Abstract:Remembering the outcomes of past experiences allows us to generate future expectations and shape selection in the long-term. A growing number of studies has shown that learned positive reward values impact spatial memory-based attentional biases on perception. However, whether memory-driven attentional biases extend to punishment-related values has received comparatively less attention. Here, we manipulated whether recent spatial contextual memories became associated with successful avoidance of punishment (po… Show more
“…Even a single association with a modest monetary gain or avoidance of loss during the last learning block leads to performance improvements in the subsequent attention-orienting session, even though reward is irrelevant to the orienting task. Target stimuli during the orienting task elicit larger visual P1 potentials when previously associated with reward or punishment avoidance (Doallo et al., 2013, Suárez-Suárez et al., 2019).…”
Memories are about the past, but they serve the future. Memory research often emphasizes the former aspect: focusing on the functions that reconstitute (re-member) experience and elucidating the various types of memories and their interrelations, timescales, and neural bases. Here we highlight the prospective nature of memory in guiding selective attention, focusing on functions that use previous experience to anticipate the relevant events about to unfold-to ''premember'' experience. Memories of various types and timescales play a fundamental role in guiding perception and performance adaptively, proactively, and dynamically. Consonant with this perspective, memories are often recorded according to expected future demands. Using working memory as an example, we consider how mnemonic content is selected and represented for future use. This perspective moves away from the traditional representational account of memory toward a functional account in which forward-looking memory traces are informationally and computationally tuned for interacting with incoming sensory signals to guide adaptive behavior.
“…Even a single association with a modest monetary gain or avoidance of loss during the last learning block leads to performance improvements in the subsequent attention-orienting session, even though reward is irrelevant to the orienting task. Target stimuli during the orienting task elicit larger visual P1 potentials when previously associated with reward or punishment avoidance (Doallo et al., 2013, Suárez-Suárez et al., 2019).…”
Memories are about the past, but they serve the future. Memory research often emphasizes the former aspect: focusing on the functions that reconstitute (re-member) experience and elucidating the various types of memories and their interrelations, timescales, and neural bases. Here we highlight the prospective nature of memory in guiding selective attention, focusing on functions that use previous experience to anticipate the relevant events about to unfold-to ''premember'' experience. Memories of various types and timescales play a fundamental role in guiding perception and performance adaptively, proactively, and dynamically. Consonant with this perspective, memories are often recorded according to expected future demands. Using working memory as an example, we consider how mnemonic content is selected and represented for future use. This perspective moves away from the traditional representational account of memory toward a functional account in which forward-looking memory traces are informationally and computationally tuned for interacting with incoming sensory signals to guide adaptive behavior.
“…Participants were significantly faster at detecting the target object within familiar versus novel scenes, indicating that memories can be used to guide attention to a target’s expected location in complex visual scenes (Summerfield et al, 2006). A recent study modified a similar paradigm to study whether punishment-related memories likewise drive attention allocation (Suárez-Suárez, Rodríguez Holguín, Cadaveira, Nobre, & Doallo, 2019). Contextual memories paired with punishment avoidance led to faster responses to targets presented at remembered locations.…”
Section: Mechanisms Of Attention and Memory Interactions In Depressionmentioning
Depression is theorized to be caused in part by biased cognitive processing of emotional information. Yet, prior research has adopted a reductionist approach that does not characterize how biases in cognitive processes such as attention and memory work together to confer risk for this complex multifactorial disorder. Grounded in affective and cognitive science, we highlight four mechanisms to understand how attention biases, working memory difficulties, and long-term memory biases interact and contribute to depression. We review evidence for each mechanism and highlight time- and context-dependent dynamics. We outline methodological considerations and recommendations for research in this area. We conclude with directions to advance the understanding of depression risk, cognitive training interventions, and transdiagnostic properties of cognitive biases and their interactions.
“…Notably, such a benefit is not restricted to threat-related aversive stimuli but can also be seen in faster response times to stimuli associated with the loss of money, which is a secondary 'learned' reward (Bucker & Theeuwes, 2016;Small et al, 2005;Suarez-Suarez, Holguin, Cadaueira, Nobre, & Doallo, 2019). For example, in an object-in-scene learning task attentional orienting to the incorrect location was faster when subjects lost money for the object at that location in prior encounters compared to a neutral or positive outcome (Doallo, Patai, & Nobre, 2013;Suarez-Suarez et al, 2019). Similarly, when subjects are required to discriminate a peripherally presented target object they detect the stimulus faster following a short (20ms) spatial pre-cue when the cued stimulus is linked to a monetary loss (Bucker & Theeuwes, 2016).…”
Visual attention involves both enhancing the processing of stimuli that may lead to reward and avoiding the processing of stimuli that may lead to loss. But compared to reward expectancy little is known how loss-avoidance mediates attention. One hypothesis holds that attention is more efficiently deployed when expecting larger gains and larger losses as both increase motivational saliency. Alternatively, expecting larger penalties might reduce attentional efficacy and ‘loom’ larger than gains. Here, we tested these opposing views in four monkeys with a feature-token learning task that quantifies attentional efficacy by increasing distractor load from multidimensional objects. During learning the number of token rewards gained for correct choices and lost for incorrect choices were varied. We found that expecting larger gains improves attentional efficacy and learning speed as long as distractor load was low. In contrast, expecting larger losses impairs attentional efficacy and this impairment increases with distractor load and uncertainty about the relevance of visual features. These findings functionally dissociate the contributions of expecting gains and losses on attentional learning, suggesting that they operate via separate control pathways. One pathway is linked to avoiding loss by slowing down learning and disrupting attention non-selectively, while the second pathway enhances learning selectively for higher valued target features up to a limited distractor load. These results illustrate the strength and the limitation of motivational regulation of attentional efficacy during learning.Significance statementVisual attention is biased to objects promising higher expected gains, but it should likewise be biased to avoid processing objects associated with incurring a loss. Clarifying the roles of gains and losses to modulate attention is critical for understanding the possible neuronal processes that determine how efficient attention is deployed in real-world learning scenarios. This study clarifies that gains and losses have surprisingly strong, opposite effects on the efficacy of attentional deployment in environments with low and high distractor load. Attentional efficacy is enhanced with increased gains primarily when there are few distractors, while attentional efficacy is impaired with increased loss expectancy particularly when there are many distractors. These results clarify that avoiding loss and striving for gains affect attentional efficacy via partly distinct mechanisms.
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