Parsimonious statistical learning models for low-flow estimation

Statistical learning methods offer a promising approach for low-flow regionalization. We examine seven statistical learning models (Lasso, linear, and nonlinear-model-based boosting, sparse partial least squares, principal component regression, random forest, and support vector regression) for the prediction of winter and summer low flow based on a hydrologically diverse dataset of 260 catchments in Austria. In order to produce sparse models, we adapt the recursive feature elimination for variable preselection and propose using three different variable ranking methods (conditional forest, Lasso, and linear model-based boosting) for each of the prediction models. Results are evaluated for the low-flow characteristic Q95 (Pr(Q>Q95)=0.95) standardized by catchment area using a repeated nested cross-validation scheme. We found a generally high prediction accuracy for winter (RCV2 of 0.66 to 0.7) and summer (RCV2 of 0.83 to 0.86). The models perform similarly to or slightly better than a top-kriging model that constitutes the current benchmark for the study area. The best-performing models are support vector regression (winter) and nonlinear model-based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all the models with only a small loss of performance. The so-obtained learning models are more parsimonious and thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and nonlinear models reveals that nonlinear processes can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add nonlinear effects. When performing low-flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.

Reward learning and statistical learning independently influence attentional priority of salient distractors in visual search

Existing research demonstrates different ways in which attentional prioritization of salient nontarget stimuli is shaped by prior experience: Reward learning renders signals of high-value outcomes more likely to capture attention than signals of low-value outcomes, whereas statistical learning can produce attentional suppression of the location in which salient distractor items are likely to appear. The current study combined manipulations of the value and location associated with salient distractors in visual search to investigate whether these different effects of selection history operate independently or interact to determine overall attentional prioritization of salient distractors. In Experiment 1, high-value and low-value distractors most frequently appeared in the same location; in Experiment 2, high-value and low-value distractors typically appeared in distinct locations. In both experiments, effects of distractor value and location were additive, suggesting that attention-promoting effects of value and attention-suppressing effects of statistical location-learning independently modulate overall attentional priority. Our findings are consistent with a view that sees attention as mediated by a common priority map that receives and integrates separate signals relating to physical salience and value, with signal suppression based on statistical learning determined by physical salience, but not incentive salience.

EXPRESS: Are animates special? Exploring the effects of selective attention and animacy on visual statistical learning

Our visual system is built to extract regularities in how objects within our visual environment appear in relation to each other across time and space (‘visual statistical learning’). Existing research indicates that visual statistical learning is modulated by selective attention. Our attentional system prioritises information that enables behaviour; for example, animates are prioritised over inanimates (the ‘animacy advantage’). The present study examined the effects of selective attention and animacy on visual statistical learning in young adults (N = 284). We tested visual statistical learning of attended and unattended information across four animacy conditions: (i) living things that can self-initiate movement (animals); (ii) living things that cannot self-initiate movement (fruits and vegetables); (iii) non-living things that can generate movement (vehicles); and (iv) non-living things that cannot generate movement (tools and kitchen utensils). We implemented a four-point confidence-rating scale as an assessment of participants’ awareness of the regularities in the visual statistical learning task. There were four key findings. First, selective attention plays a critical role by modulating visual statistical learning. Second, animacy does not play a special role in visual statistical learning. Third, visual statistical learning of attended information cannot be exclusively accounted for by unconscious knowledge. Fourth, performance on the visual statistical learning task is associated with the proportion of stimuli that were named or labelled. Our findings support the notion that visual statistical learning is a powerful mechanism by which our visual system resolves an abundance of sensory input over time.

Match me if you can: Evidence for a domain-general visual comparison ability

Visual comparison—comparing visual stimuli (e.g., fingerprints) side by side and determining whether they originate from the same or different source (i.e., “match”)—is a complex discrimination task involving many cognitive and perceptual processes. Despite the real-world consequences of this task, which is often conducted by forensic scientists, little is understood about the psychological processes underpinning this ability. There are substantial individual differences in visual comparison accuracy amongst both professionals and novices. The source of this variation is unknown, but may reflect a domain-general and naturally varying perceptual ability. Here, we investigate this by comparing individual differences (N = 248 across two studies) in four visual comparison domains: faces, fingerprints, firearms, and artificial prints. Accuracy on all comparison tasks was significantly correlated and accounted for a substantial portion of variance (e.g., 42% in Exp. 1) in performance across all tasks. Importantly, this relationship cannot be attributed to participants’ intrinsic motivation or skill in other visual-perceptual tasks (visual search and visual statistical learning). This paper provides novel evidence of a reliable, domain-general visual comparison ability.

Statistical learning in patients in the minimally conscious state

When listening to an unknown language, listeners could learn the transitional probability between syllables and group frequently co-occurred syllables into a whole unit. Such statistical learning ability has been demonstrated for both pre-verbal infants and adults, even during passive listening. Here, we investigated whether statistical learning occurred in patients in minimally conscious state (MCS) and patients emerged from the minimally conscious state (EMCS) using electroencephalography (EEG). We presented to participants an isochronous sequence of syllables, which were composed of either 2-word real phrases or 2-word artificial phrases that were defined by the transitional probability between words. An inter-trial phase coherence (ITPC) analysis revealed that the phrase-rate EEG response was weakened in EMCS patients compared with healthy individuals, and was even more severely weakened in MCS patients. Although weak, the phrase-rate response or its harmonics remained statistically significant in MCS patients, suggesting that the statistical learning ability was preserved in MCS patients. The word-rate response was also weakened with a decreased level of consciousness. The harmonics of the word-rate response, however,were more salient in MCS than EMCS patients in the alpha and beta bands. Together with previous studies, the current results suggest that MCS patients retain residual learning ability, which can potentially be harnessed to induce neural plasticity, and that different frequency bands are differentially related to the consciousness level.

Unlocking adults’ implicit statistical learning by cognitive depletion

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.