A Tale of Two Literatures: A Fidelity-Based Integration Account of Central Tendency Bias and Serial Dependency

Ke, Tong; Dubé, Chad

doi:10.1007/s42113-021-00123-0

Cited by 14 publications

(13 citation statements)

References 65 publications

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“…This study raises the question: does contraction bias occur independently of short-term history effects, or does it emerge as a result of the latter? Our model provides support for the second possibility, offering a parsimonious account of how contraction bias emerges [36].…”

Section: Discussionmentioning

confidence: 75%

“…It must be noted, however, that short-term history effects (due to firing rate adaptation) do not necessarily need to be invoked in order to recover contraction bias: as long as errors are made following random samples from a distribution in the same range as that of the stimuli, contraction bias should be observed [56]. Indeed, when we manipulated the parameters of the PPC network in such a way that short-term history effects were eliminated (by removing the firing-rate adaptation), contraction bias persisted.…”

Section: Discussionmentioning

confidence: 99%

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From recency to central tendency biases in working memory: a unifying network model

Boboeva

Pezzotta

Akrami

et al. 2022

Preprint

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The central tendency bias, or contraction bias is a phenomenon where the judgment of the magnitude of items held in working memory is biased towards the average of past observations. This phenomenon has been first described more than a century ago [1] and since then, has been replicated in various decision making tasks in humans [2–10], and rodents [11, 12]. Contraction bias is assumed to be an optimal strategy by the brain, given the noisy nature of working memory. From a Bayesian perspective [7], the progressive shift of the noisy memory towards the mean of a prior distribution built from past sensory experience helps with more accurate estimates of the memory. In this work, we propose an alternative account, via short-term history biases (serial dependence) [12–15]. Our model is motivated and inspired by recent results from an auditory delayed-discrimination task in rats, where the posterior parietal cortex (PPC) has been shown to be critical to these memory effects [12]. The dynamics of our model suggests that contraction bias can emerge as a result of a volatile working memory content which makes it susceptible to shifting to the past sensory experience. The errors, at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the distribution’s mean. Our model explains both short-term history biases, as well as contraction bias towards the sensory mean for the averaged performance. The results are consistent with the role of the PPC in encoding such sensory history biases, and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas.

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Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

From recency to central tendency biases in working memory: a unifying network model

Boboeva

Pezzotta

Akrami

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Moreover, these biases increase with the retention interval, and build up over the course of an experimental session. Our interpretation of serial dependence, which also builds up over the course of a session, suggests that these two phenomena may be linked (see also [62]).…”

Section: Resultsmentioning

confidence: 99%

Rate-distortion theory of neural coding and its implications for working memory

Jakob

Gershman

2022

Preprint

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Rate-distortion theory provides a powerful framework for understanding the nature of human memory by formalizing the relationship between information rate (the average number of bits per stimulus transmitted across the memory channel) and distortion (the cost of memory errors). Here we show how this abstract computational-level framework can be realized by a model of neural population coding. The model reproduces key regularities of visual working memory, including some that were not previously explained by population coding models. We verify a novel prediction of the model by reanalyzing recordings of monkey prefrontal neurons during an oculomotor delayed response task.

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“…The latter is known in literature as the central tendency bias (i.e., the contraction of perceptual judgments toward the mean, or the midpoint of the overall stimulus distribution), as was first described more than a century ago by Hollingworth (1910) . Although serial dependence and central tendency have generally been discussed separately (e.g., Crawford, Corbin, & Landy, 2019 ; Manassi et al, 2017 ; Son et al, 2021 ), they are statistically correlated in many cases as they contract perception toward the same direction (see Tong & Dubé, 2022 ). Whether these biases by priors relating to different time windows originate from different underlying mechanisms, as suggested by the observation that their relative contributions to perception differ in specific populations ( Lieder et al, 2019 ) or from a single mechanism ( Boboeva et al, 2022 ; Tong & Dubé, 2022 ), one needs to study whether they have different characteristics, weights, and impacts on behavior.…”

Section: Discussionmentioning

confidence: 99%

Perceptual history biases in serial ensemble representation

Khayat¹,

Ahissar

Hochstein³

2023

Journal of Vision

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Ensemble perception refers to the visual system's ability to efficiently represent groups of similar objects as a unified percept using their summary statistical information. Most studies focused on extraction of current trial averages, giving little attention to prior experience effects, although a few recent studies found that ensemble mean estimations contract toward previously presented stimuli, with most of these focusing on explicit perceptual averaging of simultaneously presented item ensembles. Yet, the time element is crucial in real dynamic environments, where we encounter ensemble items over time, aggregating information until reaching summary representations. Moreover, statistical information of objects and scenes is learned over time and often implicitly and then used for predictions that shape perception, promoting environmental stability. Therefore, we now focus on temporal aspects of ensemble statistics and test whether prior information, beyond the current trial, biases implicit perceptual decisions. We designed methods to separate current trial biases from those of previously seen trial ensembles. In each trial, six circles of different sizes were presented serially, followed by two test items. Participants were asked to choose which was present in the sequence. Participants unconsciously rely on ensemble statistics, choosing stimuli closer to the ensemble mean. To isolate the influence of earlier trials, the two test items were sometimes equidistant from the current trial mean. Results showed membership judgment biases toward current trial mean, when informative (largest effect). On equidistant trials, judgments were biased toward previously experienced stimulus statistics. Comparison of similar conditions with a shifted stimulus distribution ruled out a bias toward an earlier, presession, prototypical diameter. We conclude that ensemble perception, even for temporally experienced ensembles, is influenced not only by current trial mean but also by means of recently seen ensembles and that these influences are somewhat correlated on a participant-by-participant basis.

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A Tale of Two Literatures: A Fidelity-Based Integration Account of Central Tendency Bias and Serial Dependency

Cited by 14 publications

References 65 publications

From recency to central tendency biases in working memory: a unifying network model

From recency to central tendency biases in working memory: a unifying network model

Rate-distortion theory of neural coding and its implications for working memory

Perceptual history biases in serial ensemble representation

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