Investigating mechanisms of the attentional repulsion effect: A diffusion model analysis.

This article investigates the decisional and attentional drivers of the attentional repulsion effect (ARE) using the diffusion decision model (DDM). The ARE is a phenomenon in which a subjective expansion of space is experienced outside the focus of attention. It is thought to occur due to changes in the functioning of visual cell receptive fields. The DDM is a model of the decision-making process that assumes responses are selected by sequentially sampling an encoded representation of a stimulus until sufficient evidence has been accumulated favoring one response alternative over the other. The model decomposes observed choice and response times into different latent variables corresponding to the rate of evidence accumulation, response caution, response bias, and the time course of stimulus encoding and response execution. In this article, we interpret changes in the rate of evidence accumulation as primarily reflecting perceptual-driven changes in stimulus representation. We interpret changes in response bias as primarily reflecting decision-level changes. We utilize the DDM's ability to estimate these variables independently to explore how they are each affected by cueing manipulations to clarify whether the ARE emerges due to attentional or decisional drivers, or some combination of the two. The results of this study could shed light on the mechanisms underlying the ARE, and has implications in our understanding of spatial attention.

Uncovering the cognitive mechanisms underlying the gaze cueing effect.

The gaze cueing effect is the tendency for people to respond faster to targets appearing at locations gazed at by others, compared with locations gazed away from by others. The effect is robust, widely studied, and is an influential finding within social cognition. Formal evidence accumulation models provide the dominant theoretical account of the cognitive processes underlying speeded decision-making, but they have rarely been applied to social cognition research. In this study, using a combination of individual-level and hierarchical computational modelling techniques, we applied evidence accumulation models to gaze cueing data (three data sets total, N = 171, 139,001 trials) for the first time to assess the relative capacity that an attentional orienting mechanism and information processing mechanisms have for explaining the gaze cueing effect. We found that most participants were best described by the attentional orienting mechanism, such that response times were slower at gazed away from locations because they had to reorient to the target before they could process the cue. However, we found evidence for individual differences, whereby the models suggested that some gaze cueing effects were driven by a short allocation of information processing resources to the gazed at location, allowing for a brief period where orienting and processing could occur in parallel. There was exceptionally little evidence to suggest any sustained reallocation of information processing resources neither at the group nor individual level. We discuss how this individual variability might represent credible individual differences in the cognitive mechanisms that subserve behaviourally observed gaze cueing effects.

A revised diffusion model for conflict tasks.

The recently developed diffusion model for conflict tasks (DMC) Ulrich et al. (Cognitive Psychology, 78, 148-174, 2015) provides a good account of data from all standard conflict tasks (e.g., Stroop, Simon, and flanker tasks) within a common evidence accumulation framework. A central feature of DMC's processing dynamics is that there is an initial phase of rapid accumulation of distractor evidence that is then selectively withdrawn from the decision mechanism as processing continues. We argue that this assumption is potentially troubling because it could be viewed as implying qualitative changes in the representation of distractor information over the time course of processing. These changes suggest more than simple inhibition or suppression of distractor information, as they involve evidence produced by distractor processing "changing sign" over time. In this article, we (a) develop a revised DMC (RDMC) whose dynamics operate strictly within the limits of inhibition/suppression (i.e., evidence strength can change monotonically, but cannot change sign); (b) demonstrate that RDMC can predict the full range of delta plots observed in the literature (i.e., both positive-going and negative-going); and (c) show that the model provides excellent fits to Simon and flanker data used to benchmark the original DMC at both the individual and group level. Our model provides a novel account of processing differences across Simon and flanker tasks. Specifically, that they differ in how distractor information is processed on congruent trials, rather than incongruent trials: congruent trials in the Simon task show relatively slow attention shifting away from distractor information (i.e., location) while complete and rapid attention shifting occurs in the flanker task. Our new model highlights the importance of considering dynamic interactions between top-down goals and bottom-up stimulus effects in conflict processing.

Are there jumps in evidence accumulation, and what, if anything, do they reflect psychologically? An analysis of Lévy Flights models of decision-making.

According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, [Formula: see text]. The LF model produces sudden large "jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e., as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.

Using mixture modeling to examine differences in perceptual decision-making as a function of the time and method of participant recruitment.

We examine whether perceptual decision-making differs as a function of the time in the academic term and whether the participant is an undergraduate participating for course credit, a paid in-person participant, or a paid online participant recruited via Amazon Mechanical Turk. We use a mixture modeling approach within an evidence accumulation framework that separates stimulus-driven responses from contaminant responses, allowing us to distinguish between performance when a participant is engaged in the task and the consistency in this task focus. We first report a survey showing cognitive psychologists expect performance and response caution to be lower among undergraduate participants recruited at the end of the academic term compared to those recruited near the start, and highest among paid in-person participants. The findings from two experiments using common paradigms revealed very little evidence of time-of-semester effects among course credit participants on accuracy, response time, efficiency of information processing (when engaged in the task), caution, and non-decision time, or consistency in task focus. However, paid in-person participants did tend to be more accurate than the other two groups. Groups showed similar effects of speed/accuracy emphasis on response caution and of discrimination difficulty on information processing efficiency, but the effect of speed/accuracy emphasis on information processing efficiency was less consistent among groups. We conclude that online crowdsourcing platforms can provide quality perceptual decision-making data, but recommend that mixture modeling be used to adequately account for data generated by processes other than the psychological phenomena under investigation.

The impact of cognitive resource constraints on goal prioritization.

Many decisions we face daily entail deliberation about how to coordinate resources shared between multiple, competing goals. When time permits, people appear to approach these goal prioritization problems by analytically considering all goal-relevant information to arrive at a prioritization decision. However, it is not yet clear if this normative strategy extends to situations characterized by resource constraints such as when deliberation time is scarce or cognitive load is high. We evaluated the questions of how limited deliberation time and cognitive load affect goal prioritization decisions across a series of experiments using a gamified experimental task, which required participants to make a series of interdependent goal prioritization decisions. We fit several candidate models to experimental data to identify decision strategy adaptations at the individual subject-level. Results indicated that participants tended to opt for a simple heuristic strategy when cognitive resources were constrained rather than making a general tradeoff between speed and accuracy (e.g., the type of tradeoff that would be predicted by evidence accumulation models). The most common heuristic strategy involved disproportionately weighing information about goal deadlines compared to other goal-relevant information such as the goal's difficulty and the goal's subjective value.

Individual Differences in Decision Strategy Relate to Neurochemical Excitability and Cortical Thickness.

The speed-accuracy trade-off (SAT), whereby faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during the performance of almost all daily tasks. To date, computational modeling has implicated the latent decision variable of response caution (thresholds), the amount of evidence required for a decision to be made, in the SAT. Previous imaging has associated frontal regions, notably the left prefrontal cortex and the presupplementary motor area (pre-SMA), with the setting of such caution levels. In addition, causal brain stimulation studies, using transcranial direct current stimulation (tDCS), have indicated that while both of these regions are involved in the SAT, their role appears to be dissociable. tDCS efficacy to impact decision-making processes has previously been linked with neurochemical concentrations and cortical thickness of stimulated regions. However, to date, it is unknown whether these neurophysiological measures predict individual differences in the SAT, and brain stimulation effects on the SAT. Using ultra-high field (7T) imaging, here we report that instruction-based adjustments in caution are associated with both neurochemical excitability (the balance between GABA+ and glutamate) and cortical thickness across a range of frontal regions in both sexes. In addition, cortical thickness, but not neurochemical concentrations, was associated with the efficacy of left prefrontal and superior medial frontal cortex (SMFC) stimulation to modulate performance. Overall, our findings elucidate key neurophysiological predictors, frontal neural excitation, of individual differences in latent psychological processes and the efficacy of stimulation to modulate these.SIGNIFICANCE STATEMENT The speed-accuracy trade-off (SAT), faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during most daily tasks. The SAT is often investigated by explicitly instructing participants to prioritize speed or accuracy when responding to stimuli. Using ultra-high field (7T) magnetic resonance imaging (MRI), we found that individual differences in the extent to which participants adjust their decision strategies with instruction related to neurochemical excitability (ratio of GABA+ to glutamate) and cortical thickness in the frontal cortex. Moreover, brain stimulation to the left prefrontal cortex and the superior medial frontal cortex (SMFC) modulated performance, with the efficacy specifically related to cortical thickness. This work sheds new light on the neurophysiological basis of decision strategies and brain stimulation.

Modality independent or modality specific? Common computations underlie confidence judgements in visual and auditory decisions.

The mechanisms that enable humans to evaluate their confidence across a range of different decisions remain poorly understood. To bridge this gap in understanding, we used computational modelling to investigate the processes that underlie confidence judgements for perceptual decisions and the extent to which these computations are the same in the visual and auditory modalities. Participants completed two versions of a categorisation task with visual or auditory stimuli and made confidence judgements about their category decisions. In each modality, we varied both evidence strength, (i.e., the strength of the evidence for a particular category) and sensory uncertainty (i.e., the intensity of the sensory signal). We evaluated several classes of computational models which formalise the mapping of evidence strength and sensory uncertainty to confidence in different ways: 1) unscaled evidence strength models, 2) scaled evidence strength models, and 3) Bayesian models. Our model comparison results showed that across tasks and modalities, participants take evidence strength and sensory uncertainty into account in a way that is consistent with the scaled evidence strength class. Notably, the Bayesian class provided a relatively poor account of the data across modalities, particularly in the more complex categorisation task. Our findings suggest that a common process is used for evaluating confidence in perceptual decisions across domains, but that the parameter settings governing the process are tuned differently in each modality. Overall, our results highlight the impact of sensory uncertainty on confidence and the unity of metacognitive processing across sensory modalities.

Sustainability, Collective Self-Regulation, and Human-Nature Interdependence.

Like any organism, humanity constructs its niche and adapts to the rest of nature by modifying available materials around them. In the era that some have dubbed the "Anthropocene," human niche construction has gone so far as to threaten the planetary climate system. The central question of sustainability is how humanity can collectively self-regulate niche construction, that is, humanity's relationship with the rest of nature. In this article, we argue that to resolve the collective self-regulation problem for sustainability, sufficiently accurate and relevant aspects of causal knowledge about the functioning of complex social-ecological systems need to be cognized, communicated, and collectively shared. More specifically, causal knowledge about human-nature interdependence-how humans interact with each other and the rest of nature-is critical for coordinating cognitive agents' thoughts, feelings, and actions for the greater good without falling into the trap of free riding. Here, we will develop a theoretical framework to consider the role of causal knowledge about human-nature interdependence in collective self-regulation for sustainability, review the relevant empirical research primarily focusing on climate change, and take stock of what is currently known and what we need to investigate in the future.

The causal role of the prefrontal and superior medial frontal cortices in the incidental manipulation of decision strategies.

A key strategic decision one must make in virtually every task context concerns the speed accuracy trade-off (SAT). Experimentally, this ubiquitous phenomenon, whereby response speed and task accuracy are inversely related, is typically studied by explicitly instructing participants to adjust their strategy: by either focusing on speed, or on accuracy. Computational modelling has been applied to deconvolve the latent decision processes involved in the SAT, with considerable evidence suggesting that response caution (the amount of evidence needed for a decision to be reached) is a key variable in the setting of SAT strategy. Neuroimaging has implicated the prefrontal cortex, the pre-supplementary motor area (preSMA), and the striatum in the setting of response caution. In addition, brain stimulation has provided causal evidence for the involvement of the left prefrontal cortex and superior medial frontal cortex (SMFC, which includes the preSMA) in adjustments of response caution following explicit instructions, although stimulation of the two regions has dissociable effects. Here, in a double-blind and preregistered study we investigated the role of these two regions using an incidental manipulation of SAT strategy - via stimulus signal variability - which has previously been shown to influence decision confidence. We again found tDCS applied to both regions modulated response caution, and there was a dissociation: stimulating prefrontal cortex increased, and stimulating SMFC decreased, response caution. These findings provide further support for key, but dissociable, roles of these brain regions in decision strategies whether they are implemented explicitly or incidentally.

Causal evidence for dissociable roles of the prefrontal and superior medial frontal cortices in decision strategies.

The speed-accuracy trade-off (SAT) is arguably the most robust finding in cognitive psychology. This simple and intuitive effect (the faster subjects respond, the more likely they are to make an error) has been the subject of extensive empirical and modeling work to ascertain the underlying latent process(es). One such process is response caution-the amount of evidence to be acquired before a decision is reached-with debate regarding the involvement of another latent variable, the rate of evidence accumulation. Neuroimaging has implicated two frontal regions as neural substrates of the SAT: the posterior lateral prefrontal cortex and the pre-supplementary motor area (preSMA; part of the superior medial frontal cortex; SMFC). However, there is no causal evidence for these regions' involvement in the SAT, nor is it clear what role each plays in the underlying processes. In a double-blind, preregistered study, we applied cathodal transcranial direct current stimulation (offline) to the prefrontal and SMFC. The SAT was measured using a dot-motion task, with differing response instructions (focus on accuracy, speed, or both). The linear ballistic accumulator model indicated performance modulations were driven by response caution. Moreover, both target regions modulated caution but in opposing directions: Prefrontal stimulation increased, and SMFC stimulation decreased, caution. Discriminability (difference between correct and error evidence accumulation rates) was predominantly affected by stimulation targeting the SMFC and did not vary with response instructions. Overall, the findings indicate that while both the SMFC and the prefrontal cortex are causally involved in the SAT, they play distinct roles in this phenomenon. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The climate commons dilemma: how can humanity solve the commons dilemma for the global climate commons?

In the era when human activities can fundamentally alter the planetary climate system, a stable climate is a global commons. However, the need to develop the economy to sustain the growing human population poses the Climate Commons Dilemma. Although citizens may need to support policies that forgo their country's economic growth, they may instead be motivated to grow their economy while freeriding on others' efforts to mitigate the ongoing climate change. To examine how to resolve the climate commons dilemma, we constructed a Climate Commons Game (CCG), an experimental analogue of the climate commons dilemma that embeds a simple model of the effects of economic activities on global temperature rise and its eventual adverse effects on the economy. The game includes multiple economic units, and each participant is tasked to manage one economic unit while keeping global temperature rise to a sustainable level. In two experiments, we show that people can manage the climate system and their economies better when they regarded the goal of environmentally sustainable economic growth as a singular global goal that all economic units collectively pursue rather than a goal to be achieved by each unit individually. In addition, beliefs that everyone shares the knowledge about the climate system help the group coordinate their economic activities better to mitigate global warming in the CCG. However, we also found that the resolution of the climate commons dilemma came at the cost of exacerbating inequality among the economic units in the current constrains of the CCG. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10584-021-02989-2.

Effects of tDCS on visual statistical learning.

Visual statistical learning describes the encoding of structure in sensory input, and it has important consequences for cognition and behaviour. Higher-order brain regions in the prefrontal and posterior parietal cortices have been associated with statistical learning behaviours. Yet causal evidence of a cortical contribution remains limited. In a recent study, the modulation of cortical activity by transcranial direct current stimulation (tDCS) disrupted statistical learning in a spatial contextual cueing phenomenon; supporting a cortical role. Here, we examined whether the same tDCS protocol would influence statistical learning assessed by the Visual Statistical Learning phenomenon (i.e., Fiser and Aslin, 2001), which uses identity-based regularities while controlling for spatial location. In Experiment 1, we employed the popular exposure-test design to tap the learning of structure after passive viewing. Using a large sample (N = 150), we found no effect of the tDCS protocol when compared to a sham control nor to an active control region. In Experiment 2 (N = 80), we developed an online task that was sensitive to the timecourse of learning. Under these task conditions, we did observe a stimulation effect on learning, consistent with the previous work. The way tDCS affected learning appeared to be task-specific; expediting statistical learning in this case. Together with the existing evidence, these findings support the hypothesis that cortical areas are involved in the visual statistical learning process, and suggest the mechanisms of cortical involvement may be task-dependent and dynamic across time.

Dual target search: Attention tuned to relative features, both within and across feature dimensions.

Current models of attention propose that we can tune attention in a top-down controlled manner to a specific feature value (e.g., shape, color) to find specific items (e.g., a red car; feature-specific search). However, subsequent research has shown that attention is often tuned in a context-dependent manner to the relative features that distinguish a sought-after target from other surrounding nontarget items (e.g., larger, bluer, and faster; relational search). Currently, it is unknown whether search will be feature-specific or relational in search for multiple targets with different attributes. In the present study, observers had to search for 2 targets that differed either across 2 stimulus dimensions (color, motion; Experiment 1) or within the same stimulus dimension (color; Experiment 2: orange/redder or aqua/bluer). We distinguished between feature-specific and relational search by measuring eye movements to different types of irrelevant distractors (e.g., relatively matching vs. feature-matching). The results showed that attention was biased to the 2 relative features of the targets, both across different feature dimensions (i.e., motion and color) and within a single dimension (i.e., 2 colors; bluer and redder). The results were not due to automatic intertrial effects (dimension weighting or feature priming), and we found only small effects for valid precueing of the target feature, indicating that relational search for two targets was conducted with relative ease. This is the first demonstration that attention is top-down biased to the relative target features in dual target search, which shows that the relational account generalizes to multiple target search. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Correction: Causal knowledge promotes behavioral self-regulation: An example using climate change dynamics.

[This corrects the article DOI: 10.1371/journal.pone.0184480.].

Diffusing the bilingual lexicon: Task-based and lexical components of language switch costs.

Bilingual speakers show a response time (RT) cost when switching between languages. These costs could reflect the organization of language in a shared bilingual lexicon (Grainger, Midgley, & Holcomb, 2010) or a domain general cognitive processing cost (Green & Abutalebi, 2013). To test these accounts, we analysed RT distributions of bilingual (Spanish-English) performance on generalized lexical decision (GLD) tasks using Ratcliff (1978) diffusion model. Experiment 1 revealed that language switches decrease the rate of evidence accumulation (drift rate) and slow the cognitive processes that occur prior to decision-making (non-decision time). Experiment 2 showed that the anticipation of language switches did not change these effects. The results suggest that language switch costs originate from a combination of at least two loci: lexical access and a task-specific decision process.

Information Processing Under Reward Versus Under Punishment.

Much is known about the effects of reward and punishment on behavior, yet little research has considered how these incentives influence the information-processing dynamics that underlie decision making. We fitted the linear ballistic accumulator to data from a perceptual-judgment task to examine the impacts of reward- and punishment-based incentives on three distinct components of information processing: the quality of the information processed, the quantity of that information, and the decision threshold. The threat of punishment lowered the average quality and quantity of information processed, compared with the prospect of reward or no performance incentive at all. The threat of punishment also induced less cautious decision making by lowering people's decision thresholds relative to the prospect of reward. These findings suggest that information-processing dynamics are determined not only by objective properties of the decision environment but also by the higher order goals of the system.

Combining error-driven models of associative learning with evidence accumulation models of decision-making.

As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings.

The separable effects of feature precision and item load in visual short-term memory.

Visual short-term memory (VSTM) has been described as being limited by the number of discrete visual objects, the aggregate quantity of information across multiple visual objects, or some combination of the two. Many recent studies examining these capacity limitations have shown that increasing the number of items in VSTM increases the frequency and magnitude of errors in a participant's recall of the stimulus. This increase in response dispersion has been interpreted as a loss of precision in an item's representation as the number of items in memory increases, possibly due to a change in the tuning of the underlying representation. However, increased response dispersion can also be caused by a reduction in the total memory strength available for decision making as a consequence of a reduction in the total amount of a fixed resource representing a stimulus. We investigated the effects of load on the precision of memory representations in a fine orientation discrimination task. Accuracy was well captured by extending a simple sample-size model of VSTM, using a tuning function to account for the effect of orientation precision on performance. The best model of the data was one in which the item strength decreased progressively with memory load at all stimulus exposure durations but in which tuning bandwidth was invariant. Our results imply that memory strength and feature precision are experimentally dissociable attributes of VSTM.

Limitations of pure encoding capacity accounts of visual short-term memory phenomena: Reply to Bundesen (2018).

In his commentary, Bundesen (2018) argued that limited encoding capacity can account for the near-equivalent set size effects on performance under conditions of simultaneous and sequential presentation reported by Sewell, Lilburn, and Smith (2014). While we agree that limited encoding capacity could, in principle, account for this equivalency, we argue that such an account rests on a number of fortuitous temporal coincidences. In particular, we note that pure encoding capacity limitations appear ill equipped to explain near-equivalent simultaneous-sequential performance across a range of stimulus exposure durations, set sizes, and with stimuli with quite different attentional demands. (PsycINFO Database Record