In a typical Pavlovian fear conditioning procedure, a neutral cue is paired with foot shock. As a result, the shock-paired cue acquires many properties (McDannald, 2023). The most well-known property is that a shock-paired cue (or context) can elicit freezing (Bolles and Collier, 1976). Pavlovian conditioning procedures measuring freezing have been used to great effect to reveal neural bases of fear (LeDoux et al., 1988; De Oca et al., 1998; Goosens and Maren, 2001; Koo et al., 2004; Monfils et al., 2009; Li et al., 2013; Massi et al., 2023).

A shock-paired cue can also suppress reward seeking (Estes and Skinner, 1941), a phenomenon referred to as conditioned suppression (Killcross et al., 1997). Using conditioned suppression procedures, we recently demonstrated that a shock-paired auditory cue elicits locomotion, rearing, and jumping, in addition to freezing (Chu et al., 2024). Though consistent with some studies (Gruene et al., 2015; Totty et al., 2021; Mitchell et al., 2022; Le et al., 2024; Borkar et al., 2024; Chanthongdee et al., 2024), observing such a diversity of fear-conditioned behaviors is uncommon. In rats, and across a variety of settings, freezing is a predominant fear-conditioned behavior (Lay et al., 2020; Liu et al., 2022; Trott et al., 2022; Williams-Spooner et al., 2022; Fam et al., 2023; Ng and Sangha, 2023; Ng et al., 2024; Yau et al., 2024). It is possible the diversity we observed was due to the complexity of our experimental design (2–3 cues, including an uncertainty cue), the specific stimuli used (complex auditory cues), or the numerous shock presentations (50+). Further, our results were descriptive. If there is a robust relationship between a rat’s fear conditioning status (paired vs. unpaired cue and shock presentations) and these diverse behaviors, then we should be able to predict an individual’s status from ethograms spanning all behaviors.

The goal of the present experiments was to comprehensively quantify behaviors evoked by a shock-paired cue in a simple, visual fear conditioning setting. We then sought to determine if ethograms can predict rats’ fear conditioning status (paired vs. unpaired). Mildly food-deprived, Long Evans rats (half female) were trained to nose poke for food, then received Pavlovian fear conditioning over a baseline of rewarded poking. All rats received a 10-s visual cue paired or unpaired with foot shock. First, we identified the lowest shock intensity that would support conditioned suppression (in the manner of Holland, 1979). In Experiment 1, rats received 0.15 or 0.25 mA foot shock. In Experiment 2, rats received 0.35 or 0.50 mA foot shock. All rats received eight total shock presentations, thereby making these experiments simpler and shorter than our prior work (Chu et al., 2024). Extinction testing (four total cue light illuminations) was designed to reveal behaviors acquired by the visual cue during conditioning, rather than the reduction of those behaviors over trials and sessions.

TTL-triggered cameras were programmed to capture frames at 200-ms intervals before, during, and following cue light illumination. For Experiment 2, 22,272 frames were hand scored for 12 behaviors under categories of Immobility (freezing and stretching), Horizontal movement (locomotion and backpedaling), Vertical movement (rearing, scaling, and jumping), and Reward-related behavior (nose port and food cup). Light-directed variants of each vertical movement type were differentiated. Ethograms spanning all 12 behaviors were constructed from the scored frames. Multivariate and univariate ANOVA performed on the ethogram data revealed behaviors that differed between groups. Linear discriminant analyses determined if group membership (paired vs. unpaired) can be classified from ethogram data.

Our first objective was to determine the lowest foot shock intensity capable of supporting conditioned suppression to a visual cue. Rats were shaped to nose poke in a central port to receive a food reward, delivered via a cup directly below. The cue light was positioned above the central port. Cue light illumination resulted in bright, focal illumination of the light itself as well as diffuse illumination of the entire context, which was otherwise dark (Figure 1A). In Experiment 1, rats received cue light illumination paired or unpaired with a 0.25 or 0.15 mA foot shock. In Experiment 2, rats received cue light illumination paired or unpaired with a 0.50 or 0.35 mA foot shock (Figure 1B).

Figure 1 with 1 supplement see all

Download asset Open asset

Scoring 12 behaviors with high inter-rater reliability and low inter-rater confusion

We defined and scored 12 behaviors in 200-ms intervals around cue light illumination. These behaviors were drawn from existing literature (Holland, 1977; Fanselow, 1982; Chu et al., 2024), as well as our observations. These behaviors were divided into four different categories: Immobile (freezing and stretching), Reward (cup and port), Vertical (rearing, scaling, and jumping), and Horizontal (locomotion and backpedaling). Within the Vertical category, we separated rearing and scaling that were specifically directed toward the light. This was done because light-directed behavior reflects stimulus orienting, while non-light-directed behavior reflects context exploration.

We focused our scoring efforts on Experiment 2: conditioning session 2 and the extinction session. During these sessions, paired, but not unpaired rats, receiving the 0.50 mA and 0.35 mA foot shock intensities demonstrated robust nose poke suppression. Six observers blind to group, sex, and intensity scored 22,272 total frames (see ‘Materials and methods’ for blinding details): 11,136 from the second conditioning session and 11,136 from the extinction session. Additionally, these six observers scored 696 frames spanning eight separate comparison trials. We determined inter-rater reliability by calculating the % identical observations for each observer-observer pair across the 696 frames (Figure 2A). The mean inter-rater reliability was 72.41%. Although inter-rater reliability was higher when fewer behaviors were present within a trial (e.g., 77.7% when three were present), the inter-rater reliability remained high, at 71.9%, even when eight behaviors were present (Figure 2B).

Figure 2

Download asset Open asset

Disagreement rarely involved two observers assigning different, specific behaviors to a single frame. For example, a disagreement in which one observer assigns jump to a frame, while another observer assigns rear, is a seldom occurrence. Instead, disagreement occurred when one observer assigned background to a frame while the other observer assigned a specific behavior to that frame. To quantify disagreements, we constructed a confusion matrix (Figure 2C) composed of every single-frame judgment for every observer–observer pair. Identical observations for every frame (zero confusion) appear as a diagonal line across the matrix (top left to bottom right). Confusing a specific behavior and background appears as a horizontal line on the top edge or a vertical line on the left edge. Confusing a specific behavior for another specific behavior appears as values occurring between the diagonal and edges. The confusion matrix shows the vast majority of values fall on the diagonal, with most of the remaining values on the left edge. The confusion matrix results reveal high scoring specificity.

Ethograms reveal diverse fear-conditioned behaviors, including locomotion

We constructed 200-ms resolution ethograms for paired (Figure 3A) and unpaired (Figure 3B) rats from Experiment 2. For both groups, baseline behavior largely consisted of rearing, food cup, and port behavior, with very small amounts of freezing and locomotion. Paired and unpaired behavior dramatically separated during cue light illumination. Paired rats increased freezing and locomotion during cue illumination, then increased locomotion, backpedaling, and jumping during and following shock presentation. At the same time, paired rats decreased rearing and food cup behavior. By contrast, unpaired rats increased light scaling, rearing, and light rearing during cue light illumination. Although increases in light rearing were observed in paired rats, these increases were blunted compared to unpaired rats. Trial-by-trial ethograms are shown in Figure 3—figure supplement 1.

Figure 3 with 3 supplements see all

Download asset Open asset

In support of group differences across all behaviors, MANOVA for all 12 behaviors [factors: time (87, 200-ms bins), group (paired vs. unpaired), intensity (0.50 mA vs. 0.35 mA), and sex (female vs. male)] revealed a significant group × time interaction (F_1032,24768 = 2.50, p=7.41 × 10^–124). MANOVA additionally revealed a significant group × time × sex interaction (F_1032,24768 = 1.13, p=0.003) and a significant group × time × intensity interaction (F_1032,24768 = 1.64, p=5.47 × 10^–33), which we return to in our univariate analyses.

To reveal behavior-specific differences between groups, we performed univariate ANOVA using a Bonferroni-corrected p-value of 0.004167 (0.05/12) to reduce type 1 error. Univariate ANOVA (factors same as MANOVA) found significant group × time interactions for 8 of the 12 behaviors: freeze (F_86,2064 = 2.13, p=1.85 × 10^–8; Figure 3C), locomote (F_86,2064 = 3.72, p=5.04 × 10^–26; Figure 3D), backpedal (F_86,2064 = 14.95, p=2.34 × 10^–159; Figure 3E), jump (F_86,2064 = 3.23, p=2.98 × 10^–20; Figure 3F), light scale (F_86,2064 = 1.53, p=0.003; Figure 3G), rear (F_86,2064 = 1.75, p=3.79 × 10^–5; Figure 3H), light rear (F_86,2064 = 1.47, p=0.004; Figure 3I), and cup (F_86,2064 = 2.05, p=8.75 × 10^–8; Figure 3J). The eight behaviors differentially expressed by paired and unpaired rats varied in direction and timing. Paired rats showed greater freezing during the cue light period, greater locomotion during the cue and post-cue light periods, and greater backpedaling and jumping during the post-cue light period. Unpaired rats showed greater light scaling only during the cue light period, but greater rearing, light rearing, and food cup behavior during the cue and post-cue light periods.

Univariate ANOVA (factors same as above) further found significant group × time × intensity interactions for 3 of the 12 behaviors: locomote (F_86,2064 = 1.52, p=0.002), jump (F_86,2064 = 2.58, p=5.34 × 10^–13), and backpedal (F_86,2064 = 1.63, p=2.64 × 10^–63). Ethograms separating 0.50 mA and 0.35 mA rats are shown in Figure 3—figure supplement 2. Paired rats receiving the 0.50 mA foot shock predominantly jumped and locomoted following shock presentation. Paired rats receiving the 0.35 mA foot shock predominantly backpedaled then locomoted following shock presentation. Unpaired rats did not jump, locomote, or backpedal at either shock intensity.

Finally, univariate ANOVA found significant group × time × sex interactions for 3 of the 12 behaviors: rear (F_86,2064 = 1.80, p=1.41 × 10^–5), jump (F_86,2064 = 1.93, p=1.12 × 10^–6), and backpedal (F_86,2064 = 1.63, p=2.79 × 10^–4). Line graphs separately plotting female and male rats for each of the three behaviors are shown in Figure 3—figure supplement 3. Sex differences were not all or none – both paired females and males reduced rearing and increased jumping and backpedaling. Instead, differences amounted to timing and magnitude. Paired female rats suppressed rearing during late cue illumination, while males suppressed post-cue. Paired female rats had a longer jump bout, on average, compared to males following shock presentation. Paired males showed greater levels of backpedaling compared to females following shock presentation.

Behavioral diversity and locomotion persist in extinction

To determine if any behaviors were differentially expressed in the absence of foot shock, we constructed complete ethograms for paired (Figure 4A) and unpaired (Figure 4B) rats during extinction testing. As during conditioning baseline behavior largely consisted of food-related behavior and rearing, and was similar for paired and unpaired rats. Paired and unpaired behavior diverged following cue light illumination. Paired rats increased freezing and locomotion during cue illumination, then increased locomotion following cue offset. Unpaired rats increased scaling throughout cue illumination and maintained high levels of light rearing throughout cue and post-cue light periods. Both scaling and light rearing were suppressed in paired rats. Trial-by-trial ethograms are shown in Figure 4—figure supplement 1.

Figure 4 with 3 supplements see all

Download asset Open asset

In support of group differences across all behaviors, MANOVA for all 12 behaviors [factors: time (87, 200-ms bins), group (paired vs. unpaired), intensity (0.50 mA vs. 0.35 mA), and sex (female vs. male)] revealed a significant group × time interaction (F_1032,24768 = 1.50, p=4.80 × 10^–22). MANOVA additionally revealed a significant group × time × intensity interaction (F_1032,24768 = 1.21, p=1.21 × 10^–6) but no significant group × time × sex interaction (F_1032,24768 = 0.98, p=0.70). Revealing behavior-specific differences, univariate ANOVA (factors same as MANOVA) found significant group x time interactions for 4 of the 12 behaviors: freeze (F_86,2064 = 2.13, p=1.85 × 10^–8; Figure 4C), locomote (F_86,2064 = 3.72, p=5.04 × 10^–26; Figure 4D), scale (F_86,2064 = 1.53, p=0.003; Figure 4E), and light rear (F_86,2064 = 1.47, p=0.004; Figure 4F). Paired rats showed greater freezing only during the cue light period, as well as greater locomotion during the cue and post-cue light periods. Unpaired rats showed greater scaling and light rearing during the cue and post-cue light periods.

Univariate ANOVA found a significant group × time × intensity interaction only for rearing (F_86,2064 = 2.32, p=3.87 × 10^–4). Ethograms separating 0.50 and 0.35 mA rats are shown in Figure 4—figure supplement 2. Paired rats receiving the 0.50 mA foot shock suppressed rearing during cue illumination, while 0.35 mA rats did not.

Ethograms predict fear conditioning status

If there is a robust, bidirectional relationship between fear conditioning status and behavior, it should be possible to use ethogram data to predict fear conditioning status (paired vs. unpaired). To test this, we turned to linear discriminant analysis. We used ethogram data to create a linear array for each of the 64 sessions (32 conditioning and 32 extinction). We trained then tested linear classifiers to predict paired vs. unpaired group membership based on the ethogram data. The data of primary interest was mean ± SEM group classification accuracy, with chance classification being 50%.

We first performed linear discriminant analysis for the total ethogram data (1044 value array for each session, 12 behaviors × 87 samples). We compared linear discriminant analysis for the total ethogram data to two kinds of shuffled ethogram data. Session-shuffled ethogram data were shuffled by row, meaning that the relationship between group membership and ethogram was lost. Temporal-shuffled ethogram data were shuffled by column, meaning the group membership information was intact, but the temporal structure of behavior was lost.

Ethograms predicted group membership, and prediction did not deteriorate when temporal structure was lost. Mean ± SEM classification accuracy for the total ethogram data was 82.73 ± 0.003%, exceeding chance (one-sample t-test, p=3.87 × 10^–102; Figure 5A). Mean ± SEM classification accuracy for session-shuffled data was 50.23 ± 0.008%, which did not differ from chance (one-sample t-test, p=0.76). Mean ± SEM classification accuracy for the temporal-shuffled ethogram data was 83.53 ± 0.003%, exceeding chance (one-sample t-test, p=2.77 × 10^–111). Total ethogram and temporal-shuffled ethogram classification each exceeded session-shuffled classification (independent samples t-test, both ps<1 × 10^–10) but did not differ from one another (independent samples t-test, p=0.074).

Figure 5

Download asset Open asset

To determine which trial components provided the most group information, we broke the 87 frames into baseline (frames 1–25, Figure 5B), cue light illumination (frames 26–75, Figure 5C), and post-cue light (frames 76–87, Figure 5D) periods. Group membership could not be classified from baseline ethogram data. Indeed, group classification was below chance for the total ethogram data (46.11 ± 0.005%; one-sample t-test, p=1.19 × 10^–13) and the temporal-shuffled ethogram data (45.53 ± 0.004%; one-sample t-test, p=2.30 × 10^–18). Group membership was readily classified during the cue light illumination period [total ethogram data (76.53 ± 0.003%; one-sample t-test, p=1.48 × 10^–98); temporal-shuffled ethogram data (75.98 ± 0.003%; one-sample t-test, p=4.24 × 10^–94)] as well as during the post-cue light period [total ethogram data (73.83 ± 0.004%; one-sample t-test, p=1.52 × 10^–75); temporal-shuffled ethogram data (73.81 ± 0.004%; one-sample t-test, p=1.30 × 10^–77)]. Total cue ethogram and temporal-shuffled cue ethogram classification each exceeded session-shuffled classification (independent samples t-test, both ps<1 × 10^–10) but did not differ from one another (independent samples t-test, both ps>0.2). The same t-test significance pattern was obtained for post-cue group classification. Observing comparable classification during the cue and post-cue light periods means that group classification cannot be simply attributed to shock responding.

Here we show that a shock-paired visual cue elicits diverse behaviors. Among these behaviors, we observed low levels of freezing. Extending our prior results, we observed robust locomotion, which spiked around the time of expected foot shock. Fear-conditioned locomotion was sensitive to shock intensity and was observed in both sexes. Finally, we show that ethograms can predict whether a rat had paired or unpaired cue and shock presentations. Ethograms spanning all 12 quantified behaviors surpassed predictions based on a subset of behaviors, including immobile behaviors: freezing and stretching.

We have previously observed diverse behaviors elicited by a fear-conditioned auditory cue, including locomotion (Chu et al., 2024). In addition to utilizing conditioned suppression (Estes and Skinner, 1941), those experiments used less common designs (Berg et al., 2014). Rats underwent multi-cue discrimination consisting of danger and safety cues (CS+ vs. CS-) or danger, uncertainty, and safety cues (deterministic and probabilistic cue-shock relationships). Rats received 12–16 fear conditioning sessions and up to 96 shocks. This number of shocks is comparable to overtraining procedures (Maren, 1998), in which compensatory brain and behavioral responses can be observed following brain manipulations such as neurotoxic lesions of the basolateral amygdala (Zimmerman et al., 2007). Thus, it is possible that extended training produces fear behaviors that are not observed with limited training. Finally, rats were presented with more complex auditory cues than commonly employed pure tones (LeDoux et al., 1990) or white noise (Maren et al., 2001). Design complexity, extended training, and stimulus complexity may have acted independently or combined to produce behavioral diversity and fear-conditioned locomotion. However, these factors do not account for our present findings. Behavioral diversity and robust fear-conditioned locomotion can be observed in rats receiving limited visual cue/shock pairings.

Our current and prior studies corroborate findings from other laboratories (Gruene et al., 2015; Gruene et al., 2015; Fadok et al., 2017) observing locomotion to a shock-paired cue, but are notable for their simplicity and associative basis. Using a serial compound conditioning procedure (tone → white noise → shock), Le and colleagues found robust flight to white noise presentation (Le et al., 2024). Robust flight was only observed when the serial compound was paired with shock. Totty and colleagues also observed that white noise elicited flight whether it was the first or second compound stimulus (Totty et al., 2021). Conditioned flight to white noise in a serial compound may be a stimulus-specific instance of flight. Gruene and colleagues first demonstrated darting to a shock-paired auditory cue (Gruene et al., 2015). Darting has since been demonstrated in additional studies (Mitchell et al., 2022; Mitchell et al., 2024). Although darting in their rats is comparable to locomotion in our paired rats, the associative nature of darting has yet to be demonstrated via the use of an unpaired control. Observing robust fear-conditioned locomotion in a simple, associative setting provides a strong behavioral basis to uncover neural circuits for this essential but overlooked fear behavior (Borkar et al., 2024).

Behavioral neuroscience (Hsu and Yttri, 2021; Iglesias et al., 2023; Luxem et al., 2023; Goodwin et al., 2024) and Pavlovian fear conditioning (Cai et al., 2020; Chanthongdee et al., 2024) are moving toward machine scoring of behavior. Unsupervised machine scoring may reveal behaviors not previously observed in response to a fear-conditioned cue (Chanthongdee et al., 2024) as it does not rely on assumptions made about which behaviors should be present or absent (Hsu and Yttri, 2021). Supervised machine scoring, such as convolutional neural networks (Wu et al., 2020), trains a model based on hand-scored human data. A supervised scoring approach is useful when known behaviors – such as freezing and locomotion – are firmly established in a behavioral setting. Scored frames from the current experiments, as well as our prior study (Chu et al., 2024), are free to download via repository explicitly for these purposes. Machine scoring holds promise to make comprehensive ethograms – like in our studies – more prevalent in the field.

Comprehensive ethograms are necessary to reveal the neural basis of Pavlovian fear conditioning, as opposed to the neural basis of conditioned freezing. This is because Pavlovian fear conditioning is a collection of behaviors and processes rather than a single behavior or process (McDannald, 2023). If freezing was the totality of Pavlovian fear conditioning, as expressed through overt behavior, we should observe two results. First, freezing should be specific to a shock-paired cue. We observe this. Second, paired versus unpaired fear conditioning status should be predicted equally well by freezing alone vs. freezing + all other behaviors. We do not observe this for freezing or any behavior. This means that focusing on any individual behavior will miss information about fear conditioning status. A stronger, bidirectional relationship between fear conditioning status and behavior is observed when all behaviors present are considered.

Here we show that a shock-paired visual cue elicits diverse behaviors, including locomotion and freezing. Our results solidify and extend a directional relationship from Pavlovian fear conditioning → behavior. We further demonstrate that the strongest, reverse directional relationship (behavior → Pavlovian fear conditioning) is obtained when all behaviors present are considered. Observing diverse behaviors within a single conditioning procedure has stronger parallels to the diverse symptoms that define anxiety disorders. Comprehensive behavioral approaches, as performed here, will be essential in revealing more complete neural bases for Pavlovian fear conditioning, with greater relevance to disordered anxiety.

Raw frames and scored behaviors have been deposited: https://doi.org/10.7910/DVN/Z4YJRJ.

1. 1. McDannald MA

   (2025) Harvard Dataverse

   Behavior frames and observer judgments from 32 rats (16 females) receiving Pavlovian fear conditioning to a visual cue.

   https://doi.org/10.7910/DVN/Z4YJRJ

1. 1. Berg BA
   2. Schoenbaum G
   3. McDannald MA

   (2014) The dorsal raphe nucleus is integral to negative prediction errors in Pavlovian fear

   The European Journal of Neuroscience 40:3096–3101.

   https://doi.org/10.1111/ejn.12676

   • PubMed
   • Google Scholar
2. 1. Bolles RC
   2. Collier AC

   (1976) The effect of predictive cues on freezing in rats

   Animal Learning & Behavior 4:6–8.

   https://doi.org/10.3758/BF03211975

   • Google Scholar
3. 1. Borkar CD
   2. Stelly CE
   3. Fu X
   4. Dorofeikova M
   5. Le QSE
   6. Vutukuri R
   7. Vo C
   8. Walker A
   9. Basavanhalli S
   10. Duong A
   11. Bean E
   12. Resendez A
   13. Parker JG
   14. Tasker JG
   15. Fadok JP

   (2024) Top-down control of flight by a non-canonical cortico-amygdala pathway

   Nature 625:743–749.

   https://doi.org/10.1038/s41586-023-06912-w

   • PubMed
   • Google Scholar
4. 1. Cai LX
   2. Pizano K
   3. Gundersen GW
   4. Hayes CL
   5. Fleming WT
   6. Holt S
   7. Cox JM
   8. Witten IB

   (2020) Distinct signals in medial and lateral VTA dopamine neurons modulate fear extinction at different times

   eLife 9:e54936.

   https://doi.org/10.7554/eLife.54936

   • PubMed
   • Google Scholar
5. 1. Chanthongdee K
   2. Fuentealba Y
   3. Wahlestedt T
   4. Foulhac L
   5. Kardash T
   6. Coppola A
   7. Heilig M
   8. Barbier E

   (2024) Comprehensive ethological analysis of fear expression in rats using DeepLabCut and SimBA machine learning model

   Frontiers in Behavioral Neuroscience 18:1440601.

   https://doi.org/10.3389/fnbeh.2024.1440601

   • PubMed
   • Google Scholar
6. 1. Chu A
   2. Gordon NT
   3. DuBois AM
   4. Michel CB
   5. Hanrahan KE
   6. Williams DC
   7. Anzellotti S
   8. McDannald MA

   (2024) A fear conditioned cue orchestrates A suite of behaviors in rats

   eLife 13:e82497.

   https://doi.org/10.7554/eLife.82497

   • PubMed
   • Google Scholar
7. 1. De Oca BM
   2. DeCola JP
   3. Maren S
   4. Fanselow MS

   (1998) Distinct regions of the periaqueductal gray are involved in the acquisition and expression of defensive responses

   The Journal of Neuroscience 18:3426–3432.

   https://doi.org/10.1523/JNEUROSCI.18-09-03426.1998

   • PubMed
   • Google Scholar
8. 1. Estes WK
   2. Skinner BF

   (1941) Some quantitative properties of anxiety

   Journal of Experimental Psychology 29:390–400.

   https://doi.org/10.1037/h0062283

   • Google Scholar
9. 1. Fadok JP
   2. Krabbe S
   3. Markovic M
   4. Courtin J
   5. Xu C
   6. Massi L
   7. Botta P
   8. Bylund K
   9. Müller C
   10. Kovacevic A
   11. Tovote P
   12. Lüthi A

   (2017) A competitive inhibitory circuit for selection of active and passive fear responses

   Nature 542:96–100.

   https://doi.org/10.1038/nature21047

   • PubMed
   • Google Scholar
10. 1. Fam J
    2. Chieng B
    3. Westbrook RF
    4. Laurent V
    5. Holmes NM

    (2023) Second-order fear conditioning involves formation of competing stimulus-danger and stimulus-safety associations

    Cerebral Cortex 33:1843–1855.

    https://doi.org/10.1093/cercor/bhac176

    • PubMed
    • Google Scholar
11. 1. Fanselow MS

    (1982) The postshock activity burst

    Animal Learning & Behavior 10:448–454.

    https://doi.org/10.3758/BF03212284

    • Google Scholar
12. 1. Goodwin NL
    2. Choong JJ
    3. Hwang S
    4. Pitts K
    5. Bloom L
    6. Islam A
    7. Zhang YY
    8. Szelenyi ER
    9. Tong X
    10. Newman EL
    11. Miczek K
    12. Wright HR
    13. McLaughlin RJ
    14. Norville ZC
    15. Eshel N
    16. Heshmati M
    17. Nilsson SRO
    18. Golden SA

    (2024) Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience

    Nature Neuroscience 27:1411–1424.

    https://doi.org/10.1038/s41593-024-01649-9

    • PubMed
    • Google Scholar
13. 1. Goosens KA
    2. Maren S

    (2001) Contextual and auditory fear conditioning are mediated by the lateral, basal, and central amygdaloid nuclei in rats

    Learning & Memory 8:148–155.

    https://doi.org/10.1101/lm.37601

    • PubMed
    • Google Scholar
14. 1. Gruene TM
    2. Flick K
    3. Stefano A
    4. Shea SD
    5. Shansky RM

    (2015) Sexually divergent expression of active and passive conditioned fear responses in rats

    eLife 4:e11352.

    https://doi.org/10.7554/eLife.11352

    • PubMed
    • Google Scholar
15. 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
16. 1. Holland PC

    (1979) The effects of qualitative and quantitative variation in the US on individual components of Pavlovian appetitive conditioned behavior in rats

    Animal Learning & Behavior 7:424–432.

    https://doi.org/10.3758/BF03209696

    • Google Scholar
17. 1. Hsu AI
    2. Yttri EA

    (2021) B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors

    Nature Communications 12:5188.

    https://doi.org/10.1038/s41467-021-25420-x

    • PubMed
    • Google Scholar
18. 1. Iglesias AG
    2. Chiu AS
    3. Wong J
    4. Campus P
    5. Li F
    6. Liu ZN
    7. Bhatti JK
    8. Patel SA
    9. Deisseroth K
    10. Akil H
    11. Burgess CR
    12. Flagel SB

    (2023) InHibition of dopamine neurons prevents incentive value encoding of a reward cue: With revelations from deep phenotyping

    The Journal of Neuroscience 43:7376–7392.

    https://doi.org/10.1523/JNEUROSCI.0848-23.2023

    • PubMed
    • Google Scholar
19. 1. Killcross S
    2. Robbins TW
    3. Everitt BJ

    (1997) Different types of fear-conditioned behaviour mediated by separate nuclei within amygdala

    Nature 388:377–380.

    https://doi.org/10.1038/41097

    • PubMed
    • Google Scholar
20. 1. Koo JW
    2. Han JS
    3. Kim JJ

    (2004) Selective neurotoxic lesions of basolateral and central nuclei of the amygdala produce differential effects on fear conditioning

    The Journal of Neuroscience 24:7654–7662.

    https://doi.org/10.1523/JNEUROSCI.1644-04.2004

    • PubMed
    • Google Scholar
21. 1. Lay BP
    2. Pitaru AA
    3. Boulianne N
    4. Esber GR
    5. Iordanova MD

    (2020) Different methods of fear reduction are supported by distinct cortical substrates

    eLife 9:e55294.

    https://doi.org/10.7554/eLife.55294

    • PubMed
    • Google Scholar
22. 1. Le QSE
    2. Hereford D
    3. Borkar CD
    4. Aldaco Z
    5. Klar J
    6. Resendez A
    7. Fadok JP

    (2024) Contributions of associative and non-associative learning to the dynamics of defensive ethograms

    eLife 12:RP90414.

    https://doi.org/10.7554/eLife.90414

    • PubMed
    • Google Scholar
23. 1. LeDoux JE
    2. Iwata J
    3. Cicchetti P
    4. Reis DJ

    (1988) Different projections of the central amygdaloid nucleus mediate autonomic and behavioral correlates of conditioned fear

    The Journal of Neuroscience 8:2517–2529.

    https://doi.org/10.1523/JNEUROSCI.08-07-02517.1988

    • PubMed
    • Google Scholar
24. 1. LeDoux JE
    2. Cicchetti P
    3. Xagoraris A
    4. Romanski LM

    (1990) The lateral amygdaloid nucleus: sensory interface of the amygdala in fear conditioning

    The Journal of Neuroscience 10:1062–1069.

    https://doi.org/10.1523/JNEUROSCI.10-04-01062.1990

    • PubMed
    • Google Scholar
25. 1. Li H
    2. Penzo MA
    3. Taniguchi H
    4. Kopec CD
    5. Huang ZJ
    6. Li B

    (2013) Experience-dependent modification of a central amygdala fear circuit

    Nature Neuroscience 16:332–339.

    https://doi.org/10.1038/nn.3322

    • PubMed
    • Google Scholar
26. 1. Liu J
    2. Totty MS
    3. Melissari L
    4. Bayer H
    5. Maren S

    (2022) Convergent coding of recent and remote fear memory in the basolateral amygdala

    Biological Psychiatry 91:832–840.

    https://doi.org/10.1016/j.biopsych.2021.12.018

    • PubMed
    • Google Scholar
27. 1. Luxem K
    2. Sun JJ
    3. Bradley SP
    4. Krishnan K
    5. Yttri E
    6. Zimmermann J
    7. Pereira TD
    8. Laubach M

    (2023) Open-source tools for behavioral video analysis: Setup, methods, and best practices

    eLife 12:e79305.

    https://doi.org/10.7554/eLife.79305

    • PubMed
    • Google Scholar
28. 1. Maren S

    (1998) Overtraining does not mitigate contextual fear conditioning deficits produced by neurotoxic lesions of the basolateral amygdala

    The Journal of Neuroscience 18:3088–3097.

    https://doi.org/10.1523/JNEUROSCI.18-08-03088.1998

    • PubMed
    • Google Scholar
29. 1. Maren S
    2. Yap SA
    3. Goosens KA

    (2001) The amygdala is essential for the development of neuronal plasticity in the medial geniculate nucleus during auditory fear conditioning in rats

    The Journal of Neuroscience 21:RC135.

    https://doi.org/10.1523/JNEUROSCI.21-06-j0001.2001

    • PubMed
    • Google Scholar
30. 1. Massi L
    2. Hagihara KM
    3. Courtin J
    4. Hinz J
    5. Müller C
    6. Fustiñana MS
    7. Xu C
    8. Karalis N
    9. Lüthi A

    (2023) Disynaptic specificity of serial information flow for conditioned fear

    Science Advances 9:eabq1637.

    https://doi.org/10.1126/sciadv.abq1637

    • PubMed
    • Google Scholar
31. 1. McDannald MA

    (2023) Pavlovian fear conditioning is more than you think it is

    The Journal of Neuroscience 43:8079–8087.

    https://doi.org/10.1523/JNEUROSCI.0256-23.2023

    • PubMed
    • Google Scholar
32. 1. Mitchell JR
    2. Trettel SG
    3. Li AJ
    4. Wasielewski S
    5. Huckleberry KA
    6. Fanikos M
    7. Golden E
    8. Laine MA
    9. Shansky RM

    (2022) Darting across space and time: parametric modulators of sex-biased conditioned fear responses

    Learning & Memory 29:171–180.

    https://doi.org/10.1101/lm.053587.122

    • PubMed
    • Google Scholar
33. Preprint

    1. Mitchell JR
    2. Vincelette L
    3. Tuberman S
    4. Sheppard V
    5. Bergeron E
    6. Calitri R
    7. Clark R
    8. Cody C
    9. Kannan A
    10. Keith J
    11. Parakoyi A
    12. Pikus M
    13. Vance V
    14. Ziane L
    15. Brenhouse H
    16. Laine MA
    17. Shansky RM

    (2024) Behavioral and Neural Correlates of Diverse Conditioned Fear Responses in Male and Female Rats

    bioRxiv.

    https://doi.org/10.1101/2024.08.20.608817

    • Google Scholar
34. 1. Monfils MH
    2. Cowansage KK
    3. Klann E
    4. LeDoux JE

    (2009) Extinction-reconsolidation boundaries: key to persistent attenuation of fear memories

    Science 324:951–955.

    https://doi.org/10.1126/science.1167975

    • PubMed
    • Google Scholar
35. 1. Ng KH
    2. Sangha S

    (2023) Encoding of conditioned inhibitors of fear in the infralimbic cortex

    Cerebral Cortex 33:5658–5670.

    https://doi.org/10.1093/cercor/bhac450

    • PubMed
    • Google Scholar
36. 1. Ng K
    2. Pollock M
    3. Escobedo A
    4. Bachman B
    5. Miyazaki N
    6. Bartlett EL
    7. Sangha S

    (2024) Suppressing fear in the presence of a safety cue requires infralimbic cortical signaling to central amygdala

    Neuropsychopharmacology 49:359–367.

    https://doi.org/10.1038/s41386-023-01598-0

    • PubMed
    • Google Scholar
37. 1. Nikbakht N
    2. Diamond ME

    (2021) Conserved visual capacity of rats under red light

    eLife 10:e66429.

    https://doi.org/10.7554/eLife.66429

    • PubMed
    • Google Scholar
38. 1. Totty MS
    2. Warren N
    3. Huddleston I
    4. Ramanathan KR
    5. Ressler RL
    6. Oleksiak CR
    7. Maren S

    (2021) Behavioral and brain mechanisms mediating conditioned flight behavior in rats

    Scientific Reports 11:8215.

    https://doi.org/10.1038/s41598-021-87559-3

    • PubMed
    • Google Scholar
39. 1. Trott JM
    2. Hoffman AN
    3. Zhuravka I
    4. Fanselow MS

    (2022) Conditional and unconditional components of aversively motivated freezing, flight and darting in mice

    eLife 11:e75663.

    https://doi.org/10.7554/eLife.75663

    • PubMed
    • Google Scholar
40. 1. Williams-Spooner MJ
    2. Delaney AJ
    3. Westbrook RF
    4. Holmes NM

    (2022) Prediction error determines whether nmda receptors in the basolateral amygdala complex are involved in pavlovian fear conditioning

    The Journal of Neuroscience 42:4360–4379.

    https://doi.org/10.1523/JNEUROSCI.2156-21.2022

    • PubMed
    • Google Scholar
41. Conference

    1. Wu CY
    2. Chun-Chieh Shih A
    3. Huang HC
    4. Yu CP
    5. Chen CC

    (2020) Refined prediction of mouse and human actions based on a data-selective multiple-stage approach of 3d convolutional neural networks

    2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI).

    https://doi.org/10.1109/TAAI51410.2020.00052

    • Google Scholar
42. 1. Yau JOY
    2. Li A
    3. Abdallah L
    4. Lisowksi L
    5. McNally GP

    (2024) State- and circuit-dependent opponent processing of fear

    The Journal of Neuroscience 44:e0857242024.

    https://doi.org/10.1523/JNEUROSCI.0857-24.2024

    • PubMed
    • Google Scholar
43. 1. Zimmerman JM
    2. Rabinak CA
    3. McLachlan IG
    4. Maren S

    (2007) The central nucleus of the amygdala is essential for acquiring and expressing conditional fear after overtraining

    Learning & Memory 14:634–644.

    https://doi.org/10.1101/lm.607207

    • PubMed
    • Google Scholar

Author details

1. David C Williams

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Conceptualization, Data curation, Methodology, Project administration

   Competing interests

   No competing interests declared

2. Amanda Chu

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Nicholas T Gordon

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Aleah M DuBois

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

5. Suhui Qian

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Genevieve Valvo

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Selena Shen

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Jacob B Boyce

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

9. Anaise C Fitzpatrick

   Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Mahsa Moaddab

    Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

    Contribution

    Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

11. Emma L Russell

    Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Liliuokalani H Counsman

    Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

13. Michael A McDannald

    Boston College Department of Psychology and Neuroscience, Chestnut Hill, United States

    Contribution

    Conceptualization, Formal analysis, Funding acquisition, Visualization, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    michael.mcdannald@bc.edu

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0001-8525-1260

• 518

  views

• 71

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.