Behavioral neuroscience最新文献

The ability to remember sequences of events is fundamental to episodic memory. While rodent studies have examined sex and estrous cycle in episodic-like spatial memory tasks, little is known about these biological variables in memory for sequences of events that depend on representations of temporal context. We investigated the role of sex and estrous cycle in rats during training and testing stages of a cross-species validated sequence memory task (Jayachandran et al., 2019). Rats were trained on a two four-odor sequence memory task delivered on opposite ends of a linear track. Training occurred in six successive stages starting with learning to poke in a nose-port for ≥ 1.2 s; eventually demonstrating sequence memory by holding their nose in the port ≥ 1 s for in-sequence odors and < 1 s for out-of-sequence odors. Performance was analyzed across sex and estrous cycle (proestrus, estrus, metestrus, and diestrus), the latter being determined by cellular composition of a daily vaginal lavage. We found no evidence of sex differences in asymptotic sequence memory performance, similar to humans performing an analogous task (Reeders et al., 2021). Likewise, no differences in sequence memory performance were found across the estrous cycle. Some caveats are that males acquired out-of-sequence trials faster during training with a 3-odor sequence, but this apparent advantage did not carry over to the 4-odor sequence. Additionally, males had shorter poke times overall which seem consistent with a decreased overall response inhibition because they occurred regardless of sequence demands. Together, these results suggest sex and estrous cycle are not major factors in sequence memory capacities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Recent primate studies suggest a potential link between pupil size and subjectively elapsed duration. Here, we sought to investigate the relationship between pupil size and perceived duration in human participants performing two temporal bisection tasks in the subsecond and suprasecond interval ranges. In the subsecond task, pupil diameter was greater during stimulus processing when shorter intervals were overestimated but also during and after stimulus offset when longer intervals were underestimated. By contrast, in the suprasecond task, larger pupil diameter was observed only in the late stimulus offset phase prior to response prompts when longer intervals were underestimated. This pattern of results suggests that pupil diameter relates to an error monitoring mechanism in interval timing. These results are at odds with a direct relationship between pupil size and the perception of duration but suggest that pupillometric variation might play a key role in signifying errors related to temporal judgments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

There has been a long-standing debate on where on the time axis the transition between time perception and time estimation (i.e., the cognitive reconstruction of time) can be located. According to Fraisse (1984), time perception applies to intervals < 300 ms, whereas intervals > 1 s are subject to time estimation. While there is good empirical evidence for this notion, it might be possible to further pinpoint the threshold. In two experiments, an auditory temporal generalization (TG) task in the range of 400 ms was used to compare event-related potentials (ERPs) with findings from an analogous task using standard durations in the range of 200 ms. As an ERP correlate of actively processed durations around 400 ms, offset latency of a medial central/centroparietal contingent negative variation (CNV) was identified. Thus, durations of around 400 ms may be coded as the duration of mental processes and, hence, are cognitively reconstructed (time estimation). This contrasts with again replicated ERP correlates of TG in the 200-ms range, which involve amplitude modulations of stationary P300/P500 components and suggest an immediate evaluation of durations around 200 ms. It is concluded that the P300 span may denote the transition between time perception and time estimation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The behavioral and neural mechanisms by which distracters delay interval timing behavior are currently unclear. Distracters delay timing in a considerable dynamic range: Some distracters have no effect on timing ("run"), whereas others seem to "stop" timing; some distracters restart ("reset") the entire timing mechanisms at their offset, whereas others seem to capture attentional resources long after their termination ("over-reset"). While the run-reset range of delays is accounted for by the Time-Sharing Hypothesis (Buhusi, 2003, 2012), the behavioral and neural mechanisms of "over-resetting" are currently uncertain. We investigated the role of novelty (novel/familiar) and significance (consequential/inconsequential) in the time-delaying effect of distracters and the role of medial prefrontal cortex (mPFC) catecholamines by local infusion of norepinephrine-dopamine reuptake inhibitor (NDRI) nomifensine in a peak-interval (PI) procedure in rats. Results indicate differences in time delay between groups, suggesting a role for both novelty and significance: inconsequential, familiar distracters "stopped" timing, novel distracters "reset" timing, whereas appetitively conditioned distracters "over-reset" timing. mPFC infusion of nomifensine modulated attentional capture by appetitive distracters in a "U"-shaped fashion, reduced the delay after novel distracters, but had no effects after inconsequential, familiar distracters. These results were not due to nomifensine affecting either timing accuracy, precision, or peak response rate. Results may help elucidate the behavioral and physiological mechanisms underlying interval timing and attention to time and may contribute to developing new treatment strategies for disorders of attention. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The involvement of the cerebellum in suprasecond interval timing (i.e., timing in the seconds to minutes range) is controversial. A limited amount of evidence from humans, nonhuman primates, and rodents has shown that the lateral cerebellum, including the lateral cerebellar nucleus (LCN), may be necessary for successful suprasecond timing performance. However, many existing studies have pitfalls, such as limited timing outcome measures and confounded task demands. In addition, many existing studies relied on well-trained subjects. This approach may be a drawback, as the cerebellum is hypothesized to carry out ongoing error correction to limit timing variability. By using only experienced subjects, past timing studies may have missed a critical window of cerebellar involvement. In the experiments described here, we pharmacologically inactivated the rat LCN across three different peak interval timing tasks. We structured our tasks to address past confounds, collect timing variability measures, and characterize performance during target duration acquisition. Across these various tasks, we did not find strong support for cerebellar involvement in suprasecond interval timing. Our findings support the existing distinction of the cerebellum as a subsecond interval timing brain region. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Sensory perception, motor control, and cognition necessitate reliable timing in the range of milliseconds to seconds, which implies the existence of a highly accurate timing system. Yet, partly owing to the fact that temporal processing is modulated by contextual factors, perceived time is not isomorphic to physical time. Temporal estimates exhibit regression to the mean of an interval distribution (global context) and are also affected by preceding trials (local context). Recent Bayesian models of interval timing have provided important insights regarding these observations, but questions remain as to how exposure to past intervals shapes perceived time. In this article, we provide a brief overview of Bayesian models of interval timing and their contribution to current understanding of context effects. We then proceed to highlight recent developments in the field concerning precision weighting of Bayesian evidence in both healthy timing and disease and the neurophysiological and neurochemical signatures of timing prediction errors. We further aim to bring attention to current outstanding questions for Bayesian models of interval timing, such as the likelihood conceptualization. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

This special issue provides a representative snapshot of cutting-edge behavioral neuroscience research on sense of time, cognitive and behavioral functioning, and neural processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The role of dopamine (DA) as a reward prediction error (RPE) signal in reinforcement learning (RL) tasks has been well-established over the past decades. Recent work has shown that the RPE interpretation can also account for the effects of DA on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a readout of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of RL and interval timing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Animals routinely learn to associate environmental stimuli and self-generated actions with their outcomes such as rewards. One of the most popular theoretical models of such learning is the reinforcement learning (RL) framework. The simplest form of RL, model-free RL, is widely applied to explain animal behavior in numerous neuroscientific studies. More complex RL versions assume that animals build and store an explicit model of the world in memory. To apply these approaches to explain animal behavior, typical neuroscientific RL models make implicit assumptions about how real animals represent the passage of time. In this perspective, I explicitly list these assumptions and show that they have several problematic implications. I hope that the explicit discussion of these problems encourages the field to seriously examine the assumptions underlying timing and reinforcement learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).