Cannabis is among the most widespread, popular substances worldwide (Degenhardt et al., 2013). Among its various psychotropic effects, cannabis has long been known to change the perception of time. Time perception is a fundamental dimension of experience, and its relationship with psychopathology is well-recognized (Amadeo et al., 2022; Kent et al., 2023). Given the evidence that suggests the potential implications of cannabinoids compound on mental health (Escelsior et al., 2020), this review aims to summarize the available evidence on cannabis, time perception, and psychopathology.

The Endocannabinoid System and Cannabis Effects

Cannabis is a plant that contains several psychotropic active compounds (Degenhardt et al., 2010). Among them, delta-9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) are of particular importance and display opposite effects in many domains: while Δ9-THC increase in pulse rate, alters time judgment, or might induce psychiatric outcomes like anxiety or psychosis, CBD, when administered together with Δ9-THC, prevents most of its adverse effects. In addition, CBD made the Δ9-THC effects more pleasurable for the subjects (Atakan, 2012). Cannabis mainly exerts its manifold effects on the body and brain through the endocannabinoid system (ECBs), which has been characterized only recently as a widespread system that regulates different neurobiological functions (Curran et al., 2016). The ECBs act as a neuromodulatory system that regulates the integration of internal stimuli with the perception of the external environment. In this framework, the ECBs modulates the sensation of pain and influences motivation, reward, emotional homeostasis, synaptic plasticity, learning, and memory; these levels of integration are essential for the adaptation of behavioral responses such as fearful reactions, anxiety, and stress response (Lutz et al., 2015; Lu and Mackie, 2016). The ECBs act mainly through the cannabinoid receptors CB1 and CB2 (Devane et al., 1988; Munro et al., 1993; Console-Bram et al., 2012). Whereas CB1 is expressed in different brain areas such as the cingulate gyrus, frontal cortex, secondary somatosensory and motor cortex, hippocampus, amygdala, basal ganglia, and cerebellum (Mackie, 2005), CB2 is is primarily expressed on immune system cells, including microglia and astrocytes, lymphocytes, macrophages, and NK cells (Matias and Di Marzo, 2007; Stella, 2010). Other receptors within the ECBs are transient receptor potential (TRP), especially TRPV1, G protein-coupled receptor 55 (GPR55), and peroxisome proliferator-activated receptor (PPAR) α and γ (Zygmunt et al., 2002; O’Sullivan, 2007; Ryberg et al., 2007; Puente et al., 2011; Escelsior et al., 2020). The ECBs receptors are the target for endogenous ligands such as anandamide (AEA) and 2-arachidonoyl glycerol (2-AG) (Atsak et al., 2015; Curran et al., 2016; Lu and Mackie, 2016) or a plethora exogenous ligand like the Cannabis sativa phytocannabinoids Δ9-THC, cannabidiol, or Δ8-THC (Ashton, 2001; Console-Bram et al., 2012). The Δ9-THC exerts its action by activating presynaptic CB1 also inducing a differentiated modulation of other neurotransmitter systems (Fig. 1).

Fig. 1
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Effects of cannabis exposure on neurotransmitters signaling. Red = increased; blue = decreased 

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The intake of Cannabis sativa induces a wide variety of effects spanning from euphoria, time and space perceptual changes, decreased alertness and anxiety, analgesia, sedation, hunger and impairments in cognitive and psychomotor performance (Ashton, 2001; Andrade, 2016) to severe anxiety, panic, dysphoric states, manic or mixed episodes, paranoia, suicidal thoughts, and psychosis (Ashton, 2001; Henquet et al., 2006; Moore et al., 2007; Agrawal et al., 2011). Furthermore, a large amount of literature confirms that cannabis consumption can predict specific harmful behaviors, including suicide attempts, self-injurious behaviors (Escelsior et al., 2021), or restlessness and aggressive behaviors (Ashton, 2001; Curran et al., 2016), especially in subjects with a history of psychiatric disorders.

Among the diverse effects of cannabinoid compounds, alterations in time perception induced by cannabis are intriguing in terms of a potential link between fundamental perceptual changes and psychopathology. These were highlighted as early as the foundational study by Weil and colleagues, which was the first to objectively measure the effects of cannabis on time estimation. The study was conducted on nine marijuana näive subjects and specifically found that the main active constituent of cannabis, Δ9-THC, could modify subjective time perception in the range of seconds to minutes in a dose-dependent way (Weil et al., 1979). This finding first prompted a line of research that, however, has received little attention (Tinklenberg et al., 1976; McDonald et al., 2003).

Time Perception, Psychopathology, and Neurobiology

Our understanding of the world is based on a hierarchy of dimensions, whereby time holds the first level (Navon, 1978). One of the primary keys to survival and adaptation to the world is the ability to time the sequence of events and make predictions in a seconds-to-minute range (Meck and Benson, 2002). This capacity influences the reach of motor goals and, in humans, is crucial for organizing thoughts in language and communication (Atakan, 2012). Usually, the ability to “time” and coordinate actions and thoughts is pre-reflective and unconscious. Thus, the existence of an “internal clock” setting the pace and tuning peripheral clocks in several organs was postulated (Madison, 2001; Ivry and Schlerf, 2008). Drugs, organic and psychiatric diseases, context, and perceptual modalities can influence time perception, causing motor, emotional, behavioral, and cognitive disturbances (Coull et al., 2011).

Time Perception and Psychopathology

The distortion of the sense of time, with the loss of the regular continuity of past, present, and future, has been described in the twentieth century by phenomenologists as one central subjective experience of schizophrenia (Binswanger, 1960; Erwin Straus, 1960; Minkowski E., 1970; Blankenburg W, 1971; Kimura Bin, 1985; K. Jaspers, 1997; ELVEVÅG et al., 2003). More recently, the “cognitive dysmetria theory” postulated that cognitive and behavioral symptoms of schizophrenia might be underpinned by the alteration of the cerebello-thalamo-cortical-circuit, well known for its implication in temporal processes; this pattern of dysconnectivity would lead to the loss of the fluidity and temporal sequence of thoughts and actions characterizing “normal cognition and behavior” (Andreasen et al., 1998; Cao and Cannon, 2019; Escelsior et al., 2019).

Consistent with these theories, recent studies have confirmed that schizophrenia is associated with a wide range of difficulties in time measurement, including those related to time perception and time production (Carroll et al., 2009a, 2009b, 2009c; Bolbecker et al., 2014). Stanghellini and colleagues reported that the fragmentation of time experience (disruption of time flowing, déjà vu/vecu, premonitions about oneself and the external world) specifically characterizes anomalous time experience (ATE) in schizophrenia (Stanghellini et al., 2015). A recent meta-analysis showed that the perception of time in schizophrenia might be prolonged or shortened compared to objective time; this finding may reflect a more variable internal clock in patients with schizophrenia (Thoenes and Oberfeld, 2017). Similarly, disturbances in temporal processes are also found in individuals with high schizotypy scores or diagnosed with a prodromal syndrome (i.e., clinical high risk; CHR) and may constitute a specific vulnerability trait (Penney et al., 2005; Lee et al., 2006; Reed and Randell, 2014). Consistent with this hypothesis, a recent study found that individuals at clinical high risk for psychosis had poorer temporal accuracy compared with control subjects, and the degree of temporal inaccuracy was associated with abnormal connectivity in the cerebellar circuitry (Osborne et al., 2021).

Neurobiology of Time Perception

Time perception is thought to result from a network-like activity rather than the consequence of a single area’s activity (Droit-Volet and Meck, 2007; Gómez et al., 2014; Hass and Durstewitz, 2016). The neuronal processes of different temporal scales depend on the differential recruitment of a distributed brain network (Paton and Buonomano, 2018) that permits rapid adaptations of an organism to environmental temporal characteristics, thus modeling cognitive functions and motor activity (Praamstra, 2006; Avanzino et al., 2016). Two detailed reviews highlight the role of cortical activity localizing it in specific areas such as the dorsolateral prefrontal cortex (dlPFC), supplementary motor area (SMA), and right inferior frontal cortex (IFC) (Droit-Volet et al., 2013; Lake et al., 2016). Other brain areas involved in time perception are the cerebellum, left inferior insula, left putamen, and hippocampus (Droit-Volet et al., 2013; Gómez et al., 2014; Thoenes and Oberfeld, 2017; Ptacek et al., 2019). Time processing seems to rely on different areas or networks depending on the different durations, with cerebello-thalamo-cortical circuit mainly involved in sub-second intervals and cortico-striatal circuitry mainly involved in second-to-minute intervals processing (Schubotz et al., 2000; Pouthas and Macar, 2005; Wiener et al., 2010; van Wassenhove et al., 2011; Carvalho et al., 2016; Lošák et al., 2016; Apaydın et al., 2018; Mioni et al., 2020). The perception of time involves brain areas crucial for motor behavior. Therefore, damage in regions such as the cerebellum and basal ganglia can affect motor and perceptual timing, thus proving that spatial and temporal processing work together to shape accurate actions, thoughts, or emotions in specific temporal windows (Atakan, 2012).

Several neurotransmitter systems participate in that function, especially the dopaminergic (Droit-Volet et al., 2013; Gómez et al., 2014; Wiener et al., 2014a; Hass and Durstewitz, 2016; Lake et al., 2016; Thoenes and Oberfeld, 2017). Not surprisingly, among drugs, those affecting the dopaminergic activity seem to influence timing: particularly, dopamine-antagonists like first-generation antipsychotics (i.e., haloperidol) decrease the inner clock speed, while dopamine-agonists, including amphetamine, cocaine, or cannabis, increase it (Rammsayer and Vogel, 1992). Other examples of dopamine involvement in time perception come from studies on neurological conditions, such as Parkinson’s disease and Huntington’s chorea, which can alter time perception (O’Boyle et al., 1996; Rowe et al., 2010), as well as from pharmacological studies which reported improvements in time perception after tolcapone (COMT inhibitor that induces an increased dopaminergic tone in the PFC) administration (Mitchell et al., 2018) or genetic studies that reported a worsening in time perception performances associated to D2/ANKK1-Taq1a A1 polymorphism (Wiener et al., 2014b).

Aims and Objectives

Unlike the well-recognized role of cannabis exposure in promoting the onset of psychiatric symptoms and illnesses such as psychotic disorders, the field of cannabis-induced alterations of temporal perception and their psychopathological, cognitive, and behavioral correlates is still poorly characterized. Considering that distortions of time perception have been suggested to be a fundamental alteration linked to psychotic disorders, this systematic review aims to reassume and present the current evidence concerning the alteration of time perception induced by cannabis exposure and their cognitive, psychopathological, and neurobiological correlates.

Methods

Data Sources

The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were followed for the present review (Page et al., 2021). A literature search on PubMed, Cochrane Library, and Embase with no year restriction was conducted on April 3, 2023. We searched for a combination of the following search string: “cannabi* AND (temporality OR “time estimation” OR “time perception” OR “time processing” OR timing OR “interval timing” OR “motor timing” OR “perceptual timing” OR “implicit timing” OR “explicit timing”).”

Eligibility Criteria

We sought to include all studies (1) written in English, (2) with an experimental or observational design, and (3) directly investigating the effects of cannabis on timing behavior in clinical and preclinical samples. Since the number/type of studies proved insufficient to conduct a meta-analysis, we extracted and tabulated data relative to sample characteristics, study design, type of intervention, outcome measures, and main findings. We excluded from data collection: (1) reviews and meta-analyses and (2) case reports. Titles and abstracts were screened for inclusion by two blinded researchers (A.E. and M.B.M.), who determined if the studies met the inclusion criteria. Manuscripts were assessed independently by the two researchers, and discrepancies were clarified by consent in a meeting with another researcher (G.S.). Firstly, the researchers screened articles by title and abstract and after by full text. Duplicates, review articles, and articles not fulfilling the search criteria were removed.

Results

Study Selection

Our search strategy yielded 1332 studies, out of which 38 satisfied our inclusion criteria, and full texts were screened (Fig. 2).

Fig. 2
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Flow chart

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Study Characteristics

Table 1 reports study characteristics. Of these, 11 articles were about experiments conducted on animals (rats, mice, hamsters, rabbits, chimpanzees) (Carlini et al., 1974; Han and Robinson, 2001; Crystal et al., 2003; de Solages et al., 2008; Pietr et al., 2010; Sanford et al., 2008; Steinmetz and Freeman, 2010; Oleson et al., 2014). The remainder of the studies on human subjects included 3743 cannabis users, 1121 individuals who do not use cannabis, 68 occasional or non-smoking cannabis users, and 10 former cannabis users.

Table 1 Cognitive, psychopathological, and neurobiological correlates of time alterations in cannabis users
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Study designs were the following: 37 studies had an experimental design, and one was observational (anonymous online survey) (Cuttler, Mischley, and Sexton 2016).

In most studies, marijuana cigarettes with different levels of Δ9-THC were used, although in some studies, CBD was assessed. Most of the studies did not consider the differences among the ways of administration: most of the studies used inhalation, others oral administration or i.v. administration. Placebo and alcohol were mainly used in controlled trials. A double-blind method was used in 9 of the studies. In clinical samples, most of the subjects involved were male healthy volunteers with cannabis frequency use going from occasional to regular.

Cognitive Assessments

Cognitive functions were assessed with Wechsler adult intelligence scale (WAIS) (Dornbush and Kokkevi, 1976; Mendhiratta et al., 1978), WAIS-revised (WAIS-R) (OʼLeary et al., 2003a), American National Adult Reading Test (ANART) (Boggs et al., 2018), national adult reading test (NART) (Sewell et al., 2013a), word association test (Borg et al., 1975), Cambridge Neuropsychological Test Automated Battery Rapid Visual Processing (CANTAB-RVP) (Boggs et al., 2018), Cambridge Neuropsychological Test Automated Battery Motor Screening Task (CANTAB-MOT) (Boggs et al., 2018), and full scale IQ (FSIQ) (Takagi et al., 2011).

Time Processing Assessments

The most common research modality we found assesses “interval timing,” which refers to perception and behavioral control with respect to time in the seconds to minutes range (Matell and McGovern, 2018). Most research on the effects of cannabis on timing has involved timing intervals using three different methods: (1) in “time estimation” tasks, the subject is asked to estimate how long an interval is (for example, 7 s); (2) in “time production” tasks, the subject is asked to produce the interval set by the experimenter (for example, 7 s); (3) in “time reproduction” tasks, the subject is asked to reproduce the interval presented by the interviewer (for example, counting out 7 s).

Time processing was assessed with eStroop task (Asmaro et al., 2014), standard Stroop task (TAKAGI et al., 2011), Go-NoGo task (TAKAGI et al., 2011), car simulator task (Bech et al., 1973), Grooved Pegboard (Boggs et al., 2018), paced fingertapping task (Boggs et al., 2018), simple reaction time task (SRT) (Borg et al., 1975), complex reaction time task (CRT) (Borg et al., 1975), time estimation task (TE) (Weil et al., 1979; Jones and Stone, 1970; Dornbush et al., 1971; Vachon et al., 1974; Borg et al., 1975; Dornbush and Kokkevi, 1976; Soueif, 1976; Han and Robinson, 2001; Wenzel and Cheer, 2014), time production task (TPT) (Carlini et al., 1974; Borg et al., 1975; Tinklenberg et al., 1976), corneal reflex (Carlini et al., 1974), rope climbing (Carlini et al., 1974), goal-directed serial alternation (Casswell and Marks 1973), typing (Chopra, 1973), A-B interresponse (Conrad et al., 1979), reaction time task (RT) (Dornbush et al., 1971; Mendhiratta et al., 1978), time perception task (TP) (Mendhiratta et al., 1978; Heishman et al., 1997), whisking task (Pietr et al., 2010), fixed consecutive number task (McLaughlin et al., 2017), temporal integration inventory (TII) (Melges, 1970), temporal extension inventory (Melges, 1971), pencil tapping task (Mendhiratta et al., 1978), speed and accuracy test (Mendhiratta et al., 1978), conditioned eyeblink response (Steinmetz and Freeman, 2010; Steinmetz et al., 2012a), fixed interval schedules (Oleson et al., 2014), continuous performance test (CPT) (Vachon et al., 1974), automated digit symbol substitution test (ADSST) (Vachon et al., 1974), digit symbol substitution test (DSST) (Weil et al., 1979; Jones and Stone, 1970; Borg et al., 1975).

Symptom Assessment

Psychiatric symptoms were assessed with Likert scale (Asmaro et al., 2014), Beck depression inventory version II (BDI-II) (Asmaro et al., 2014), state-trait anxiety inventory (STAI) (Asmaro et al., 2014), Structured Clinical Interview for DSM-IV Axis I (SCID-I) (Steinmetz et al., 2012a; Boggs et al., 2018), Structured Clinical Interview for DSM-IV Axis II (SCID-II) (Melges, 1970, 1971; Steinmetz et al., 2012a), Dixon depersonalization index (DDI) (Melges, 1970), Nowlis-Green mood adjective check list (MACL) (Melges, 1970, 1971), Maudsley personality inventory short scale (MPI) (Mendhiratta et al., 1978), Child Behavior Check List Youth Self Report (CBCL-YSR) (Takagi et al., 2011), and Positive and Negative Affect Schedule (PANAS) (Takagi et al., 2011).

Neuroimaging Assessment

O’Leary and colleagues used water-PET (OʼLeary et al., 2003b), whereas Klumpers et al. used RS-fMRI (Klumpers et al., 2012).

Effects of Cannabis on Time Perception in Preclinical Models

In their study, Carlini and colleagues found that, depending on its pharmacological potency, marijuana determines different levels of corneal areflexia, reduction of motor activity, and catatonic behaviors in rabbits (Carlini et al., 1974). Pietr and colleagues, while studying the effects of cannabinoids on whisking in rats, found that channels controlling amplitude, but not timing, were modulated by CB1 receptor (Pietr et al., 2010). Acuna-Goycolea, Obrietan, and van den Pol found that, besides the motor alteration, cannabinoid receptor activation markedly alters the capacity of light to entrain the core clock timing process. The release of endogenous cannabinoids (endocannabinoids) influences clock phasing in dark-adapted mice (Acuna-Goycolea et al., 2010). These results were replicated in hamsters (Sanford et al., 2008). Considering the alterations of motor and circadian responses induced by cannabis administration, it is unsurprising that different authors reported alterations of time perceptions in preclinical models. In this framework, an early study by Conrad and colleagues described a dose-dependent time overestimation induced by Δ9-THC oral administration in a population of chimpanzees with highly efficient and stable schedule-controlled timing behavior (Conrad et al., 1979). Similarly, in rats, the administration of WIN55,212-2, a CB1 receptor agonist, induced dose-related decreases in sensitivity to time (Crystal et al., 2003) and in conditioned blink response amplitude (Steinmetz and Freeman 2010). Opposite results of shortened and lengthened time responses were obtained respectively after administering CB1 receptor agonist or antagonist in rats (Han and Robinson, 2001). Similarly, McLaughlin et al. showed that the blockage of the CB1 receptor-induced underestimation of time perception (McLaughlin et al., 2017), whereas Oleson et al. demonstrated that CB1 receptor agonist-induced a dose-dependent increase in dopamine release and accelerated temporal processing in mice (Oleson et al., 2014). Using a differential reinforcement of low rate (DRL) task in rats, Wan-Ting Liao and colleagues demonstrated that activation of CB1 receptors in the medial entorhinal cortex (MEC) disrupts the functional connectivity between the hippocampal CA1 region and MEC, essential for the transmission of temporal-associated information (Liao et al., 2020).

Effects of Cannabis on Time Perception in Clinical Samples

In a PET study, O’Leary and colleagues assessed brain perfusion during an internal timing task before and after smoking marijuana and placebo cigarettes in 12 recreational cannabis users compared with a group of 12 daily users. After smoking cannabis, counting rates and self-paced tapping increased in both groups. This was also associated with increased blood flow in the cerebellum. This finding may indicate the existence of a single internal clock in the cerebellum that controls different activities, such as counting, and that this clock is affected by cannabis use (OʼLeary et al., 2003b). In an RS-fMRI study on healthy volunteers, Klumpers and colleagues found that Δ9-THC administration determines both increases and decreases in functional brain connectivity in brain regions with high densities of CB1 receptors, such as cerebellum and dorsal frontal cortical regions. These regions may be linked to Δ9-THC-induced CNS effects, such as postural stability, feeling high, and altered time perception (Klumpers et al., 2012).

In a study comparing the effects of alcohol and cannabis on the estimation of time and distance, Δ9-THC showed a more marked effect than alcohol on time estimation in a dose-dependent way. Specifically, the subjective estimation worsened further than the objective measurement (Bech et al., 1973).

Similarly, Carlini et al. compared the cognitive and behavioral effects of three samples of Brazilian marihuana with different ∆9-THC content, finding that the higher the Δ9-THC content, the higher the disruption of the time production task was (Carlini et al., 1974). In a study on recreational cannabis users, McDonald and colleagues found that, in a time estimation task, Δ9-THC increased estimates of the duration of short intervals while not affecting estimates of longer intervals (McDonald et al., 2003).

Boggs and colleagues found that the acute i.v. administration of Δ9-THC led to significant deficits in fine motor performance and timing, while gross motor performance was unaltered (Boggs et al., 2018). Other authors tested simple and complex reaction time, time estimation, and time production and found that cannabis significantly worsened subjects’ performances in all test scores (Casswell and Marks, 1973; Borg et al., 1975). Similarly, subjects that smoked cannabis took more time to complete a typing task (Chopra, 1973). Dornbush and colleagues produced two articles about the association between Δ9-THC and time: in the first one, they found that auditory or visual reaction time was positively related to Δ9-THC dose, while time estimation was unaffected (Dornbush et al., 1971); in the second study, they noticed a significant overestimation in a time estimation task (Dornbush and Kokkevi, 1976). Likewise, another article reported time overestimation in time estimation but not in time production tasks, although experienced users showed some sort of pharmacologic sensitization (Jones and Stone, 1970). Moreover, in one study, subjects had to judge time by duration production after smoking cigarettes with or without Δ9-THC; Δ9-THC increased the subjective time rate (Hicks et al., 1984a). In a study comparing the effects of Δ9-THC and cannabinol (CBN) on time perception and heart rate, Karniol and colleagues found that Δ9-THC produced increased heart rate and time underestimation, while CBN had no effect. In combination, the two drugs produced no further effect on heart rate; on the contrary, they induced significant overestimates and underestimates of the passage of 1 min (Karniol et al., 1975). Interestingly, Melges et al. reported that Δ9-THC impaired the results of cognitive tasks by causing, in a dose-dependent way, “temporal disintegration”: this expression refers to the difficulty of the individual in retaining, keeping track of, and sequencing memories, perceptions, and expectations that are relevant to his goals. Subjectively, this state is perceived as confusion of past, present, and future, thus contributing to the sensation of the self as unreal or weird typical of depersonalization, another common symptom of cannabis intoxication (Melges, 1970). Casswell and colleagues compared cannabis-experienced and naïve smokers: a temporal disintegration following cannabis abuse was found in both groups (Casswell and Marks, 1973). In another study on that topic, subjects exposed to oral marijuana reported in a time concentration inventory a discontinuous subjective experience of time passing with an overfocus on to present and a foreshortening of the awareness span into the future (Melges, 1971).

In contrast, other results reported that cannabis users were significantly more oriented toward the past (King and Manaster, 1975). Mendhiratta et al. found that subjects who smoked cannabis scored more poorly in reaction time and time perception tests (Mendhiratta et al., 1978). Similarly, another article reported that the current cannabis consumption impaired both the acquisition and the timing of the conditioned eyeblink reflex and that former cannabis utilizers still had shorter latencies. Thus, it has been demonstrated that heavy cannabis use negatively influences timing-related synaptic plasticity within the cerebellum (Steinmetz et al., 2012a). Sewell et al. reported that cannabis intake induced time overestimation and underproduction in a non-dose-dependent correlation; moreover, like Jones and colleagues previously reported, the authors found that chronic cannabis consumption did not alter baseline time perception (Sewell et al., 2013b). Previously, another paper found that cannabis use induced a significant time underproduction, corresponding to the increase in heart rate and the onset of subjective feelings of drug effects (Tinklenberg et al., 1976). These findings may indicate that Δ9-THC causes an increase in the internal clock speed. Likewise, Vachon and colleagues assessed a shorter time production due to Δ9-THC use (Vachon et al., 1974). Weil and colleagues reported increased time estimations after cannabis administration (Weil et al., 1979). Another article evidenced that cannabis use had a significant impact on every speed task and initial reaction time (Soueif, 1976). In contrast, Takagi and colleagues found no difference in reaction time between cannabis and a control group (Takagi et al., 2011). Similarly, in another study comparing the effects of alcohol and smoked marijuana effects, it was found that none of the drugs affected time perception and reaction time tests (Heishman et al., 1997).

Interestingly, indomethacin pre-treatment abolished the profound effect of Δ9-THC on time estimation and production, suggesting a role of prostaglandins in the neurophysiologic mechanisms responsible for some of the typical effects of Δ9-THC, particularly the alteration of time perception (Perez-Reyes et al., 1991a). Finally, in a study conducted through an online survey addressed to cannabis users, men participants reported a subjective alteration in time perception more frequently than women (Cuttler et al., 2016).

Discussion

Since the 1970s, studies have shown that cannabis can modify the experience of time, a very important part of how we experience the world. By modifying the perception of time, cannabis may elicit various forms of psychopathology. We reviewed the existing literature on the effects of cannabis use on time perception.

We found that cannabis use is associated with a distorted sense of time, and this alteration is strictly linked with impaired short-term memory, motor behavior, and decision-making. Studies evaluating time estimation evidence that cannabis intoxication, both in clinical and preclinical models, mainly conducts to time overestimation in a dose-dependent way. Otherwise, results on time production and reproduction failed to converge on homogeneous evidence. These latter inconclusive results may be attributed to the significant limitations of most of the studies. Some limitations specifically regard the study populations: in fact, most of the studies have small sample sizes; moreover, being participants mainly men volunteers, gender differences in cannabis-induced symptoms were not evaluated (Pattij et al., 2008; Prieto-Arenas et al., 2022); some studies included chronic cannabis users, not considering the potential impact of factors such as CB1 downregulation and tolerance (Weil et al., 1979; Dornbush and Kokkevi, 1976; Heishman et al., 1997; OʼLeary et al., 2003b), while others did not assess previous exposure (Dornbush et al., 1971; Karniol et al., 1975; Hicks et al., 1984b; Perez-Reyes et al., 1991b; McDonald et al., 2003).

In many studies, the pharmacokinetics of cannabinoids are not taken into account: despite the well-known nonlinear and biphasic effects of cannabinoids, in most of the research, only one dose was administered to participants (Jones and Stone, 1970; Vachon et al., 1974; Tinklenberg et al., 1976; Hicks et al., 1984b; Heishman et al., 1997; OʼLeary et al., 2003b). Notwithstanding, studies used different ways of administration, not considering that both inhalation and oral administration are associated with individual and intra-individual differences in pharmacokinetics. For example, oral administration is associated with lower pick plasma levels and protracted Δ9-THC effects compared to inhaled or i.v. administration (Ohlsson et al., 1980).

Other limitations concern timing tasks: time perception was evaluated only in a few studies using time estimation and time production tasks (Jones and Stone, 1970; Perez-Reyes et al., 1991a). Additionally, only a few studies used the prevention of “internal counting” techniques, which are helpful in increasing the accuracy of timing tasks (Weil et al., 1979; McDonald et al., 2003). Another considerable limit is the inability to discriminate, through simple timing tasks, the effects of Δ9-THC on clock, memory, and decision phases of temporal discrimination. Indeed, motor timing and time estimation may be indivisible functions, as they mainly involve the same brain regions (Ranganathan and D’Souza, 2006). Differentiating temporal stimuli may be necessary to disentangle the Δ9-THC effects on timing from those on memory and decision-making.

Unfortunately, only scarce literature explored the linkage between the cannabis-induced alterations of time processing and the emergence of specific psychiatric symptoms, such as depersonalization or psychosis (Melges, 1970).

Impairment in time processing seems to involve brain networks and areas rich in CB1 receptors, known to be implicated in the pathogenesis of schizophrenia. There is evidence that the endocannabinoid system is a widespread system implicated in executive functions by modulating dopaminergic and glutamatergic transmission on cortical and striatal networks (Pattij et al., 2008). Furthermore, it has been proposed that the interaction between the endocannabinoid system and DA receptors may indirectly influence time perception. Indeed, DA transmission has been linked to the speed of the internal clock (Cheng et al., 2016), with DA receptor agonists speeding it up and DA receptor antagonists slowing it down (Frederick, 1996; Meck, 2006). It has been found that cannabinoids induce an augmentation in dopamine concentrations within the striatum via CB1 and CB2 receptors influencing time perception (Oleson et al., 2014; Wenzel and Cheer, 2014). As previously pointed out, cannabis users and schizophrenia patients share the tendency to overestimate time intervals, probably induced by dopamine-related accelerated time procession (Droit-Volet and Meck, 2007). Acceleration of the internal clock has been related to the hypervigilant state typical of the delusional mood and the productive symptoms of psychosis (Peterburs et al., 2013; Lošák et al., 2016). Hypervigilance would lead to the “aberrant salience” phenomenon by inducing cognitive and perceptual biases in interpreting external stimuli.

In conclusion, further studies about the effects of cannabis on time perception and executive and cognitive functions are needed to understand better the neurobiology of timing and its implication in debilitating disorders such as schizophrenia. To this purpose, it would be worth studying how exactly dopamine exerts its actions in altering time perception, knowing which step is affected.

Moreover, cannabis-related timing alterations seem to be associated with alterations of synaptic plasticity (Steinmetz et al., 2012b), activity (Klumpers et al., 2012), and metabolism (OʼLeary et al., 2003) of the cerebellum. Given its widely recognized role both in schizophrenia and sub-second time perception, we believe it would be essential to investigate the role of the cerebello-thalamo-cortical circuit in temporal alterations, particularly in cannabis-induced psychosis.

These goals would not only be of significant theoretical value but would pave the way for new therapeutic strategies for treating schizophrenia based on the modulation of the endocannabinoid system.