Base-rate sensitivity through implicit learning

Wismer, Andrew; Bohil, Corey J.

doi:10.1371/journal.pone.0179256

Cited by 6 publications

(4 citation statements)

References 42 publications

(41 reference statements)

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“…However, there are many everyday tasks in which the probability of a set of alternatives needs to be assessed based on one’s past experience with the outcomes of the task. The importance of experience has been demonstrated in studies examining differences between experience-based and description-based decisions [10, 11] and in perceptual-categorization tasks with unequal probability, in which response feedback leads to performance that is closer to optimal than observational feedback [12, 13]. While these studies demonstrate the importance of experience on decision-making behavior, they do not describe how experience influences expectation formation.…”

Section: Introductionmentioning

confidence: 99%

Human online adaptation to changes in prior probability

Norton

Acerbi

et al. 2019

PLoS Comput Biol

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Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure: probability was held constant for a period of time before jumping to another probability state that was randomly selected from a predetermined set of probability states. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies—from approximately Bayesian to simple heuristics—that the observers may have adopted to update their beliefs about probabilities. While a number of models provided decent fits to the data, model comparison favored a model in which probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias, and a flexible variant of the Bayesian change-point detection model with incorrect beliefs. We interpret the former as a simpler, more biologically plausible explanation suggesting that the mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales.

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Section: Introductionmentioning

confidence: 99%

Human online adaptation to changes in prior probability

Norton

Acerbi

et al. 2019

PLoS Comput Biol

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“…As such, it could explain the avoidance effect without categorisation at the superordinate level of human-made and natural. Given recent research has suggested that base-rate neglect is driven by explicit processes (Bohil & Wismer, 2015;Wismer & Bohil, 2017), it is likely that the effect seen here (if base-rate neglect is the correct explanation) would be sensitive to whether participants are learning word-location associations under conditions that preclude explicit awareness.…”

Section: Schema-irrelevant Information and Avoidancementioning

confidence: 79%

Schematic information influences memory and generalisation behaviour for schema-relevant and -irrelevant information

Cockcroft¹,

Berens²,

Gaskell³

et al. 2021

Preprint

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Schemas modulate memory performance for schema-congruent and -incongruent information. However, it is typically assumed they do not influence behaviour for information irrelevant to themselves. We assessed memory and generalisation behaviour for information related to an underlying pattern, where a schema could be extracted (schema-relevant), and information that was unrelated and therefore irrelevant to the extracted schema (schema-irrelevant). Using precision measures of long-term memory, where participants learnt associations between words and locations around a circle, we assessed memory and generalisation for schema-relevant and -irrelevant information. Words belonged to two semantic categories: human-made and natural. For one category, word-locations were clustered around one point on the circle (clustered condition), while the other category had word-locations randomly distributed (non-clustered condition). The presence of an underlying pattern in the clustered condition allows for the extraction of a schema that can support both memory and generalisation. At test, participants were presented with old (memory) and new (generalisation) words, requiring them to identify a remembered location or make a best guess. The presence of the clustered pattern modulated memory and generalisation. In the clustered condition, participants placed old and new words in locations consistent with the underlying pattern. In contrast, for the non-clustered condition, participants were less likely to place old and new non-clustered words in locations consistent with the clustered condition. We therefore provide evidence that the presence of schematic information modulates memory and generalisation for schema-relevant and -irrelevant information. Our results highlight the need to carefully construct appropriate schema-irrelevant control conditions such that behaviour in these conditions is not modulated by the presence of a schema. Theoretically, models of schema processing need to account for how the presence of schematic information can have consequences for information that is technically irrelevant to itself.

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“…In our research, we did not assume that the base-rate dependence requires an intentional or explicit learning of the base-rates; as a matter of fact, there is no reason to assume that base-rate sensitivity cannot be acquired from implicit learning (cf. Wismer and Bohil 2017 ). Moreover, at least in Experiment 1, the participants were instructed at study that one colour font is more frequent in a particular font size than in another, and they used this knowledge to inform their guessing strategy, in spite of the fact that they neglected the base-rates in their memory for context.…”

Section: Discussionmentioning

confidence: 98%

Does context recollection depend on the base-rate of contextual features?

Nieznański,

Obidziński,

Ford

2023

Cogn Process

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Episodic recollection is defined by the re-experiencing of contextual and target details of a past event. The base-rate dependency hypothesis assumes that the retrieval of one contextual feature from an integrated episodic trace cues the retrieval of another associated feature, and that the more often a particular configuration of features occurs, the more effective this mutual cueing will be. Alternatively, the conditional probability of one feature given another feature may be neglected in memory for contextual features since they are not directly bound to one another. Three conjoint recognition experiments investigated whether memory for context is sensitive to the base-rates of features. Participants studied frequent versus infrequent configurations of features and, during the test, they were asked to recognise one of these features with (vs. without) another feature reinstated. The results showed that the context recollection parameter, representing the re-experience of contextual features in the dual-recollection model, was higher for frequent than infrequent feature configurations only when the binding of feature information was made easier and the differences in the base-rates were extreme, otherwise no difference was found. Similarly, base-rates of features influenced response guessing only in the condition with salient differences in base-rates. The Bayes factor analyses showed that the evidence from two of our experiments favoured the base-rate neglect hypothesis over the base-rate dependency hypothesis; the opposite result was obtained in the third experiment, but only when high base-rate disproportion and facilitated feature binding conditions were used.

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Base-rate sensitivity through implicit learning

Cited by 6 publications

References 42 publications

Human online adaptation to changes in prior probability

Human online adaptation to changes in prior probability

Schematic information influences memory and generalisation behaviour for schema-relevant and -irrelevant information

Does context recollection depend on the base-rate of contextual features?

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