Relative versus absolute stimulus control in the temporal bisection task

Temporal encoding: Relative and absolute representations of time guide behavior

Temporal information-processing is critical for adaptive behavior and goal-directed action. It is thus crucial to understand how the temporal distance between behaviorally relevant events is encoded to guide behavior. However, research on temporal representations has yielded mixed findings as to whether organisms utilize relative versus absolute judgments of time intervals. To address this fundamental question about the timing mechanism, we tested mice in a duration discrimination procedure in which they learned to correctly categorize tones of different durations as short or long. After being trained on a pair of target intervals, the mice were transferred to conditions in which cue durations and corresponding response locations were systematically manipulated so that either the relative or absolute mapping remained constant. The findings indicate that transfer occurred most readily when relative relationships of durations and response locations were preserved. In contrast, when subjects had to re-map these relative relations, even when positive transfer initially occurred based on absolute mappings, their temporal discrimination performance was impaired, and they required extensive training to re-establish temporal control. These results demonstrate that mice can represent experienced durations both as having a certain magnitude (absolute representation) and as being shorter or longer of the two durations (an ordinal relation to other cue durations), with relational control having a more enduring influence in temporal discriminations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?

Timing underlies a variety of functions, from walking to perceiving causality. Neural timing models typically fall into one of two categories-"ramping" and "population-clock" theories. According to ramping models, individual neurons track time by gradually increasing or decreasing their activity as an event approaches. To time different intervals, ramping neurons adjust their slopes, ramping steeply for short intervals and vice versa. In contrast, according to "population-clock" models, multiple neurons track time as a group, and each neuron can fire nonlinearly. As each neuron changes its rate at each point in time, a distinct pattern of activity emerges across the population. To time different intervals, the brain learns the population patterns that coincide with key events. Both model categories have empirical support. However, they often differ in plausibility when applied to certain behavioral effects. Specifically, behavioral data indicate that the timing system has a rich computational capacity, allowing observers to spontaneously compute novel intervals from previously learned ones. In population-clock theories, population patterns map to time arbitrarily, making it difficult to explain how different patterns can be computationally combined. Ramping models are viewed as more plausible, assuming upstream circuits can set the slope of ramping neurons according to a given computation. Critically, recent studies suggest that neurons with nonlinear firing profiles often scale to time different intervals-compressing for shorter intervals and stretching for longer ones. This "temporal scaling" effect has led to a hybrid-theory where, like a population-clock model, population patterns encode time, yet like a ramping neuron adjusting its slope, the speed of each neuron's firing adapts to different intervals. Here, we argue that these "relative" population-clock models are as computationally plausible as ramping theories, viewing population-speed and ramp-slope adjustments as equivalent. Therefore, we view identifying these "speed-control" circuits as a key direction for evaluating how the timing system performs computations. Furthermore, temporal scaling highlights that a key distinction between different neural models is whether they propose an absolute or relative time-representation. However, we note that several behavioral studies suggest the brain processes both scales, cautioning against a dichotomy.

Unraveling sources of stimulus control in a temporal discrimination task

In temporal discriminations tasks, more than one stimulus may function as a time marker. We studied two of them in a matching-to-sample task, the sample keylight and the houselight that signaled the intertrial interval (ITI). One group of pigeons learned a symmetrical matching-to-sample task with two samples (2 s or 18 s of a center keylight) and two comparisons (red and green side keys), whereas another group of pigeons learned an asymmetrical matching-to-sample task with three samples (2 s, 6 s, and 18 s) and two comparisons (red and green). In the asymmetrical task, 6-s and 18-s samples shared the same comparison. In a subsequent retention test, both groups showed a preference for the comparison associated with the longer samples, a result consistent with the hypothesis that pigeons based their choices on the duration elapsed since the offset of the houselight (i.e., sample duration + retention interval). Results from two no-sample tests further corroborated the importance of the ITI illumination as a time marker: When the ITI was illuminated, the proportion of choices correlated positively with the retention interval; when the ITI was darkened, choices fell to random levels. However, the absolute value of choice proportions suggested that the sample stimulus was also a time marker. How multiple stimuli acquire control over behavior and how they combine remains to be worked out.

Binding time: Evidence for integration of temporal stimulus features

Several lines of evidence suggest that during processing of events, the features of these events become connected via episodic bindings. Such bindings have been demonstrated for a large number of visual and auditory stimulus features, like color and orientation, or pitch and loudness. Importantly, most visual and auditory events typically also involve temporal features, like onset time or duration. So far, however, whether temporal stimulus features are also bound into event representations has never been tested directly. The aim of the present study was to investigate possible binding between stimulus duration and other features of auditory events. In Experiment 1, participants had to respond with two keys to a low or high pitch sinus tone. Critically, the tones were presented with two different presentation durations. Sequential analysis of RT data indicated binding of stimulus duration into the event representation: at pitch repetitions, performance was better when both pitch and duration repeated, relative to when only pitch repeated and duration switched. This finding was replicated with loudness as relevant stimulus feature in Experiment 2. In sum, the results demonstrate that temporal features are bound into auditory event representations. This finding is an important advancement for binding theory in general, and raises several new questions for future research.

Time-based event expectations employ relative, not absolute, representations of time

When the timing of an event is predictable, humans automatically form implicit time-based event expectations. We investigated whether these expectations rely on absolute (e.g., 800 ms) or relative (e.g., a shorter duration) representations of time. In a choice-response task with two different pre-target intervals, participants implicitly learned that targets were predictable by interval durations. In a test phase, the two intervals were either considerably shortened or lengthened. In both cases, behavioral tendencies transferred from practice to test according to relative, not absolute, interval duration. We conclude that humans employ relative representations of time periods when forming time-based event expectations. These results suggest that learned time-based event expectations (e.g., in communication and human-machine interaction) should transfer to faster or slower environments if the relative temporal distribution of events is preserved.