Pavlovian learning is often conceived as the formation of a binary association between two events, where event #1 (the stimulus) evokes the anticipation of event #2 (the outcome). Under this naive definition, the background circumstances (the context) in which those events occur play little role. However, this simple form of learning is often augmented by higher-level cognitive systems that allow animals (including humans) to learn and retrieve context-specific associations (e.g., the word ‘shot’ might evoke different representations in a bar vs. a vaccine clinic) (Gershman, 2017; Swartzentruber, 1995; Fraser and Holland, 2019). This process is referred to as ‘contextual-gating’ (the context acting as a ‘gatekeeper’ in the selection of the appropriate associative memory).

Deficits in contextual gating are observed in several neuropsychiatric disorders including schizophrenia, autism spectrum disorder (ASD), post-traumatic stress disorders (PTSD), and substance use disorders (SUD) (Maren et al., 2013; Vermeulen, 2015; Elman et al., 2023; Hemsley, 2005; Servan-Schreiber et al., 1996; Liberzon and Abelson, 2016; Frith and Happé, 1994; Jones et al., 2013; Chatham et al., 2012), and may contribute to the intrusive thoughts and maladaptive (situation-inappropriate) behaviors that characterize these disorders. Importantly, the prevalence and symptomatology of these disorders vary between sexes. For example, the incidence of schizophrenia and ASD is higher in men (Santos et al., 2022) while women are more at risk of developing SUD or PTSD following initial drug use or trauma (Towers et al., 2023; Tolin and Foa, 2006). This disparity might be due, in part, to subtle sex differences in executive strategies (Baron-Cohen, 2002; Grissom and Reyes, 2019; Geary, 2019; Andreano and Cahill, 2009). Stress, a contributing factor in all aforementioned disorders, can further exacerbate these sex differences, resulting in sex-specific cognitive vulnerabilities (Geary, 2019; Andreano and Cahill, 2009; Goldfarb et al., 2019; Mazure et al., 2023; Bangasser et al., 2018; Klein and Corwin, 2002). Sex differences in executive functions and sensitivity to stress have been reproduced in animal models using behavioral assays of attention, impulsivity, spatial navigation, and working memory (Grissom and Reyes, 2019; Orsini and Setlow, 2017; Shansky et al., 2004; Papaleo et al., 2012). Whether these sex differences extend to the contextual gating of associative predictions remains unknown.

In animal models, the contextual gating of associative predictions can be studied in occasion-setting preparations. In positive occasion-setting, animals learn that a ‘target’ cue (X) results in a reward outcome (+) only when that cue is accompanied by a specific ‘feature’ such as a specific context (A); the same cue presented in absence of this contextual feature remains without consequence (A:X+/X−). Conversely, in negative occasion-setting, a target cue results in reward, except when accompanied by a specific feature (A:X−/X+). In both preparations, the contextual feature is thought to hierarchically modulate (positively or negatively) the association between the target cue and the outcome (Fraser and Holland, 2019; Bonardi et al., 2017). Strong evidence for this hierarchical modulation is the fact that a contextual feature can simultaneously function as a positive modulator for one target cue and as a negative modulator for another target cue (A:X+/X−/A:Y−/ Y+). In such bidirectional occasion-setting procedures all cues and contexts have equal probabilities of reward, therefore, accurate reward predictions cannot be achieved via simple associative summation but instead require the context-informed hierarchical gating of associative predictions (i.e., context-gated associative predictions).

Compared to simple forms of associative learning, the neural bases of context-gated associative predictions (or occasion-setting) remain severely understudied. Although recent studies have implicated the orbitofrontal cortex (OFC) in occasion-setting (Fraser and Janak, 2023; Meyer and Bucci, 2016; Shobe et al., 2017), most of the behavioral and neurobiological occasion-setting studies have exclusively used male subjects and/or did not explicitly address sex as a biological variable.

Therefore, the purpose of this study was twofold: (1) investigate sex differences in the (bidirectional) contextual gating of reward prediction and its sensitivity to stress, and (2) investigate sex differences in OFC engagement during the contextual gating of reward predictions. We found that female rats were slower than males to acquire context-gated reward seeking. However, once established, context-gated performance was more stable in females; it showed less variability within a session and was more resistant to acute stress. Moreover, we showed that this superior contextual control achieved by females was accompanied by an increased activation of the OFC.

Male and female rats were randomly assigned to one of four Pavlovian discrimination tasks outlined in Figure 1A–C. In these tasks [adapted from Delamater et al., 2010], all rats were exposed to two auditory cues (X and Y, 10 s) presented within two distinct, alternating, visual contexts (LIGHT and DARK; 2 min). All rats received similar amount of reward, and produced a similar behavioral response (magazine approach). However, the different reward contingencies featured in these tasks presumably promoted the emergence of different associative architectures for reward prediction (Figure 1B). In the Ctx-dep. O1 task, cue X was rewarded only in the LIGHT context and cue Y was rewarded only in DARK context; all rewarded trials resulted in the same outcome (O1). Accurate reward prediction in this task critically relies on the context-informed hierarchical modulation associative predictions (i.e., occasion-setting). In the Simple discrimination task, cue X was always rewarded and cue Y was never rewarded, regardless of context. In the No discrimination task both cues X and Y were probabilistically rewarded in both contexts. Finally, the Ctx-dep. O1/O2 task featured similar contingencies as Ctx-dep. O1, with the exception that the two rewarded trials (LIGHT: X and DARK: Y) resulted in two different outcomes (O1 and O2, respectively). In this case, not only might the visual context promote the hierarchical modulation of associative predictions (as in Ctx-dep. O1), but the context might also contribute to context-sensitive performance via simple (nonhierarchical) associations. Indeed, the use of differential outcomes allows for distinct context–outcome associations (LIGHT → O1 and DARK → O2), as well as distinct cue–outcome associations (X → O1 and Y → O2). On a given trial, if the cue and context tend to activate the same outcome representation, the converging cue- and context-evoked excitations can add up. As a result, context-sensitive performance could potentially be achieved via simple (nonhierarchical) summation process. Therefore, while rats trained in Ctx-dep. O1/O2 might engage a combination of associative processes to achieve context-sensitive behavior (including hierarchical and nonhierarchical associations), only rats trained in the Ctx-dep. O1 task critically and unambiguously rely on hierarchical associations to achieve context-sensitive behavior.

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Male rats acquire context regulation over reward seeking faster than females

Figure 1D displays the course of acquisition of discriminated Pavlovian approach in all four tasks (Ctx-dep. O1 n = 30F + 32M; Simple discrimination n = 12F + 22M; No discrimination n = 8F + 8M; Ctx-dep. O1/O2 n = 14F + 14M). In the context-dependent discrimination with a single outcome (Ctx-dep. O1), Linear Mixed Models (LMM) analysis revealed a significant Trial × Session interaction (F(17, 181.488) = 73.343, p < 0.001), as rats gradually learned to respond more during rewarded than non-rewarded trials. Critically, the analysis also revealed a significant Sex × Trial × Session interaction (F(17, 181.488) = 4.683, p < 0.001). Post hoc comparisons revealed that males and females displayed similar levels of responding to rewarded trials (ps > 0.260), but females maintained higher responding during non-rewarded trials, causing both sexes to differ in this measure from session 40 onwards (ps < 0.047). To facilitate comparisons between groups, we calculated for each animal a discrimination ratio, defined as the time in port during rewarded trials divided by the total time in port during all trials (Figure 1E). With the exception of animals trained in the No Discrimination task (for which discrimination between rewarded and non-rewarded trials was impossible by design), all training groups demonstrated a gradual increase in discrimination ratio (main Session effect: No Discr. p = 0.477; all other groups ps < 0.001). Importantly, only in Ctx-dep. O1 did we detect a main effect of Sex (F(1, 69.876) = 15.185, p < 0.001) and a Sex × Session interaction (F(17, 365.569) = 3.054, p < 0.001). Indeed, acquisition of discriminated performance was faster in males, causing significant sex differences from session 28 onwards (ps < 0.014). No main effect or interaction with Sex was detected for any other training group (ps > 0.305).

Figure 2A summarizes the performance of each group (time in port during the four trial types, LIGHT: X/DARK: X/LIGHT: Y/DARK: Y) by the end of the acquisition period (i.e., after 72 sessions for Ctx-dep. O1, or 32 session for all other groups). We next calculated the proportion of male and female rats that successfully acquired discriminated responding during the training period. The criterion for successful discrimination was defined as a time in port during rewarded trials that exceeded the time in port during non-rewarded trials by at least 50% (Time _rew ≥1.5* Time _nonrew, which corresponds to a discrimination ratio ≥0.6) for a minimum of four out of five consecutive sessions. Rats in the Ctx-dep. discrimination groups had to reach this criterion for both cues to be considered ‘discriminators’ (LIGHT:X ≥1.5* DARK:X and DARK:Y ≥1.5* LIGHT:Y). A significant sex difference in the proportion of discriminators was observed in the Ctx-dep. O1 group (χ² = 5.35; p = 0.021), as a greater proportion of males reached criterion (Figure 2B, C). No sex differences were observed in the proportion of discriminators in any other tasks.

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We then analyzed the number of sessions required for rats to reach the discrimination criterion (Figure 2D). Rats that failed to reach the discrimination criterion were excluded from this analysis. A Kruskal–Wallis one-way analysis of variance (ANOVA) revealed a significant effect of the behavioral task (H(2) = 87.361, p < 0.001) with different sessions to criterion for all the discrimination tasks (Ctx-dep. O1 > Ctx dep. O1/O2 > Simple; ps < 0.001, Dunn’s tests). Note how the introduction of differential outcomes profoundly facilitated the acquisition of context-dependent discrimination (sessions to criterion: 47.8 ± 1.7 standard error of the mean [s.e.m.] for Ctx-dep. O1 vs. 17.26 ± 1.6 s.e.m. for Ctx dep. O1/O2). This is consistent with a prior study showing a similar differential outcome effect in this task [Delamater et al., 2010].

Sex differences in the speed of acquisition (i.e., sessions to criterion) were analyzed separately within each task. In Ctx-dep. O1, males were significantly faster to reach the discrimination criterion (T(38) = 2.502, p = 0.017). No sex differences in the speed of acquisition were observed in the other tasks (ps ≥ 0.175).

These results indicate that female rats were generally slower to acquire contextual control over cue-evoked reward seeking. This sex difference in the development of discriminated performance was observed only in Ctx-dep. O1, the only task that unambiguously engages contextual modulation of reward predictions. Note that estrous cycle did not affect performance in female discriminators (Figure 2—figure supplement 1). Rats that failed to reach the discrimination criterion at the end of the acquisition period were excluded from further experimental procedures (with the exception of rats trained in the No Discrimination group).

Trial history influences context-gated reward seeking in male but not female rats

The delivery of reward, while critical for learning, can also interfere with the expression of discrimination behavior (Kuchibhotla et al., 2019). Therefore, we investigated to which extent within-session reward history (i.e., prior rewarded or non-rewarded trials) influenced performance in the Ctx-dep. O1 task, in male (n = 25) and female (n = 15) discriminators (Figure 3). Performance was analyzed over 10 consecutive sessions (sessions 71–80; 80 trials/session; 800 trials total), a period during which discrimination performance was globally stable. The first trial of a session was excluded as it was not preceded by any trial. For the remaining 790 trials (10 sessions × 79 trials), we determined if the trial was preceded by a rewarded or non-rewarded trial. This large sample size is required to ensure that enough data were collected for each possible trial history scenario. Responding during non-rewarded and rewarded trials was analyzed separately.

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Figure 3A shows the time in port during non-rewarded trials, as a function of the outcome of the previous trial (prior reward, or non-reward). A repeated measures (RM)-ANOVA revealed a main effect of Prior Rewards (F(1, 38) = 27.144; p < 0.001) as prior rewards increased responding during subsequent non-rewarded trials. The analysis showed no main effect of Sex (F(1, 38) = 1.783; p = 0.190), but a marginal Sex × Prior Reward interaction (F(1, 38) = 4.015; p = 0.052), as males were slightly more influenced by prior rewards. The effect of trial history appears to be carried exclusively by the outcome of the previous trial (t − 1) (no effect of consecutive prior rewards or non-rewards; ps ≥ 0.147, Bonferroni t-tests; Figure 3B).

We then analyzed another metric of conditioned responding: the response probability (i.e., probability subject enters the reward port during cue presentation). We reasoned that this metric might be more prone to detect brief lapses in discrimination performance (false alarms). Figure 3C shows the probability of responding to a non-rewarded cue as a function of the outcome of the prior trial (prior reward or non-reward). A RM-ANOVA revealed a main effect of Prior Reward (F(1, 38) = 25.607; p < 0.001), and a significant Sex × Prior Reward interaction (F(1, 38) = 9.247; p = 0.004). In males, prior reward increased the probability of responding on subsequent non-rewarded trials (T(38) = 43.752; p < 0.001) while in females, trial history had no effect (T(38) = 1.631; p = 0.209). As a result, males and females differed in their probability of responding to non-rewarded trials when those trials were preceded by reward (T(38) = 4.508; p = 0.040). Here again we found no influence of the number of consecutive past reward or non-rewards (ps ≥0.710, Figure 3D).

Responding to rewarded cues remained high throughout the sessions and was not influenced by trial history (ps ≥ 0.113); moreover no main effect or interaction with Sex was observed on these rewarded trials (ps ≥ 0.127) (Figure 3E–H).

These results indicate that male rats have difficulty inhibiting responding to non-rewarded (contextually irrelevant) cues following a recent reward. In contrast, female rats maintained high discrimination performance throughout the session and recent rewards had little to no effect on their discrimination accuracy. The mechanisms for the reward-induced decrease in discrimination accuracy (here observed primarily in males) are not fully understood. Rewards might temporarily lower the threshold for behavioral responding, resulting in stronger responding to weakly predictive cues (Kuchibhotla et al., 2019). Additionally, reward delivery might decrease discrimination accuracy by increasing the predictive value of the cue (and context) present during reward delivery. These direct cue → outcome (and context → outcome) associations compete with hierarchical associations for the control of behavior (Bradfield and Balleine, 2013), potentially affecting responding on the next trial (e.g., a rewarded LIGHT: X+ trial might increase the value of cue X and promote responding during the following DARK: X− trial; the same logic could be applied to the influence of the context). Further investigation is required to determine the precise mechanism(s) by which reward disrupt discrimination performance in the context-dependent discrimination task.

Acute stress disrupts context-gated reward seeking in male but not female rats

Stress affects several cognitive processes (e.g., selective attention, spatial navigation, working memory), often in a sex-dependent manner (Papaleo et al., 2012; Beiko et al., 2004; Shansky et al., 2006). However, the effect of stress on contextual modulation, and possible sex-differences in this effect, have not been formally tested. Therefore, we tested the effect of an acute stress (90 min restrain) on discrimination performance (expressed as discrimination ratio), in a subset of animals from all training groups (Ctx-dep O1: n = 11F + 15M; Simple discrimination: n = 6F + 6M; No discrimination: n = 8F + 8M; Ctx-dep O1/O2: n = 7F + 7M) (Figure 4). With the exception of the No Discrimination group, acute stress reduced discrimination accuracy in all tasks (main Stress effect: ps ≤ 0.004). However, only in the Ctx-dep. O1 task did we observe a significant Stress × Sex interaction (F(1, 24) = 5.665; p = 0.026) as only males showed reduced discrimination performance following acute stress (Stress vs. Baseline; males: T(14) = 5.165; p < 0.001; females: T(10) = 1.290; p = 0.209). No main effect or interaction with Sex was observed in any other task (ps ≥ 0.178; note however the smaller sample size in all these other groups). Therefore, although acute stress disrupted discrimination performance in all tasks, females trained in Ctx-dep. O1 appear to be less sensitive to the disrupting effect of stress in this task. This is despite the fact that female rodents typically show greater hypothalamo–pituitary–adrenal (HPA) activation in response to this type of stressor (i.e., greater stress activation) (Babb et al., 2013; Lovelock and Deak, 2020).

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Female rats show higher orbitofrontal engagement during context-gated reward predictions

The hierarchical gating of associative predictions has been linked to activity in the OFC (Fraser and Janak, 2023; Meyer and Bucci, 2016; Shobe et al., 2017). Therefore, for a subset of animals (Ctx-dep. O1: n = 7F + 9M; Simple discrimination: n = 7F + 9M; No discrimination: n = 7F + 7M; Ctx-dep. O1/O2: n = 5F + 5M), we quantified c-Fos-immunoreactive (cfos+) cells in the OFC (medial, ventral, and lateral subregions) following regular behavior testing (no-stress condition). We also quantified cfos+ in two control regions, M1J and S1J, where we did not expect task-specific activation. Compared to baseline (homecage controls), behavioral testing increased cfos+ expression in all regions of interest, regardless of task (Figure 5; homecage c-Fos+ counts are shown but not included in the following statistical analyses). A RM-ANOVA revealed a main effect of Region (F(4, 192) = 85.037; p ≤ 0.001) and a significant Task × Sex × Region interaction (F(12, 192) = 1.9; p = 0.036). Follow-up planned contrast analysis compared males and females within each task and brain region. In the Ctx-dep. O1 task, females showed increased c-fos+ expression compared to males, in all OFC subregions (T(14) ≥ 2.387; p ≤ 0.032). No sex differences were found in any other task (ps ≥ 0.255; note however the smaller sample size of the Ctx-dep. O1/O2 group). Moreover, no sex differences were found in the control regions M1J and S1J, regardless of task (ps ≥ 0.139).

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We investigated sex differences in contextual modulation of cue-evoked reward predictions and its neural correlates. Male and female rats were trained on a context-dependent discrimination task (Ctx-dep. O1) in which a contextual feature informs the validity of two reward cues (i.e., bidirectional occasion-setting). Overall, males acquired context-gated behavior faster than females. However, once established, contextual control over behavior was more robust in females; it was more stable within a session (less influence of prior reward) and more resistant to acute stress. This superior contextual control achieved by females was accompanied by elevated c-Fos expression in all OFC subregions (medial, ventral, and lateral). Critically, no sex differences (behavioral or neurobiological) were observed among animals trained on simpler discrimination tasks that do not engage contextual gating (Simple Discrimination, No discrimination, Ctx-dep. O1/O2). These results add to the growing body of literature on sex differences in motivated behaviors and value-based decision-making (Grissom and Reyes, 2019; Orsini and Setlow, 2017). Most of these sex differences were observed in behaviors informed by binary associations between cues (or actions) and rewards. To our knowledge, this study is the first to demonstrate sex differences in the context-informed hierarchical gating of associative predictions, a process that is largely independent from binary associations (Fraser and Holland, 2019; Bonardi et al., 2017).

For Ctx-dep. O1, the context (LIGHT or DARK chamber) is an occasion-setter, that is, a stimulus that modulates the response to a target cue, presumably by gating the retrieval of the appropriate cue → outcome association. However, our task differs from classic occasion-setting preparations in several ways. Historically, Pavlovian modulation has been studied in preparations in which the modulating stimuli (the occasion-setter) exerts unidirectional modulation, either potentiating (A:X+/X−) or reducing (A:X−/X+) the response to the target cue. In such preparations, the occasion-setter might also establish a direct association (excitatory or inhibitory) with the outcome (Gershman, 2017; Urcelay and Miller, 2014; Brandon and Wagner, 1991), thereby entangling the contributions of hierarchical modulation and direct stimuli-evoked predictions. To circumvent this issue, occasion-setting preparations commonly introduce a temporal gap (5–30 s) between the termination of a discrete occasion-setting stimulus and its target cue. This approach has generally been successful in limiting direct associations between the occasion-setter and the outcome, instead promoting hierarchical modulation processes (Ross and Holland, 1981; Holland, 1998; but see Bowers and Timberlake, 2017). This temporal gap however introduces a working-memory requirement (animals must remember if the target cue was preceded by the occasion-setter) which complicates results interpretations.

Here, we used a bidirectional occasion-setting preparation with a single reward outcome (Ctx-dep. O1) as an alternative approach to investigate contextual modulation (Delamater et al., 2010). In this paradigm, a contextual feature (e.g., LIGHT) positively modulates one target association (LIGHT: X+/DARK: X−) and negatively modulates another association (LIGHT: Y−/DARK: Y+). Critically, there are no direct informative associations between contextual feature and the single reward outcome (rewards are equally frequent in the LIGHT or DARK contexts), so accurate discrimination necessitates hierarchical modulation (Bonardi et al., 2017). Moreover, the contextual feature precedes and overlaps with the target cue, thereby eliminating working-memory requirements. Thus, sex differences observed in Ctx-dep. O1, strongly indicate sex differences in hierarchical (context-informed) associative modulations. Alternatively, males and females might differ in terms of attentional resources (Jentsch and Taylor, 2003). Indeed, unlike Simple Discrimination or No discrimination tasks, Ctx-dep. O1 requires high multimodal attention (animals must simultaneously attend to the visual context and auditory cues). However, no sex differences were observed in context-dependent discrimination with two outcomes (Ctx-dep. O1/O2), a task that shares similar attentional requirements with Ctx-dep. O1, but does not necessarily engage hierarchical contextual modulation. Indeed, in Ctx-dep. O1/O2, both the visual context and the auditory target cue are relevant, however the use of differential outcomes presumably allows animals to solve the task via simple (nonhierarchical) summation processes, rats simply adding up the converging context- and cue-evoked predictions. The possibility of a nonhierarchical solution to the discrimination problem might explain why almost all rats trained in the Ctx-dep. O1/O2 task learned the discrimination and at much faster rate compared to Ctx-dep. O1 (but see Delamater et al., 2010 for alternative explanation of the differential outcome effect). Together, these results indicate that the sex differences observed here are not attributable to simple associative, motivational, working-memory, or attentional processes, but are specific to the neurocomputational operations required for the hierarchical, contextual control of behavior. Note that there is no a priori reason to believe that the nature of these computations differ between sexes (no sex differences in the fundamental mechanisms of context-gated predictions). Rather male and female rats appear to differ slightly in the learning rate and magnitude/robustness of these computations. Moreover it remains to be determined if the sex differences in context-gated reward seeking are specific to the sensory modalities employed here or if those sex differences extend to other sensory modalities.

Sex differences in context-dependent behaviors have previously been reported in fear conditioning preparations where females often display generalized fear responses that evade precise contextual control (Day et al., 2016; Greiner et al., 2019; Keiser et al., 2017; Trask et al., 2020). Elevated baseline anxiety and fear reactivity in females might contribute to this relative failure to regulate fear responses (Blanchard et al., 1991; Dalla and Shors, 2009; but see Scholl et al., 2019). However, we show here that reduced contextual control in females (at least in the early stages of acquisition) is not limited to aversive behaviors but extends to appetitive, reward-seeking behaviors. This suggests a broader, domain-general, delay in the engagement of contextual, top–down regulation processes in female rodents. This is consistent with higher sign-tracking tendencies in female rats (Stringfield et al., 2019; Peterson et al., 2020; Fuentes et al., 2018; Pitchers et al., 2015; Hughson et al., 2019) – another form of appetitive behavior that reflects reduced top–down cognitive control (opposed to goal-tracking behavior) (Sarter and Phillips, 2018; Flagel and Robinson, 2017; María-Ríos and Morrow, 2020). Note that the reduced top–down contextual control displayed by females in our study was limited to delayed acquisition. Provided sufficient training, females actually established more robust contextual control over cue-evoked reward seeking than males.

The biological basis for the delayed acquisition of contextual control in females is unknown. Although estrous cycle had no effect on the expression of context-dependent behavior in female discriminators, fluctuating sex hormones (and resulting neurobiological changes) could have contributed to the slower acquisition of contextual control in females. Indeed, learning the hierarchical contextual rules for reward prediction is a complex and lengthy process that probably requires distributed plasticity across several brain regions including the striatum, amygdala, hippocampus, and neocortex (Fraser and Holland, 2019; Lee and Lee, 2013). Estrous cycle affects synaptic physiology and plasticity at all these sites via changes in dopamine reuptake (Morissette and Di Paolo, 1993; Calipari et al., 2017), neuronal excitability (Alonso-Caraballo and Ferrario, 2019; Clemens et al., 2019; Shanley et al., 2023; Scharfman et al., 2003; Blume et al., 2017), spine density (Woolley et al., 1990; Beeson and Meitzen, 2023), gene expression (Knoedler et al., 2022; Duclot and Kabbaj, 2015), or neurogenesis (Mahmoud et al., 2016). This dynamic neurobiological environment might constitute a challenge for females attempting to solve the Ctx-dep. O1 task, and might contribute to their delayed acquisition of contextual control. Interestingly, this dynamic neurobiological environment might also contribute to the more stable performance ultimately achieved by females. Indeed, in deep learning models, the injection of random ‘noise’ in (artificial) neural networks during training is commonly used to build redundancy and increase network performance and stability (Srivastava et al., 2014; Zhang et al., 2021). This ‘robustness through perturbations’ approach often comes at the expense of learning speed, similar to what we observed in females. Alternatively, or in addition, androgens (male sex hormones) – known to contribute to the male advantage on certain spatial tasks – might have contributed to males’ faster acquisition of contextual control in this task (Dohanich et al., 2009; Spritzer et al., 2011). The role of these hormonal factors, as well as potential non-gonadal sex differences, in the trade-off between learning speed and performance stability remains to be confirmed experimentally.

Regardless of the precise mechanism, our results indicate that, compared to male rats, female discriminators ultimately achieved more stable contextual control over cued reward seeking; their context-dependent discrimination performance was less affected by trial history (recent rewards) or by acute stress. This relative resilience of females trained in the Ctx-dep. O1 task is consistent with studies showing that females, while more sensitive to the arousing and anxiogenic effects of stress, are more resilient to the disrupting effects of stress on executive functions and memory (Bangasser et al., 2018; Papaleo et al., 2012). Sex differences in regional sensitivity to corticotropin-releasing factor (CRF) and CRF receptor signaling might contribute to these sex differences in behavioral responses to stress (Hupalo et al., 2019).

The behavioral sex differences observed in this study were accompanied by neural sex differences. Compared to males, females displayed increased OFC activation during Ctx-dep. O1. Importantly, no behavioral or neural sex differences were observed for any other task. Together, these results strongly implicate the OFC in the sex-biased expression of contextual modulation of reward seeking. This is consistent with recent studies demonstrating a critical role of the OFC in hierarchical control over cue-evoked behaviors (Fraser and Janak, 2023; Meyer and Bucci, 2016; Shobe et al., 2017). More generally, the OFC is proposed to encode task structure including context-dependent contingencies (Mizrak et al., 2021; Wilson et al., 2014; Farovik et al., 2015). Accordingly, the OFC appears critical for decision-making when optimal choices require knowledge of the broader task structure (i.e., when stimuli cannot be taken at face value) (Bradfield et al., 2015; Takahashi et al., 2011). Although causality remains to be established, our data suggest that the increased orbitofrontal engagement displayed by females might contribute to their resilient contextual regulation of behavior.

The root causes of the behavioral and neural sex differences in context-gated reward prediction are largely unknown and not addressed in this study. In principle, all the sex differences reported here (speed of acquisition, robustness of performance, and OFC activation) might be independent and engage different mechanisms. Alternatively, some of these effects might be causally related. Our result show that, for a comparable level of context-dependent discrimination, females show an increased engagement of the OFC compared to males (higher number of c-Fos-immunoreactive neurons). This suggests a denser (less sparse) coding of context-dependent contingencies in females’ OFC. Denser coding is generally associated with slower discrimination learning but superior fault tolerance (i.e., better resistance to noise or disturbance) (Spanne and Jörntell, 2015), exactly what we observed in females, suggesting a potential unifying mechanism for the sex differences reported here.

The contextual regulation of behavior is at the heart of cognitive control (Chatham et al., 2012; Miller and Cohen, 2001). Deficits in this process are integral to the pathophysiology of numerous neuropsychiatric disorders (Maren et al., 2013). Although extrapolation of animal data to humans requires caution, our findings of a superior OFC engagement by female rats during context-informed hierarchical control of reward seeking is consistent with human [f]MRI studies showing larger OFC grey matter volume in females (Liu et al., 2020) and superior OFC activation in females during the regulation of emotional responses (Lee et al., 2009; Welborn et al., 2009). The behavioral and neural sex differences observed here might offer insight into the biological sex differences observed in certain disorders. For instance, the initial delay in establishing contextual control observed here in females might contribute to the rapid progression of SUD or PTSD observed in women following initial drug use or trauma (Towers et al., 2023; Tolin and Foa, 2006; Becker and Chartoff, 2019; Olff, 2017). Conversely, the more stable contextual control ultimately achieved by females after extended training observed here might contribute to the better prognosis for women following behavioral and cognitive treatments for these disorders (Olff, 2017; Walitzer and Dearing, 2006; Galovski et al., 2013), and might be due, in part, to increased OFC engagement.

All data generated or analyzed during this study are included in the manuscript and supporting files; source data files have been provided for Figures 1–5 and Figure 2—figure supplement 1.

1. 1. Alonso-Caraballo Y
   2. Ferrario CR

   (2019) Effects of the estrous cycle and ovarian hormones on cue-triggered motivation and intrinsic excitability of medium spiny neurons in the Nucleus Accumbens core of female rats

   Hormones and Behavior 116:104583.

   https://doi.org/10.1016/j.yhbeh.2019.104583

   • PubMed
   • Google Scholar
2. 1. Andreano JM
   2. Cahill L

   (2009) Sex influences on the neurobiology of learning and memory

   Learning & Memory 16:248–266.

   https://doi.org/10.1101/lm.918309

   • PubMed
   • Google Scholar
3. 1. Babb JA
   2. Masini CV
   3. Day HEW
   4. Campeau S

   (2013) Sex differences in activated corticotropin-releasing factor neurons within stress-related neurocircuitry and hypothalamic–pituitary–adrenocortical axis hormones following restraint in rats

   Neuroscience 234:40–52.

   https://doi.org/10.1016/j.neuroscience.2012.12.051

   • Google Scholar
4. 1. Bangasser DA
   2. Eck SR
   3. Telenson AM
   4. Salvatore M

   (2018) Sex differences in stress regulation of arousal and cognition

   Physiology & Behavior 187:42–50.

   https://doi.org/10.1016/j.physbeh.2017.09.025

   • PubMed
   • Google Scholar
5. 1. Baron-Cohen S

   (2002) The extreme male brain theory of autism

   Trends in Cognitive Sciences 6:248–254.

   https://doi.org/10.1016/S1364-6613(02)01904-6

   • Google Scholar
6. 1. Becker JB
   2. Chartoff E

   (2019) Sex differences in neural mechanisms mediating reward and addiction

   Neuropsychopharmacology 44:166–183.

   https://doi.org/10.1038/s41386-018-0125-6

   • Google Scholar
7. 1. Beeson ALS
   2. Meitzen J

   (2023) Estrous cycle impacts on dendritic spine plasticity in rat nucleus accumbens core and shell and caudate-putamen

   The Journal of Comparative Neurology 531:759–774.

   https://doi.org/10.1002/cne.25460

   • PubMed
   • Google Scholar
8. 1. Beiko J
   2. Lander R
   3. Hampson E
   4. Boon F
   5. Cain DP

   (2004) Contribution of sex differences in the acute stress response to sex differences in water maze performance in the rat

   Behavioural Brain Research 151:239–253.

   https://doi.org/10.1016/j.bbr.2003.08.019

   • Google Scholar
9. 1. Blanchard DC
   2. Shepherd JK
   3. De Padua Carobrez A
   4. Blanchard RJ

   (1991) Sex effects in defensive behavior: baseline differences and drug interactions

   Neuroscience and Biobehavioral Reviews 15:461–468.

   https://doi.org/10.1016/s0149-7634(05)80132-0

   • PubMed
   • Google Scholar
10. 1. Blume SR
    2. Freedberg M
    3. Vantrease JE
    4. Chan R
    5. Padival M
    6. Record MJ
    7. DeJoseph MR
    8. Urban JH
    9. Rosenkranz JA

    (2017) Sex- and estrus-dependent differences in rat basolateral amygdala

    The Journal of Neuroscience 37:10567–10586.

    https://doi.org/10.1523/JNEUROSCI.0758-17.2017

    • PubMed
    • Google Scholar
11. 1. Bonardi C
    2. Robinson J
    3. Jennings D

    (2017) Can existing associative principles explain occasion setting? Some old ideas and some new data

    Behavioural Processes 137:5–18.

    https://doi.org/10.1016/j.beproc.2016.07.007

    • PubMed
    • Google Scholar
12. 1. Bowers RI
    2. Timberlake W

    (2017) Do rats learn conditional independence?

    Royal Society Open Science 4:160994.

    https://doi.org/10.1098/rsos.160994

    • PubMed
    • Google Scholar
13. 1. Bradfield LA
    2. Balleine BW

    (2013) Hierarchical and binary associations compete for behavioral control during instrumental biconditional discrimination

    Journal of Experimental Psychology 39:2–13.

    https://doi.org/10.1037/a0030941

    • Google Scholar
14. 1. Bradfield LA
    2. Dezfouli A
    3. van Holstein M
    4. Chieng B
    5. Balleine BW

    (2015) Medial orbitofrontal cortex mediates outcome retrieval in partially observable task situations

    Neuron 88:1268–1280.

    https://doi.org/10.1016/j.neuron.2015.10.044

    • PubMed
    • Google Scholar
15. 1. Brandon SE
    2. Wagner AR

    (1991) Modulation of a discrete Pavlovian conditioned reflex by a putative emotive Pavlovian conditioned stimulus

    Journal of Experimental Psychology 17:299–311.

    https://doi.org/10.1037/0097-7403.17.3.299

    • Google Scholar
16. 1. Calipari ES
    2. Juarez B
    3. Morel C
    4. Walker DM
    5. Cahill ME
    6. Ribeiro E
    7. Roman-Ortiz C
    8. Ramakrishnan C
    9. Deisseroth K
    10. Han M-H
    11. Nestler EJ

    (2017) Dopaminergic dynamics underlying sex-specific cocaine reward

    Nature Communications 8:13877.

    https://doi.org/10.1038/ncomms13877

    • PubMed
    • Google Scholar
17. 1. Chatham CH
    2. Claus ED
    3. Kim A
    4. Curran T
    5. Banich MT
    6. Munakata Y

    (2012) Cognitive control reflects context monitoring, not motoric stopping, in response inhibition

    PLOS ONE 7:e31546.

    https://doi.org/10.1371/journal.pone.0031546

    • PubMed
    • Google Scholar
18. 1. Clemens AM
    2. Lenschow C
    3. Beed P
    4. Li L
    5. Sammons R
    6. Naumann RK
    7. Wang H
    8. Schmitz D
    9. Brecht M

    (2019) Estrus-cycle regulation of cortical inhibition

    Current Biology 29:605–615.

    https://doi.org/10.1016/j.cub.2019.01.045

    • PubMed
    • Google Scholar
19. 1. Cora MC
    2. Kooistra L
    3. Travlos G

    (2015) Vaginal cytology of the laboratory rat and mouse: Review and criteria for the staging of the estrous cycle using stained vaginal smears

    Toxicologic Pathology 43:776–793.

    https://doi.org/10.1177/0192623315570339

    • PubMed
    • Google Scholar
20. 1. Dalla C
    2. Shors TJ

    (2009) Sex differences in learning processes of classical and operant conditioning

    Physiology & Behavior 97:229–238.

    https://doi.org/10.1016/j.physbeh.2009.02.035

    • Google Scholar
21. 1. Day HLL
    2. Reed MM
    3. Stevenson CW

    (2016) Sex differences in discriminating between cues predicting threat and safety

    Neurobiology of Learning and Memory 133:196–203.

    https://doi.org/10.1016/j.nlm.2016.07.014

    • PubMed
    • Google Scholar
22. 1. Delamater AR
    2. Kranjec A
    3. Fein MI

    (2010) Differential outcome effects in pavlovian biconditional and ambiguous occasion setting tasks

    Journal of Experimental Psychology. Animal Behavior Processes 36:471–481.

    https://doi.org/10.1037/a0019136

    • PubMed
    • Google Scholar
23. Book

    1. Dohanich G
    2. Korol D
    3. Shors T

    (2009) Steroids, learning and memory

    In: Dohanich G, editors. Hormones, Brain and Behavior. Elsevier. pp. 539–578.

    https://doi.org/10.1016/B978-008088783-8.00015-2

    • Google Scholar
24. 1. Duclot F
    2. Kabbaj M

    (2015) The estrous cycle surpasses sex differences in regulating the transcriptome in the rat medial prefrontal cortex and reveals an underlying role of early growth response 1

    Genome Biology 16:256.

    https://doi.org/10.1186/s13059-015-0815-x

    • PubMed
    • Google Scholar
25. 1. Duricki DA
    2. Soleman S
    3. Moon LDF

    (2016) Analysis of longitudinal data from animals with missing values using SPSS

    Nature Protocols 11:1112–1129.

    https://doi.org/10.1038/nprot.2016.048

    • PubMed
    • Google Scholar
26. 1. Elman I
    2. Ariely D
    3. Tsoy-Podosenin M
    4. Verbitskaya E
    5. Wahlgren V
    6. Wang AL
    7. Zvartau E
    8. Borsook D
    9. Krupitsky E

    (2023) Contextual processing and its alterations in patients with addictive disorders

    Addiction Neuroscience 7:100100.

    https://doi.org/10.1016/j.addicn.2023.100100

    • Google Scholar
27. 1. Farovik A
    2. Place RJ
    3. McKenzie S
    4. Porter B
    5. Munro CE
    6. Eichenbaum H

    (2015) Orbitofrontal cortex encodes memories within value-based schemas and represents contexts that guide memory retrieval

    The Journal of Neuroscience 35:8333–8344.

    https://doi.org/10.1523/JNEUROSCI.0134-15.2015

    • PubMed
    • Google Scholar
28. 1. Flagel SB
    2. Robinson TE

    (2017) Neurobiological basis of individual variation in stimulus-reward learning

    Current Opinion in Behavioral Sciences 13:178–185.

    https://doi.org/10.1016/j.cobeha.2016.12.004

    • PubMed
    • Google Scholar
29. 1. Fraser KM
    2. Holland PC

    (2019) Occasion setting

    Behavioral Neuroscience 133:145–175.

    https://doi.org/10.1037/bne0000306

    • PubMed
    • Google Scholar
30. 1. Fraser KM
    2. Janak PH

    (2023) Basolateral amygdala and orbitofrontal cortex, but not dorsal hippocampus, are necessary for the control of reward-seeking by occasion setters

    Psychopharmacology 240:623–635.

    https://doi.org/10.1007/s00213-022-06227-0

    • PubMed
    • Google Scholar
31. 1. Frith U
    2. Happé F

    (1994) Autism: beyond “theory of mind.”

    Cognition 50:115–132.

    https://doi.org/10.1016/0010-0277(94)90024-8

    • PubMed
    • Google Scholar
32. 1. Fuentes S
    2. Carrasco J
    3. Hatto A
    4. Navarro J
    5. Armario A
    6. Monsonet M
    7. Ortiz J
    8. Nadal R

    (2018) Sex-dependent impact of early-life stress and adult immobilization in the attribution of incentive salience in rats

    PLOS ONE 13:e0190044.

    https://doi.org/10.1371/journal.pone.0190044

    • PubMed
    • Google Scholar
33. 1. Galovski TE
    2. Blain LM
    3. Chappuis C
    4. Fletcher T

    (2013) Sex differences in recovery from PTSD in male and female interpersonal assault survivors

    Behaviour Research and Therapy 51:247–255.

    https://doi.org/10.1016/j.brat.2013.02.002

    • PubMed
    • Google Scholar
34. 1. Geary DC

    (2019) Evolutionary perspective on sex differences in the expression of neurological diseases

    Progress in Neurobiology 176:33–53.

    https://doi.org/10.1016/j.pneurobio.2018.06.001

    • PubMed
    • Google Scholar
35. 1. Gershman SJ

    (2017) Context-dependent learning and causal structure

    Psychonomic Bulletin & Review 24:557–565.

    https://doi.org/10.3758/s13423-016-1110-x

    • PubMed
    • Google Scholar
36. 1. Goldfarb EV
    2. Seo D
    3. Sinha R

    (2019) Sex differences in neural stress responses and correlation with subjective stress and stress regulation

    Neurobiology of Stress 11:100177.

    https://doi.org/10.1016/j.ynstr.2019.100177

    • PubMed
    • Google Scholar
37. 1. Goldman JM
    2. Murr AS
    3. Cooper RL

    (2007) The rodent estrous cycle: characterization of vaginal cytology and its utility in toxicological studies

    Birth Defects Research. Part B, Developmental and Reproductive Toxicology 80:84–97.

    https://doi.org/10.1002/bdrb.20106

    • PubMed
    • Google Scholar
38. 1. Greiner EM
    2. Müller I
    3. Norris MR
    4. Ng KH
    5. Sangha S

    (2019) Sex differences in fear regulation and reward-seeking behaviors in a fear-safety-reward discrimination task

    Behavioural Brain Research 368:111903.

    https://doi.org/10.1016/j.bbr.2019.111903

    • PubMed
    • Google Scholar
39. 1. Grissom NM
    2. Reyes TM

    (2019) Let’s call the whole thing off: evaluating gender and sex differences in executive function

    Neuropsychopharmacology 44:86–96.

    https://doi.org/10.1038/s41386-018-0179-5

    • PubMed
    • Google Scholar
40. 1. Hemsley DR

    (2005) The schizophrenic experience: taken out of context?

    Schizophrenia Bulletin 31:43–53.

    https://doi.org/10.1093/schbul/sbi003

    • PubMed
    • Google Scholar
41. 1. Holland PC

    (1977) Conditioned stimulus as a determinant of the form of the Pavlovian conditioned response

    Journal of Experimental Psychology. Animal Behavior Processes 3:77–104.

    https://doi.org/10.1037//0097-7403.3.1.77

    • PubMed
    • Google Scholar
42. 1. Holland PC

    (1980) CS-US interval as a determinant of the form of Pavlovian appetitive conditioned responses

    Journal of Experimental Psychology 6:155–174.

    https://doi.org/10.1037//0097-7403.6.2.155

    • Google Scholar
43. 1. Holland PC

    (1998) Temporal control in Pavlovian occasion setting

    Behavioural Processes 44:225–236.

    https://doi.org/10.1016/s0376-6357(98)00051-5

    • PubMed
    • Google Scholar
44. 1. Holland PC

    (2000) Trial and intertrial durations in appetitive conditioning in rats

    Animal Learning & Behavior 28:121–135.

    https://doi.org/10.3758/BF03200248

    • Google Scholar
45. 1. Hughson AR
    2. Horvath AP
    3. Holl K
    4. Palmer AA
    5. Solberg Woods LC
    6. Robinson TE
    7. Flagel SB

    (2019) Incentive salience attribution, “sensation-seeking” and “novelty-seeking” are independent traits in a large sample of male and female heterogeneous stock rats

    Scientific Reports 9:2351.

    https://doi.org/10.1038/s41598-019-39519-1

    • PubMed
    • Google Scholar
46. 1. Hupalo S
    2. Bryce CA
    3. Bangasser DA
    4. Berridge CW
    5. Valentino RJ
    6. Floresco SB

    (2019) Corticotropin-Releasing Factor (CRF) circuit modulation of cognition and motivation

    Neuroscience and Biobehavioral Reviews 103:50–59.

    https://doi.org/10.1016/j.neubiorev.2019.06.010

    • PubMed
    • Google Scholar
47. 1. Jentsch JD
    2. Taylor JR

    (2003) Sex-related differences in spatial divided attention and motor impulsivity in rats

    Behavioral Neuroscience 117:76–83.

    https://doi.org/10.1037//0735-7044.117.1.76

    • PubMed
    • Google Scholar
48. 1. Jones JAH
    2. Lim KO
    3. Wozniak JR
    4. Specker S
    5. MacDonald AW

    (2013) Context-processing abilities in chronic cocaine users

    Psychology of Addictive Behaviors 27:687–695.

    https://doi.org/10.1037/a0032237

    • PubMed
    • Google Scholar
49. 1. Karim BO
    2. Landolfi JA
    3. Christian A
    4. Ricart-Arbona R
    5. Qiu W
    6. McAlonis M
    7. Eyabi PO
    8. Khan KA
    9. Dicello JF
    10. Mann JF
    11. Huso DL

    (2003)

    Estrous cycle and ovarian changes in a rat mammary carcinogenesis model after irradiation, tamoxifen chemoprevention, and aging

    Comparative Medicine 53:532–538.

    • PubMed
    • Google Scholar
50. 1. Keiser AA
    2. Turnbull LM
    3. Darian MA
    4. Feldman DE
    5. Song I
    6. Tronson NC

    (2017) Sex differences in context fear generalization and recruitment of hippocampus and amygdala during retrieval

    Neuropsychopharmacology 42:397–407.

    https://doi.org/10.1038/npp.2016.174

    • PubMed
    • Google Scholar
51. 1. Klein LC
    2. Corwin EJ

    (2002) Seeing the unexpected: how sex differences in stress responses may provide a new perspective on the manifestation of psychiatric disorders

    Current Psychiatry Reports 4:441–448.

    https://doi.org/10.1007/s11920-002-0072-z

    • PubMed
    • Google Scholar
52. 1. Knoedler JR
    2. Inoue S
    3. Bayless DW
    4. Yang T
    5. Tantry A
    6. Davis CH
    7. Leung NY
    8. Parthasarathy S
    9. Wang G
    10. Alvarado M
    11. Rizvi AH
    12. Fenno LE
    13. Ramakrishnan C
    14. Deisseroth K
    15. Shah NM

    (2022) A functional cellular framework for sex and estrous cycle-dependent gene expression and behavior

    Cell 185:654–671.

    https://doi.org/10.1016/j.cell.2021.12.031

    • PubMed
    • Google Scholar
53. 1. Kuchibhotla KV
    2. Hindmarsh Sten T
    3. Papadoyannis ES
    4. Elnozahy S
    5. Fogelson KA
    6. Kumar R
    7. Boubenec Y
    8. Holland PC
    9. Ostojic S
    10. Froemke RC

    (2019) Dissociating task acquisition from expression during learning reveals latent knowledge

    Nature Communications 10:2151.

    https://doi.org/10.1038/s41467-019-10089-0

    • PubMed
    • Google Scholar
54. 1. Lee TMC
    2. Chan CCH
    3. Leung AWS
    4. Fox PT
    5. Gao JH

    (2009) Sex-related differences in neural activity during risk taking: an fMRI study

    Cerebral Cortex 19:1303–1312.

    https://doi.org/10.1093/cercor/bhn172

    • PubMed
    • Google Scholar
55. 1. Lee I
    2. Lee CH

    (2013) Contextual behavior and neural circuits

    Frontiers in Neural Circuits 7:84.

    https://doi.org/10.3389/fncir.2013.00084

    • PubMed
    • Google Scholar
56. 1. Liberzon I
    2. Abelson JL

    (2016) Context processing and the neurobiology of post-traumatic stress disorder

    Neuron 92:14–30.

    https://doi.org/10.1016/j.neuron.2016.09.039

    • PubMed
    • Google Scholar
57. 1. Liu S
    2. Seidlitz J
    3. Blumenthal JD
    4. Clasen LS
    5. Raznahan A

    (2020) Integrative structural, functional, and transcriptomic analyses of sex-biased brain organization in humans

    PNAS 117:18788–18798.

    https://doi.org/10.1073/pnas.1919091117

    • PubMed
    • Google Scholar
58. 1. Lovelock DF
    2. Deak T

    (2020) Acute stress imposed during adolescence has minimal effects on hypothalamic-pituitary-adrenal (HPA) axis sensitivity in adulthood in female Sprague Dawley rats

    Physiology & Behavior 213:112707.

    https://doi.org/10.1016/j.physbeh.2019.112707

    • PubMed
    • Google Scholar
59. 1. Mahmoud R
    2. Wainwright SR
    3. Galea LAM

    (2016) Sex hormones and adult hippocampal neurogenesis: Regulation, implications, and potential mechanisms

    Frontiers in Neuroendocrinology 41:129–152.

    https://doi.org/10.1016/j.yfrne.2016.03.002

    • PubMed
    • Google Scholar
60. 1. Maren S
    2. Phan KL
    3. Liberzon I

    (2013) The contextual brain: implications for fear conditioning, extinction and psychopathology

    Nature Reviews. Neuroscience 14:417–428.

    https://doi.org/10.1038/nrn3492

    • PubMed
    • Google Scholar
61. 1. María-Ríos CE
    2. Morrow JD

    (2020) Mechanisms of shared vulnerability to post-traumatic stress disorder and substance use disorders

    Frontiers in Behavioral Neuroscience 14:6.

    https://doi.org/10.3389/fnbeh.2020.00006

    • PubMed
    • Google Scholar
62. 1. Mazure CM
    2. Husky MM
    3. Pietrzak RH

    (2023) Stress as a risk factor for mental disorders in a gendered environment

    JAMA Psychiatry 80:1087–1088.

    https://doi.org/10.1001/jamapsychiatry.2023.3138

    • PubMed
    • Google Scholar
63. 1. Meyer HC
    2. Bucci DJ

    (2016) Imbalanced activity in the orbitofrontal cortex and nucleus accumbens impairs behavioral inhibition

    Current Biology 26:2834–2839.

    https://doi.org/10.1016/j.cub.2016.08.034

    • PubMed
    • Google Scholar
64. 1. Miller EK
    2. Cohen JD

    (2001) An integrative theory of prefrontal cortex function

    Annual Review of Neuroscience 24:167–202.

    https://doi.org/10.1146/annurev.neuro.24.1.167

    • PubMed
    • Google Scholar
65. 1. Mizrak E
    2. Bouffard NR
    3. Libby LA
    4. Boorman ED
    5. Ranganath C

    (2021) The hippocampus and orbitofrontal cortex jointly represent task structure during memory-guided decision making

    Cell Reports 37:110065.

    https://doi.org/10.1016/j.celrep.2021.110065

    • PubMed
    • Google Scholar
66. 1. Morissette M
    2. Di Paolo T

    (1993) Sex and estrous cycle variations of rat striatal dopamine uptake sites

    Neuroendocrinology 58:16–22.

    https://doi.org/10.1159/000126507

    • PubMed
    • Google Scholar
67. 1. Olff M

    (2017) Sex and gender differences in post-traumatic stress disorder: an update

    European Journal of Psychotraumatology 8:1351204.

    https://doi.org/10.1080/20008198.2017.1351204

    • Google Scholar
68. 1. Orsini CA
    2. Setlow B

    (2017) Sex differences in animal models of decision making

    Journal of Neuroscience Research 95:260–269.

    https://doi.org/10.1002/jnr.23810

    • PubMed
    • Google Scholar
69. 1. Papaleo F
    2. Erickson L
    3. Liu G
    4. Chen J
    5. Weinberger DR

    (2012) Effects of sex and COMT genotype on environmentally modulated cognitive control in mice

    PNAS 109:20160–20165.

    https://doi.org/10.1073/pnas.1214397109

    • PubMed
    • Google Scholar
70. 1. Peterson VL
    2. Richards JB
    3. Meyer PJ
    4. Cabrera-Rubio R
    5. Tripi JA
    6. King CP
    7. Polesskaya O
    8. Baud A
    9. Chitre AS
    10. Bastiaanssen TFS
    11. Woods LS
    12. Crispie F
    13. Dinan TG
    14. Cotter PD
    15. Palmer AA
    16. Cryan JF

    (2020) Sex-dependent associations between addiction-related behaviors and the microbiome in outbred rats

    EBioMedicine 55:102769.

    https://doi.org/10.1016/j.ebiom.2020.102769

    • PubMed
    • Google Scholar
71. 1. Pitchers KK
    2. Flagel SB
    3. O’Donnell EG
    4. Woods LCS
    5. Sarter M
    6. Robinson TE

    (2015) Individual variation in the propensity to attribute incentive salience to a food cue: influence of sex

    Behavioural Brain Research 278:462–469.

    https://doi.org/10.1016/j.bbr.2014.10.036

    • PubMed
    • Google Scholar
72. 1. Ross RT
    2. Holland PC

    (1981) Conditioning of simultaneous and serial feature-positive discriminations

    Animal Learning & Behavior 9:293–303.

    https://doi.org/10.3758/BF03197835

    • Google Scholar
73. 1. Santos S
    2. Ferreira H
    3. Martins J
    4. Gonçalves J
    5. Castelo-Branco M

    (2022) Male sex bias in early and late onset neurodevelopmental disorders: Shared aspects and differences in Autism Spectrum Disorder, Attention Deficit/hyperactivity Disorder, and Schizophrenia

    Neuroscience and Biobehavioral Reviews 135:104577.

    https://doi.org/10.1016/j.neubiorev.2022.104577

    • PubMed
    • Google Scholar
74. 1. Sarter M
    2. Phillips KB

    (2018) The neuroscience of cognitive-motivational styles: Sign- and goal-trackers as animal models

    Behavioral Neuroscience 132:1–12.

    https://doi.org/10.1037/bne0000226

    • PubMed
    • Google Scholar
75. 1. Scharfman HE
    2. Mercurio TC
    3. Goodman JH
    4. Wilson MA
    5. MacLusky NJ

    (2003) Hippocampal excitability increases during the estrous cycle in the rat: a potential role for brain-derived neurotrophic factor

    The Journal of Neuroscience 23:11641–11652.

    https://doi.org/10.1523/JNEUROSCI.23-37-11641.2003

    • PubMed
    • Google Scholar
76. 1. Scholl JL
    2. Afzal A
    3. Fox LC
    4. Watt MJ
    5. Forster GL

    (2019) Sex differences in anxiety-like behaviors in rats

    Physiology & Behavior 211:112670.

    https://doi.org/10.1016/j.physbeh.2019.112670

    • PubMed
    • Google Scholar
77. 1. Servan-Schreiber D
    2. Cohen JD
    3. Steingard S

    (1996) Schizophrenic deficits in the processing of context. A test of a theoretical model

    Archives of General Psychiatry 53:1105–1112.

    https://doi.org/10.1001/archpsyc.1996.01830120037008

    • PubMed
    • Google Scholar
78. 1. Shanley MR
    2. Miura Y
    3. Guevara CA
    4. Onoichenco A
    5. Kore R
    6. Ustundag E
    7. Darwish R
    8. Renzoni L
    9. Urbaez A
    10. Blicker E
    11. Seidenberg A
    12. Milner TA
    13. Friedman AK

    (2023) Estrous cycle mediates midbrain neuron excitability altering social behavior upon stress

    The Journal of Neuroscience 43:736–748.

    https://doi.org/10.1523/JNEUROSCI.1504-22.2022

    • PubMed
    • Google Scholar
79. 1. Shansky RM
    2. Glavis-Bloom C
    3. Lerman D
    4. McRae P
    5. Benson C
    6. Miller K
    7. Cosand L
    8. Horvath TL
    9. Arnsten AFT

    (2004) Estrogen mediates sex differences in stress-induced prefrontal cortex dysfunction

    Molecular Psychiatry 9:531–538.

    https://doi.org/10.1038/sj.mp.4001435

    • PubMed
    • Google Scholar
80. 1. Shansky RM
    2. Rubinow K
    3. Brennan A
    4. Arnsten AFT

    (2006) The effects of sex and hormonal status on restraint-stress-induced working memory impairment

    Behavioral and Brain Functions 2:8.

    https://doi.org/10.1186/1744-9081-2-8

    • PubMed
    • Google Scholar
81. 1. Shobe JL
    2. Bakhurin KI
    3. Claar LD
    4. Masmanidis SC

    (2017) Selective modulation of orbitofrontal network activity during negative occasion setting

    The Journal of Neuroscience 37:9415–9423.

    https://doi.org/10.1523/JNEUROSCI.0572-17.2017

    • PubMed
    • Google Scholar
82. 1. Spanne A
    2. Jörntell H

    (2015) Questioning the role of sparse coding in the brain

    Trends in Neurosciences 38:417–427.

    https://doi.org/10.1016/j.tins.2015.05.005

    • PubMed
    • Google Scholar
83. 1. Spritzer MD
    2. Daviau ED
    3. Coneeny MK
    4. Engelman SM
    5. Prince WT
    6. Rodriguez-Wisdom KN

    (2011) Effects of testosterone on spatial learning and memory in adult male rats

    Hormones and Behavior 59:484–496.

    https://doi.org/10.1016/j.yhbeh.2011.01.009

    • PubMed
    • Google Scholar
84. 1. Srivastava N
    2. Hinton G
    3. Krizhevsky A
    4. Sutskever I
    5. Salakhutdinov R

    (2014)

    Dropout: A simple way to prevent neural networks from overfitting

    Journal of Machine Learning Research 15:1929–1958.

    • Google Scholar
85. 1. Stringfield SJ
    2. Madayag AC
    3. Boettiger CA
    4. Robinson DL

    (2019) Sex differences in nicotine-enhanced Pavlovian conditioned approach in rats

    Biology of Sex Differences 10:37.

    https://doi.org/10.1186/s13293-019-0244-8

    • PubMed
    • Google Scholar
86. 1. Swartzentruber D

    (1995) Modulatory mechanisms in Pavlovian conditioning

    Animal Learning & Behavior 23:123–143.

    https://doi.org/10.3758/BF03199928

    • Google Scholar
87. 1. Takahashi YK
    2. Roesch MR
    3. Wilson RC
    4. Toreson K
    5. O’Donnell P
    6. Niv Y
    7. Schoenbaum G

    (2011) Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex

    Nature Neuroscience 14:1590–1597.

    https://doi.org/10.1038/nn.2957

    • PubMed
    • Google Scholar
88. 1. Tolin DF
    2. Foa EB

    (2006) Sex differences in trauma and posttraumatic stress disorder: A quantitative review of 25 years of research

    Psychological Bulletin 132:959–992.

    https://doi.org/10.1037/0033-2909.132.6.959

    • PubMed
    • Google Scholar
89. 1. Towers EB
    2. Williams IL
    3. Qillawala EI
    4. Rissman EF
    5. Lynch WJ

    (2023) Sex/gender differences in the time-course for the development of substance use disorder: A focus on the telescoping effect

    Pharmacological Reviews 75:217–249.

    https://doi.org/10.1124/pharmrev.121.000361

    • PubMed
    • Google Scholar
90. 1. Trask S
    2. Reis DS
    3. Ferrara NC
    4. Helmstetter FJ

    (2020) Decreased cued fear discrimination learning in female rats as a function of estrous phase

    Learning & Memory 27:254–257.

    https://doi.org/10.1101/lm.051185.119

    • PubMed
    • Google Scholar
91. 1. Urcelay GP
    2. Miller RR

    (2014) The functions of contexts in associative learning

    Behavioural Processes 104:2–12.

    https://doi.org/10.1016/j.beproc.2014.02.008

    • Google Scholar
92. 1. Vermeulen P

    (2015) Context blindness in autism spectrum disorder

    Focus on Autism and Other Developmental Disabilities 30:182–192.

    https://doi.org/10.1177/1088357614528799

    • Google Scholar
93. 1. Walitzer KS
    2. Dearing RL

    (2006) Gender differences in alcohol and substance use relapse

    Clinical Psychology Review 26:128–148.

    https://doi.org/10.1016/j.cpr.2005.11.003

    • PubMed
    • Google Scholar
94. 1. Welborn BL
    2. Papademetris X
    3. Reis DL
    4. Rajeevan N
    5. Bloise SM
    6. Gray JR

    (2009) Variation in orbitofrontal cortex volume: relation to sex, emotion regulation and affect

    Social Cognitive and Affective Neuroscience 4:328–339.

    https://doi.org/10.1093/scan/nsp028

    • Google Scholar
95. 1. West BT

    (2009) Analyzing longitudinal data with the linear mixed models procedure in SPSS

    Evaluation & the Health Professions 32:207–228.

    https://doi.org/10.1177/0163278709338554

    • Google Scholar
96. 1. Wilson RC
    2. Takahashi YK
    3. Schoenbaum G
    4. Niv Y

    (2014) Orbitofrontal cortex as a cognitive map of task space

    Neuron 81:267–279.

    https://doi.org/10.1016/j.neuron.2013.11.005

    • PubMed
    • Google Scholar
97. 1. Woolley CS
    2. Gould E
    3. Frankfurt M
    4. McEwen BS

    (1990) Naturally occurring fluctuation in dendritic spine density on adult hippocampal pyramidal neurons

    The Journal of Neuroscience 10:4035–4039.

    https://doi.org/10.1523/JNEUROSCI.10-12-04035.1990

    • PubMed
    • Google Scholar
98. Preprint

    1. Zhang A
    2. Lipton ZC
    3. Li M
    4. Smola AJ

    (2021) Dive into Deep Learning

    arXiv.

    https://arxiv.org/abs/2106.11342

    • Google Scholar

Author details

1. Sophie Peterson

   Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Amanda Maheras

   Competing interests

   No competing interests declared

2. Amanda Maheras

   Department of Molecular, Cellular & Developmental Biology, University of California, Santa Barbara, Santa Barbara, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Sophie Peterson

   Competing interests

   No competing interests declared

3. Brenda Wu

   Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4921-8628

4. Jose Chavira

   Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

5. Ronald Keiflin

   1. Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, United States
   2. Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   rkeiflin@ucsb.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5347-7337

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