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Prediction and Uncertainty in Associative Learning: Examining Controlled and Automatic Components of Learned Attentional Biases

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Abstract

It has been suggested that attention is guided by two factors that operate during associative learning: a predictiveness principle, by which attention is allocated to the best predictors of outcomes, and an uncertainty principle, by which attention is allocated to learn about the less-known features of the environment. Recent studies have shown that predictiveness-driven attention can operate rapidly and in an automatic way to exploit known relationships. The corresponding characteristics of uncertainty-driven attention, on the other hand, remain unexplored. In two experiments we examined whether both predictiveness and uncertainty modulate attentional processing in an adaptation of the dot probe task. This task provides a measure of automatic orientation to cues during associative learning. The stimulus-onset-asynchrony of the probe display was manipulated in order to explore temporal characteristics of predictiveness- and uncertainty-driven attentional effects. Results showed that the predictive status of cues determined selective attention, with faster attentional capture to predictive than to nonpredictive cues. In contrast, the level of uncertainty slowed down responses to the probe regardless of the predictive status of the cues. Both predictiveness- and uncertainty-driven attentional effects were very rapid (at 250 ms from cue onset) and automatically activated.

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... Attentional models of associative learning propose that our attention system filters information in order to focus the learning mechanism on those cues that will assist in making accurate predictions in the future (Esber & Haselgrove, 2011;Le Pelley, 2004;Mackintosh, 1975). It is now well established that the process of cue-outcome learning leads to changes in attention, favouring the processing of predictive cues over those that are not predictive (Feldmann-Wüstefeld, Uengoer, & Schubö, 2015;Kruschke, Kappenman, & Hetrick, 2005;Le Pelley, Vadillo, & Luque, 2013;Livesey, Harris, & Harris, 2009;Luque, Morís, Rushby, & Le Pelley, 2015;Luque, Vadillo, Gutiérrez-Cobo, & Le Pelley, 2018;Luque, Vadillo, Le Pelley, & Beesley, 2017;O'Brien & Raymond, 2012;Rehder & Hoffman, 2005;Wills, Lavric, Croft, & Hodgson, 2007). ...
... This effect was evident when the probe appeared very shortly (250 ms) after the cues. However, when the stimulus-onset-asynchrony (SOA) of the probe was increased to 1000 ms, there was no effect of cue predictiveness on attention (see also Luque, Vadillo et al., 2017). This pattern of results suggests that the predictiveness-driven bias of attention is activated automatically: given the presentation of a predictive cue, attention is oriented towards that cue regardless of the participants' effort to keep their attentional focus on the centre of the screen. ...
... In Experiment 1 we investigated the automaticity of a predictiveness-driven bias of attention by using the procedure of Luque, Vadillo et al. (2017), which combines an associative learning and a dot-probe task. In the associative learning task, two coloured shapes (cues) appeared on each trial and participants were required to make one of two responses (up or down). ...
Article
It is well established that associative learning, such as learning new cue-outcome pairings, produces changes in attention: cues that are good predictors of relevant outcomes become prioritised compared with those that are non-predictive or redundant. However, there is controversy about whether such a learnt attentional bias results from a controlled orientation of attention, or whether it can be involuntary in nature. In three experiments, participants learned that cues of certain colours were predictive or non-predictive, and we assessed attention to cues using a dot-probe task. On dot-probe trials, participants were instructed to control attention by orienting towards a cue of a certain shape (target), while trying to ignore another cue (distractor). Although the colours of the cues were critical for the associative learning task, they were irrelevant for the dot-probe task. The results show that, even though participants' controlled attention was focused on the target shape (as evident in response times and accuracy data), response times to the probe were slower (Experiments 1 and 2) and error rates were higher (Experiments 2 and 3) when the distractor was of a (previously) predictive colour. These data suggest that attention was captured involuntarily by the predictive value of the distractor, despite this being counterproductive to the task goal.
... Factors other than the strength of reward association have been shown to influence attentional modulation by reward. For example, Pearce and Hall (1980) proposed the uncertainty-based attention theory, according to which people deploy their attention more toward a stimulus feature presenting uncertainty about its association with an outcome than features without such uncertainty (Beesley, Nguyen, Pearson, & Le Pelley, 2015;Easdale, Le Pelley, & Beesley, 2019;Hogarth, Dickinson, Austin, Brown, & Duka, 2008;Le Pelley, Pearson, Porter, Yee, & Luque, 2019;Luque, Vadillo, Le Pelley, & Beesley, 2016;Walker, Luque, Le Pelley, & Beesley, 2019). In other words, observers are likely to deploy more attention to a stimulus associated with uncertainty for further processing, which can be expressed as the variance of outcomes (Fiorillo, Tobler, & Schultz, 2003;Rushworth & Behrens, 2008;Tobler, O'Doherty, Dolan, & Schultz, 2006). ...
... In other words, observers are likely to deploy more attention to a stimulus associated with uncertainty for further processing, which can be expressed as the variance of outcomes (Fiorillo, Tobler, & Schultz, 2003;Rushworth & Behrens, 2008;Tobler, O'Doherty, Dolan, & Schultz, 2006). Indeed, many studies have demonstrated that when participants infer what outcome is expected for a given stimulus in associative learning, the attentional allocation to the stimulus is modulated by the probabilistic relationship between the stimulus and its outcome Easdale et al., 2019;Hogarth et al., 2008;Luque et al., 2016;Koenig, Kadel, Uengoer, Schubö, & Lachnit, 2017a;Koenig, Uengoer, & Lachnit, 2017b). For example, in Beesley et al.'s (2015) experiment, participants were asked to predict what type of outcome would result from two artificial images. ...
... Some images resulted in a specific outcome with 100% probability, while others resulted in a particular outcome with either 70% or 30% probability. As in other studies (Easdale et al., 2019;Hogarth et al., 2008;Luque et al., 2016), the amount of attentional bias to stimuli depending on their predictability was measured to investigate attentional modulation based on the stimulus-outcome probabilistic relationship. Studies have consistently reported prolonged dwell time on stimuli with uncertainty about an upcoming outcome (De Tommaso, Mastropasqua, & Turatto, 2019;Koenig et al., 2017a). ...
Article
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The majority of previous studies on the value modulation of attention have shown that the magnitude of value-driven attentional bias correlates with the strength of reward association. However, relatively little is known about how uncertainty affects value-based attentional bias. We investigated whether attentional capture by previously rewarded stimuli is modulated by the uncertainty of the learned value without the influence of the strength of reward association. Participants were instructed to identify the line orientation in the target color circle. Importantly, each target color was associated with a different level of uncertainty by tuning the variation in reward delivery (Experiment 1) or reward magnitude (Experiment 2). Attentional interference for uncertainty-related distractors was greater than that for certainty distractors in Experiments 1 and 2. In addition, uncertainty-induced attentional bias disappeared earlier than attentional bias for certainty. The study demonstrated that uncertainty modulates value-based attentional capture in terms of strength and persistence, even when the effect of expected value remains constant.
... In a related result, Vadillo et al. (2016) found that a rise in prediction error, induced by contingency reversal, shifted attention away from the relevant cue towards the irrelevant cue. Luque et al. (2017) suggested that uncertainty-driven attention would increase with prediction error, engaging the agent in a resource-limiting attentional exploration process, where previously ignored cues or features of the environment are attended in a search for further information. ...
... Experimenrt 1 and ηp 2 =.04 in Experiment 2) and the differences between groups were only reliable in the first part of the testing phase (Trials 2 and 3) with the control group quickly overcoming that initial advantage in performance. Subtle parametric differences may be one of the reasons underlying the conflicting results in the literature such as those showing that uncertainty facilitates attention to the cues with the uncertain meaning (e.g., (Vadillo et al., 2016); see also Luque et al., 2017), facing the ones showing a more general effect affecting to cues that tend to be ignored otherwise (Torrents-Roda et al., 2021a). ...
Article
Two experiments were conducted to test the effect of experiencing associative interference on later learning. A predictive learning task was used in which human participants had to evaluate whether plants would grow or not (Outcome) after being watered with different fertilizers (Cues). Experiment 1 found that the increase in the prediction error produced by following a pre-exposed nontarget cue by the outcome, facilitated subsequent acquisition of the relationship between the pre-exposed target cue and the outcome. Experiment 2 compared whether learning about the target cue was differentially affected by experiencing two types of associative interference with the nontarget cue: Pairing the pre-exposed cue with the outcome and presenting the cue without outcome after being paired with it. The experience of associative interference with nontarget cues similarly facilitated subsequent learning about the target cue, regardless of the direction of the change in the nontarget cue-outcome relationship. It is suggested that the increase in prediction error produced by the experience of associative interference may lead to a general increase in attention that facilitates subsequent learning.
... Moreover, the model also accounts for the attentional changes occurring throughout the task, which were characterized by both the exploitation of the relevant cues as accuracy increased and the exploration of the irrelevant cues following the drop in accuracy (see Beesley et al., 2015). Our results add to recent evidence from both animal and human studies lending support to hybrid models (Beesley et al., 2015;Easdale et al., 2019;Haselgrove, Esber, Pearce, & Jones, 2010;Luque, Vadillo, Le Pelley, & Beeseley, 2017;Torrents-Rodas, Koenig, Uengoer, & Lachnit, 2020;Walker, Luque, Le Pelley, & Beesley, 2019). ...
... These effects are reminiscent of those produced by physically salient stimuli (Theeuwes, 1991(Theeuwes, , 1992Theeuwes, De Vries, & Godijn, 2003). Recently, Koenig, Kadel et al. (2017) and suggested that cues generating sustained prediction error may also exert attentional effects automatically (see also Luque et al., 2017). In the present study, the attentional changes following contingency reversal were presumably linked to a sudden rise in prediction error. ...
Article
We investigated whether a sudden rise in prediction error widens an individual’s focus of attention by increasing ocular fixations on cues that otherwise tend to be ignored. To this end, we used a discrimination learning task including cues that were either relevant or irrelevant for predicting the outcomes. Half of participants experienced contingency reversal once they had learned to predict the outcomes (reversal group, n = 30). The other half experienced the same contingencies throughout the task (control group, n = 30). As participants’ prediction accuracy increased, they showed a decrease in the number of fixations directed to the irrelevant cues. Following contingency reversal, participants in the reversal group showed a drop in accuracy, indicating a rise in prediction error, and fixated on the irrelevant cues more often than participants in the control group. We discuss the results in the context of attentional theories of associative learning.
... It has been suggested that an uncertainty type of processing might emerge when it is necessary to explore several sources of information providing uncertain predictions about the outcome (Le ), a condition that has not been fully investigated yet. While there are data in support of a hybrid model that reconciles both views Luque, Vadillo, Le Pelley, & Beesley, 2016), procedures in which there is the need to unveil the causal role of multiple uncertain cues have been overlooked, and might instead provide evidence in line with the uncertainty principle (Le . ...
... However, both factors contribute to the salience of a reward-cue, as suggested by a model of attention in associative learning recently proposed by Esber and Haselgrove (2011). Furthermore, this view is further supported by behavioral and neural evidence Fiorillo, Tobler, & Schultz 2003;Luque et al., 2016;Preuschoff, Bossaerts, & Quartz, 2006). In particular, a recent study have suggested that reward expectancy would affect the frequency of attentional capture, while reward uncertainty would affect the duration of attentional capture expressed in dwell time (Koenig et al., 2017). ...
Article
Full-text available
Reward-predicting cues attract attention because of their motivational value. A debated question regards the conditions under which the cue’s attentional salience is governed more by reward expectancy rather than by reward uncertainty. To help shedding light on this relevant issue, here, we manipulated expectancy and uncertainty using three levels of reward-cue contingency, so that, for example, a high level of reward expectancy (p = .8) was compared with the highest level of reward uncertainty (p = .5). In Experiment 1, the best reward–cue during conditioning was preferentially attended in a subsequent visual search task. This result was replicated in Experiment 2, in which the cues were matched in terms of response history. In Experiment 3, we implemented a hybrid procedure consisting of two phases: an omission contingency procedure during conditioning, followed by a visual search task as in the previous experiments. Crucially, during both phases, the reward–cues were never task relevant. Results confirmed that, when multiple reward-cue contingencies are explored by a human observer, expectancy is the major factor controlling both the attentional and the oculomotor salience of the reward–cue.
... In order to test the hypothesis that blocking (and, by extension, prediction error) produces a general change in the perceived salience of a stimulus, we need to examine whether blocking can produce a bias in the attentional processing of a stimulus that operates very rapidly and even when it is not required (unintentionally). A suitable approach comes from a study by Le Pelley et al. (2013; see also Haselgrove et al., 2016;Luque, Vadillo, Le Pelley, & Beesley, 2017;Vadillo, Orgaz, Luque, & Nelson, 2016), who tested attentional biases formed during associative learning using a variant of the dot probe task. This task is a well-established measure of rapid attentional capture by visual stimuli, used extensively in the study of attention to threatening stimuli (see Frewen, Dozois, Joanisse, & Neufeld, 2008). ...
Article
Full-text available
Blocking refers to the finding that less is learned about the relationship between a stimulus and an outcome if pairings are conducted in the presence of a second stimulus that has previously been established (via pretraining) as a reliable predictor of that outcome. Attentional models of associative learning suggest that blocking reflects a reduction in the attention paid to the blocked cue. We tested this idea in three experiments in which participants were trained in an associative learning task using a blocking procedure. Attention to stimuli was measured 250 ms after onset using an adapted version of the dot probe task. This task was presented at the beginning of each associative learning trial (Experiments 1 & 2) or in independent trials (Experiment 3). Each experiment found evidence of reduced attention to blocked stimuli as compared to pretrained stimuli, but no evidence of a corresponding difference in a control condition. In addition, this attentional bias correlated positively with the magnitude of blocking in associative learning, as measured by predictive-value judgments. Moreover, Experiments 2 and 3 found evidence of an influence of learning about predictiveness on memory for episodes involving stimuli. These findings are consistent with a central role of learned attentional biases in producing the blocking effect, and in the encoding of new memories. Although these attentional biases were likely created in an effortful way during learning, they exerted an influence on learning and memory very rapidly and independently of participants’ ongoing task goals.
... Immediately before this second phase, participants received continuity or change instructions regarding which stimuli would be important in determining the correct response in the following phase. A dot probe task was combined with the learning task throughout the experiment, as in Le Pelley et al.'s Experiment 3 [14] (see also [5,16,17]). By analyzing response times to the dot probe during Phase 2, we could examine the impact of experienced predictivess provided through training (in Phase 1) versus instructions on attentional bias. ...
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Previous studies have provided evidence that selective attention tends to prioritize the processing of stimuli that are good predictors of upcoming events over nonpredictive stimuli. In the present study we explored whether the mechanism responsible for this effect critically reflects the influence of prior experience of predictiveness (history of attentional selection of predictive stimuli), or whether it reflects a more flexible process that can be adapted to new verbally acquired knowledge. Our experiment manipulated participants' experience of the predictiveness of different stimuli over the course of trial-by-trial training; we then provided explicit verbal instructions regarding stimulus predictiveness that were designed to be either consistent or inconsistent with the previously established learned predictiveness. The effects of training and instruction on attention to stimuli were measured using a dot probe task. Results revealed a rapid attentional bias towards stimuli experienced as predictive (versus those experienced as nonpredictive), that was completely unaffected by verbal instructions. This was not due to participants' failure to recall or use instructions appropriately, as revealed by analyses of their learning about stimuli, and their memory for instructions. Overall, these findings suggest that stimuli experienced as predictive through trial-by-trial training produce a relatively inflexible attentional bias based on prior selection history, which is not (always) easily altered through instructions.
... For instance, participants tested by Vadillo et al. (2016) paid more attention to the experimental context after a sudden reversal of cue-outcome contingencies. According to Luque et al. (2016), these shifts in attention result from a rapid exploratory process directed at processing any feature of the experimental situation that might disambiguate the meaning of cues and thus avoid future prediction errors (see also Kruschke, 2001;Rosas et al., 2006a). Based on those studies, we can further speculate that a similar shift in attention may have taken place in the present experiments. ...
Article
Full-text available
Decades of research in extinction and interference show that contexts can play a critical role at disambiguating the meaning of cues that have been paired with different outcomes at different times. For instance, if a cue x is followed by outcome 1 in the first phase of an experiment and by outcome 2 in a second phase, responses to cue x tend to be consistent with outcome 2 when tested in a context similar to that of the second phase of the experiment. However, if participants are taken back to the original context of the first phase (i.e., ABA renewal) or to a completely new context (i.e., ABC or AAB renewal), their responses to x tend to be more consistent with outcome 1. Although the role of physical and temporal contexts has been well studied, other factors that can also modulate the selective retrieval of information after interference have received less attention. The present series of experiments shows how changes in cue configuration can modulate responding in a similar manner. Across five experiments using a human predictive learning task, we found that adding, removing or replacing elements from a compound cue that had undergone an interference treatment gave rise to a recovery of responding akin to that observed after context changes in AAB renewal. These results are consistent with those of previous studies exploring the effect of changes of cue configuration on interference. Taken together, these studies suggest that a change in cue configuration can have the functional properties of a context change, a finding with important implications for formal models of configural learning and for classical accounts of interference and information retrieval.
... From a different perspective, some researchers have begun to apply to the study of learning tests traditionally used in the field of attention, such as the "dot probe task" (Le Pelley, Vadillo, & Luque, 2013;Luque, Vadillo, Le Pelley, & Beesley, 2016). This test records the time it takes the participant to detect and respond to a point that can appear in any stimulus present in the task. ...
Article
Full-text available
A review of the literature suggests that attention, specifically attention to the contexts, also plays a relevant role on information retrieval. It also shows that attention to the contexts is modulated by the ambiguity of the situation, and the informative value contexts have. The virtues and limitations of different attentional theories of learning applied to the explanation of the effects of context change on retrieval of the information are discussed. This analysis uncovers the weaknesses of current research on context processing that should be corrected by future research: The need of independent measures of attention to the contexts, the evaluation of the mechanisms of contextual control, and the possibility of taking an evolutionary perspective on the effects of context change.
... Alternatively,Pearce and Hall (1980)proposed that attention instead al-locates resources to stimuli that have been associated with the most uncertainty, promoting learning and error reduction on future trials. It has been recently discussed that both forms of attention may coexist in associative learning: automatic attention akin to that proposed by Mackintosh (1975) that converges on strong predictors, and another concerned with the controlled selection of stimuli that are unreliable (Pearce and Mackintosh, 2010;Luque et al., 2016). In an event-related potential (ERP) study of the IBRE, Wills and colleagues (2014) found evidence to suggest enhanced attention to cue PR relative to PC following training. ...
Article
The inverse base-rate effect (IBRE) describes an apparent irrationality in human decision making whereby people tend to ignore category base rates and choose rarer options when classifying ambiguous stimuli. According to associative learning theories, people choose rare categories for ambiguous stimuli because rare cues draw more attention. Alternatively, inferential theories predict that people choose rare categories because ambiguous stimuli contrast more with well-established rules that predict membership in the common category. In this experiment, we used multi-voxel pattern analysis to decode which features of ambiguous stimuli, common or rare, participants are activating during an fMRI version of the IBRE task. Participants learned to predict four hypothetical diseases based on pairings of face, object, and scene cues. Objects and scenes were diagnostic cues, and predicted either a common or rare disease. Prior to the task, we collected independent localizer scans to characterize activation patterns associated with each of the different visual cues in object-selective cortex. During a test phase, ambiguous trials were presented in which a cue for the rare disease was paired with a cue for the common disease. We show that individuals engage qualitatively distinct neural processes when making rare versus common responses: choosing the rare category involved activation of cues associated with the common category. Consistent with inferential theories of base-rate neglect, our findings suggest that this surprising behavior involves a deliberative mechanism not explained by purely associative models.
... While attentional biases are usually measured in response to the presentation of salient stimuli, as for instance in Dot-Probe, or Visual Probe Tasks (Cox, Fadardi, Hosier, & Pothos, 2015;Field & Cox, 2008;; C. E. Wiers et al., 2016), anticipatory processes may also play a role in attentional biases. That is: If an individual has learned that a certain type of stimulus is likely to appear at a certain time or location, then this foreknowledge may evoke biases in pre-stimulus preparation (Le Pelley, Vadillo, & Luque, 2013;Luque et al., 2016;Notebaert, Crombez, Van Damme, De Houwer, & Theeuwes, 2011;Van Damme, Crombez, Hermans, Koster, & Eccleston, 2006). Automatic shifts in attention to or away from upcoming stimuli would be driven by their predicted outcomes, i.e. the consequences of making the shift, if and when the stimulus occurs. ...
Article
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Attentional bias variability may be related to alcohol abuse. Of potential use for studying variability is the anticipatory attentional bias: Bias due to the locations of predictively-cued rather than already-presented stimuli. The hypothesis was tested that conflicting automatic associations are related to attentional bias variability. Further, relationships were explored between anticipatory biases and individual differences related to alcohol use. 74 social drinkers performed a cued Visual Probe Task and univalent Single-Target Implicit Associations Tasks. Questionnaires were completed on risky drinking, craving, and motivations to drink or refrain from drinking. Conflict was related to attentional bias variability at the 800 ms Cue-Stimulus Interval. Further, a bias related to craving and risky drinking was found at the 400 ms Cue-Stimulus Interval. Thus, the selection of attentional responses was biased by predicted locations of expected salient stimuli. The results support a role of conflicting associations in attentional bias variability.
... On probe trials, the cues are followed by a probe stimulus instead of the emotional and neutral pictures, to which participants are required to react pressing a button on the keyboard following task instructions. Cue-related effects on performance on probe trials are thus caused by the contingency between cues and predicted emotional stimuli (Le Pelley, Vadillo, & Luque, 2013;Luque et al., 2016;Notebaert et al., 2011;Van Damme, Crombez, Hermans, Koster, & Eccleston, 2006), with no emotional stimulus actually being presented at all on that trial. The cVPT has been used to provide novel information on relationships between anticipatory attentional biases for alcohol stimuli, automatic associations and conflict between them, craving, and motives to drink or refrain from drinking (Gladwin & Vink, 2017). ...
Article
Dot-Probe or Visual Probe Tasks (VPTs) are used extensively to measure attentional biases. A novel variant termed the cued VPT (cVPT) was developed to focus on the anticipatory component of attentional bias. The current study aimed to establish an anticipatory attentional bias to threat using the cVPT and compare its split-half reliability with a typical Dot-Probe task. 120 students performed the cVPT task and Dot-Probe tasks. Essentially, the cVPT uses cues that predict the location of pictorial threatening stimuli, but on trials on which probe stimuli are presented the pictures do not appear. Hence, actual presentation of emotional stimuli did not affect responses. The reliability of the cVPT was higher at most Cue-Stimulus Intervals, and was .56 overall. A clear anticipatory attentional bias was found. In conclusion, the cVPT may be of methodological and theoretical interest. Using visually neutral predictive cues may remove sources of noise that negatively impact reliability. Predictive cues are able to bias response selection, suggesting a role of predicted outcomes in automatic processes.
... Indeed, research shows that people selectively direct their attention in ways that optimize learning (Kruschke, Kappenman, & Hetrick, 2005;Matsuka & Corter, 2008;Nosofsky, 1984;Rehder & Hoffman, 2005). In addition, people naturally attend to novel information in order to reduce uncertainty (Gottlieb, Oudeyer, Lopes, & Baranes, 2013;Luque, Vadillo Nistal, Le Pelley, & Beesley, 2017) or adapt to a changing environment (Ranganath & Rainer, 2003). Indeed, the brain and nervous system are wired to automatically orient people's attention toward novel stimuli in the environment (Gottlieb et al., 2013;Ranganath & Rainer, 2003;Sokolov, 1963). ...
Chapter
In this chapter, we describe how a simple attentional mechanism can account for a wide variety of phenomena in social perception. According to Attention Theory (Kruschke, 1996, 2003), people preferentially attend to differentiating information in order to maximize category learning. When learning multiple social categories, people attend to all features that characterize the first-learned category but shift their attention to features that uniquely distinguish a later-learned category from the first. As a result, they form a stronger impression of the later-learned social category. First, we review research on attentional processes in stereotype formation and group categorization. We show how Attention Theory can account for both category accentuation and illusory correlation in the formation of majority and minority group stereotypes. We then explain how attention shifting influences face perception and racial categorization. Second, we describe attentional processes as they relate to context-based impression formation and the influence of individual- and group-based expectancies on context-based impressions. Last, we discuss implications for impression change.
... On probe trials, the cues are followed by a probe stimulus instead of the emotional and neutral pictures, to which participants are required to react pressing a button on the keyboard following task instructions. Cue-related effects on performance on probe trials are thus caused by the contingency between cues and predicted emotional stimuli (Le Pelley, Vadillo, & Luque, 2013;Luque et al., 2016;Notebaert et al., 2011;Van Damme, Crombez, Hermans, Koster, & Eccleston, 2006), with no emotional stimulus actually being presented at all on that trial. The cVPT has been used to provide novel information on relationships between anticipatory attentional biases for alcohol stimuli, automatic associations and conflict between them, craving, and motives to drink or refrain from drinking (Gladwin & Vink, 2017). ...
Article
Dot‐probe or visual probe tasks (VPTs) are used extensively to measure attentional biases. A novel variant termed the cued VPT (cVPT) was developed to focus on the anticipatory component of attentional bias. This study aimed to establish an anticipatory attentional bias to threat using the cVPT and compare its split‐half reliability with a typical dot‐probe task. A total of 120 students performed the cVPT task and dot‐probe tasks. Essentially, the cVPT uses cues that predict the location of pictorial threatening stimuli, but on trials on which probe stimuli are presented the pictures do not appear. Hence, actual presentation of emotional stimuli did not affect responses. The reliability of the cVPT was higher at most cue–stimulus intervals and was .56 overall. A clear anticipatory attentional bias was found. In conclusion, the cVPT may be of methodological and theoretical interest. Using visually neutral predictive cues may remove sources of noise that negatively impact reliability. Predictive cues are able to bias response selection, suggesting a role of predicted outcomes in automatic processes.
... One aspect that has been shown to influence attentional prioritization is the emotionality of stimuli: people show an attentional bias towards emotion-laden stimuli, relative to emotionally neutral stimuli (Anderson, 2005;Ohman, Flykt, & Esteves, 2001;Vuilleumier & Huang, 2009). Notably, it has been argued that attentional prioritization of emotional stimuli can be relatively automatic, reflecting a rapid and unintentional psychological process that is neither voluntary nor oriented to achieve the goals of the current task (Evans, 2008;Feldmann-Wüstefeld, Uengoer, & Schubö, 2015;Luque, Vadillo, Le Pelley, & Beesley, 2017;Shone, Harris, & Livesey, 2015). One influential suggestion has been that emotional stimuli can be processed via a 'low-road' path from the amygdala that bypasses those cortical regions associated with higher processing (LeDoux, 2000;Morris, Ohman, & Dolan, 1999;Tamietto & de Gelder, 2010). ...
Article
Facial emotion constitutes an important source of information, and rapid processing of this information may bring adaptive advantages. Previous evidence suggests that emotional faces are sometimes prioritised for cognitive processing. Three experiments used an emotion-induced blindness task to examine whether this prioritisation occurs in a purely stimulus-driven fashion or whether it emerges only when the faces are task-relevant. Angry or neutral faces appeared as distractors in a rapid serial visual presentation sequence, shortly before a target that participants were required to identify. Either the emotion (Experiment 1) or gender (Experiments 2 and 3) of the distractor face indicated whether a correct/incorrect response to the target would produce reward/punishment, or not. The three experiments found that reward-related faces impaired subsequent target identification, replicating previous results. Target identification accuracy was also impaired following angry faces, compared with neutral faces, demonstrating an emotion-induced attentional bias. Importantly, this impairment was observed even when face emotion was entirely irrelevant to the participants’ ongoing task (in Experiments 2 and 3), suggesting that rapid processing of the facial emotion might arise (at least in part) from the operation of relatively automatic cognitive–perceptual processes.
... Immediately before this second phase, participants received continuity or change instructions regarding which stimuli would be important in determining the correct response in the following phase. A dot probe task was combined with the learning task throughout the experiment, as in Le Pelley et al.'s Experiment 3 [14] (see also [5,16,17]). By analyzing response times to the dot probe during Phase 2, we could examine the impact of experienced predictivess provided through training (in Phase 1) versus instructions on attentional bias. ...
Article
Full-text available
Previous studies have provided evidence that selective attention tends to prioritize the processing of stimuli that are good predictors of upcoming events over nonpredictive stimuli. Moreover, studies using eye-tracking to measure attention demonstrate that this attentional bias towards predictive stimuli is at least partially under voluntary control and can be flexibly adapted via instruction. Our experiment took a similar approach to these prior studies, manipulating participants’ experience of the predictiveness of different stimuli over the course of trial-by-trial training; we then provided explicit verbal instructions regarding stimulus predictiveness that were designed to be either consistent or inconsistent with the previously established learned predictiveness. Critically, we measured the effects of training and instruction on attention to stimuli using a dot probe task, which allowed us to assess rapid shifts of attention (unlike the eye-gaze measures used in previous studies). Results revealed a rapid attentional bias towards stimuli experienced as predictive (versus those experienced as nonpredictive), that was completely unaffected by verbal instructions. This was not due to participants’ failure to recall or use instructions appropriately, as revealed by analyses of their learning about stimuli, and their memory for instructions. Overall, these findings suggest that rapid attentional biases such as those measured by the dot probe task are more strongly influenced by our prior experience during training than by our current explicit knowledge acquired via instruction.
... Turning to attention, participants in the uncertain condition spent longer looking at cues overall (as a proportion of the trial time) compared to participants in the certain condition (see also Beesley et al., 2015;Easdale, Le Pelley, & Beesley, 2019;Luque, Vadillo, Le Pelley, & Beesley, 2017). Participants also spent a greater proportion of trial-time fixating on predictive cues over non-predictive cues: this greater attention to more informative cues suggests "attentional exploitation." ...
Article
The exploitation-exploration (EE) trade-off describes how, when making a decision, an organism must often choose between a safe alternative with a known pay-off, and one or more riskier alternatives with uncertain pay-offs. Recently, the concept of the EE trade-off has been extended to the examination of how organisms distribute limited attentional resources between several stimuli. This work suggests that when the rules governing the environment are certain, participants learn to “exploit” by attending preferentially to cues that provide the most information about upcoming events. However, when the rules are uncertain, people “explore” by increasing their attention to all cues that may provide information to help in predicting upcoming events. In the current study, we examine how uncertainty affects the EE trade-off in attention using a contextual two-armed bandit task, where participants explore with both their attention and their choice behavior. We find evidence for an influence of uncertainty on the EE trade-off in both choice and attention. These findings provide support for the idea of an EE trade-off in attention, and that uncertainty is a primary motivator for exploration in both choice and attentional allocation.
... learned predictiveness effect, see:Beesley et al., 2015;Griffiths & Mitchell, 2008; Haselgrove et al., 2015;Le Pelley et al., 2011;Le Pelley & McLaren, 2003;Le Pelley et al., 2013;Le Pelley et al., 2009;Le Pelley et al., 2010;Lochmann & Wills, 2003;Mitchell et al., 2012). Conversely, much of the research which provides support for the Pearce-Hall model has been found using animal subjects (see:Dickinson, Hall & Mackintosh, 1976;Haselgrove, Esber, Pearce & Jones, 2010;Kaye & Pearce, 1984;Swan & Pearce, 1988;Wilson, Boumphrey & Pearce, 1992) with only recent, relatively scant, support coming from studies involving humans (see: Beesley, Nguyen, Pearson, Le Pelley, 2015;Easdale, Le Pelley & Beesley, 2017;Griffiths, Johnson & Mitchell, 2011;Luque, Vadillo, Le Pelley & Beesley, 2017). ...
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Over the last forty years, experimental support for different models of associative learning has come from a range of phenomena. Support for the Rescorla-Wagner (1972) model comes from blocking and overshadowing experiments; however, this model is unable to explain the findings of latent inhibition experiments. The Mackintosh (1975) model, on the other hand, is able to accommodate the findings from blocking, overshadowing and latent inhibition experiments, as well as discrimination learning, relative validity, learned irrelevance, intra-/extra-dimensional shift (IDS/EDS) and learned predictiveness experiments. The model proposed by Pearce and Hall (1980) is also able to explain the findings of blocking, overshadowing and latent inhibition experiments, but in addition to this it is also able to accommodate the effects of partial reinforcement and negative transfer. In an attempt to unify the theories into a single model that is able to explain all the aforementioned phenomena, Le Pelley (2004) proposed a hybrid model of associative learning, but it was not easily able to incorporate the effects of learned value. Alternatively, Esber and Haselgrove (2011) proposed a model that reconciles the influence of predictiveness and uncertainty into a single mechanism for attentional allocation, and this model was better able to explain the experimental findings of learned value. Theories of associative learning claim that a cue’s predictive validity determines the amount of attention it attracts and to what extent it is subsequently learned about (e.g. Mackintosh, 1975; Pearce & Hall, 1980). In Chapter 2, using eye-tracking methodology during a learned predictiveness task, several measures of overt attention were recorded and compared on trials where the predictive contingency was certain or less certain. Findings revealed that, at a within-trial level, good predictors of an outcome attracted more attention compared to irrelevant cues. Although, at a between-trial level, uncertain trials attracted more attention compared to certain trials. These findings provide support for the conflicting attentional modulation predictions made by the Mackintosh (1975) and Pearce-Hall (1980) models. Consequently, these findings can only be fully explained by appealing to a model of associative learning that incorporates both the principles of predictiveness and uncertainty (e.g. Le Pelley, 2004; Esber & Haselgrove, 2011). Prior to eye-tracking becoming more widely available as a measure of overt visual attention, stimulus associability was used as an indirect measure of attention since it is assumed that the speed at which a stimulus is learned about reflects the amount of attention it attracts. This is demonstrated in the IDS/EDS task which consistently finds that IDS are easier than EDS because in the IDS condition the higher associability of the predictive dimension in Stage 1 facilitates learning when generalised into Stage 2. Until now, eye gaze during an IDS/EDS task has not been investigated to determine whether the effect results from a shift in overt attention from Stage 1 into Stage 2. Chapter 3 revealed that participants acquired an attentional bias towards predictive cues in Stage 1 which transferred into Stage 2; however, in the EDS condition this bias was maintained only very briefly. Eye-tracking during learned predictiveness tasks using adult participants has revealed that cues which are good predictors of an outcome attract more overt visual attention than cues which are irrelevant. However, thus far, little research has investigated whether good predictors of reinforcement and non-reinforcement show a comparable effect. Moreover, it is currently unclear whether children and non-human animals demonstrate the learned predictiveness effect. Chapter 4 employed the same design and stimuli to examine eye gaze towards cues during a simple learned predictiveness task (AX+, AY+, BX-, BY-) in adults, children and an orangutan. Results revealed that all participants demonstrated the learned predictiveness effect, directing more attention towards cues that were good predictors of the outcome compared with cues that were irrelevant. However, for adult humans this effect was only present on reinforced trials and questionnaire data suggested they had only learned about one of the predictive contingencies. Contemporary discussions of associative learning have emphasised the importance of a cue’s predictive relevance in determining learned variations in attention. However, most theoretical accounts of the effect do not capture the notion of prediction – only associative strength, or relative associative strength (e.g. Mackintosh, 1975). In Chapter 5, letters were established as congruent or incongruent cues of other letters presented simultaneously or serially with a target cue. Results revealed no difference in the amount of attention directed towards congruent and incongruent cues if stimuli were presented simultaneously or serially when participants were required to respond to the identity of the target cue. However, an attentional bias towards congruent cues compared to incongruent cues was found when cues were presented serially, if participants were permitted to predict the identity of the target before its onset.
... Controlled attention will be engaged in order to reduce uncertainty (Pearce & Hall, 1980;, but the ability to do this may be weakened as task difficulty increases. In contrast, attention paid to predictive cues may reflect a more automatic form of attention (Le Pelley, Vadillo, & Luque, 2013;Luque, Vadillo, Le Pelley, & Beesley, 2017; but see Mitchell, Griffiths, Seetoo, & Lovibond, 2012, for a potential role of controlled processing in the predictiveness effect). When task difficulty is low the effect of controlled attention paid to uncertain cues may be greater than the automatic attention paid to predictive cues, but as task difficulty increases the cognitive resources required to engage controlled attention are decreased such that automatic attention wins out over controlled attention. ...
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Attention determines which cues receive processing and are learned about. Learning, however, leads to attentional biases. In the study of animal learning, in some circumstances, cues that have been previously predictive of their consequences are subsequently learned about more than are nonpredictive cues, suggesting that they receive more attention. In other circumstances, cues that have previously led to uncertain consequences are learned about more than are predictive cues. In human learning, there is a clear role for predictiveness, but a role for uncertainty has been less clear. Here, in a human learning task, we show that cues that led to uncertain outcomes were subsequently learned about more than were cues that were previously predictive of their outcomes. This effect occurred when there were few uncertain cues. When the number of uncertain cues was increased, attention switched to predictive cues. This pattern of results was found for cues (1) that were uncertain because they led to 2 different outcomes equally often in a nonpredictable manner and (2) that were used in a nonlinear discrimination and were not predictive individually but were predictive in combination with other cues. This suggests that both the opposing predictiveness and uncertainty effects were determined by the relationship between individual cues and outcomes rather than the predictive strength of combined cues. These results demonstrate that learning affects attention; however, the precise nature of the effect on attention depends on the level of task complexity, which reflects a potential switch between exploration and exploitation of cues. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
... Different versions of the two-phase paradigm have been adopted in a great wealth of studies showing the persistence of reward-based attentional capture also with more complex stimuli and with a variety of tasks in different modalities (Anderson, 2016b), as well in the spatial (Anderson & Kim, 2018) and temporal (Raymond & O'Brien, 2009) domains of attention (for reviews see Bourgeois et al., 2016;Chelazzi et al., 2013;Failing & Theeuwes, 2017b;Luque et al., 2017). ...
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A great wealth of studies has investigated the capacity of motivationally relevant stimuli to bias attention, suggesting that reward predicting cues are prioritized even when reward is no longer delivered and when attending to such stimuli is detrimental to reward achievement. Despite multiple procedures have been adopted to unveil the mechanisms whereby reward cues gain attentional salience, some open questions remain. Indeed, mechanisms different from motivation can be responsible for the capture of attention triggered by the reward cue. In addition, we note that at present only a few studies have sought to address whether the cue attractiveness dynamically follows changes in the associated reward value. Investigating how and to what extent the salience of the reward cue is updated when motivation changes, could help shedding light on how reward-cues attain and maintain their capacity to attract attention, and therefore on apparent irrational attentive behaviors. K E Y W O R D S attention, devaluation, reward, salience, selection history
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Surprising violations of outcome expectancies have long been known to enhance the associability of Pavlovian cues; that is, the rate at which the cue enters into further associations. The adaptive value of such enhancements resides in promoting new learning in the face of uncertainty. However, it is unclear whether associability enhancements reflect increased associative plasticity within a particular behavior system, or whether they can facilitate learning between a cue and any arbitrary outcome, as suggested by attentional models of conditioning. Here, we show evidence consistent with the latter hypothesis. Violating the outcome expectancies generated by a cue in an appetitive setting (feeding behavior system) facilitated subsequent learning about the cue in an aversive setting (defense behavior system). In addition to shedding light on the nature of associability enhancements, our findings offer the neuroscientist a behavioral tool to dissociate their neural substrates from those of other, behavior system- or valence-specific changes. Moreover, our results present an opportunity to utilize associability enhancements to the advantage of counterconditioning procedures in therapeutic contexts.
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fMRI investigations have examined the extent to which reward and punishment motivation are associated with common or opponent neural systems, but such investigations have been limited by confounding variables and methodological constraints. The present study aimed to address limitations of earlier approaches and more comprehensively evaluate the extent to which neural activation associated with reward and punishment motivation reflects opponent or shared systems. Participants completed a modified monetary incentive delay task, which involved the presentation of a cue followed by a target to which participants were required to make a speeded button press. Using a factorial design, cues indicated whether monetary reward and/or loss (i.e., cues signaled probability of reward, punishment, both, or neither) could be expected depending upon response speed. Neural analyses evaluated evidence of (a) directionally opposing effects by testing for regions of differential activation for reward and punishment anticipation, (b) mutual inhibition by testing for interactive effects of reward and punishment anticipation within a factorial design, and (c) opposing effects on shared outputs via a psychophysiological interaction analysis. Evidence supporting all three criteria for opponent systems was obtained. Collectively, present findings support conceptualizing reward and punishment motivation as opponent forces influencing brain and behavior and indicate that shared activation does not suggest the operation of a common neural mechanism instantiating reward and punishment motivation.
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We investigated whether the temporal dynamics of attention could be used to reconcile exploitative and explorative attentional learning theories. Participants trained on a categorisation task where some stimuli were predictive (P) of the correct response while others were non-predictive (NP). These stimuli were then used in a dot probe task in which we varied the stimulus onset asynchrony (SOA) between the cues and the target. Participants responded faster to the target when it appeared over a P cue at each SOA. The reaction time advantage towards the P cues increased proportionally with SOA, suggesting that participants were strategically processing the cues. Target-elicited N2pc amplitudes at short SOAs suggested that P cues were preferentially processed, consistent with exploitation. However, the amplitudes at a longer SOA suggested that after the P cues were processed, they were inhibited. This inhibition could bias attention towards other currently ambiguous stimuli, consistent with exploration.
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Past research in animals has suggested that attention is distributed to exploit known relationships between stimuli (e.g., Mackintosh, 1975) and explore stimuli whose consequences are uncertain (e.g., Pearce & Hall, 1980). The resulting changes in attention influence how animals learn new information involving those stimuli. While there is strong support for exploitative attention and its effects on learning in humans, the evidence for exploratory attention is less well developed. Two experiments examined whether preferential allocation of attention (as measured by eye-gaze) to cues associated with uncertainty leads to more rapid learning of new associations involving these cues in the future. In each experiment, participants first learned about compounds containing one predictive cue and one non-predictive cue. The level of uncertainty during this first stage of training was also manipulated: cue-outcome relationships were either deterministic (certain) or probabilistic (uncertain). In a second stage, new cue-outcome relationships were trained and the uncertainty of these relationships could be resolved by learning about the previously non-predictive cues. As a result of the manipulation of uncertainty in the first stage, some participants experienced a sudden onset of uncertainty at the start of this second stage, while others experienced a stable level of uncertainty throughout the experiment. Experiment 1 showed that participants who experienced an onset of uncertainty learned novel cue-outcome associations faster than participants for whom uncertainty was constant. Furthermore, participants experiencing unexpected uncertainty showed a greater increase in attention to cues in Stage 2. When the first stage of training was extended in Experiment 2 a larger difference in the rate of learning between the two conditions was observed in the second stage. We argue that this represents evidence for an effect of exploratory attention on rate of learning in humans.
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Two experiments examined biases in selective attention during contextual cuing of visual search. When participants were instructed to search for a target of a particular colour, overt attention (as measured by the location of fixations) was biased strongly towards distractors presented in that same colour. However, when participants searched for targets that could be presented in one of two possible colours, overt attention was not biased between the different distractors, regardless of whether these distractors predicted the location of the target (repeating) or did not (randomly arranged). These data suggest that selective attention in visual search is guided only by the demands of the target detection task (the attentional set) and not by the predictive validity of the distractor elements.
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We sought to provide evidence for a combined effect of two attentional mechanisms during associative learning. Participants' eye movements were recorded as they predicted the outcomes following different pairs of cues. Across the trials of an initial stage, a relevant cue in each pair was consistently followed by one of two outcomes, while an irrelevant cue was equally followed by either of them. Thus, the relevant cue should have been associated with small relative prediction errors, compared to the irrelevant cue. In a later stage, each pair came to be followed by one outcome on a random half of the trials and by the other outcome on the remaining half, and thus there should have been a rise in the overall prediction error. Consistent with an attentional mechanism based on relative prediction error, an attentional advantage for the relevant cue was evident in the first stage. On the other hand, in accordance with a mechanism linked to overall prediction error, the attention paid to both types of cues increased at the beginning of the second stage. These results showed up in both dwell times and within-trial patterns of fixations, and they were predicted by a hybrid model of attention.
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Allocating attention to significant events, such as a salient object, is effortless. Our brain is effective on this type of processing because doing so is generally beneficial for survival. However, a salient object could also be distracting and ignoring it costs a large amount of cognitive resource. In the present study, we conducted two behavioral experiments to investigate the effect of learned suppression of a salient color. Particularly, we were interested in the effect of learning in a new task context in which the previously suppressed color was task irrelevant. In Experiment 1, we trained the participants for five days with explicit instruction to suppress a color singleton distractor in a visual search task. We measured the effect of training with a dot probe task before and after the training. Colors in the dot probe task only served as the background and were not associated with the position of the target dot. However, we found that attention was involuntarily biased away from the previously suppressed color. In Experiment 2, the color singleton could either be the target or distractor in the visual search task, making the suppression of the color singleton inefficient for task performance. The results showed no training effect in the dot probe task after this manipulation. These findings provided direct evidence for the learned low priority of attention after training to suppress the color singleton distractor.
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Besides visual salience and observers' current intention, prior learning experience may influence deployment of visual attention. Associative learning models postulate that observers pay more attention to stimuli previously experienced as reliable predictors of specific outcomes. To investigate the impact of learning experience on deployment of attention, we combined an associative learning task with a visual search task and measured event-related potentials of the EEG as neural markers of attention deployment. In the learning task, participants categorized stimuli varying in color/shape with only one dimension being predictive of category membership. In the search task, participants searched a shape target while disregarding irrelevant color distractors. Behavioral results showed that color distractors impaired performance to a greater degree when color rather than shape was predictive in the learning task. Neurophysiological results show that the amplified distraction was due to differential attention deployment (N2pc). Experiment 2 showed that when color was predictive for learning, color distractors captured more attention in the search task (ND component) and more suppression of color distractor was required (PD component). The present results thus demonstrate that priority in visual attention is biased toward predictive stimuli, which allows learning experience to shape selection. We also show that learning experience can overrule strong top-down control (blocked tasks, Experiment 3) and that learning experience has a longer-term effect on attention deployment (tasks on two successive days, Experiment 4). © 2015 Society for Psychophysiological Research.
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Previous animal-learning studies have shown that the effect of the predictive history of a cue on its associability depends on whether priority was set to the absolute or relative predictiveness of that cue. The present study tested this assumption in a human contingency-learning task. In both experiments, one group of participants was trained with predictive and nonpredictive cues that were presented according to an absolute-predictiveness principle (either continuously or partially reinforced cue configurations), whereas a second group was trained with co-occurring cues that differed in predictiveness (emphasizing the relative predictive validity of the cues). In both groups, later test discriminations were learned more readily if the discriminative cues had been predictive in the previous learning stage than if they had been nonpredictive. These results imply that both the absolute and relative predictiveness of a cue lead positive transfer with regard to its associability. The data are discussed with respect to attentional models of associative learning.
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Attention provides the gateway to cognition, by selecting certain stimuli for further analysis. Recent research demonstrates that whether a stimulus captures attention is not determined solely by its physical properties, but is malleable, being influenced by our previous experience of rewards obtained by attending to that stimulus. Here we show that this influence of reward learning on attention extends to task-irrelevant stimuli. In a visual search task, certain stimuli signaled the magnitude of available reward, but reward delivery was not contingent on responding to those stimuli. Indeed, any attentional capture by these critical distractor stimuli led to a reduction in the reward obtained. Nevertheless, distractors signaling large reward produced greater attentional and oculomotor capture than those signaling small reward. This counterproductive capture by task-irrelevant stimuli is important because it demonstrates how external reward structures can produce patterns of behavior that conflict with task demands, and similar processes may underlie problematic behavior directed toward real-world rewards. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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Prior to each target letter string presented visually to 120 university students in a speeded word–nonword classification task, either {bird, body, building,} or {xxx} appeared as a priming event. Five types of word-prime/word-target trials were used: bird-robin, bird-arm, body-door, body-sparrow, and body-heart. The stimulus onset asynchrony (SOA) between prime and target letter string varied between 250 and 2,000 msec. At 2,000-msec SOA, reaction times (RTs) on bird-robin type trials were faster than on xxx-prime trials (facilitation), whereas RTs on bird-arm type trials were slower than on xxx-prime (inhibition). As SOA decreased, the facilitation effect on bird-robin trials remained constant, but the inhibition effect on bird-arm decreased until, at 250-msec SOA, there was no inhibition. For Shift conditions at 2,000-msec SOA, facilitation was obtained on body-door type trials and inhibition was obtained on body-sparrow type. These effects decreased as SOA decreased until there was no facilitation or inhibition. On body-heart type trials, there was an inhibition effect at 2,000 msec SOA, which decreased as SOA decreased until, at 250-msec SOA, it became a facilitation effect. Results support the theory of M. I. Posner and S. R. Snyder (1975) that postulated 2 distinct components of attention: a fast automatic inhibitionless spreading-activation process and a slow limited-capacity conscious-attention mechanism. (27 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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This research provides evidence that there are 2 competing attentional mechanisms in category learning. Attentional persistence directs attention to attributes previously found to be predictive, whereas attentional contrast directs attention to attribute values that have not already been associated with a category. Three experiments provided evidence for these mechanisms. Experiments 1 and 2 provided evidence for persistence because increased attention to an attribute followed training in which that attribute was relevant. These experiments also provided evidence for contrast because attention was also increased to the values of an attribute when the values of another, more salient attribute had already been associated with categories. Experiment 3 provided evidence that persistence operates primarily at the level of attributes, whereas contrast operates at the level of attribute values.
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Three visual-search experiments tested whether the preattentive parallel stage can-selectively guide the attentive stage to a particular known-to-be-relevant target feature. Subjects searched multielement displays for a salient green circle that had a unique form when surrounded by green nontarget squares or had a unique color when surrounded by red nontarget circles. In the distractor conditions, a salient item in the other dimension was present as well. As an extension of earlier findings (Theeuwes, 1991), the results showed that complete top-down selectivity toward a particular feature was not possible, not even after extended and consistent practice. The results reveal that selectivity depends on the relative discriminability of the stimulus dimensions: the presence of an irrelevant item with a unique color interferes with parallel search for a unique form, and vice versa.
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Learning theory and computational accounts suggest that learning depends on errors in outcome prediction as well as changes in processing of or attention to events. These divergent ideas are captured by models, such as Rescorla-Wagner (RW) and temporal difference (TD) learning on the one hand, which emphasize errors as directly driving changes in associative strength, vs. models such as Pearce-Hall (PH) and more recent variants on the other hand, which propose that errors promote changes in associative strength by modulating attention and processing of events. Numerous studies have shown that phasic firing of midbrain dopamine (DA) neurons carries a signed error signal consistent with RW or TD learning theories, and recently we have shown that this signal can be dissociated from attentional correlates in the basolateral amygdala and anterior cingulate. Here we will review these data along with new evidence: (i) implicating habenula and striatal regions in supporting error signaling in midbrain DA neurons; and (ii) suggesting that the central nucleus of the amygdala and prefrontal regions process the amygdalar attentional signal. However, while the neural instantiations of the RW and PH signals are dissociable and complementary, they may be linked. Any linkage would have implications for understanding why one signal dominates learning in some situations and not others, and also for appreciating the potential impact on learning of neuropathological conditions involving altered DA or amygdalar function, such as schizophrenia, addiction or anxiety disorders.
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Two experiments used eye-tracking procedures to investigate the relationship between attention and associative learning in human participants. These experiments found greater overt attention to cues experienced as predictive of the outcomes with which they were paired, than to cues experienced as nonpredictive. Moreover, this attentional bias persisted into a second training phase when all cues were equally predictive of the outcomes with which they were paired, and it was accompanied by a related bias in the rate of learning about these cues. These findings are consistent with the attentional model of associative learning proposed by Mackintosh (1975), but not with that proposed by Pearce and Hall (1980).
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Four appetitive Pavlovian conditioning experiments with rats examined the rate at which the discrimination between compounds AY and AX was solved relative to the discrimination between compounds AY and BY. In Experiments 1 and 2, these discriminations were preceded by training in which A and B were continuously reinforced and X and Y were partially reinforced. Consistent with the Pearce and Hall (1980) model, the results showed that the AY/AX discrimination was solved more readily than the AY/BY discrimination. In Experiments 3 and 4, the discriminations were preceded by feature-positive training in which trials with AX and BY signaled food but trials with X and Y did not. Consistent with the Mackintosh (1975) model, the results showed that the AY/BY discrimination was solved more readily than the AY/AX discrimination. These results are discussed with respect to a hybrid model of conditioning and attention.
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Three localized, visual pattern stimuli were trained as predictive signals of auditory outcomes. One signal partially predicted an aversive noise in Experiment 1 and a neutral tone in Experiment 2, whereas the other signals consistently predicted either the occurrence or absence of the noise. The expectation of the noise was measured during each signal presentation, and only participants for whom this expectation demonstrated contingency knowledge showed differential attention to the signals. Importantly, when attention was measured by visual fixations, the contingency-aware group attended more to the partially predictive signal than to the consistent predictors in both experiments. This profile of visual attention supports the Pearce and Hall (1980)17. Pearce , J. M. and Hall , G. 1980. A model for Pavlovian learning: Variations in the effectiveness of conditioned but not unconditioned stimuli. Psychological Review, 87: 532–552. [CrossRef], [PubMed], [Web of Science ®]View all references theory of the role of attention in associative learning.
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In three experiments the orienting response (OR) to a light was reduced by presenting rats with a serial conditioning schedule in which the light was always followed by a tone, and the tone was intermittently paired with food. The original strength of the OR was then restored by changing the nature of the serial conditioning: the light was followed either by the tone and food, or by nothing. Experiments 1 and 2 demonstrated that this change resulted in an increase in the OR relative to control groups, which continued to receive the original training. Experiment 3 revealed a similar effect using a within-subjects design. A further finding from the first two experiments was that the recovery of the OR was accompanied by an enhancement in the conditionability of the light. Overall, the results suggest that the accuracy with which the light signals the events that immediately follow it is an important determinant of the strength of the OR it elicits.
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This book provides a foundation to the principles of psychology. It draws upon the natural sciences, avoiding metaphysics, for the basis of its information. According to James, this book, assuming that thoughts and feelings exist and are vehicles of knowledge, thereupon contends that psychology, when it has ascertained the empirical correlation of the various sorts of thought or feeling with definite conditions of the brain, can go no farther as a natural science. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Investigated the strength of the orienting response to a light in 3 inhibitory conditioning experiments, using 72 male Sprague-Dawley rats. In Exp I, the occurrence of the light was negatively correlated with food delivery; this procedure resulted in a decline in the strength of the orienting response. A more rapid decline in the strength of this response was observed in Ss receiving the light and food presented randomly or the light presented alone. In the remaining experiments, a discrimination procedure was used in which the light was presented, nonreinforced, simultaneously with a tone. On reinforced trials, the tone was presented alone and was followed either directly by food (Exp II) or by a clicker that signaled food (Exp III). The results are smiliar to those of Exp I. It is concluded that the strength of the orienting response to a light may reflect the amount of attention or central processing that it receives, which itself is determined by the accuracy with which its immediate consequences are predicted. (29 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Prominent models of attentional control assert a dichotomy between top-down and bottom-up control, with the former determined by current selection goals and the latter determined by physical salience. This theoretical dichotomy, however, fails to explain a growing number of cases in which neither current goals nor physical salience can account for strong selection biases. For example, equally salient stimuli associated with reward can capture attention, even when this contradicts current selection goals. Thus, although 'top-down' sources of bias are sometimes defined as those that are not due to physical salience, this conception conflates distinct--and sometimes contradictory--sources of selection bias. We describe an alternative framework, in which past selection history is integrated with current goals and physical salience to shape an integrated priority map.
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How do individuals decide to act based on a rewarding status quo versus an unexplored choice that might yield a better outcome? Recent evidence suggests that individuals may strategically explore as a function of the relative uncertainty about the expected value of options. However, the neural mechanisms supporting uncertainty-driven exploration remain underspecified. The present fMRI study scanned a reinforcement learning task in which participants stop a rotating clock hand in order to win points. Reward schedules were such that expected value could increase, decrease, or remain constant with respect to time. We fit several mathematical models to subject behavior to generate trial-by-trial estimates of exploration as a function of relative uncertainty. These estimates were used to analyze our fMRI data. Results indicate that rostrolateral prefrontal cortex tracks trial-by-trial changes in relative uncertainty, and this pattern distinguished individuals who rely on relative uncertainty for their exploratory decisions versus those who do not.
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Models of attentional allocation in associative learning are typically structured according to one of two guiding principles: the predictiveness principle, which posits that attention is paid to cues that have reliably predicted an outcome in the past, or the uncertainty principle, which states that attention is paid to cues about which little is known. Both principles are well supported by studies of animals. However, in studies of human learning, there is very little direct empirical support for the uncertainty principle. In the study reported here, we addressed this gap by investigating negative transfer, a phenomenon that may provide unique support for the uncertainty principle. In two human learning experiments using an allergist task, we replicated the primary findings of previous research on animal learning. We believe that these data provide the first direct evidence for the uncertainty principle in human associative learning.
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Theories of selective attention in associative learning posit that the salience of a cue will be high if the cue is the best available predictor of reinforcement (high predictiveness). In contrast, a different class of attentional theory stipulates that the salience of a cue will be high if the cue is an inaccurate predictor of reinforcement (high uncertainty). Evidence in support of these seemingly contradictory propositions has led to: (i) the development of hybrid attentional models that assume the coexistence of separate, predictiveness-driven and uncertainty-driven mechanisms of changes in cue salience; and (ii) a surge of interest in identifying the neural circuits underpinning these mechanisms. Here, we put forward a formal attentional model of learning that reconciles the roles of predictiveness and uncertainty in salience modification. The issues discussed are relevant to psychologists, behavioural neuroscientists and neuroeconomists investigating the roles of predictiveness and uncertainty in behaviour.
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The results of several recent studies of human associative learning indicate that people will learn more rapidly about cues that have previously been experienced as predictive of events of significance, as compared with cues previously experienced as nonpredictive. Notably, however, these experiments have typically established this prior predictiveness by means of pretraining with multiple, simultaneously presented cues, some of which are more predictive than others. The present experiments instead investigated the influence of prior predictiveness on future learning when this predictiveness was established via pretraining with individual cues, each of which was the best available predictor of the outcome with which it was paired. Results indicate that, following this pretraining, human participants again show better learning about previously predictive cues than about previously nonpredictive cues.
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The capacity for voluntary action is seen as essential to human nature. Yet neuroscience and behaviourist psychology have traditionally dismissed the topic as unscientific, perhaps because the mechanisms that cause actions have long been unclear. However, new research has identified networks of brain areas, including the pre-supplementary motor area, the anterior prefrontal cortex and the parietal cortex, that underlie voluntary action. These areas generate information for forthcoming actions, and also cause the distinctive conscious experience of intending to act and then controlling one's own actions. Volition consists of a series of decisions regarding whether to act, what action to perform and when to perform it. Neuroscientific accounts of voluntary action may inform debates about the nature of individual responsibility.
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In two experiments rats initially received appetitive Pavlovian conditioning with an auditory conditioned stimulus (CS). Subsequently this stimulus was presented in compound with a novel light and paired with the same appetitive reinforcer. In keeping with the outcome of many such experiments on blocking, there was very little evidence of appetitive conditioning during subsequent independent presentations of the light. Of main concern in the present experiments, however, was the influence of this training on the strength of the orienting response directed towards the light. When the light was first presented it elicited a strong orienting response. The strength of this response declined rapidly when the light was presented in compound with the previously trained auditory CS but more slowly when it was paired with the reinforcer either by itself or in conjunction with an initially neutral auditory stimulus. It is suggested that the extent to which the events following the light are accurately predicted determines the strength of orientation towards this stimulus.
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The strength of orienting toward a light that signaled food was studied in five experiments. Experiment 1 demonstrated that this response declined in strength during conditioning but was temporarily restored during extinction. In Experiment 2 the light was again paired immediately with the unconditioned stimulus (US), whereas in Experiments 3 and 4 it signaled a tone which in turn signaled the US. In these three experiments we again found that continuous reinforcement resulted in a decline in the strength of light orientation. These studies also revealed that under conditions of partial reinforcement, orientation to the light was sustained. Experiment 5 demonstrated that the decline in light orientation with a continuous reinforcement procedure can be retarded either by preexposing the light for a number of trials prior to conditioning or by intermixing reinforced light trials with nonreinforced presentations of a tone. This experiment also revealed that reversing the reinforcement contingency associated with the tone restored orientation to the light. This pattern of results can be most readily explained by the proposal that the strength of orientation toward the light is inversely related to the predictive accuracy of this stimulus.
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Part 1 of this discussion summarizes several formal models of exicitatory classical conditioning. It is suggested that a central problem for all of them is the explanation of cases in which learning does not occur in spite of the fact that the CS is a signal for the reinforcer. A new model is proposed that deals with this problem by specifying that certain procedures cause a CS to lose effectiveness; in particular, it is argued that a CS will lose associability when its consequences are accurately predicted. In contrast to other current models, the effectiveness of the reinforcer remains constant throughout conditioning. Part 2 presents a reformulation of the nature of the learning produced by inhibitory-conditioning procedures and a discussion of the way in which such learning can be accommodated within the model outlined for excitatory learning. (47 ref)
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Recent visual half-field studies using relatively long stimulus onset asynchronies (SOAs) and non-associated category members (e.g. deer-pony) as word pair stimuli have indicated that with a low proportion of related stimuli, automatic priming of non-associated category information is larger in the right than in the left hemisphere [Chiarello and co-workers]. The present study examined semantic priming of non-associated category members, with a low proportion of related stimuli in the visual fields/hemispheres across SOAs of 165, 250, 500, and 750 msec. Eighty normal, right-handed subjects were tested (20 subjects/SOA). The results revealed a left hemisphere advantage in priming at the 165 msec SOA, whereas the right hemisphere advantage reported in earlier studies was observed at the longest 750 msec SOA only. The results challenge the view that a larger range of related meanings is activated in the right than in the left hemisphere. Rather, the time course of semantic activation may be different in the hemispheres.