Constraints on Statistical Learning Across Species

Both human and nonhuman organisms are sensitive to statistical regularities in sensory inputs that support functions including communication, visual processing, and sequence learning. One of the issues faced by comparative research in this field is the lack of a comprehensive theory to explain the relevance of statistical learning across distinct ecological niches. In the current review we interpret cross-species research on statistical learning based on the perceptual and cognitive mechanisms that characterize the human and nonhuman models under investigation. Considering statistical learning as an essential part of the cognitive architecture of an animal will help to uncover the potential ecological functions of this powerful learning process.

Generation of Hypotheses in Wason's 2–4–6 Task: an Information Theory Approach

We explored the “context of discovery” in Wason's 2–4–6 task, focusing on how the first hypothesis is generated. According to Oaksford and Chater (1994a) people generate hypotheses extracting “common features”, or regularities, from the available triples, but their model does not explain why some regularities contribute to the hypothesis more than do other regularities. Our conjecture is that some regularities contribute to the hypothesis more than do other regularities because people estimate the amount of information in the perceived regularities and try to preserve as much information as possible in their initial hypotheses. Experiment 1, which used two initial triples, showed that the presence of high-information relational regularities in the initial triples affected the information in the initial hypotheses more than did the presence of low-information object regularities. Experiment 2 extended the results to the classic situation in which only one initial triple is given. It also suggested that amount of information is the only aspect of the structure of the triple that affects hypotheses generation. Experiment 3 confirmed the latter finding: Although relations are commonly distinguished between first-order and higher order relations, the latter being most important for generating hypotheses (Gentner, 1983), higher order relations do have an effect on Wason's 2–4–6 task only if their presence increases information. In the conclusion we discuss the statistical soundness of human hypotheses generation processes, and we ask an unanswered question: Amount of information explains why some regularities are preferred to others, but only within a set of “nonarbitrary” regularities; there are object regularities that are rich in information content, but are considered “arbitrary”, and are not used in generating hypotheses. Which formal property can distinguish between these two sets of regularities?

Causal and noncausal conditionals: An integrated model of interpretation and reasoning

We present an integrated model for the understanding of and the reasoning from conditional statements. Central assumptions from several approaches are integrated into a causal path model. According to the model, the cognitive availability of exceptions to a conditional reduces the subjective conditional probability of the consequent, given the antecedent. This conditional probability determines people's degree of belief in the conditional, which in turn affects their willingness to accept logically valid inferences. In addition to this indirect pathway, the model contains a direct pathway: Availability of exceptional situations directly reduces the endorsement of valid inferences. We tested the integrated model with three experiments using conditional statements embedded in pseudonaturalistic cover stories. An explicitly mentioned causal link between antecedent and consequent was either present (causal conditionals) or absent (arbitrary conditionals). The model was supported for the causal but not for the arbitrary conditional statements.

Statistical contingency has a different impact on preparation judgements than on causal judgements

Previous studies on causal learning showed that judgements about the causal effect of a cue on an outcome depend on the statistical contingency between the presence of the cue and the outcome. We demonstrate that statistical contingency has a different impact on preparation judgements (i.e., judgements about the usefulness of responses that allow one to prepare for the outcome). Our results suggest that preparation judgements primarily reflect information about the outcome in prior situations that are identical to the test situation. These findings also add to previous evidence showing that people can use contingency information in a flexible manner depending on the type of test question.

Working memory capacity and the construction of spatial mental models in comprehension and deductive reasoning

We asked 149 high-school students who were pretested for their working memory capacity (WMC) to read spatial descriptions relating to five objects and to evaluate conclusions asserting an unmentioned relationship between two of the objects. Unambiguous descriptions were compatible with a single spatial arrangement, whereas ambiguous descriptions permitted two arrangements; a subset of the ambiguous descriptions still determined the relation asserted in the conclusion, whereas another subset did not. Two groups of participants received different instructions: The deduction group should accept conclusions only if they followed with logical necessity from the description, whereas the comprehension group should accept a conclusion if it agreed with their representation of the arrangement. Self-paced reading times increased on sentences that introduced an ambiguity, replicating previous findings in deductive reasoning experiments. This effect was also found in the comprehension group, casting doubt on the interpretation that people consider multiple possible arrangements online. Responses to conclusions could be modelled by a multinomial processing model with four parameters: the probability of constructing a correct mental model, the probability of detecting an ambiguity, and two guessing parameters. Participants with high and with low WMC differed mainly in the probability of successfully constructing a mental model.

Categorical Reasoning from Multiple Diagrams

Syllogistic reasoning from categorical premise pairs is generally taken to be a multistep process. Quantifiers ( all, no, some, some …not) must be interpreted, representations constructed, and conclusions identified from these. Explanations of performance have been proposed in which errors may occur at any of these stages. The current paper contrasts (a) representation explanations of performance, in which errors occur because not all possible representations are constructed, and/or mistakes are made when doing so (e.g., mental models theory), and (b) conclusion identification explanations, in which errors occur even when information has been correctly and exhaustively represented, due to systematic difficulties that people may have when identifying particular conclusions, or in identifying conclusions in particular circumstances. Three experiments are reported, in which people identified valid conclusions from diagrams analogous to Euler circles, so that the first two stages of reasoning from premise pairs were effectively removed. Despite this, several phenomena associated with reasoning from premise pairs persisted, and it is suggested that whereas representation explanations may account for some of these phenomena, conclusion identification explanations, which have never previously been considered, are required for others.

Statistical Learning in Specific Language Impairment: A Meta-Analysis

PURPOSE

The current meta-analysis provides a quantitative overview of published and unpublished studies on statistical learning in the auditory verbal domain in people with and without specific language impairment (SLI). The database used for the meta-analysis is accessible online and open to updates (Community-Augmented Meta-Analysis), which facilitates the accumulation and evaluation of previous and future studies on statistical learning in this domain.

METHOD

A systematic literature search identified 10 unique experiments examining auditory verbal statistical learning in 213 participants with SLI and 363 without SLI, aged between 6 and 19 years. Data from qualifying studies were extracted and converted to Hedges' g effect sizes.

RESULTS

The overall standardized mean difference between participants with SLI and participants without SLI was 0.54, which was significantly different from 0 (p < .001, 95% confidence interval [0.36, 0.71]).

CONCLUSION

Together, the results of our meta-analysis indicate a robust difference between people with SLI and people without SLI in their detection of statistical regularities in the auditory input. The detection of statistical regularities is, on average, not as effective in people with SLI compared with people without SLI. The results of this meta-analysis are congruent with a statistical learning deficit hypothesis in SLI.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.5558074.

Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis

Research has not yet reached a consensus on why humans match probabilities instead of maximise in a probability learning task. The most influential explanation is that they search for patterns in the random sequence of outcomes. Other explanations, such as expectation matching, are plausible, but do not consider how reinforcement learning shapes people's choices. We aimed to quantify how human performance in a probability learning task is affected by pattern search and reinforcement learning. We collected behavioural data from 84 young adult participants who performed a probability learning task wherein the majority outcome was rewarded with 0.7 probability, and analysed the data using a reinforcement learning model that searches for patterns. Model simulations indicated that pattern search, exploration, recency (discounting early experiences), and forgetting may impair performance. Our analysis estimated that 85% (95% HDI [76, 94]) of participants searched for patterns and believed that each trial outcome depended on one or two previous ones. The estimated impact of pattern search on performance was, however, only 6%, while those of exploration and recency were 19% and 13% respectively. This suggests that probability matching is caused by uncertainty about how outcomes are generated, which leads to pattern search, exploration, and recency.

Contextual variability and exemplar strength in phonotactic learning

Phonotactics-constraints on the position and combination of speech sounds within syllables-are subject to statistical differences that gradiently affect speaker and listener behavior (e.g., Vitevitch & Luce, 1999). What statistical properties drive the acquisition of such constraints? Because they are naturally highly correlated, previous work has been unable to dissociate the contribution of 2 properties: contextual variability (the number of unique phonological contexts in which a phonotactic pattern appears) and exemplar strength (the overall number of times the pattern appears). Using an artificial language learning paradigm, 3 experiments disentangled the effects of variability and strength, indexed by type and token frequency, respectively, on the learning of gradient phonotactics. When the 2 factors were decorrelated (Experiment 2), participants showed greater generalization of patterns advantaged for contextual variability, but not those advantaged for exemplar strength. When the 2 factors were anticorrelated (Experiment 3), participants preferred patterns advantaged in contextual variability, even though they were disadvantaged for exemplar strength. These results suggest that contextual variability is the key force driving phonotactic learning, as it allows learners to home in on the invariant features of the input. (PsycINFO Database Record

Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co-occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities.

Statistical learning in social action contexts

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli.

Cognitive flexibility impairment and reduced frontal cortex BDNF expression in the ouabain model of mania

Central infusion of the Na+/K+-ATPase inhibitor, ouabain in rats serves as an animal model of mania because it leads to hyperactivity, as well as reproduces ion dysregulation and reduced brain-derived neurotrophic factor (BDNF) levels similar to that observed in bipolar disorder. Bipolar disorder is also associated with cognitive inflexibility and working memory deficits. It is unknown whether ouabain treatment in rats leads to similar cognitive flexibility and working memory deficits. The present study examined the effects of an intracerebral ventricular infusion of ouabain in rats on spontaneous alternation, probabilistic reversal learning and BDNF expression levels in the frontal cortex. Ouabain treatment significantly increased locomotor activity, but did not affect alternation performance in a Y-maze. Ouabain treatment selectively impaired reversal learning in a spatial discrimination task using an 80/20 probabilistic reinforcement procedure. The reversal learning deficit in ouabain-treated rats resulted from an impaired ability to maintain a new choice pattern (increased regressive errors). Ouabain treatment also decreased sensitivity to negative feedback during the initial phase of reversal learning. Expression of BDNF mRNA and protein levels was downregulated in the frontal cortex which also negatively correlated with regressive errors. These findings suggest that the ouabain model of mania may be useful in understanding the neuropathophysiology that contributes to cognitive flexibility deficits and test potential treatments to alleviate cognitive deficits in bipolar disorder.

Suboptimal choice in rats: Incentive salience attribution promotes maladaptive decision-making

Stimuli that are more predictive of subsequent reward also function as better conditioned reinforcers. Moreover, stimuli attributed with incentive salience function as more robust conditioned reinforcers. Some theories have suggested that conditioned reinforcement plays an important role in promoting suboptimal choice behavior, like gambling. The present experiments examined how different stimuli, those attributed with incentive salience versus those without, can function in tandem with stimulus-reward predictive utility to promote maladaptive decision-making in rats. One group of rats had lights associated with goal-tracking as the reward-predictive stimuli and another had levers associated with sign-tracking as the reward-predictive stimuli. All rats were first trained on a choice procedure in which the expected value across both alternatives was equivalent but differed in their stimulus-reward predictive utility. Next, the expected value across both alternatives was systematically changed so that the alternative with greater stimulus-reward predictive utility was suboptimal in regard to primary reinforcement. The results demonstrate that in order to obtain suboptimal choice behavior, incentive salience alongside strong stimulus-reward predictive utility may be necessary; thus, maladaptive decision-making can be driven more by the value attributed to stimuli imbued with incentive salience that reliably predict a reward rather than the reward itself.

Statistical learning: a powerful mechanism that operates by mere exposure

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website.

Who "believes" in the Gambler's Fallacy and why?

Humans possess a remarkable ability to discriminate structure from randomness in the environment. However, this ability appears to be systematically biased. This is nowhere more evident than in the Gambler's Fallacy (GF)-the mistaken belief that observing an increasingly long sequence of "heads" from an unbiased coin makes the occurrence of "tails" on the next trial ever more likely. Although the GF appears to provide evidence of "cognitive bias," a recent theoretical account (Hahn & Warren, 2009) has suggested the GF might be understandable if constraints on actual experience of random sources (such as attention and short term memory) are taken into account. Here we test this experiential account by exposing participants to 200 outcomes from a genuinely random (p = .5) Bernoulli process. All participants saw the same overall sequence; however, we manipulated experience across groups such that the sequence was divided into chunks of length 100, 10, or 5. Both before and after the exposure, participants (a) generated random sequences and (b) judged the randomness of presented sequences. In contrast to other accounts in the literature, the experiential account suggests that this manipulation will lead to systematic differences in postexposure behavior. Our data were strongly in line with this prediction and provide support for a general account of randomness perception in which biases are actually apt reflections of environmental statistics under experiential constraints. This suggests that deeper insight into human cognition may be gained if, instead of dismissing apparent biases as failings, we assume humans are rational under constraints. (PsycINFO Database Record

Incremental implicit learning of bundles of statistical patterns

Forming an accurate representation of a task environment often takes place incrementally as the information relevant to learning the representation only unfolds over time. This incremental nature of learning poses an important problem: it is usually unclear whether a sequence of stimuli consists of only a single pattern, or multiple patterns that are spliced together. In the former case, the learner can directly use each observed stimulus to continuously revise its representation of the task environment. In the latter case, however, the learner must first parse the sequence of stimuli into different bundles, so as to not conflate the multiple patterns. We created a video-game statistical learning paradigm and investigated (1) whether learners without prior knowledge of the existence of multiple "stimulus bundles" - subsequences of stimuli that define locally coherent statistical patterns - could detect their presence in the input and (2) whether learners are capable of constructing a rich representation that encodes the various statistical patterns associated with bundles. By comparing human learning behavior to the predictions of three computational models, we find evidence that learners can handle both tasks successfully. In addition, we discuss the underlying reasons for why the learning of stimulus bundles occurs even when such behavior may seem irrational.

Levodopa medication improves incidental sequence learning in Parkinson's disease

Empirical evidence suggests that levodopa medication used to treat the motor symptoms of Parkinson's disease (PD) may either improve, impair or not affect specific cognitive processes. This evidence led to the 'dopamine overdose' hypothesis that levodopa medication impairs performance on cognitive tasks if they recruit fronto-striatal circuits which are not yet dopamine-depleted in early PD and as a result the medication leads to an excess of dopamine. This hypothesis has been supported for various learning tasks including conditional associative learning, reversal learning, classification learning and intentional deterministic sequence learning, on all of which PD patients demonstrated significantly worse performance when tested on relative to off dopamine medication. Incidental sequence learning is impaired in PD, but how such learning is affected by dopaminergic therapy remains undetermined. The aim of the current study was to investigate the effect of dopaminergic medication on incidental sequence learning in PD. We used a probabilistic serial reaction time task (SRTT), a sequence learning paradigm considered to make the sequence less apparent and more likely to be learned incidentally rather than intentionally. We compared learning by the same group of PD patients (n=15) on two separate occasions following oral administration of levodopa medication (on state) and after overnight withdrawal of medication (off state). Our results demonstrate for the first time that levodopa medication enhances incidental learning of a probabilistic sequence on the serial reaction time task in PD. However, neither group significantly differed from performance of a control group without a neurological disease, which indicates the importance of within group comparisons for identifying deficits. Levodopa medication enhanced incidental learning by patients with PD on a probabilistic sequence learning paradigm even though the patients were not aware of the existence of the sequence or their acquired knowledge. The results suggest a role in acquiring incidental motor sequence learning for dorsal striatal areas strongly affected by dopamine depletion in early PD.

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning

Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment.

Anchors aweigh: The impact of overlearning on entrenchment effects in statistical learning

Previous research has revealed that when learners encounter multiple artificial languages in succession only the first is learned, unless there are contextual cues correlating with the change in structure or if exposure to the second language is protracted. These experiments provided a fixed amount of exposure irrespective of when learning occurred. Here, the authors presented learners with 2 consecutive artificial languages testing learning after each minute of familiarization. In Experiment 1, learners received fixed input, and the authors replicated the primacy effect. In Experiment 2, learners advanced to the second language immediately following robust learning of the first language (thereby limiting additional exposure past the point of learning). Remarkably, learners tended to acquire and retain both languages, although contextual cues did not boost performance further. Notably, there was no correlation between performance on this task and a flanker task that measured inhibitory control. Overall, the findings suggest that anchoring effects in statistical learning may be because of overlearning. We speculate that learners may reduce their attention to the input once they achieve a low level of estimation uncertainty. (PsycINFO Database Record

Machine learning, statistical learning and the future of biological research in psychiatry

Psychiatric research has entered the age of 'Big Data'. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other 'omic' measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.

Failure to Construct and Transfer Correct Representations across Probability Problems

Previous studies carried out on “purely random” situations (with dice or poker chips) show the difficulties encountered by people in such situations, however simple they may be. In fact, in this type of situation, prior knowledge guides spontaneous representations, and the “errors” observed could be explained by the activation of “implicit models” which form the basis of erroneous representations. 42 statistically naïve undergraduates were given several variants of a probability problem on which errors are common. In a learning phase, subjects were given four problems involving geometric figures which were pairwise related by complementarity and equivalence relations. In a subsequent transfer phase, they were given a fifth problem involving poker chips, which was structurally isomorphic to the fourth geometric-figures problem. The findings show that people do not realize the relations between problems, and that transfer occurred only for the subset of subjects who performed correctly on the training problems of the learning phase. These results appear to have some significant implications in teaching mathematical concepts.

Retributive Reactions to Suspected Offenders: The Importance of Social Categorizations and Guilt Probability

In the current research, the author investigates the influence of social categorizations on retributive emotions (e.g., anger) and punishment intentions when people evaluate suspected offenders as independent observers. It is argued that information that guilt is certain or uncertain (i.e., guilt probability) has different consequences for retributive reactions to ingroup and outgroup suspects. In correspondence with predictions, results of four experiments showed that people reacted more negatively to ingroup than outgroup suspects when guilt was certain but that people reacted more negatively to outgroup than ingroup suspects when guilt was uncertain. It is concluded that guilt probability moderates the influence of social categorizations on people’s retributive reactions to suspected offenders.

The “Ripple Effect”: Cultural Differences in Perceptions of the Consequences of Events

Previous research has demonstrated that people from East Asian cultural backgrounds make broader, more complex causal attributions than do people from Western cultural backgrounds. In the current research, the authors hypothesized that East Asians also would be aware of a broader, more complex distribution of consequences of events. Four studies assessed cultural differences in perceptions of the consequences of (a) a shot in a game of pool, (b) an area being converted into a national park, (c) a chief executive officer firing employees, and (d) a car accident. Across all four studies, compared to participants from Western cultural backgrounds, participants from East Asian cultural backgrounds were more aware of the indirect, distal consequences of events. This pattern occurred on a variety of measures, including spontaneously generated consequences, estimations of an event's impact on subsequent events, perceived responsibility, and predicted affective reactions. Implications for our understanding of cross-cultural psychology and social perception are discussed.