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

Pelley, Mike E. Le; Ung, Rhonda; Mine, Chisato; Most, Steven B.; Watson, Poppy; Pearson, Daniel; Theeuwes, Jan

doi:10.3758/s13414-021-02426-7

Cited by 14 publications

(12 citation statements)

References 54 publications

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“…It remains to be determined whether spatial certainty cues can always be best explained by anticipatory mechanisms, and incentive value cues counterfactual mechanisms, or whether task demands and cue formations can modify where and how each account best predicts performance. For example, in the current study (as well as many of the investigations into the influence of incentive value associations on visual prioritisation ( Anderson, Laurent, and Yantis 2011 ; Garner, Bowman, and Raymond 2021 ; Itthipuripat et al 2019 ; Kim and Anderson 2021 ; Le Pelley et al, 2015 ; Le Pelley et al, 2022 ; MacLean, Diaz, and Giesbrecht 2016 ; Stănişor et al 2013 ; Stankevich and Geng 2014 )), incentive value is not predictive of target location under the key testing conditions, and therefore offers no benefit for finding the target. It would be interesting to test within a single set of participants whether shifting between value cueing conditions that are predictive and non-predictive of upcoming target locations likewise shifts individuals between anticipatory and counterfactual styles of cue influence.…”

Section: Discussionmentioning

confidence: 58%

“…performance. For example, in the current study (as well as many of the investigations into the influence of incentive value associations on visual prioritisation(Anderson, Laurent, and Yantis 2011;Garner, Bowman, and Raymond 2021;Itthipuripat et al 2019;Kim and Anderson 2021;Le Pelley et al, 2015;Le Pelley et al, 2022;MacLean, Diaz, and Giesbrecht 2016;Stănişor et al 2013; Stankevich and Geng 2014)), incentive value is not predictive of target location under the key testing conditions, and therefore offers no benefit for finding the target. ItGarner et al …”

mentioning

confidence: 55%

“…Although these two sources of expectation have comparable influences on visual priorities, i.e. improved performance at a signalled location at the expense of performance at other locations, a growing number of studies suggest that the influence of spatial and incentive value expectations are exerted via independent mechanisms ( Stankevich and Geng 2014 ; Garner, Bowman, and Raymond 2021 ; Kim and Anderson 2021 ; Le Pelley et al, 2022 ). For example, Garner et al ( 2021 ) parametrically manipulated spatial certainty by adjusting the reliability of a central spatial cue, and pit these cues against high and low value target and distractor locations.…”

Section: Introductionmentioning

confidence: 99%

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On the Influence of Spatial and Value Attentional Cues Across Individuals

Garner¹,

Lovell-Kane²,

Carroll³

et al. 2022

Journal of Cognition

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The visual world provides a myriad of cues that can be used to direct information processing. How does the brain integrate predictive information from disparate sources to modify visual priorities, and are combination strategies consistent across individuals? Previous evidence shows that cues predictive of the value of a visually guided task (incentive value) and cues that signal where targets may occur (spatial certainty) act independently to bias attention. Anticipatory accounts propose that both cues are encoded into an attentional priority map, whereas the counterfactual account argues that incentive value cues instead induce a reactive encoding of losses based on the direction of attention. We adjudicate between these alternatives and further determine whether there are individual differences in how attentional cues are encoded. 149 participants viewed two coloured placeholders that specified the potential value of correctly identifying an imminent target. Prior to the target's presentation, an endogenous spatial cue indicated the target's likely location. The anticipatory and counterfactual accounts were used to motivate parametric regressors that were compared in their explanatory power of the data, at the group level and on data stratified by a clustering algorithm. Clustering revealed 2 subtypes; whereas all individuals use spatial certainty cues a subset does not use incentive value cues. When incentive value cues are used their influence reflects a counterfactual loss function. The data support the counterfactual account and show that theories of motivated attention must account for the non-uniform influence of incentive value on visual priorities.

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Section: Discussionmentioning

confidence: 58%

mentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Influence of Spatial and Value Attentional Cues Across Individuals

Garner¹,

Lovell-Kane²,

Carroll³

et al. 2022

Journal of Cognition

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“…Interestingly, previous research suggests that both reward value and regularities can modulate the priority map for attentional selection (Kim & Anderson, 2021; Le Pelley et al, 2022; Ogden et al, 2023; Stankevich & Geng, 2014). For instance, an object previously associated with a reward can increase the reaction time in a visual search task, even if it is irrelevant to the task (Anderson et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Contributions of statistical learning to learning from reward feedback

Yazdanpanah,

Wang,

Trepka

et al. 2024

Preprint

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Natural environments are abundant with patterns and regularities. It has been demonstrated that learning these regularities, especially through statistical learning, can greatly influence perception, memory, and other cognitive functions. Using a novel experimental paradigm involving two orthogonal tasks, we investigated whether regularities in the environment can enhance reward learning. In one task, human participants predicted the next stimulus in a sequence by recognizing regularities in a feature. In a separate, multidimensional learning task, they learned the predictive value of a different set of stimuli, based on reward feedback received after choosing between pairs of stimuli. Using both model-free and model-based approaches, we found that participants used regularities about features from the sequence-prediction task to bias their behavior in the learning task, resulting in the values associated with the regular feature having a greater influence. Fitting of choice behavior revealed that these effects were more consistent with attentional modulations of learning, rather than decision making, due to regularity manipulation. Specifically, the learning rates for the feature with regularity were higher, especially when learning from the forgone option during unrewarded trials. This demonstrates that feature regularities can intensify the confirmation bias observed in reward learning. Our results suggest that by enhancing learning about certain features, detecting regularities in the environment can reduce dimensionality and thus mitigate the curse of dimensionality in reward learning. Such interactions between statistical and reward learning have important implications for learning in naturalistic settings.

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“…positive notes, small things, reduces the risks of behavioral impediments and detachments from the class discussions and activities. Furthermore, it is believed that rewards system statistically influences attentional priority of salient distractors in class which shapes all learning outcome in the classroom setup (Gbollie & Keamu, 2017;Le Pelley et al, 2022). Table 4 the correlational results of the respondents' profile and their perception on the use of the reward system using Spearman's Rho Correlation.…”

Section: Category Mean 443 Strongly Agreementioning

confidence: 99%

Teachers’ Use of Reward System: Inputs for Students Motivation Enhancement

VIRAY-CASTILLEJOS

2023

AIDE-IRJ

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This study determined the teacher’s perception of the reward system in the classroom. The study made use of the descriptive quantitative research design. The study was conducted in the School Division of Cauayan City in San Fermin Elementary School, San Isidro Elementary School, and North Central School for the academic year 2021-2022. This paper facilitated a researcher-made questionnaire to gather data on the teachers’ profiles, motivation forms, and perceptions of using the strategy. A Likert Scale was used to analyze the perception of the respondents. Also, the researcher used Spearman’s Rho to draw a correlation between the profile of the respondents relative to their perception and form of motivation used in their elementary classroom. The results revealed that the teachers often used different forms of motivation are small items. Also, teachers perceived the rewards system to be effective for all pupils. Moreover, the elementary teachers strongly agree that using this strategy benefits the classroom facilitators. Hence students’ academic performance increased, and the student’s participation became active with a reward system. In conclusion, the study found that the teachers’ profile and perception of using the reward strategy, higher educational attainment, length of service, and grade level were significantly related. Correspondingly, the forms of motivations of the teachers are linked to the quality of higher educational attainment of the teachers and the grade level they are handling. Thus, the perception of the use of the reward strategy and its forms depends on the profession’s service, the quality of students’ foundation, such as distractors and needs, and the pedagogical mastery acquired through pursuing higher education. The school should encourage teachers to improve the mechanism reward system in their classes. Further study should be made in integrating a reward system or a combination of external strategies on a particular subject and performance competencies.

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

Cited by 14 publications

References 54 publications

On the Influence of Spatial and Value Attentional Cues Across Individuals

On the Influence of Spatial and Value Attentional Cues Across Individuals

Contributions of statistical learning to learning from reward feedback

Teachers’ Use of Reward System: Inputs for Students Motivation Enhancement

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