Temporal binding and internal clocks: No evidence for general pacemaker slowing.

The perception of time is distorted by many factors (e.g., arousal, temperature, age etc.), but is it possible that causality would affect our perception of time? We investigate timing changes in the temporal binding effect, which refers to a subjective shortening of the interval between actions and their outcomes. Four experiments investigated whether binding may be due to variations in the rate of an internal clock. Specifically, we asked whether binding reflects changes to a general timing system, or a dedicated clock unique to causal sequences. We developed a novel experimental paradigm (embedded interval estimation procedure) in which participants made temporal judgments of either causal or non causal intervals, or the duration of an event embedded within that interval. Stimuli and modality were combined factorially, with interval markers and embedded events being either visual or auditory. While we replicated the temporal binding effect, we found no evidence for commensurate changes to time perception of the embedded event, which suggests that temporal binding is effected by changes to a specific and dedicated, rather than a general clock system

The role of time perception in temporal binding: Impaired temporal resolution in causal sequences

Causality affects our perception of time; events that appear as causally related are perceived as closer together in time than unrelated events. This effect is known as temporal binding. One potential explanation of this effect is that causality slows an “internal clock” that is used in interval estimation. To explore this hypothesis, we first examined participants’ perceived duration of a range of intervals between a causal action and an effect, or between two unrelated events. If (apparent) causality slows the internal clock, then plotting perceived duration against actual duration should reveal a shallower slope in the causality condition (a relative compression of perceived time). This pattern was found. We then examined an interesting corollary: that a slower rate during causal sequences would result in reduced temporal acuity. This is what we found: Duration discrimination thresholds were higher for causal compared to non-causal sequences. These results are compatible with a clock-slowing account of temporal binding. Implications for sensory recalibration accounts of binding are discussed

Temporal Binding, Causation, and Agency: Developing a New Theoretical Framework

In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency.” However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause–effect sequence. In those contexts, it has been interpreted as a top-down effect on perception reflecting a belief in causality. These two views need not be in conflict with one another, if one thinks of them as concerning two separate mechanisms through which temporal binding can occur. In this paper, we explore an alternative possibility: that there is a unitary way of explaining temporal binding both within and outside the context of voluntary action as a top-down effect on perception reflecting a belief in causality. Any such explanation needs to account for ways in which agency, and factors connected with agency, has been shown to affect the strength of temporal binding. We show that principles of causal inference and causal selection already familiar from the literature on causal learning have the potential to explain why the strength of people's causal beliefs can be affected by the extent to which they are themselves actively involved in bringing about events, thus in turn affecting binding

The developmental profile of temporal binding: From childhood to adulthood.

Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6–7, 7–8, and 9–10 years) and adults on a novel interval estimation task. In Experiment 1, participants made judgements about the time interval between (a) their button press and a rocket launch, and (b) a non-causal predictive signal and rocket launch. In Experiment 2, an additional causal condition was included in which participants made judgements about the interval between an experimenter’s button press and the launch of a rocket. Temporal binding was demonstrated consistently and did not change in magnitude with age: estimates of delay were shorter in causal contexts for both adults and children. In addition, the magnitude of the binding effect was greater when participants themselves were the cause of an outcome compared with when they were mere spectators. This suggests that although causality underlies the binding effect, intentional action may modulate its magnitude. Again, this was true of both adults and children. Taken together, these results are the first to suggest that the binding effect is present and developmentally constant from childhood into adulthood

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Time and Causality

The problem of how humans and other intelligent systems construct causal representations from non-causal perceptual evidence has occupied scholars in cognitive science for many decades. Most contemporary approaches agree with David Hume that patterns of covariation between two events of interest are the critical input to the causal induction engine, irrespective of whether this induction is believed to be grounded in the formation of associations (Shanks & Dickinson, 1987), rule-based evaluation (White, 2004), appraisal of causal powers (Cheng, 1997), or construction of Bayesian Causal Networks (Pearl, 2000). Recent research, however, has repeatedly demonstrated that an exclusive focus on covariation while neglecting contiguity (another of Hume's cues) results in ecologically invalid models of causal inference. Temporal spacing, order, variability, predictability, and patterning all have profound influence on the type of causal representation that is constructed. The influence of time upon causal representations could be seen as a bottom-up constraint (though current bottom-up models cannot account for the full spectrum of effects). However, causal representations in turn also constrain the perception of time: Put simply, two causally related events appear closer in subjective time than two (equidistant) unrelated events. This reversal of Hume's conjecture, referred to as Causal Binding (Buehner & Humphreys, 2009) is a top-down constraint, and suggests that our representations of time and causality are mutually influencing one another. At present, the theoretical implications of this phenomenon are not yet fully understood. Some accounts link it exclusively to human motor planning (appealing to mechanisms of cross-modal temporal adaptation, or forward learning models of motor control). However, recent demonstrations of causal binding in the absence of human action, and analogous binding effects in the visual spatial domain, challenge such accounts in favour of Bayesian Evidence Integration. This Research Topic reviews and further explores the nature of the mutual influence between time and causality, how causal knowledge is constructed in the context of time, and how it in turn shapes and alters our perception of time. We draw together literatures from the perception and cognitive science, as well as experimental and theoretical papers. Contributions investigate the neural bases of binding and causal learning/perception, methodological advances, and functional implications of causal learning and perception in real time

Awareness of voluntary and involuntary causal actions and their outcomes

This article revisits Haggard, Clark, and Kalogeras’s (2002) seminal discovery of temporal binding between intentional actions and their consequences, and repulsion between involuntary actions and subsequent events. Careful analysis of the original experimental set-up reveals a confound between agency and causality, questioning the validity of temporal binding as a measure of agency, particularly in light of recent research findings that temporal binding is rooted in causality rather than intentionality (Buehner & Humphreys, 2009; Buehner, 2012). An experiment that contrasts voluntary against involuntary actions, while preserving the causal nature of these actions, replicates the original temporal binding effect for voluntary causal actions and also finds a weak binding effect for involuntary causal actions, rather than repulsion as originally reported by Haggard et al. It appears that temporal repulsion between involuntary actions and subsequent events is ameliorated in the presence of a causal relationship and that experiencing unagentic movements dampens causal binding

Understanding the past, predicting the future: causation, not intentional action, is the root of temporal binding

Temporal binding refers to a subjective shortening of elapsed time between actions and their resultant consequences. Originally, it was thought that temporal binding is specific to motor learning and arises as a consequence of either sensory adaptation or the associative principles of the forward model of motor command. Both of these interpretations assume that the binding effect is rooted in the motor system and, critically, that it is driven by intentional action planning. The research reported here demonstrates that both intentional actions and mechanical causes result in temporal binding, which suggests that intentional action is not necessary for temporal binding and that binding results from the causal relation linking actions with their consequences. Intentional binding is thus a special case of more general causal binding, which can be explained by a theory of Bayesian ambiguity reductio