing but from the cooperative activity of many cortical areas. In brief, in the frequency discrimination task S1 neurons represent the stimulus frequency both in the phase lock and mean firing-rate responses. Downstream from S1, neurons encode the stimulus frequencies in their firing rates, employing a dual manner, with positive and negative monotonic responses. This coding scheme is used as a subtraction mechanism to preserve information about f1 not only during the stimulus period, but also during the working memory and the comparison period to generate a neural computation consistent with the animal report (Hernández et al. Historically, premotor cortices have been associated with motor commands (Caminiti et al.

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