Inertia in value-driven attention

Trichotomy revisited: A monolithic theory of attentional control

The control of attention was long held to reflect the influence of two competing mechanisms of assigning priority, one goal-directed and the other stimulus-driven. Learning-dependent influences on the control of attention that could not be attributed to either of those two established mechanisms of control gave rise to the concept of selection history and a corresponding third mechanism of attentional control. The trichotomy framework that ensued has come to dominate theories of attentional control over the past decade, replacing the historical dichotomy. In this theoretical review, I readily affirm that distinctions between the influence of goals, salience, and selection history are substantive and meaningful, and that abandoning the dichotomy between goal-directed and stimulus-driven mechanisms of control was appropriate. I do, however, question whether a theoretical trichotomy is the right answer to the problem posed by selection history. If we reframe the influence of goals and selection history as different flavors of memory-dependent modulations of attentional priority and if we characterize the influence of salience as a consequence of insufficient competition from such memory-dependent sources of priority, it is possible to account for a wide range of attention-related phenomena with only one mechanism of control. The monolithic framework for the control of attention that I propose offers several concrete advantages over a trichotomy framework, which I explore here.

Effects of outcome revaluation on attentional prioritisation of reward-related stimuli

Stimuli associated with rewards can acquire the ability to capture our attention independently of our goals and intentions. Here, we examined whether attentional prioritisation of reward-related cues is sensitive to changes in the value of the reward itself. To this end, we incorporated an instructed outcome devaluation (Experiment 1a), "super-valuation" (Experiment 1b), or value switch (Experiment 2) into a visual search task, using eye-tracking to examine attentional prioritisation of stimuli signalling high- and low-value rewards. In Experiments 1a and 1b, we found that prioritisation of high- and low-value stimuli was insensitive to devaluation of a previously high-value outcome, and super-valuation of a previously low-value outcome, even when participants were provided with further experience of receiving that outcome. In Experiment 2, following a value-switch manipulation, we found that prioritisation of a high-value stimulus could not be overcome with knowledge of the new values of outcomes alone. Only when provided with further experience of receiving the outcomes did patterns of attentional prioritisation of high- and low-value stimuli switch, in line with the updated values of the outcomes they signalled. To reconcile these findings, we suggest that participants were motivated to engage in effortful updating of attentional control settings when there was a relative difference between reward values at test (Experiment 2) but that previous settings were allowed to persist when both outcomes had the same value at test (Experiments 1a and 1b). These findings provide a novel framework to further understand the role of cognitive control in driving reward-modulated attention and behaviour.

Value-driven attention and associative learning models: a computational simulation analysis

Value-driven attentional capture (VDAC) refers to a phenomenon by which stimulus features associated with greater reward value attract more attention than those associated with smaller reward value. To date, the majority of VDAC research has revealed that the relationship between reward history and attentional allocation follows associative learning rules. Accordingly, a mathematical implementation of associative learning models and multiple comparison between them can elucidate the underlying process and properties of VDAC. In this study, we implemented the Rescorla-Wagner, Mackintosh (Mac), Schumajuk-Pearce-Hall (SPH), and Esber-Haselgrove (EH) models to determine whether different models predict different outcomes when critical parameters in VDAC were adjusted. Simulation results were compared with experimental data from a series of VDAC studies by fitting two key model parameters, associative strength (V) and associability (α), using the Bayesian information criterion as a loss function. The results showed that SPH-V and EH- α outperformed other implementations of phenomena related to VDAC, such as expected value, training session, switching (or inertia), and uncertainty. Although V of models were sufficient to simulate VDAC when the expected value was the main manipulation of the experiment, α of models could predict additional aspects of VDAC, including uncertainty and resistance to extinction. In summary, associative learning models concur with the crucial aspects of behavioral data from VDAC experiments and elucidate underlying dynamics including novel predictions that need to be verified.

Neural correlates of value-driven spatial orienting

Reward learning has been shown to habitually guide overt spatial attention to specific regions of a scene. However, the neural mechanisms that support this bias are unknown. In the present study, participants learned to orient themselves to a particular quadrant of a scene (a high-value quadrant) to maximize monetary gains. This learning was scene-specific, with the high-value quadrant varying across different scenes. During a subsequent test phase, participants were faster at identifying a target if it appeared in the high-value quadrant (valid), and initial saccades were more likely to be made to the high-value quadrant. fMRI analyses during the test phase revealed learning-dependent priority signals in the caudate tail, superior colliculus, frontal eye field, anterior cingulate cortex, and insula, paralleling findings concerning feature-based, value-driven attention. In addition, ventral regions typically associated with scene selection and spatial information processing, including the hippocampus, parahippocampal gyrus, and temporo-occipital cortex, were also implicated. Taken together, our findings offer new insights into the neural architecture subserving value-driven attention, both extending our understanding of nodes in the attention network previously implicated in feature-based, value-driven attention and identifying a ventral network of brain regions implicated in reward's influence on scene-dependent spatial orienting.

The persistence of value-driven attention capture is task-dependent

Visual features previously associated with reward can capture attention even when task-irrelevant, a phenomenon known as value-driven attention capture (VDAC). VDAC persists without reinforcement, unlike other forms of learning, where removing reinforcement typically leads to extinction. In five experiments, factors common to many studies were manipulated to examine their impact on VDAC and its extinction. All experiments included learning and test phases. During learning, participants completed a visual search task during which one of two target colors was associated with a reward, and the other with no reward. During test, 1 week later, participants completed another visual search task in which the reward association was not reinforced. When a rewarded feature remained task-relevant (Experiment 1), VDAC was observed. When the rewarded feature was made task-irrelevant (Experiments 2-5) there was no evidence of a VDAC effect, except when the target feature was physically salient and there was a reduction in the frequency of exposure to the reward-associated feature (Experiment 5). We failed to find evidence of VDAC in Experiments 2-4, suggesting that VDAC may depend on the demands of the task resulting in vulnerability to VDAC. When VDAC was observed, extinction was also observed. This indicates that VDAC is subject to extinction as would be expected from an effect driven by reinforcement learning.

Reward magnitude enhances early attentional processing of auditory stimuli

Reward associations are known to shape the brain's processing of visual stimuli, but relatively less is known about how reward associations impact the processing of auditory stimuli. We leveraged the high-temporal resolution of electroencephalography (EEG) and event-related potentials (ERPs) to investigate the influence of low- and high-magnitude stimulus-reward associations in an auditory oddball task. We associated fast, correct detection of certain auditory target stimuli with larger monetary rewards, and other auditory targets with smaller rewards. We found enhanced attentional processing of the more highly rewarded target stimuli, as evidenced by faster behavioral detection of those stimuli compared with lower-rewarded stimuli. Neurally, higher-reward associations enhanced the early sensory processing of auditory targets. Targets associated with higher-magnitude rewards had higher amplitude N1 and mismatch negativity (MMN) ERP components than targets associated with lower-magnitude rewards. Reward did not impact the latency of these early components. Higher-reward magnitude also decreased the latency and increased the amplitude of the longer-latency P3 component, suggesting that reward also can enhance the final processing stages of auditory target stimuli. These results provide insight into how the sensory and attentional neural processing of auditory stimuli is modulated by stimulus-reward associations and the magnitude of those associations, with higher-magnitude reward associations yielding enhanced auditory processing at both early and late stages compared with lower-magnitude reward associations.

The past, present, and future of selection history

The last ten years of attention research have witnessed a revolution, replacing a theoretical dichotomy (top-down vs. bottom-up control) with a trichotomy (biased by current goals, physical salience, and selection history). This third new mechanism of attentional control, selection history, is multifaceted. Some aspects of selection history must be learned over time whereas others reflect much more transient influences. A variety of different learning experiences can shape the attention system, including reward, aversive outcomes, past experience searching for a target, target‒non-target relations, and more. In this review, we provide an overview of the historical forces that led to the proposal of selection history as a distinct mechanism of attentional control. We then propose a formal definition of selection history, with concrete criteria, and identify different components of experience-driven attention that fit within this definition. The bulk of the review is devoted to exploring how these different components relate to one another. We conclude by proposing an integrative account of selection history centered on underlying themes that emerge from our review.

Testing reward-cue attentional salience: Attainment and dynamic changes

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.

Coherence and cognition in the cortex: the fundamental role of parvalbumin, myelin, and the perineuronal net

The calcium binding protein parvalbumin is expressed in interneurons of two main morphologies, the basket and chandelier cells, which target perisomatic domains on principal cells and are extensively interconnected in laminar networks by synapses and gap junctions. Beyond its utility as a convenient cellular marker, parvalbumin is an unambiguous identifier of the key role that these interneurons play in the fundamental functions of the cortex. They provide a temporal framework for principal cell activity by propagating gamma oscillation, providing coherence for cortical information processing and the basis for timing-dependent plasticity processes. As these parvalbumin networks mature, they are physically and functionally stabilised by axonal myelination and development of the extracellular matrix structure termed the perineuronal net. This maturation correlates with the emergence of high-speed, highly energetic activity and provides a coherent foundation for the unique ability of the cortex to cross-correlate activity across sensory modes and internal representations.