Perceptual bias and response bias in temporal bisection

Raslear, Thomas G.

doi:10.3758/bf03207153

Cited by 42 publications

(35 citation statements)

References 17 publications

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“…Overall, the amount of bias manifested in choice behavior and the optimality of temporal decisions were highly modulated by the overall reinforcement rate. Taken together, our findings demonstrate the importance of non-temporal factors in guiding temporal decisions (see, for instance, Galtress and Kirkpatrick 2009;Raslear 1985;Stubbs 1976;Wearden 1992;Whitaker et al 2008) in a context where these modulations were necessary for adaptiveness. Further studies are needed to better characterize the precise nature of the interaction between probabilistic information and decision outputs as well as the latent processing dynamics.…”

Section: Discussionmentioning

confidence: 63%

Stimulus probability effects on temporal bisection performance of mice (Mus musculus)

Akdoğan

Balcı

2015

Anim Cogn

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In the temporal bisection task, participants classify experienced stimulus durations as short or long based on their temporal similarity to previously learned reference durations. Temporal decision making in this task should be influenced by the experienced probabilities of the reference durations for adaptiveness. In this study, we tested the temporal bisection performance of mice (Mus musculus) under different short and long reference duration probability conditions implemented across two experimental phases. In Phase 1, the proportion of reference durations (compared to probe durations) was 0.5, whereas in Phase 2 it was increased to 0.8 to further examine the adjustment of choice behavior with more frequent reference duration presentations (under higher reinforcement rate). Our findings suggest that mice developed adaptive biases in their choice behaviors. These adjustments in choice behavior were nearly optimal as the mice maximized their gain to a great extent which required them to monitor stimulus probabilities as well as the level of variability in their temporal judgments. We further found that short but not long categorization response times were sensitive to stimulus probability manipulations, which in turn suggests an asymmetry between short and long categorizations. Finally, we investigated the latent decision processes underlying the bias manifested in subjects' choice behavior within the diffusion model framework. Our results revealed that probabilistic information influenced the starting point and the rate of evidence accumulation process. Overall, the stimulus probability effects on choice behavior were modulated by the reinforcement rate. Our findings illustrate that mice can adapt their temporal behaviors with respect to the probabilistic contingencies in the environment.

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Section: Discussionmentioning

confidence: 63%

Stimulus probability effects on temporal bisection performance of mice (Mus musculus)

Akdoğan

Balcı

2015

Anim Cogn

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“…What is less clear is what the data say about the relation of subjective to objective time, or whether time is under-or over-estimated as opposed to veridically perceived. The same data could have arisen if β = 1 (i.e., μ(t) = t) and the assumption that S mp = μ(t mp ) is removed, implying that observers perceive duration veridically but for some reason they do not set the anchor at μ(t mp ) during the training phase (Raslear, 1985;Allan and Gerhardt, 2001;Allan, 2002a,b). And the same shift could have been caused also with μ(t) = t and by reinstating the assumption that S mp = μ(t mp ) if observers had a non-null indifference region (i.e., δ = 0) and responded with bias when undecided ( Figure 5).…”

Section: Summary and Discussion Of Single-presentation Methodsmentioning

confidence: 96%

The Role of Parametric Assumptions in Adaptive Bayesian Estimation.

Alcalá-Quintana

Garcı́a-Pérez

2004

Psychological Methods

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Time perception is studied with subjective or semi-objective psychophysical methods. With subjective methods, observers provide quantitative estimates of duration and data depict the psychophysical function relating subjective duration to objective duration. With semi-objective methods, observers provide categorical or comparative judgments of duration and data depict the psychometric function relating the probability of a certain judgment to objective duration. Both approaches are used to study whether subjective and objective time run at the same pace or whether time flies or slows down under certain conditions. We analyze theoretical aspects affecting the interpretation of data gathered with the most widely used semi-objective methods, including single-presentation and paired-comparison methods. For this purpose, a formal model of psychophysical performance is used in which subjective duration is represented via a psychophysical function and the scalar property. This provides the timing component of the model, which is invariant across methods. A decisional component that varies across methods reflects how observers use subjective durations to make judgments and give the responses requested under each method. Application of the model shows that psychometric functions in single-presentation methods are uninterpretable because the various influences on observed performance are inextricably confounded in the data. In contrast, data gathered with paired-comparison methods permit separating out those influences. Prevalent approaches to fitting psychometric functions to data are also discussed and shown to be inconsistent with widely accepted principles of time perception, implicitly assuming instead that subjective time equals objective time and that observed differences across conditions do not reflect differences in perceived duration but criterion shifts. These analyses prompt evidence-based recommendations for best methodological practice in studies on time perception.

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“…Bisiach, Ricci, Lualdi, and Colombo (1998) proposed a method to eliminate the influence of response bias from estimates obtained with the landmark task, but comments on it will be deferred to the “Discussion” section. Analogous considerations have led to questioning the interpretability of results obtained with the temporal bisection task (e.g., Allan, 2002; Raslear, 1985). …”

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confidence: 99%

The bisection point across variants of the task

Garcı́a-Pérez

Peli

2014

Atten Percept Psychophys

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Bisection tasks are used in research on normal space and time perception and to assess the perceptual distortions accompanying neurological disorders. Several variants of the bisection task are used, which often yield inconsistent results, prompting the question of which variant is most dependable and which results are to be trusted. We addressed this question using theoretical and experimental approaches. Theoretical performance in bisection tasks is derived from a general model of psychophysical performance that includes sensory components and decisional processes. The model predicts how performance should differ across variants of the task, even when the sensory component is fixed. To test these predictions, data were collected in a within-subjects study with several variants of a spatial bisection task, including a two-response variant in which observers indicated whether a line was transected to the right or left of the midpoint, a three-response variant (which included the additional option to respond “midpoint”), and a paired-comparison variant of the three-response format. The data supported the model predictions, revealing that estimated bisection points were least dependable with the two-response variant, because this format confounds perceptual and decisional influences. Only the three-response paired-comparison format can separate out these influences. Implications for research in basic and clinical fields are discussed.

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Perceptual bias and response bias in temporal bisection

Cited by 42 publications

References 17 publications

Stimulus probability effects on temporal bisection performance of mice (Mus musculus)

Stimulus probability effects on temporal bisection performance of mice (Mus musculus)

The Role of Parametric Assumptions in Adaptive Bayesian Estimation.

The bisection point across variants of the task

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