Attention In Associative Learning Research Articles

Aging and the role of attention in associative learning.

In this study, we investigated whether age-related deficits in cue-outcome associative learning (e.g., Mutter, Atchley, & Plumlee, 2012; Mutter, DeCaro, & Plumlee, 2009; Mutter, Haggbloom, Plumlee, & Schirmer, 2006; Mutter & Williams, 2004) might be due to a decline in older adults' ability to modulate attention to relevant and irrelevant cues. In the first 2 experiments, we used standard blocking and highlighting tasks to indirectly measure the ability to shift attention away from irrelevant stimuli toward relevant, predictive cues (e.g., Kruschke, Kappenman, & Hetrick, 2005). Although there were age differences in prediction accuracy, like young adults, older adults learned to shift attention toward predictive stimuli and ignore irrelevant or less predictive stimuli. This attentional effect was unrelated to either working memory or executive function suggesting that it did not involve voluntary control processes. The third experiment provided further support for this idea. We alternated a category learning task with a dot probe task to more directly assess the development of automatic attentional biases. There were again age differences in category prediction, but young and older adults alike responded more rapidly to the location of a dot probe cued by a stimulus experienced as predictive during the learning task than one cued by a stimulus experienced as nonpredictive. These findings provide converging evidence that even though cue-outcome prediction declines with age, the ability to modulate attention based on the predictive relevance of cues during associative learning remains intact. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Supplemental Material for Aging and the Role of Attention in Associative Learning

Supplemental Material for Aging and the Role of Attention in Associative Learning

Secondary visual cortex is critical to the expression of surprise-induced enhancements in cue associability in rats.

Considerable evidence indicates that reinforcement prediction error, the difference between the obtained and expected reinforcer values, modulates attention to potential cues for reinforcement. The surprising delivery or omission of a reinforcer enhances the associability of the stimuli that were present when the error was induced, so that they more readily enter into new associations in the future. Previous research from our laboratory identified brain circuit elements critical to the enhancement of stimulus associability by omission of an expected event and to the subsequent expression of that altered associability in more rapid learning. A key finding was that the rat posterior parietal cortex was essential during the encoding, consolidation and retrieval of associability memories that were altered by the surprising omission of an expected event in a serial prediction task. Here, we found that the function of adjacent secondary visual cortex was critical only to the expression of altered cue associability in that same task. This specialization of function is discussed in the context of broader cortical and subcortical networks for modulation of attention in associative learning, as well as recent anatomical investigations that suggest that the rodent posterior parietal cortex overlaps with and may subsume secondary visual cortex.

Dorsolateral striatum is critical for the expression of surprise-induced enhancements in cue associability.

The dorsolateral striatum (DLS) is frequently implicated in sensory-motor integration, including the performance of sensory orienting responses (ORs) and learned stimulus-response habits. Our laboratory previously identified a role for the DLS in rats' performance of conditioned ORs to Pavlovian cues for food delivery. Here, we considered whether DLS is also critical to another aspect of attention in associative learning, the surprise-induced enhancement of cue associability. A large behavioral literature shows that a cue present when an expected event is omitted enters into new associations more rapidly when that cue is subsequently paired with food. Research from our laboratory has shown that both cue associability enhancements and conditioned ORs depend on the function of a circuit that includes the amygdala central nucleus and the substantia nigra pars compacta. In three experiments, we explored the involvement of DLS in surprise-induced associability enhancements, using a three-stage serial prediction task that permitted separation of DLS function in registering surprise (prediction error) and enhancing cue associability, and in using that increased associability to learn more rapidly about that cue later. The results showed that DLS is critical to the expression, but not the establishment, of the enhanced cue associability normally produced by surprise in this task. They extend the role of DLS and the amygdalo-nigro-striatal circuit underlying learned orienting to more subtle aspects of attention in associative learning, but are consistent with the general notion that DLS is more important in the expression of previously acquired tendencies than in their acquisition.

Amygdalo-striatal interaction in the enhancement of stimulus salience in associative learning.

Function of the central nucleus of the amygdala (CeA) is critical to 2 aspects of attention in associative learning: the conditioning of orienting responses (ORs) to cues paired with food, and the enhancement of cue salience by the surprising omission of expected events. Such salience enhancements have been found to depend on interactions within a circuit that includes CeA, the substantia nigra pars compacta (SNc), the substantia innominata (SI), and the posterior parietal cortex (PPC). The acquisition and expression of conditioned ORs requires interactions among CeA, SNc, and the dorsal lateral striatum (DLS), but not SI or PPC. Here, we considered whether CeA-DLS interactions are also important in surprise-induced salience enhancements in a serial prediction task. Rats received unilateral lesions of CeA and DLS, either contralaterally, which disrupted interactions between those structures, or ipsilaterally, which produced comparable damage to each structure but permitted interactions between them in 1 hemisphere. Rats with ipsilateral lesions of CeA and DLS showed the salience enhancements normally observed in this task, but rats with contralateral lesions of those structures did not. Thus, convergence of information processing by CeA and DLS is essential for surprise-induced salience enhancements, as well as for conditioned ORs.

Role of lateral hypothalamus in two aspects of attention in associative learning.

Orexin (hypocretin) and melanin-concentrating hormone (MCH) neurons are unique to the lateral hypothalamic (LH) region, but project throughout the brain. These cell groups have been implicated in a variety of functions, including reward learning, responses to stimulants, and the modulation of attention, arousal and the sleep/wakefulness cycle. Here, we examined roles for LH in two aspects of attention in associative learning shown previously to depend on intact function in major targets of orexin and MCH neurons. In experiments 1 and 2, unilateral orexin-saporin lesions of LH impaired the acquisition of conditioned orienting responses (ORs) and bilaterally suppressed FOS expression in the amygdala central nucleus (CeA) normally observed in response to food cues that provoke conditioned ORs. Those cues also induced greater FOS expression than control cues in LH orexin neurons, but not in MCH neurons. In experiment 3, unilateral orexin-saporin lesions of LH eliminated the cue associability enhancements normally produced by the surprising omission of an expected event. The magnitude of that impairment was positively correlated with the amount of LH damage and with the loss of orexin neurons in particular, but not with the loss of MCH neurons. We suggest that the effects of the LH orexin-saporin lesions were mediated by their effect on information processing in the CeA, known to be critical to both behavioral phenomena examined here. The results imply close relations between LH motivational amplification functions and attention, and may inform our understanding of disorders in which motivational and attentional impairments co-occur.

Modeling attention in associative learning: Two processes or one?

Certain studies of associative learning show that attention is more substantial to cues that have a history of being predictive of an outcome than to cues that are irrelevant. At the same time, other studies show that attention is more substantial to cues whose outcomes are uncertain than to cues whose outcomes are predictable. This has led to the suggestion of there being two kinds of attention in associative learning: one based upon a mechanism that allocates attention to a cue on the basis of its predictiveness, the other based upon a mechanism that allocates attention to a cue on the basis of its prediction error (e.g., Le Pelley, Quarterly Journal of Experimental Psychology, 57B, 193-243, 2004). As an alternative, it has been demonstrated that the effects of both predictiveness and uncertainty can be accounted for with only one kind of attention: one that emphasizes the role of prediction (Esber & Haselgrove, Proceedings of the Royal Society B, 278, 2553-2561, 2011). Here, we consider the alternative: whether the effects of predictiveness and uncertainty can be reconciled with a model of learning that emphasizes the role of prediction error (Pearce, Kaye, & Hall, 1982). Simulations of this model reveal that, in many cases, it too is able to account for the influence of predictiveness and uncertainty in associative learning.

Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning.

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.

Attention and expectation in human predictive learning: The role of uncertainty

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) theory of the role of attention in associative learning.

Attentional control of associative learning—A possible role of the central cholinergic system

How does attention interact with learning? Kruschke [Kruschke, J.K. (2001). Toward a unified Model of Attention in Associative Learning. J. Math. Psychol. 45, 812–863.] proposed a model (EXIT) that captures Mackintosh's [Mackintosh, N.J. (1975). A theory of attention: Variations in the associability of stimuli with reinforcement. Psychological Review, 82(4), 276–298.] framework for attentional modulation of associative learning. We developed a computational model that showed analogous interactions between selective attention and associative learning, but is significantly simplified and, in contrast to EXIT, is motivated by neurophysiological findings. Competition among input representations in the internal representation layer, which increases the contrast between stimuli, is critical for simulating these interactions in human behavior. Furthermore, this competition is modulated in a way that might be consistent with the phasic activation of the central cholinergic system, which modulates activity in sensory cortices. Specifically, phasic increases in acetylcholine can cause increased excitability of both pyramidal excitatory neurons in cortical layers II/III and cortical GABAergic inhibitory interneurons targeting the same pyramidal neurons. These effects result in increased attentional contrast in our model. This model thus represents an initial attempt to link human attentional learning data with underlying neural substrates.

Role of Substantia Nigra–Amygdala Connections in Surprise-Induced Enhancement of Attention

Coding of prediction error by midbrain dopamine neurons has been examined extensively in the framework of associative learning theory. Most of this research has focused on the role of prediction error in determining the reinforcement value of unconditioned stimuli: poorly predicted ("surprising") outcomes are more effective reinforcers and produce a greater dopamine response than well predicted outcomes. However, surprise also enhances attention to cues that signal poorly predicted outcomes. Previous reports from our laboratories demonstrated that circuitry, including the amygdala central nucleus (CeA), the cholinergic neurons of the substantia innominata/nucleus basalis region, and their innervation of the posterior parietal cortex, is critical to these surprise-induced enhancements of attention in associative learning. The present study considered the origin of prediction error information important for the operation of this system by examining the effects of disrupting communication between the midbrain substantia nigra pars compacta (SNc) and the CeA. Rats received unilateral lesions of the SNc and lesions of the CeA in either the contralateral or ipsilateral hemisphere. Contralateral lesions eliminated the surprise-induced enhancement of attention and learning that was displayed by rats with ipsilateral control lesions. These results show that SNc-CeA communication is critical to mechanisms by which the coding of prediction error by midbrain dopamine neurons is translated into enhancement of attention and learning modulated by the cholinergic system.

Toward a Unified Model of Attention in Associative Learning

Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276–298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke & N. J. Blair, 2000, Psychonomic Bulletin & Review, 7, 636–645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other “irrational” phenomena in associative learning, including base rate effects, perseveration of attention through relevance shifts, overshadowing, and the extrapolation of rules near exceptions.