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. 2021 Mar 25;24(4):102223. doi: 10.1016/j.isci.2021.102223

Cyclic alternation of quiet and active sleep states in the octopus

Sylvia Lima de Souza Medeiros 1,2,3, Mizziara Marlen Matias de Paiva 1,3, Paulo Henrique Lopes 4,7, Wilfredo Blanco 4,5,7, Françoise Dantas de Lima 6, Jaime Bruno Cirne de Oliveira 1, Inácio Gomes Medeiros 1,7, Eduardo Bouth Sequerra 1, Sandro de Souza 1,5,7, Tatiana Silva Leite 6, Sidarta Ribeiro 1,2,3,8,∗
PMCID: PMC8101055  PMID: 33997665

Summary

Previous observations suggest the existence of ‘Active sleep’ in cephalopods. To investigate in detail the behavioral structure of cephalopod sleep, we video-recorded four adult specimens of Octopus insularis and quantified their distinct states and transitions. Changes in skin color and texture and movements of eyes and mantle were assessed using automated image processing tools, and arousal threshold was measured using sensory stimulation. Two distinct states unresponsive to stimulation occurred in tandem. The first was a ‘Quiet sleep’ state with uniformly pale skin, closed pupils, and long episode durations (median 415.2 s). The second was an ‘Active sleep’ state with dynamic skin patterns of color and texture, rapid eye movements, and short episode durations (median 40.8 s). ‘Active sleep’ was periodic (60% of recurrences between 26 and 39 min) and occurred mostly after ‘Quiet sleep’ (82% of transitions). These results suggest that cephalopods have an ultradian sleep cycle analogous to that of amniotes.

Subject areas: Biological Sciences, Zoology, Ethology

Graphical abstract

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Highlights

  • •

    Octopus has ‘Quiet’ and ‘Active sleep’, with different episode duration and periodicity

  • •

    States differ on arousal thresholds, skin color and texture, and eye and mantle movement

  • •

    The results suggest that octopus has a sleep cycle analogous to that of amniotes

Biological Sciences; Zoology; Ethology

Introduction

Sleep is a well-studied behavior in amniotes such as mammals (Vanderwolf, 1969; Timo-Iaria et al., 1970; Delorme et al., 1964), birds (Low et al., 2008; Ayala-Guerrero et al., 1988), and some reptiles (Shein-Idelson et al., 2016; Libourel et al., 2018; Norimoto et al., 2020). Electrophysiological recordings in amniotes show distinct spectral profiles that comprise two major alternating sleep states, one quiet and another active (Gervasoni et al., 2004; Noda et al., 1969; Steriade et al., 1993). Much less is known about neurobiological rhythms in invertebrates because electrophysiological recordings remain very challenging in these animals, due to technical difficulties caused by a soft body, a rigid carapace, or life in the aquatic environment (Hendricks et al., 2000).

Despite these limitations, the study of invertebrate sleep has advanced using behavioral criteria originally developed to investigate mammalian sleep. These criteria comprise stereotyped or species-specific postures, maintenance of behavioral quiescence, elevated arousal threshold, state reversibility by sensory stimulation, responsiveness, and homeostatic regulation able to cause sleep rebound after deprivation (Hendricks et al., 2000; Campbell and Toblew, 1984; Greenspan et al., 2001; Meisel et al., 2011).

Among cephalopods, Octopus vulgaris (Cuvier, 1797) (Octopodidae: Cephalopod) meets all the criteria to define sleep (Meisel et al., 2011). For example, Octopus vulgaris specimens choose a preferred resting place and assume a typical posture of head lowered, arms curled around the body, motionless body except for sporadic shrinking and rapid, random movement of the suckers, pale body color, narrowed or completely closed eye pupils, and reduced ventilation rate (Meisel et al., 2011). Quiescent animals exposed to vibratory stimulation showed an elevated arousal threshold and state reversibility after intense stimulation. Furthermore, deprivation of the quiescence state led to a rest rebound (Meisel et al., 2011).

The sleep criteria mentioned earlier, except for the increase in the arousal threshold during quiescence, have also been met by Sepia officinalis (Linnaeus, 1758; Frank et al., 2012; Iglesias et al., 2019). Behavioral observations suggest the existence of a sleep state in which the animal is partially buried in the substrate with pupils closed and another sleep state with rapid chromatophore changes accompanied by skin-texture changes, rapid eye movements, and arm twitching. Altogether, the behavioral evidence points to the existence of an ultradian rhythm (Frank et al., 2012; Iglesias et al., 2019).

Despite the mounting evidence that cephalopods display a Quiet/Active sleep cycle akin to amniotes, one can still argue that perhaps the specimens were not asleep but rather in a state of quiet alertness. This is a possibility because (1) responsiveness to stimulation was not tested in Sepia officinalis and (2) the studies of Octopus vulgaris investigated the quiescence state with pale body pattern, but not the ‘Active sleep’ state. Here we set out to address these gaps through a comprehensive behavioral quantification of all the sleep and waking states observed in Octopus insularis (Leite and Haimovici, 2008). Based on previous research (Meisel et al., 2011; Frank et al., 2012; Iglesias et al., 2019), we hypothesized that this species displays at least two consecutive quiescence states unresponsive to stimulation, comprising ‘Quiet sleep’ and ‘Active sleep’ states. To test this hypothesis, animals were video-recorded and systematically exposed to sensory stimuli during each state of interest to assess potential differences in the arousal threshold across the wake-sleep cycle (Figure 1).

Figure 1.

Figure 1

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Experimental scheme

Animals were first subjected to acclimatization, followed by behavioral video recordings to assess the wake-sleep cycle and then a protracted period, during which the arousal threshold was measured. Visual stimulation was performed in three animals (octopuses 2, 3, and 4), whereas the vibratory stimulation test was performed in only one animal (octopus 3), which initially did not respond to the visual stimulation.

Results

Ethogram analysis reveals different quiescence states

The behaviors scored during the day were ‘Active’ state, ‘Alert’ state and five variations of the quiescence state, categorized as Quiet with open pupils (‘QOP’) (Figure 2A; Video S1), Quiet with closed pupil (‘Quiet sleep’) (Figure 2B; Video S2), Quiet with the ‘half and half’ skin pattern (‘QHH’) (Figure 2C; Video S3), Quiet with dynamic body pattern and movement of the eyes (‘Active sleep’) (Figure 2D; Video S4), and Quiet with only one eye movement (‘QOEM’) (Figure 2E; Video S5) (Table 1). For the behaviors recorded during the night, we observed the following types of quiescence states: ‘QOP’ (Video S6), ‘Quiet sleep’ (Video S7), ‘Active sleep’ (Video S8), and ‘QOEM’ (Video S9).

Figure 2.

Figure 2

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Behaviors observed during the different quiescence states

A total of five states were observed during quiescence: (A) Quiet state with open pupils; (B) ‘Quiet sleep’ with closed pupils; (C) Quiet with half and half skin pattern; (D) ‘Active sleep’ (quiet with dynamic pattern and eye movement); (E) Quiet with the movement of only one eye. See descriptions of each state in Table 1.

Table 1.

Description of the different quiescence states found in Octopus insularis

States Description
Quiet with dynamic body pattern and eye movements (ACTIVE SLEEP) The animal dynamically changes the skin color and texture and moves both eyes while contracting the suckers and the body, with muscular twitches.
Quiet with closed pupil (QUIET SLEEP) Quiescence with the pupils of the eyes narrowed to a slit and pale body color. The animal can also sporadically display random movements of the suckers and arm tips without touching the environmental surfaces. However, these movements are much softer and slower than during the ‘Active sleep’ detailed above.
Quiet with open pupil (QOP) Quiescence with generally pale body color, head lowered and motionless body, except for respiratory movements, and sporadic shrinking.
Quiet with the half and half body pattern (QHH) The animal is head lowered and motionless and suddenly changes the skin pattern, which is generally pale during this quiescence state, to the ‘half and half’ skin pattern.
Quiet with only one eye movement (QOEM) A cyclic one eye movement behavior, appearing pupil contraction and dilation together with exophthalmos.
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Video S1. The behavior Quiet with open pupil (‘QOP’) recorded during the day, related to Table 1 and Figure 2
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Video S2. The behavior ‘Quiet sleep’ recorded during the day, related to Table 1 and Figure 2
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Video S3. The behavior Quiet with half and half body pattern (‘QHH’) recorded during the day, related to Table 1 and Figure 2
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Video S4. The behavior ‘Active sleep’ recorded during the day, related to Table 1 and Figure 2
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Video S5. The behavior Quiet with only one eye movement (‘QOEM’) recorded during the day, related to Table 1 and Figure 2
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Video S6. The behavior Quiet with open pupil (‘QOP’) recorded during the night, related to Table 1
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Video S7. The behavior ‘Quiet sleep’ recorded during the night, related to Table 1
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Video S8. The behavior ‘Active sleep’ recorded during the night, related to Table 1
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Video S9. The behavior Quiet with only one eye movement (‘QOEM’) recorded during the night, related to Table 1
Download video file (140.3MB, mp4)

Among the quiescent states, ‘QHH’, ‘Quiet sleep’, and ‘Active sleep’ resembled sleep behavior. To further investigate this resemblance we performed the arousal threshold test during these states and during the ‘Alert’ state, which allowed for a comparison of differences in responsiveness to sensory stimulation.

Measuring arousal threshold

Visual stimulation test

The arousal response elicited by visual stimulation was analyzed in only three animals, because a few days after we finished the wake-sleep recordings of octopus 1, this animal began to show signs of decrease in health condition, such as less interest for food, so we excluded it from the arousal threshold tests.

These tests were performed when the octopuses were in the states ‘Alert’, Half and half (‘QHH’), ‘Quiet sleep’ and ‘Active sleep’. The behavioral states showed significant differences in time spent to react to the visual stimulation (Kruskal-Wallis p = 1.30 × 10−12). The highest latency was observed in the ‘Active sleep’ state (median 32.0 s, first quartile 22.5 s, third quartile 38.0 s), followed by the ‘Quiet sleep’ (median 6.0 s, first quartile 4.0 s, third quartile 9.0 s), ‘QHH’ (median 6.0 s, first quartile 4.0 s, third quartile 8.0 s), and finally the ‘Alert’ state with the shortest latency (median 4.0 s, first quartile 2.0 s, third quartile 6.0 s) (Figure 3A, upper panel).

Figure 3.

Figure 3

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Reaction times after visual or vibratory stimulation

(A) The top panel shows the latency in seconds (y axis) of the animals' reaction on visual test when they were in the behaviors: ‘Alert’ state, ‘Quiet with half and half’ (QHH), ‘Quiet sleep’, and ‘Active sleep’ (x axis). The highest latency was during ‘Active sleep’ and the lowest was during the ‘Alert state.’ The bottom panel shows the percentage of absence of reaction for the same states. ‘Active sleep’ was the behavior with more absence of reaction, whereas the ‘Alert’ state had less absence of reaction.

(B) Frequency of animal's reaction on vibratory test (20 trials for each behavior). The lines represent the animals' behaviors: ‘Alert’ (orange), ‘QHH’ (light blue), ‘Quiet sleep’ (dark blue), and ‘Active sleep’ (green). On the x axis, the category “Before impact” comprises trials when the animal reacted even before the hammer's impact, i.e. in response to the hammer's movement. The inclination angles were 10° (low), 20° (medium), 30° (strong), and 40° (maximum impact). When the animal did not react even with the maximum impact, the trial was scored as “maximum Impact with absence of reaction.” The ‘Alert’ state and ‘QHH’ showed the highest frequency of responses “before impact” (10 times), and this value decreased as the impact increased. The reaction frequency for these behaviors was below 2 times when the impact was maximum. ‘Quiet sleep’ showed little variation in frequency across different hammer impacts. Furthermore, it was more difficult to evoke a reaction when the animal was in ‘Active sleep’, so that the reaction frequency value increased as the hammer's angle increased.

The ‘Active sleep’ had the lowest number of stimulation trials with any reaction (highest number of stimuli classified as “absence of reaction”), followed by ‘Quiet sleep’, ‘QHH’ and the ‘Alert’ state (Figure 3A, bottom panel). Besides, the chi-square pairwise comparison showed significant differences regarding the absence of reaction between the states: ‘Quiet sleep’ and ‘Alert’ state (chi-squared test, p = 6.11 × 10−10), ‘Quiet sleep’ and ‘QHH’ (chi-squared test, p = 3.51 × 10−4), ‘Alert state’ and ‘QHH’ (chi-squared test, p = 1.39 × 10−2), ‘Alert state’ and ‘Active sleep’ (chi-squared test, p = 2.20 × 10−16), ‘Active sleep’ and ‘QHH’ (chi-squared test, p = 1.02 × 10−8). The only states that did not differ significantly from each other were ‘Active sleep’ and ‘Quiet sleep’ (chi-squared test, p = 0.06).

Vibratory stimulation test

The vibratory stimulation test was performed with one octopus (n = 1), and the results showed (Figure 3B) that in the ‘Alert’ and ‘QHH’ states, most responses were recorded at level 1. The ‘Quiet sleep’ state showed slower reactions, with a predominance of levels 2 and 4. The slowest reactions occurred during the ‘Active sleep’ state: during most trials, the animal responded only at level 5 or showed complete absence of reaction. The Pearson's chi-squared test showed that there were significant differences in response time across the behaviors (p = 1.93 × 10−4). There were more responses at levels 4 and 5 or “absence of reaction” for ‘Quiet sleep’ and ‘Active sleep’ states than for ‘Alert’ and ‘QHH’ states. Although the chi-squared pairwise-comparison showed that the ‘Active sleep’ responses differed significantly from those sampled during the ‘Alert’ state (p = 5.52 × 10−5) or QHH (p = 1.70 × 10−5), responses during ‘Quiet sleep’ were not significantly different from those sampled within the ‘Alert’ state (p = 0.11) or ‘QHH’ (p = 0.08).

Characterization of spontaneous behavioral alternation

The day-time behavioral states analyzed showed distinct distributions of episode durations: The ‘Active sleep’ (median 40.8 s, first quartile 25.8 s, third quartile 52.2 s) and the ‘QHH’ (median 10.2 s, first quartile 7.2 s, third quartile 13.8 s) states displayed much shorter durations than the ‘Alert’ state (median 2.83 min, first quartile 1.32 min, third quartile 6.9 min) and ‘Quiet sleep’ (median 6.92 min, first quartile 2.63 min, third quartile 15.04 min) states (Figure 4A). The ‘Quiet sleep’ state was the one with the most variation in duration. Kruskal-Wallis tests followed by Wilcoxon pairwise comparisons were conducted to determine whether there was any pattern of the duration of this state in relation to the states preceding or following it. These analyses also aimed to investigate the relationship between ‘Active sleep’ and ‘Quiet sleep’ episodes. The states that immediately preceded the ‘Quiet sleep’ state were ‘Alert’, ‘QOP’, ‘QHH’, and ‘Active sleep’, and the durations of ‘Quiet sleep’ episodes preceded by ‘Active sleep’ episodes were significantly shorter (Kruskal-Wallis p = 4.91 × 10−7) (Figure 5A).

Figure 4.

Figure 4

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Characterization of the behaviors observed in Octopus insularis

Each color represents one behavior analyzed during the experiment.

(A) Boxplot and histogram of all animals' behavior observed through the video recordings during the 12 h of the light period. Data from the behaviors with shorter durations were zoomed in (top right inset).

(B) Pie chart showing the proportion of the total duration of each behavior for all animals.

Figure 5.

Figure 5

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Duration of ‘Quiet sleep’ episodes as a function of precedent or subsequent states

States that occurred less than 10 times before or after ‘Quiet sleep’ were discarded from this analysis.

(A) Boxplot and histogram showing the duration in minutes (y axis) of ‘Quiet sleep’ episodes that occurred after the behaviors ‘Alert’, Quiet with open pupil (‘QOP’), Quiet with half and half (‘QHH’), and ‘Active sleep’ (x axis). The behaviors ‘Alert’, ‘QOP’, and ‘QHH’ occurred before long ‘Quiet sleep’ episodes, with medians 10.73, 7.41, and 5.84, respectively. ‘Active sleep’ episodes preceded the shortest ‘Quiet sleep’ episodes.

(B) Boxplot and histogram showing the duration in min (y axis) of ‘Quiet sleep’ episodes that occurred prior to the behaviors ‘Alert’, ‘QHH’, and ‘Active sleep’ (x axis). Long ‘Quiet sleep’ episodes (median value of 13.33 min) often led to ‘Active sleep’ episodes. ‘Active sleep’ was the only behavior that occurred after ‘Quiet sleep’ episodes with durations above 30 min. ‘QHH’ episodes were preceded by ‘Quiet sleep’ episodes of short duration (median 3.9 min), whereas ‘Alert’ episodes were preceded by ‘Quiet sleep’ episodes of intermediate duration (median 4.6 min).

The states immediately following the ‘Quiet sleep’ state were ‘Alert’, ‘QHH’, and ‘Active sleep’, and only the duration of the ‘Quiet sleep’ episodes immediately followed by ‘Active sleep’ episodes differed from the others by being significantly longer (Kruskal-Wallis p = 1.18 × 10−14) (Figure 5B).

The large variation in the duration of ‘Quiet sleep’ episodes prompted us to analyze more deeply the relationship between the durations of ‘Quiet sleep’ episodes and the likelihood of observing a neighboring ‘Active sleep’ episode. First, we looked for differences in the duration of ‘Quiet sleep’ episodes when they immediately preceded or did not immediately precede ‘Active sleep’ episodes: using Kernel Density Estimation, we plotted the durations distribution of the ‘Quiet sleep’ episodes immediately preceding ‘Active sleep’ episodes and of the ‘Quiet sleep’ episodes not immediately preceding ‘Active sleep’ episodes. These distributions show that the ‘Quiet sleep’ episodes that precede ‘Active sleep’ episodes are generally longer than those that do not precede ‘Active sleep’ episodes. The Kolmogorov-Smirnov test showed that these distributions were significantly different (p = 1.36 × 10−5). Figure S1A suggests that it is more likely that an octopus initiates an ‘Active sleep’ episode when they have been in a ‘Quiet sleep’ episode for a long time period. For this reason, we further sorted the ‘Quiet sleep’ episodes in two distinct categories: long and short. To classify these behaviors more accurately, we used a non-arbitrary parameter to split them into long and short ‘Quiet sleep’ episodes. Namely, we calculated the median time between (1) the mode of the distribution of ‘Quiet sleep’ episodes that occurred immediately before ‘Active sleep’ episodes and (2) the mode of other ‘Quiet sleep’ episodes that did not occur immediately before an ‘Active sleep’ episode (Figure S1A). In this way, short ‘Quiet sleep’ episodes had durations ≤6.51 min, whereas long ‘Quiet sleep’ episodes lasted >6.51 min.

Next, Kernel Density Estimation and the Kolmogorov-Smirnov test were used to compare the distribution of durations for ‘Quiet sleep’ episodes occurring immediately after ‘Active sleep’ episodes versus ‘Quiet sleep’ episodes occurring not immediately after ‘Active sleep’ episodes (p = 0.22). The distributions of the ‘Active sleep’ episodes immediately preceding ‘Quiet sleep’ episodes and not immediately preceding ‘Quiet sleep’ episodes were also compared (p = 0.98). Likewise, we further compared the ‘Active sleep’ episodes immediately succeeding ‘Quiet sleep’ episodes and not immediately succeeding ‘Quiet sleep’ episodes (p = 0.27). None of these comparisons showed significant differences (Figures S1B–S1D). These results indicate that there is a relationship between the duration of ‘Quiet sleep’ episodes and the subsequent occurrence of ‘Active sleep’ episodes, whereas the duration of ‘Active sleep’ episodes has no relationship with the occurrence of subsequent ‘Quiet sleep’ episodes. Importantly, short ‘Quiet sleep’ episodes are rarely followed by ‘Active sleep’ episodes.

The behaviors with the highest total time during the diurnal video recordings were the ‘Alert’ state (37.85%), ‘QOP’ (28.42%), and long ‘Quiet sleep’ (22.63%), whereas the ones with the lowest total time were ‘Active’ (6.42%), short ‘Quiet sleep’ (3.5%), ‘QHH’ body pattern (0.57%), ‘Active sleep’ (0.52%), and ‘QOEM’ (0.09%) (Figure 4B).

The behaviors with the highest frequencies per day were ‘Alert’ (35.56%) and ‘QOP’ (31.31%), whereas the behaviors ‘QOEM’ (0.50%) and ‘Active sleep’ (7.81%) had the lowest frequencies (Table S1).

There was a significant difference between the duration of almost all states observed. All quiescence behaviors differed except ‘QOEM.’ In addition, the ‘Active’, ‘Alert’, and ‘QOP’ states in which the octopus is possibly awake differed from all other quiescence states (‘Quiet sleep’, ‘Active sleep’, and ‘QHH’) except ‘QOEM’, as shown in Table S2.

Ultradian cyclicity of ‘active sleep’ and long ‘quiet sleep’ episodes

To analyze the cyclic pattern comprising the long ‘Quiet sleep’ and the ‘Active sleep,’ we excluded the feeding period because it interfered in the sleep cycle. From the total of 74 long ‘Quiet sleep’ intervals (time spent between the end of one episode and the beginning of the next episode of the same behavior), 60% had durations between 6.77 min and 25.48 min, i.e., with a wide range of durations (Figure 6A; Table S3 and Figure S2). From the total of 79 ‘Active sleep’ intervals, 60% had duration between 29.58 min and 32.98 min, i.e. with a narrow range of durations (Figure 6B; Table S4).

Figure 6.

Figure 6

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Ultradian Cyclicity of ‘Active sleep’ and long ‘Quiet sleep’ episodes

Each background color represents one individual octopus

(A) ‘Quiet sleep’ characterization: (Top panel) plots of intervals between long ‘Quiet sleep’ with histogram on the right; (Bottom panel) boxplot and histogram for the durations of intervals between long ‘Quiet sleep’ episodes for each octopus.

(B) ‘Active sleep’ characterization: (Top panel) plots of intervals between ‘Active sleep’ with its histogram distributions on the right; (Bottom panel) boxplot and histogram for the ‘Active sleep’ interval durations of each octopus. For better visualization of the distribution, we excluded from the plot the data points 227.78 min and 150.45 min of the ‘Active sleep’ intervals and data point 173.47 min of the long ’Quiet sleep’ intervals.

A hypnogram was elaborated from the behavioral observation of one representative animal (octopus 3) for 200 continuous minutes in order to exemplify the ultradian cyclicity. Concomitantly with the hypnogram, we analyzed skin changes in color and texture, represented respectively by general skin color and localized skin patterning dynamics (Figure 7). Tracking of eyes and mantle movements was also performed for the same period (Figure S3). These parameters varied substantially across behaviors. For instance, during ‘Quiet sleep’ there was less variation of these parameters and during ‘Active sleep’ there was a more accentuated variation of the measures, whereas during the ‘Alert’ state we observed a constant and not extreme oscillation of skin color and texture, with mantle and eye movements.

Figure 7.

Figure 7

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Hypnogram with corresponding measurements of general skin color dynamics and localized skin patterning dynamics

The figure depicts the behaviors of one representative animal (octopus 3) along 200 min of diurnal recordings, beginning at 06:36:50 and finishing at 09:56:50. The plot of general skin color dynamics depicts the mean color from two regions of interest delimited over the head and dorsal mantle (detailed in Figure 7). The plot of localized skin patterning dynamics shows changes in skin texture represented by the variation of the number of white pixels captured by the Canny algorithm along time. For both skin dynamic analyses, a marked variation of color and texture concomitant with the occurrence of ‘Active sleep’ can be observed.

General skin color dynamics across behavioral states

The pixel analysis of mean brightness values showed characteristic profiles of chromatophore color changes for each behavioral state (Figure 8A; Figure S4; Figure 7 second panel). When animals were in the ‘Alert’ state, brightness intensities recorded from the skin kept oscillating (mantle—median 0.56, first quartile 0.22, third quartile 1.25; head—median 0.75, first quartile 0.34, third quartile 1.59), whereas during the ‘Quiet sleep’ state there was very little variation in the measurements (mantle—median 0.21, first quartile 0.09, third quartile 0.47; head—median 0.25, first quartile 0.11, third quartile 0.61). However, during ‘Active sleep’ episodes there was a conspicuous and abrupt decrease in the measures, followed by intense brightness variation throughout the duration of the episode (mantle—median 2.09, first quartile 0.93, third quartile 3.94; head—median 2.8, first quartile 1.35, third quartile 5.14). This difference in color variation between these three states was confirmed by the statistical analyses for head and mantle (Kruskal-Wallis, head p = 2.20 × 10−16; mantle p = 2.20 × 10−16). The pairwise comparisons (Wilcoxon) with Bonferroni adjustment showed that in the head and mantle there were more variations in skin color during ‘Active sleep’ than during ‘Quiet sleep’ (head p = 2.00 × 10−16; mantle p = 2.00 × 10−16) and ‘Alert’ state (head p = 2.00 × 10−16; mantle p = 2.00 × 10−16). There was also more color variation during the ‘Alert’ state than during ‘Quiet sleep’ (head p = 2.00 × 10−16; mantle p = 5.30 × 10−12). These results are shown by the raw data and Gaussian filter of the number of white pixels over time (Figure 8A) and by the median of the variations shown by each octopus (Figure 8B). The other octopuses presented similar profiles, as shown in the Figure S4.

Figure 8.

Figure 8

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General skin color dynamics across ‘Quiet sleep’, ‘Active sleep’, and the ‘Alert’ state

(A) The left panel shows changes in skin color represented by the variation of the ROI's mean brightness (y axis) along 174 s (x axis) for five transitions across ‘Quiet sleep’ (blue zone), ‘Active sleep’ (green zone), and the ‘Alert’ state (orange zone) from octopus 3. The intervals analyzed for ‘Quiet sleep’ and the ‘Alert state’ had the same duration of the ‘Active sleep’ episode that occurred between them. The examples are organized from the shortest ‘Active sleep’ episode, on the top, to the longest, on the bottom. The green line indicates the head's color and the blue line indicates the mantle's color. Both signals are well followed by the Gaussian filter (red line). All the behavioral transitions show a similar profile, with little value variation during ‘Quiet sleep’ and the ‘Alert’ state, and with a marked variation during ‘Active sleep’, so that both extreme values (highlighted by the blue and red dots) occurred during ‘Active sleep’. The right panel shows a picture of the octopus with the ROI's selected for head (green) and mantle (blue) when the animal color achieved its maximum value (most pale) and minimum value (most dark).

(B) Scatterplots showing the medians of delta (500 ms) Gaussian values for the color analysis of each behavior per octopus (n = 4). The top panel shows results for the head, and the bottom panel shows results for the mantle. Note that the mantle was properly visible in two animals only, so octopuses 1 and 2 were not included in the mantle color analyses. The color variation in all animals followed a general profile in which the highest color variation occurred during ‘Active sleep’.

Localized skin patterning dynamics across behavioral states

The localized skin dynamics is caused by texture changes due to extension and retraction of the papillae and changes in color patterning within specific body regions. These dynamic changes in skin were investigated using the Canny algorithm, which captured edges in the image frames. The edges were visualized through white pixels that indicate the skin papillae's texture and the color patterning complexity (Figure 9A; Figure 7, third panel; Figure S5). There was a significant difference in variation of skin texture and complexity of color patterns across ‘Quiet sleep’ (median 1.00, first quartile 0.00, third quartile 3.00), ‘Active sleep’ (median 8.00, first quartile 3.00, third quartile 4. 16.00), and the ‘Alert’ state (median 2.00, first quartile 1.00, third quartile 5.00) for all the octopuses (Kruskal-Wallis p = 2.20 × 10−16). The pairwise comparisons (Wilcoxon) with Bonferroni adjustment showed that there was significantly more variation in skin texture during ‘Active sleep’ than during ‘Quiet sleep’ (p = 2.00 × 10−16) and ‘Alert’ state (p = 2.00 × 10−16). There was also significantly more variation during the ‘Alert’ state than during ‘Quiet sleep’ (p = 3.40 × 10−16), as shown by the raw data and Gaussian filter of the number of white pixels over time (Figure 9A) and by the median of the variations shown by each octopus (Figure 9B). The octopuses 1, 2, and 4 presented similar profiles, as shown in the Figure S5.

Figure 9.

Figure 9

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Localized skin patterning dynamics across ‘Quiet sleep’, ‘Active sleep’, and the ‘Alert’ state

(A) The left panel shows changes in skin texture represented by the variation of the number of white pixels (y axis) along 174 s (x axis) for five transitions across ‘Quiet sleep’ (blue zone), ‘Active sleep’ (green zone), and the ‘Alert’ state (orange zone) from octopus 3. The intervals analyzed for ‘Quiet sleep’ and Alert had the same duration of the ‘Active sleep’ that occurred between them. The examples are organized from the shortest ‘Active sleep’ episode, on the top, to the longest, on the bottom. The red line is a Gaussian filter and follows closely the real data, represented by the black line. All the behavioral transitions show a similar profile, with little variation during ‘Quiet sleep’ and the ‘Alert’ state, and with a marked variation during ‘Active sleep’, so that both extreme values occurred during ‘Active sleep’ and all the highest values occurred after the lowest values. The top value among the five sampled transitions is on the last line, having 1,367 white pixels and the lowest value is on the first line, with 281 white pixels. The right panel shows the moment during ‘Active sleep’ when the animal displayed minimum body pattern complexity, represented by the minimal number of white pixels captured by the Canny edges algorithm (blue dots on the graphs and minimal white pixels column); the maximal count of white pixels are represented by the red dots on the graphs and maximum white pixels (red) column.

(B) Scatterplots showing the medians of delta (500 ms) Gaussian values for the behavioral analysis of each octopus (n = 4). The median values of ‘Active sleep’ are mostly higher than during other behaviors (except for one transition in octopus 1, when the value did not change across behaviors).

Eye movements across behavioral states

For each octopus we analyzed eye movements for five periods comprising a natural sequence of ‘Quiet sleep’, ‘Active sleep’, and the ‘Alert’ state. The octopuses showed significant differences in eye movements across states (Kruskal-Wallis p = 2.20 × 10−16). Besides, pairwise comparisons (Wilcoxon) with Bonferroni adjustment show that the increase rate during ‘Active sleep’ (median 0.56, first quartile 0.32, third quartile 1.32) was significant in comparison with ‘Quiet sleep’ (median 0.36, first quartile 0.21, third quartile 0.79) (p = 2.00 × 10−16) and ‘Alert’ (median 0.44, first quartile 0.25, third quartile 1.02) (p = 5.20 × 10−15). There was also a significant increase in eye movements during the ‘Alert’ state in comparison with ‘Quiet sleep’ (p = 8.60 × 10−7) (Figure 10).

Figure 10.

Figure 10

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Eye movements across ‘Quiet sleep’, ‘Active sleep’, and the ‘Alert’ state

(A) The left panel shows the sum of the position variation for the left and right eyes (y axis) over time (x axis) for five transitions between ‘Quiet sleep’ (blue zone), ‘Active sleep’ (green zone), and the ‘Alert’ state (orange zone) for octopus 3. The intervals analyzed for ‘Quiet sleep’ and the ‘Alert’ state had the same duration of the ‘Active sleep’ that occurred between them. The examples are organized from the shortest ‘Active sleep’, on top, to the longest, on the bottom. The red line on the graph is a Gaussian filter and follows closely the real data, represented by the black line. The right panels display an example of the region where marks were placed on each eye to perform the tracking, with zoomed-in images on the right.

(B) Scatterplots show the medians of delta (300 ms) Gaussian values for the behavioral analysis of each octopus (n = 4).

Mantle ventilation movements across behavioral states

The octopuses 3 and 4 showed significant differences in mantle movement across ‘Quiet sleep’ (median 0.70, first quartile 0.29, third quartile 4.67), ‘Active sleep’ (median 1.12, first quartile 0.41, third quartile 4.60), and the ‘Alert’ state (median 0.67, first quartile 0.28, third quartile 3.23) (Kruskal-Wallis p = 1.43 × 10−8). The pairwise comparison (Wilcoxon) with Bonferroni adjustment shows that the increased rate during ‘Active sleep’ was significant in comparison with ‘Quiet sleep’ (p = 2.90 × 10−5) and the ‘Alert’ state (p = 6.50 × 10−12). Besides, the increase in mantle movements during ‘Quiet sleep’ in comparison with the ‘Alert’ state was also significant (p = 0.04). Figure 11 shows in detail for octopus 3 the variation of mantle position over time for the behavioral sequences comprising ‘Quiet sleep’, ‘Active sleep’, and the ‘Alert’ state (Figures 11A) and the scatterplot with the medians of these transitions for both octopuses (Figures 11B).

Figure 11.

Figure 11

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Mantle ventilation movements across ‘Quiet sleep’, ‘Active sleep’, and the ‘Alert’ state

(A) The left panel is showing sum of the position variation of seven different spots chosen in the mantle for the tracking (y axis) along the time (x axis) for five transitions between ‘Quiet sleep’ (blue zone), ‘Active sleep’ (green zone), and the ‘Alert’ state (orange zone) from octopus 3. The intervals analyzed for ‘Quiet sleep’ and the ‘Alert’ state had the same duration of the ‘Active sleep’ that occurred between them. The five examples are organized from the shortest ‘Active sleep’ episode, on top, to the longest episode, on the bottom. The red line on the graph is a Gaussian filter and follows closely the real data, represented by the black line. The right panels display an example of the region where seven marks (red circles) were placed on the mantle to perform the tracking, with zoomed-in images on the right.

(B) Scatterplots showing the medians of delta (500 ms) Gaussian values for the behavioral analysis of each octopus (n = 2).

Behavioral transitions show a characteristic pattern

To quantify comprehensively how specimens of Octopus insularis transit between specific behavioral states, the entire dataset recorded during daytime was subjected to graph analysis. The graphs of behavioral transitions show that some of the states were more correlated with each other and that there was a pattern in the sequence of their transitions (Figure 12). ‘Active sleep’ occurred after ‘Quiet sleep’ 82% of the times, with 18% coming from short ‘Quiet sleep’ episodes and 64% from long ‘Quiet sleep’ episodes. The behaviors with the highest probability to occur after long ‘Quiet sleep’ were ‘Active sleep’ (57%), the ‘Alert’ state (24%), and ‘QHH’ (16%). Furthermore, ‘QHH’ was more strongly correlated with quiet behaviors, with 50% of the inputs coming from ‘QOP’ and 45% of all short ‘Quiet sleep’ outputs leading to ‘QHH’. The ‘Quiet sleep’ episodes were mostly preceded by ‘QHH’, the ‘Alert’ state, and ‘QOP’, but almost never by ‘Active sleep’, with significant differences between their frequencies (chi-squared test, p = 2.20 × 10−16). When this occurred the ‘Quiet sleep’ episodes were almost invariably short (chi-squared test, p = 4.27 × 10−3).

Figure 12.

Figure 12

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Proportions of transitions between behaviors

Graphs show the proportions of behavioral transitions that occurred along the 180 h and 49 min of video recordings (n = 4 octopuses). Each node of the graph represents one behavior described on the ethogram and the edges indicate transitions.

(A) Percentage of the total inputs for each behavior.

(B) Percentage of total outputs for each behavior.

Discussion

In the present work, we showed that Octopus insularis displays two different quiescence states that fulfill the behavioral criteria for sleep, namely ‘Quiet sleep’ and ‘Active sleep’. We characterized these states with regard to general body skin color dynamics, local skin patterning dynamics, eye and mantle movements, episode duration, episode periodicity, transition probabilities, and arousal thresholds. Altogether, the results point to a cyclic-state dynamics in which the behavioral sequence ‘Quiet sleep’ to ‘Active sleep’ to ‘Alert’ state is prevalent.

‘Quiet sleep’ has already been observed in Octopus vulgaris (Meisel et al., 2011), but ‘Active sleep’ has not yet been described in the literature for the octopus. However, this ‘Active sleep’ is similar to the behavior named REM-like sleep in Sepia officinalis (Iglesias et al., 2019), by analogy with rapid-eye movement (REM) sleep in mammals. In both species there were eye movements, dynamic chromatophore patterning, and a sudden simultaneous darkening of the mantle and head chromatophores at state onset (Frank et al., 2012; Iglesias et al., 2019). Another similarity between the ‘Active sleep’ here described and the REM-like state described in Sepia officinalis (Iglesias et al., 2019) is the presence of an ultradian rhythm for both behaviors, with a characteristic cycle. The periodicity of REM-like sleep in Sepia officinalis was 36.34 ± 1.46 min, whereas in Octopus insularis 60% of the intervals between ‘Active sleep’ episodes had durations between 29.58 min and 32.98 min. Importantly, an ultradian sleep rhythm is also observed in mammals (Trachsel et al., 1991), birds (Walker and Berger, 1972), and in the reptile bearded dragon Pogona vitticeps (Ahl, 1926; Norimoto et al., 2020).

Regarding this periodicity, octopus 4 differed a bit from the others with regard to durations of the intervals between consecutive ‘Active sleep’ episodes. Reptiles, such as the bearded dragon, can change the period of neural oscillations according to the environment's temperature, so that an increase in temperature leads to a decrease in periodicity (Shein-Idelson et al., 2016). However, considering that during this study the water temperature was kept identical for all animals and that octopus 4 was the female with the highest body weight and probably the most mature animal assessed (Lima et al., 2014), it is possible that Octopus insularis specimens undergo changes in their sleep cycles as they age.

During quiescence animals also presented the behavior ‘QHH’, which was also observed in O. vulgaris during resting periods (Meisel et al., 2011). For this reason, we included this state in the arousal threshold experiment. The visual latency test performed with Octopus insularis showed a significant and gradual increase in arousal threshold from the ‘Alert’ state to QHH, ‘Quiet sleep’, and ‘Active sleep’. These results strongly suggest the existence of different sleep states in octopuses.

We also hypothesized that the “half and half” body pattern is related to a rest behavior, something also reported for Octopus vulgaris, including an alternation of the body half that gets dark a few minutes later (Figure 2C) (Meisel et al., 2011). However, other authors have reported that this behavior is related to intraspecific interactions with other cephalopods, such as in the squid Sepioteuthis sepioidea (Blainville, 1823; Mather, 2016). In our experiment, it is possible that the octopuses were seeing their own reflection in the aquarium wall and the ‘QHH’ is a response to environmental stimuli rather than a resting behavior. Nevertheless, in agreement with our hypothesis, the arousal threshold during ‘QHH’ was significantly higher in comparison with the ‘Alert’ state, and the peaks occurred during periods of quiescence. Thus, this state may be related to a lighter sleep state preceding or interspersing the ‘Quiet sleep’.

The ‘Quiet state with only one eye movement’ (‘QOEM’), which has never been reported for any cephalopod species, also seems to be a rest behavior. However, further studies must investigate whether this unusual type of eye movement can also occur with both eyes simultaneously, whether it can also be observed in the natural environment, and what are the physiological processes underlying it. The fact that this behavior also occurred during the night, with lights off, indicates that it is not triggered as a response of the reflexive glass tank (Video S9).

Our study demonstrates that quiescent states shown by specimens of Octopus insularis fit most of the behavioral criteria for sleep. The existence in cephalopods of at least two different sleep states within an ultradian wake-sleep cycle contrasts with the apparent existence of a single sleep state in other mollusks, such as in Aplysia californica (Cooper, 1863; Vorster and Born, 2017) and Lymnaea stagnalis (Linnaeus, 1758; Stephenson and Lewis, 2011). If extended to multiple species of non-cephalopod mollusks, this difference suggests an independent evolution of ‘Active sleep’ in cephalopods and amniotes.

One of the main interests in the field is to establish parallels between the ‘Quiet sleep’ and ‘Active sleep’ states presented here and the different physiological responses found in the mammalian non-REM (NREM) and REM sleep states. Given that the ‘Active sleep’ state has not been described for octopuses before, it is necessary to investigate whether the similarity of this state with REM sleep goes beyond the behavioral similarities observed in this work, such as elevated arousal threshold, eye movements, and body twitches (Aserinsky and Kleitman, 1955; Dement, 1958; Dillon and Webb, 1965). The similarities between the ‘Quiet sleep’ of octopuses and NREM sleep in vertebrates, such as behavioral quiescence, with only brief and minimal movements (Muzet et al., 1972), and increased arousal threshold (Neckelmann and Ursin, 1993), prompt the need for further investigation on whether octopuses and mammas undergo similar physiological processes. Recordings of brain activity during ‘Quiet sleep’ and ‘Active sleep’ can be used to search for the occurrence of typical neural oscillations found in mammals (Steriade et al., 1993, Hobson and Mccarley, 1971), but there is of course a major limitation in this comparison, because the vertebrate and cephalopod brains are not homologous and differ substantially in tissue organization. Furthermore, given that it is very challenging to successfully place electrodes in the octopus brain, (Brown et al., 2006) remains the only published electrophysiological study of octopus' sleep. However, although it shows interesting findings in four specimens of O. vulgaris, such as increase in neural activity during quiescence, the video recordings were sampled using time lapse. For this reason, we can speculate that ‘Active sleep’, which has a brief episode duration, could have been severely undersampled. Furthermore, it is possible that during such increase in brain activity the animal was actually undergoing a short ‘Active sleep’ episode as described here, which may have been missed in the video record. Therefore, we recommend further studies in this field to assess brain electrophysiology in continuous behavioral recordings.

As proposed by (Reiter et al., 2018), it is reasonable that chromatophore expansion could serve as a proxy for motor neuron activity, because each chromatophore is controlled by a small number of motor neurons, and each motor neuron controls a small number of chromatophores (Reed, 1995). Therefore, the skin patterning computational analyses can be useful to understand neural activity of these animals. The analysis of skin color and texture dynamics revealed differences in chromatophore pattern and papillae exposure across episodes of the ‘Alert state’, ‘Quiet sleep’, and ‘Active sleep’. During the ‘Alert’ state there were oscillations, whereas during ‘Quiet sleep’ they remained relatively constant and during the ‘Active sleep’ they change abruptly, in accordance with the dynamic body pattern. Furthermore, the onset of ‘Active sleep’ episodes was characterized by a sudden and simultaneous darkening of the mantle and head, as described for what has been called REM-like state in Sepia officinalis (Iglesias et al., 2019). In addition, we also found a moderate increase in eye movements and ventilation rate during ‘Active sleep’.

Conclusions

Despite the great evolutionary distance between vertebrates and invertebrates, the sleep behavior of Octopus insularis shares many features with the sleep of amniotes, including the ultradian cyclic pattern observed for the ‘Active sleep’ state. The recent evidence of ‘Active Sleep’ in Drosophila (Tainton-Heap et al., 2021) suggests strong selection pressure across evolution for an alternation between ‘Quiet’ and ‘Active’ sleep states. The occurrence of the non-REM/REM alternation in mammals, birds, and in some reptiles, such as in the bearded dragon and in the argentine tegu Salvator merianae (Duméril and Bibron, 1839; Libourel et al., 2018; Shein-Idelson et al., 2016), points to a common origin of the wake-sleep cycle in these groups of animals, which share a common ancestor (Libourel et al., 2018). However, considering that cephalopods split from vertebrates more than 500 million years ago (Vitti, 2013; Shu et al., 2001), it is likely that the sleep behaviors observed here, despite their similarity to those found in amniotes, are analogous rather than homologous to these states.

By the same token, the fact that analogous nervous systems such as the cephalopod brain and the vertebrate brain evolved similar behavioral sequences across the wake-sleep cycle is strongly suggestive of convergent evolution. Cephalopods have evolved de novo neural structures termed lobes, including the vertical lobe that is involved in long-term memory and shares some functional features with the mammalian hippocampus (Gutnick et al., 2016; Nixon and Young, 2003). Indeed, cephalopod evolution seems to have converged with vertebrates with regard to the neural mechanisms underlying learning (Gutnick et al., 2016; Shomrat et al., 2015; Hochner et al., 2003). It remains to be investigated whether the physiological functions of sleep, in this far-evolving taxon, also resemble the functions performed in amniotes, such as metabolic detoxification (Xie et al., 2013; Hablitz et al., 2019) and cognitive processing (Boyce et al., 2016; Blanco et al., 2015).

Limitations of the study

A more accurate analysis of the behavior ‘QHH’ would involve estimating the arousal threshold using the visual stimulus to selectively affect only the dark side or the pale side separately. We tried to do it, but it proved very difficult to have the animal stay long enough in the same position, to ensure that it was really seeing the monitor with only one eye. Furthermore, even if we could establish beyond doubt that just one eye was able to see the monitor, the other eye still could be stimulated by reflections on the aquarium's glass.

If ‘QHH’ is in fact a sleep state, it may be akin to the unihemispheric sleep observed in bird and aquatic mammals (Rattenborg et al., 1999, 2000, 2016; Ridgway et al., 2006; Lyamin et al., 2002; Mascetti, 2016). Unihemispheric sleep occurs during slow wave sleep (SWS), and the eye corresponding to the sleeping hemisphere remains closed. However, another limitation of this study was that we were not able to compare the pupil contractions recorded from the pale and dark sides during the ‘QHH’. Pupil size was difficult to measure because (1) ‘QHH’ is a behavior with reduced frequency and very short duration; (2) we were often able to observe only one of the eyes because of the camera position; (3) commonly the eye from the dark side also gets dark, making it difficult to distinguish the pupil from the rest of the eye; and (4) given the asymmetries in skin color, we could not assume that both eyes had their pupils equally open or closed.

For a deeper understanding of ‘Active sleep’ it will be important to quantify more precisely the occurrence of body twitches. Because the twitches are distributed all over the octopus' body, variations of focus and body position made these twitches difficult to quantify here. Another caveat of the present study is that we did not assess sleep rebound. This hallmark of sleep has been documented in Octopus vulgaris (Brown et al., 2006; Meisel et al., 2011), so it is quite likely that Octopus insularis also has a sleep homeostatic regulation.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Sidarta Ribeiro (sidartaribeiro@neuro.ufrn.br).

Materials availability

Our study did not generate new unique reagents.

Data and code availability

Original materials, data and code have been deposited in OSF at https://osf.io/f6jyu/?view_only=c8341a1535ad472d908f7a5b629e99a3.

Methods

All methods can be found in the accompanying transparent methods supplemental file.

Acknowledgments

Funding was obtained from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Project - Ciências do Mar II-23038.004807/2014- 01, Undergraduate scientific research scholarship from CNPq, from the State University of Rio Grande do Norte (UERN), grants #308775/2015-5 and #408145/2016-1 from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and grant #2013/07699-0 from the São Paulo Research Foundation (FAPESP) Center for Neuromathematics. We thank the Mind the Graph platform (www.mindthegraph.com) for facilitating figure preparation. We thank Annie da Costa Souza and Renato Junqueira de Souza Dantas for helping with animal collection in the field, Alberto Medeiros for photography edition, Claudio Queiroz for granting access to software, and the staff of the Brain Institute and BioME (UFRN) for their indispensable technical and logistical assistance, especially Ana Elvira Oliveira and Eronildo Lira de Santana.

Author contributions

SLSM, SR, and TSL conceived and designed the experiments and literature search; SLSM and SS secured laboratory space and funds for the research; SLSM and SR wrote the paper and PHL contributed with the writing; WB and EBS contributed with study design and data interpretation; SLSM and MMMP performed the experiments and collected and analyzed the data; SLSM, MMMP, and FDL performed specimen collection in the field; FDL, SLSM, PHL, and IGM performed the statistical analysis; SLSM, MMMP, PHL, JBCO, IGM, and SR prepared figures and/or tables; SR, TSL, FDL, WB, EBS, and SS reviewed the paper and approved the final version.

Declaration of interests

The authors declare no competing interests.

Published: March 25, 2021

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2021.102223.

Supplemental information

Document S1. Transparent methods, Figures S1–S5, and Tables S1–S4
mmc1.pdf (1.7MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Video S1. The behavior Quiet with open pupil (‘QOP’) recorded during the day, related to Table 1 and Figure 2
Download video file (97.2MB, mp4)
Video S2. The behavior ‘Quiet sleep’ recorded during the day, related to Table 1 and Figure 2
Download video file (188.2MB, mp4)
Video S3. The behavior Quiet with half and half body pattern (‘QHH’) recorded during the day, related to Table 1 and Figure 2
Download video file (139.6MB, mp4)
Video S4. The behavior ‘Active sleep’ recorded during the day, related to Table 1 and Figure 2
Download video file (139.1MB, mp4)
Video S5. The behavior Quiet with only one eye movement (‘QOEM’) recorded during the day, related to Table 1 and Figure 2
Download video file (94.6MB, mp4)
Video S6. The behavior Quiet with open pupil (‘QOP’) recorded during the night, related to Table 1
Download video file (97.4MB, mp4)
Video S7. The behavior ‘Quiet sleep’ recorded during the night, related to Table 1
Download video file (212.5MB, mp4)
Video S8. The behavior ‘Active sleep’ recorded during the night, related to Table 1
Download video file (107.3MB, mp4)
Video S9. The behavior Quiet with only one eye movement (‘QOEM’) recorded during the night, related to Table 1
Download video file (140.3MB, mp4)
Document S1. Transparent methods, Figures S1–S5, and Tables S1–S4
mmc1.pdf (1.7MB, pdf)

Data Availability Statement

Original materials, data and code have been deposited in OSF at https://osf.io/f6jyu/?view_only=c8341a1535ad472d908f7a5b629e99a3.

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