Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy.

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.

Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems.

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires' Disease which is known for high mortality rates, particularly in the elderly. With caseloads continuing to increase, further research is needed to improve our understanding of optimized sampling schema and safe limits of L. pneumophila, in part to target improved treatment options and realistic population-level risk modeling. Particularly in healthcare and other high-risk locations these become crucial and time sensitive needs. Therefore, we conceptualized this research as a means of incorporating easily measured physiochemical water quality parameters and generalization of the unique ecology of building water systems to build a computational model that can allow for more rapid and accurate decision making. This research uses the specific machine learning (ML) method called statistical learning theory to incorporate concentration of host cells, such as native amoeba, and physiochemical water quality parameters to estimate the probability of observing ranges of Legionella gene copy concentrations. Using data from previously published research on Legionella prevalence in a large building, our ML method trains the model on the relative impacts of physiochemical parameters on likely amoeba host cell occurrences. The model is expanded to estimate host cell concentrations using correlations and regressions operated through LASSO algorithms. After categorization variables from these results are then used to inform a logistic regression to provide an estimate of the probability of Legionella gene copy concentration ranges. In summary, conventional results generated by logistic regression and multiple linear regression quantified the associations among ecological conditions in the water and ability to predict a likely range of Legionella concentration in a management focused way. Further, two ML methods, PCA and LASSO, demonstrated feasibility in accurate real-time monitoring of Legionella through physiochemical indicators as evidenced with good accuracy of predictions based for validation results. Furthermore results demonstrate the vital need to account for the impact of water quality on building on host cells, and via their quantified water microbial ecology, not just Legionella concentrations.

Anticipating multisensory environments: Evidence for a supra-modal predictive system.

Our perceptual experience is generally framed in multisensory environments abundant in predictive information. Previous research on statistical learning has shown that humans can learn regularities in different sensory modalities in parallel, but it has not yet determined whether multisensory predictions are generated through a modality-specific predictive mechanism or instead, rely on a supra-modal predictive system. Here, across two experiments, we tested these hypotheses by presenting participants with concurrent pairs of predictable auditory and visual low-level stimuli (i.e., tones and gratings). In different experimental blocks, participants had to attend the stimuli in one modality while ignoring stimuli from the other sensory modality (distractors), and perform a perceptual discrimination task on the second stimulus of the attended modality (targets). Orthogonal to the task goal, both the attended and unattended pairs followed transitional probabilities, so targets and distractors could be expected or unexpected. We found that participants performed better for expected compared to unexpected targets. This effect generalized to the distractors but only when relevant targets were expected. Such interactive effects suggest that predictions may be gated by a supra-modal system with shared resources across sensory modalities that are distributed according to their respective behavioural relevance.

English-learning infants developing sensitivity to vowel phonotactic cues to word segmentation.

Previous research has shown that when domain-general transitional probability (TP) cues to word segmentation are in conflict with language-specific stress cues, English-learning 5- and 7-month-olds rely on TP, whereas 9-month-olds rely on stress. In two artificial languages, we evaluated English-learning infants' sensitivity to TP cues to word segmentation vis-a-vis language-specific vowel phonotactic (VP) cues-English words do not end in lax vowels. These cues were either consistent or conflicting. When these cues were in conflict, 10-month-olds relied on the VP cues, whereas 5-month-olds relied on TP. These findings align with statistical bootstrapping accounts, where infants initially use domain-general distributional information for word segmentation, and subsequently discover language-specific patterns based on segmented words. RESEARCH HIGHLIGHTS: Research indicates that when transitional probability (TP) conflicts with stress cues for word segmentation, English-learning 9-month-olds rely on stress, whereas younger infants rely on TP. In two artificial languages, we evaluated English-learning infants' sensitivity to TP versus vowel phonotactic (VP) cues for word segmentation. When these cues conflicted, 10-month-olds relied on VPs, whereas 5-month-olds relied on TP. These findings align with statistical bootstrapping accounts, where infants first utilize domain-general distributional information for word segmentation, and then identify language-specific patterns from segmented words.

Reduced contextual uncertainty facilitates learning what to attend to and what to ignore.

Variability in the search environment has been shown to affect the capture of attention by salient distractors, as attentional capture is reduced when context variability is low. However, it remains unclear whether this reduction in capture is caused by contextual learning or other mechanisms, grounded in generic context-structure learning. We set out to test this by training participants (n = 200) over two sessions in a visual search task, conducted online, where they gained experience with a small subset of search displays, which significantly reduced capture of attention by colour singletons. In a third session, we then tested participants on a mix of familiar and novel search displays and examined whether this reduction in capture was specific to familiar displays, indicative of contextual cueing effects, or would generalise to novel displays. We found no capture by the singleton in either the familiar or novel condition. Instead, our findings suggested that reduced statistical volatility reduced capture by allowing the development of generic predictions about task-relevant locations and features of the display. These findings add to the current debate about the determinants of capture by salient distractors by showing that capture is also affected by generic task regularities and by the volatility of the learning environment.

Reward History and Statistical Learning Independently Impact Attention Search: An ERP Study.

Selection history is widely accepted as a vital source in attention control. Reward history indicates that a learned association captures attention even when the reward is no longer presented, while statistical learning indicates that a learned probability exerts its influence on attentional control (facilitation or inhibition). Existing research has shown that the effects of the reward history and statistical learning are additive, suggesting that these two components influence attention priority through different pathways. In the current study, leveraging the temporal resolution advantages of EEG, we explored whether these two components represent independent sources of attentional bias. The results revealed faster responses to the target at the high-probability location compared to low-probability locations. Both the target and distractor at high-probability locations elicited larger early Pd (50-150 ms) and Pd (150-250 ms) components. The reward distractor slowed the target search and elicited a larger N2pc (180-350 ms). Further, no interaction between statistical learning and the reward history was observed in RTs or N2pc. The different types of temporal progression in attention control indicate that statistical learning and the reward history independently modulate the attention priority map.

Distributional learning drives statistical deafening.

Humans and other animals use information about how likely it is for something to happen. The absolute and relative probability of an event influences a remarkable breadth of behaviors, from foraging for food to comprehending linguistic constructions -- even when these probabilities are learned implicitly. It is less clear how, and under what circumstances, statistical learning of simple probabilities might drive changes in perception and cognition. Here, across a series of 29 experiments, we probe listeners' sensitivity to task-irrelevant changes in the probability distribution of tones' acoustic frequency across tone-in-noise detection and tone duration decisions. We observe that the task-irrelevant frequency distribution influences the ability to detect a sound and the speed with which perceptual decisions are made. The shape of the probability distribution, its range, and a tone's relative position within that range impact observed patterns of suppression and enhancement of tone detection and decision making. Perceptual decisions are also modulated by a newly discovered perceptual bias, with lower frequencies in the distribution more often and more rapidly perceived as longer, and higher frequencies as shorter. Perception is sensitive to rapid distribution changes, but distributional learning from previous probability distributions also carries over. In fact, massed exposure to a single point along the dimension results in a sustained 'statistical deafening' along a range of subsequently encountered frequencies. This seemingly maladaptive loss of sensitivity - occurring entirely in the absence of feedback or reward - points to a gain mechanism that suppresses sensitivity to regions along a perceptual dimension that are less likely to be encountered.

The order of stimuli matters when learning second-order transitional probabilities.

The order of stimuli within sequences and the transitional probabilities (TPs) it generates are central information in sequence processing. However, less is known about what type of information and how it is extracted by general learning mechanisms. The present study focused on statistical learning of second-order TPs. Second-order TPs are involved when only the combination of two stimuli predicts the third. In a first experiment, TPs depended crucially on the order of presentation of a pair A - B , which led to different predictions depending on the order of the stimuli (i.e., ABC vs. BAF). Eight visuomotor sequences governed by second-order TPs were used and response times (RTs) were recorded for each transition. The task included a learning phase followed by a switch phase during which the second-order TP were reversed (e.g., the sequences ABC and BAF became respectively ABF and BAC). A decrease of RTs between the second and the third stimulus during the learning phase and an increase of RTs during the switch phase suggested that variations of orders within second-order TPs could be learned. Further analyses, however, indicated that such learning was difficult for most participants. A second experiment showed that the difficulty of learning was not solely due to the difficulty to pick up the effect of order of presentation, but that learning second-order transitional probabilities in addition to order would be the main obstacle. These experiments suggest that statistical learning is capable of learning complex associations, even if this remains a challenge for human cognition.

Statistical Learning Facilitates Access to Awareness.

Statistical learning is a powerful mechanism that enables the rapid extraction of regularities from sensory inputs. Although numerous studies have established that statistical learning serves a wide range of cognitive functions, it remains unknown whether statistical learning impacts conscious access. To address this question, we applied multiple paradigms in a series of experiments (N = 153 adults): Two reaction-time-based breaking continuous flash suppression (b-CFS) experiments showed that probable objects break through suppression faster than improbable objects. A preregistered accuracy-based b-CFS experiment showed higher localization accuracy for suppressed probable (versus improbable) objects under identical presentation durations, thereby excluding the possibility of processing differences emerging after conscious access (e.g., criterion shifts). Consistent with these findings, a supplemental visual-masking experiment reaffirmed higher localization sensitivity to probable objects over improbable objects. Together, these findings demonstrate that statistical learning alters the competition for scarce conscious resources, thereby potentially contributing to established effects of statistical learning on higher-level cognitive processes that require consciousness.

Identifying Transfer Learning in the Reshaping of Inductive Biases.

Transfer learning, the reuse of newly acquired knowledge under novel circumstances, is a critical hallmark of human intelligence that has frequently been pitted against the capacities of artificial learning agents. Yet, the computations relevant to transfer learning have been little investigated in humans. The benefit of efficient inductive biases (meta-level constraints that shape learning, often referred as priors in the Bayesian learning approach), has been both theoretically and experimentally established. Efficiency of inductive biases depends on their capacity to generalize earlier experiences. We argue that successful transfer learning upon task acquisition is ensured by updating inductive biases and transfer of knowledge hinges upon capturing the structure of the task in the inductive bias that can be reused in novel tasks. To explore this, we trained participants on a non-trivial visual stimulus sequence task (Alternating Serial Response Times, ASRT); during the Training phase, participants were exposed to one specific sequence for multiple days, then on the Transfer phase, the sequence changed, while the underlying structure of the task remained the same. Our results show that beyond the acquisition of the stimulus sequence, our participants were also able to update their inductive biases. Acquisition of the new sequence was considerably sped up by earlier exposure but this enhancement was specific to individuals showing signatures of abandoning initial inductive biases. Enhancement of learning was reflected in the development of a new internal model. Additionally, our findings highlight the ability of participants to construct an inventory of internal models and alternate between them based on environmental demands. Further, investigation of the behavior during transfer revealed that it is the subjective internal model of individuals that can predict the transfer across tasks. Our results demonstrate that even imperfect learning in a challenging environment helps learning in a new context by reusing the subjective and partial knowledge about environmental regularities.

Joint weight optimization for partial domain adaptation via kernel statistical distance estimation.

The goal of Partial Domain Adaptation (PDA) is to transfer a neural network from a source domain (joint source distribution) to a distinct target domain (joint target distribution), where the source label space subsumes the target label space. To address the PDA problem, existing works have proposed to learn the marginal source weights to match the weighted marginal source distribution to the marginal target distribution. However, this is sub-optimal, since the neural network's target performance is concerned with the joint distribution disparity, not the marginal distribution disparity. In this paper, we propose a Joint Weight Optimization (JWO) approach that optimizes the joint source weights to match the weighted joint source distribution to the joint target distribution in the neural network's feature space. To measure the joint distribution disparity, we exploit two statistical distances: the distribution-difference-based L2-distance and the distribution-ratio-based χ2-divergence. Since these two distances are unknown in practice, we propose a Kernel Statistical Distance Estimation (KSDE) method to estimate them from the weighted source data and the target data. Our KSDE method explicitly expresses the two estimated statistical distances as functions of the joint source weights. Therefore, we can optimize the joint weights to minimize the estimated distance functions and reduce the joint distribution disparity. Finally, we achieve the PDA goal by training the neural network on the weighted source data. Experiments on several popular datasets are conducted to demonstrate the effectiveness of our approach. Intro video and Pytorch code are available at https://github.com/sentaochen/Joint-Weight-Optimation. Interested readers can also visit https://github.com/sentaochen for more source codes of the related domain adaptation, multi-source domain adaptation, and domain generalization approaches.

Successful sensitization of 2.5-year-olds to other-race faces through bimodal training.

The present study investigated the potential for sensitizing 2.5-year-old Caucasian infants to other-race faces (Asian faces). In the domain of face perception, infants become less sensitive to facial distinctions of other-race faces through perceptual narrowing at the end of the first year of life. Nevertheless, infants around 12 months can regain their sensitivity to other-race faces. For instance, exposing them to a specific statistical distribution and employing the mechanisms of statistical learning is one way to enhance their discriminatory abilities towards other-race faces. Following this idea, we investigated if even older infants around 2.5 years can be sensitized to other-race faces. We trained the infants with a bimodal distribution of a morphed continuum of Asian female faces with faces closer to the endpoints presented most frequently. We assessed infants' discrimination of Asian faces by measuring their looking times after the training phase. The 2.5-year-olds showed a difference in looking times after the training, indicating that the exposure to a bimodal frequency distribution led to a successful discrimination between Asian faces. These findings demonstrate that 2.5-year-olds can be sensitized to other-race faces by exposing them to a bimodal distribution of such faces, underlining the plasticity of face perception in childhood.

Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

Attending is not enough: Responding to targets is needed for across-trial statistical learning.

Recent evidence shows that observers are able to learn across-trial regularities as indicated by faster responses to targets whose location was predicted by the target's location on the preceding trial. The present study investigated whether responding to both targets of the pair, as was the case in studies thus far, was needed for learning to occur. Participants searched for a shape singleton target and responded to the line inside. There were two across-trial predicting-predicted regularities regarding target locations: if the target appeared at one specific location on a given trial, it would appear at another specific location on the next trial. Unlike previous experiments, for one of these regularity pairs a response was only needed on either the first or the second target in the pair. Experiment 1 showed that across-trial learning only occurred when responding was required to both targets of a pair. If the response to one target of a pair had to be withheld, no learning occurred. Experiment 2 showed that the absence of learning cannot be attributed to carry-over inhibition resulting from not having to respond. After learning across-trial contingencies, learning remained in place even when the response to the first target of the pair had to be withheld. Our findings show that the execution of the (arbitrary) simple key-press response for both trials of the pair was needed for across-trial statistical learning to occur, whereas solely attending target locations did not result in any learning.

Sensitive periods in language development: Do children outperform adults on auditory word-form segmentation?

Children are more successful language learners than adults, yet the nature and cause of this phenomenon are still not well understood. Auditory statistical learning from speech has been a prominent focus of research in the field of language development because it is regarded as a fundamental learning mechanism underlying word segmentation in early language acquisition. However, a handful of studies that investigated developmental trajectories for auditory statistical learning found no clear child advantages. The degree to which the learning task measures explicit rather than implicit mechanisms might obscure a potential advantage for younger learners, as suggested by recent findings. In the present study, we compared children aged 7-12 years and young adults on an adapted version of the task that disentangles explicit and implicit contributions to learning. They were exposed to a continuous stream of speech sounds comprising four repeating trisyllabic pseudowords. Learning of the hidden words was tested (a) online through a target-detection task and (b) offline via a forced-choice word recognition test that included a memory judgement procedure. Both measures revealed comparable learning abilities. However, children's performance on the recognition task showed evidence for both explicit and implicit word knowledge while adults appeared primarily sensitive to explicit memory. Since implicit memory is more stable in time than explicit memory, we suggest that future work should focus more on developmental differences in the nature of the memory that is formed, rather than the strength of learning, when trying to understand child advantages in language acquisition.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation.

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

The proactive and reactive mechanisms of learned spatial suppression.

Selection history refers to the notion that previous allocations of attention or suppression have the potential to elicit lingering and enduring selection biases that are isolated from goal-driven or stimulus-driven attention. However, in the singleton detection mode task, manipulating the selection history of distractors cannot give rise to pure proactive inhibition. Therefore, we employed a combination of a working memory task and a feature search mode task, simultaneously recording cortical activity using EEG, to investigate the mechanisms of suppression guided by selection history. The results from event-related potential and reaction times showed an enhanced inhibitory performance when the distractor was presented at the high-probability location, along with instances where the target appeared at the high-probability location of distractors. These findings demonstrate that a generalized proactive inhibition bias is learned and processed independent of cognitive resources, which is supported by selection history. In contrast, reactive rejection toward the low-probability location was evident through the Pd component under varying cognitive resource conditions. Taken together, our findings indicated that participants learned proactive inhibition when the distractor was at the high-probability location, whereas reactive rejection was involved at low-probability location.

Prediction of Cell Survival Rate Based on Physical Characteristics of Heavy Ion Radiation.

The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910-2, the local regression 9.2110-2, and the random forest 1.96 × 10-2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power.

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses.

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

Statistically learned associations among objects bias attention.

A growing body of research suggests that semantic relationships among objects can influence the control of attention. There is also some evidence that learned associations among objects can bias attention. However, it is unclear whether these findings are due to statistical learning or existing semantic relationships. In the present study, we examined whether statistically learned associations among objects can bias attention in the absence of existing semantic relationships. Participants searched for one of four targets among pairs of novel shapes and identified whether the target was present or absent from the display. In an initial training phase, each target was paired with an associated distractor in a fixed spatial configuration. In a subsequent test phase, each target could be paired with the previously associated distractor or a different distractor. In our first experiment, the previously associated distractor was always presented in the same pair as the target. Participants were faster to respond when this distractor was present on target-present trials. In our second experiment, the previously associated distractor was presented in a different pair than the target in the test phase. In this case, participants were slower to respond when this distractor was present on both target-present and target-absent trials. Together, these findings provide clear evidence that statistically learned associations among objects can bias attention, analogous to the effects of semantic relationships on attention.