Training attention improves decision making in individuals with elevated self-reported depressive symptoms

Depression is often characterized by attentional biases toward negative items and away from positive items, which likely affects reward and punishment processing. Recent work has reported that training attention away from negative stimuli reduced this bias and reduced depressive symptoms. However, the effect of attention training on subsequent learning has yet to be explored. In the present study, participants were required to learn to maximize reward during decision making. Undergraduates with elevated self-reported depressive symptoms received attention training toward positive stimuli prior to performing the decision-making task (n = 20; active training). The active-training group was compared to two other groups: undergraduates with elevated self-reported depressive symptoms who received placebo training (n = 22; placebo training) and a control group with low levels of depressive symptoms (n = 33; nondepressive control). The placebo-training depressive group performed worse and switched between options more than did the nondepressive controls on the reward maximization task. However, depressives that received active training performed as well as the nondepressive controls. Computational modeling indicated that the placebo-trained group learned more from negative than from positive prediction errors, leading to more frequent switching. The nondepressive control and active-training depressive groups showed similar learning from positive and negative prediction errors, leading to less-frequent switching and better performance. Our results indicate that individuals with elevated depressive symptoms are impaired at reward maximization, but that the deficit can be improved with attention training toward positive stimuli.     

Mapping emotions through time: how affective trajectories inform the language of emotion

The words used to describe emotions can provide insight into the basic processes that contribute to emotional experience. We propose that emotions arise partly from interacting evaluations of one's current affective state, previous affective state, predictions for how these may change in the future, and the experienced outcomes following these predictions. These states can be represented and inferred from neural systems that encode shifts in outcomes and make predictions. In two studies, we demonstrate that emotion labels are reliably differentiated from one another using only simple cues about these affective trajectories through time. For example, when a worse-than-expected outcome follows the prediction that something good will happen, that situation is labeled as causing anger, whereas when a worse-than-expected outcome follows the prediction that something bad will happen, that situation is labeled as causing sadness. Emotion categories are more differentiated when participants are required to think categorically than when participants have the option to consider multiple emotions and degrees of emotions. This work indicates that information about affective movement through time and changes in affective trajectory may be a fundamental aspect of emotion categories. Future studies of emotion must account for the dynamic way that we absorb and process information.     

Cross-situational Learning From Ambiguous Egocentric Input Is a Continuous Process: Evidence Using the Human Simulation Paradigm

Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real-world contexts in which learning occurs. Thus, the feasibility of generalizing cross-situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments examining cross-situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross-situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners’ performance gradually improved over time. This improvement was driven in part by learners’ use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word-object mappings. These results suggest that word learning is a continuous process by means of real-time information integration.     

Temporal characteristics of overt attentional behavior during category learning

Many theories of category learning incorporate mechanisms for selective attention, typically implemented as attention weights that change on a trial-by-trial basis. This is because there is relatively little data on within-trial changes in attention. We used eye tracking and mouse tracking as fine-grained measures of attention in three complex visual categorization tasks to investigate temporal patterns in overt attentional behavior within individual categorization decisions. In Experiments 1 and 2, we recorded participants’ eye movements while they performed three different categorization tasks. We extended previous research by demonstrating that not only are participants less likely to fixate irrelevant features, but also, when they do, these fixations are shorter than fixations to relevant features. We also found that participants’ fixation patterns show increasingly consistent temporal patterns. Participants were faster, although no more accurate, when their fixation sequences followed a consistent temporal structure. In Experiment 3, we replicated these findings in a task where participants used mouse movements to uncover features. Overall, we showed that there are important temporal regularities in information sampling during category learning that cannot be accounted for by existing models. These can be used to supplement extant models for richer predictions of how information is attended to during the buildup to a categorization decision.     

Cortical oscillations and sensory predictions

Many theories of perception are anchored in the central notion that the brain continuously updates an internal model of the world to infer the probable causes of sensory events. In this framework, the brain needs not only to predict the causes of sensory input, but also when they are most likely to happen. In this article, we review the neurophysiological bases of sensory predictions of "what' (predictive coding) and 'when' (predictive timing), with an emphasis on low-level oscillatory mechanisms. We argue that neural rhythms offer distinct and adapted computational solutions to predicting 'what' is going to happen in the sensory environment and 'when'.     

Effects of Instructor Attractiveness on Learning

Although a considerable body of research has examined the impact of student attractiveness on instructors, little attention has been given to the influence of instructor attractiveness on students. This study tested the hypothesis that persons would perform significantly better on a learning task when they perceived their instructor to be high in physical attractiveness. To test the hypothesis, participants listened to an audio lecture while viewing a photograph of instructor. The photograph depicted either a physically attractive instructor or a less attractive instructor. Following the lecture, participants completed a forced choice recognition task covering material from the lecture. Consistent with the predictions; attractive instructors were associated with more learning. Finally, we replicated previous findings demonstrating the role attractiveness plays in person perception.     

The predicting brain: Unconscious repetition, conscious reflection and therapeutic change

Neuroscience indicates that 'repetition' is fundamental to brain function. The brain non-consciously predicts what is most likely to happen and sets in motion perceptions, emotions, behaviors and interpersonal responses best adapted to what is expected-before events occur. Predictions enable individuals to be ready 'ahead of time' so reactions occur rapidly and smoothly when events occur. The brain uses past learning as the guide for what to expect in the future. Because of prediction, present experience and responses are shaped by the past. Predictions from early life can be deeply encoded and enduring. Predictions based on the past allow for more efficient brain function in the present, but can lead to mistakes. When what is predicted does not occur, consciousness can be engaged to monitor and correct the situation. But if a perception or emotion seems reasonable for the situation, a person might not notice an error, and a maladaptive 'repetition' may remain unchanged. The author discusses how predictions contribute to psychological defenses and transference repetition, and how conscious self-reflection facilitates therapeutic change. The neuroscience of prediction indicates why, in certain cases, active engagement by the analyst may be necessary. The author makes the argument for use of a 'neuroscience interpretation'.     

A neural autopilot theory of habit: Evidence from consumer purchases and social media use

This article applies a two-process “neural autopilot” model to field data. The autopilot model hypothesizes that habitual choice occurs when the reward from a behavior has low numerical “doubt” (i.e., reward prediction errors are small). The model toggles between repeating a previous choice (habit) when doubt is low and making a goal-directed choice when doubt is high. The model has ingredients established in animal learning and cognitive neuroscience and is simple enough to make nonobvious predictions. In two empirical applications, we fit the model to field data on purchases of canned tuna and posting on the Chinese social media site Weibo. This style of modeling is called “structural” because there is a theoretical model of how different variables influence choices by agents (the “structure”), which tightly restricts how hidden variables lead to observed choices. There is empirical support for the model, more strongly for tuna purchases than for Weibo posting, relative to a baseline “reduced-form” model in which current choices are correlated with past choices without a mechanistic (structural) explanation. An interesting set of predictions can also be derived about how consumers react to different kinds of changes in prices and qualities of goods (this is called “counterfactual analysis”).     

Effects of feedback reliability on feedback-related brain activity: A feedback valuation account

Adaptive decision making relies on learning from feedback. Because feedback sometimes can be misleading, optimal learning requires that knowledge about the feedback’s reliability be utilized to adjust feedback processing. Although previous research has shown that feedback reliability indeed influences feedback processing, the underlying mechanisms through which this is accomplished remain unclear. Here we propose that feedback processing is adjusted by the adaptive, top-down valuation of feedback. We assume that unreliable feedback is devalued relative to reliable feedback, thus reducing the reward prediction errors that underlie feedback-related brain activity and learning. A crucial prediction of this account is that the effects of feedback reliability are susceptible to contrast effects. That is, the effects of feedback reliability should be enhanced when both reliable and unreliable feedback are experienced within the same context, as compared to when only one level of feedback reliability is experienced. To evaluate this prediction, we measured the event-related potentials elicited by feedback in two experiments in which feedback reliability was varied either within or between blocks. We found that the fronto-central valence effect, a correlate of reward prediction errors during reinforcement learning, was reduced for unreliable feedback. But this result was obtained only when feedback reliability was varied within blocks, thus indicating a contrast effect. This suggests that the adaptive valuation of feedback is one mechanism underlying the effects of feedback reliability on feedback processing.     

Event perception: a mind-brain perspective

People perceive and conceive of activity in terms of discrete events. Here the authors propose a theory according to which the perception of boundaries between events arises from ongoing perceptual processing and regulates attention and memory. Perceptual systems continuously make predictions about what will happen next. When transient errors in predictions arise, an event boundary is perceived. According to the theory, the perception of events depends on both sensory cues and knowledge structures that represent previously learned information about event parts and inferences about actors' goals and plans. Neurological and neurophysiological data suggest that representations of events may be implemented by structures in the lateral prefrontal cortex and that perceptual prediction error is calculated and evaluated by a processing pathway, including the anterior cingulate cortex and subcortical neuromodulatory systems.     

Gavagai Is as Gavagai Does: Learning Nouns and Verbs From Cross‐Situational Statistics

Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross‐situational learning studies have shown that word‐object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust when objects are moving, and that verbs can also be learned from similar scenes without additional syntactic information. Furthermore, we show that both nouns and verbs can be acquired simultaneously, thus resolving category‐level as well as individual word‐level ambiguity. However, nouns were learned more quickly than verbs, and we discuss this in light of previous studies investigating the noun advantage in word learning.     

Automatic and strategic effects in the guidance of attention by working memory representations

Theories of visual attention suggest that working memory representations automatically guide attention toward memory-matching objects. Some empirical tests of this prediction have produced results consistent with working memory automatically guiding attention. However, others have shown that individuals can strategically control whether working memory representations guide visual attention. Previous studies have not independently measured automatic and strategic contributions to the interactions between working memory and attention. In this study, we used a classic manipulation of the probability of valid, neutral, and invalid cues to tease apart the nature of such interactions. This framework utilizes measures of reaction time (RT) to quantify the costs and benefits of attending to memory-matching items and infer the relative magnitudes of automatic and strategic effects. We found both costs and benefits even when the memory-matching item was no more likely to be the target than other items, indicating an automatic component of attentional guidance. However, the costs and benefits essentially doubled as the probability of a trial with a valid cue increased from 20% to 80%, demonstrating a potent strategic effect. We also show that the instructions given to participants led to a significant change in guidance distinct from the actual probability of events during the experiment. Together, these findings demonstrate that the influence of working memory representations on attention is driven by both automatic and strategic interactions.     

Model-based and model-free Pavlovian reward learning: Revaluation,revision, and revelation

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.     

Visual learning of statistical relations among nonadjacent features: Evidence for structural encoding

Recent results suggest that observers can learn, unsupervised, the co-occurrence of independent shape features in viewed patterns (e.g., Fiser & Aslin, 2001). A critical question with regard to these findings is whether learning is driven by a structural, rule-based encoding of spatial relations between distinct features or by a pictorial, template-like encoding, in which spatial configurations of features are embedded in a “holistic” fashion. In two experiments, we test whether observers can learn combinations of features when the paired features are separated by an intervening spatial “gap”, in which other, unrelated features can appear. This manipulation both increases task difficulty and makes it less likely that the feature combinations are encoded simply as larger unitary features. Observers exhibited learning consistent with earlier studies, suggesting that unsupervised learning of compositional structure is based on the explicit encoding of spatial relations between separable visual features. More generally, these results provide support for compositional structure in visual representation.     

Analyzing behavior implied by EWA learning: An emphasis on distinguishing reinforcement from belief learning

An important issue in the field of learning is to what extent one can distinguish between behavior resulting from either belief or reinforcement learning. Previous research suggests that it is difficult or even impossible to distinguish belief from reinforcement learning: belief and reinforcement models often fit the empirical data equally well. However, previous research has been confined to specific games in specific settings. In the present study we derive predictions for behavior in games using the EWA learning model (e.g., Camerer & Ho, 1999), a model that includes belief learning and a specific type of reinforcement learning as special cases. We conclude that belief and reinforcement learning can be distinguished, even in 2×2 games. Maximum differentiation in behavior resulting from either belief or reinforcement learning is obtained in games with pure Nash equilibria with negative payoffs and at least one other strategy combination with only positive payoffs. Our results help researchers to identify games in which belief and reinforcement learning can be discerned easily.     

Consensus effects in categorization decisions

A previous study (Gilat et al., J. Exp. Psychol. Appl. 3 (1997) 83) has shown that the incentive to reach consensus can raise the tendency to rely on base rates in signal detection decisions and can reduce the probability that less likely events will be accurately classified. This phenomenon was named the “consensus effect”. The current study assesses the conditions under which this effect develops and in particular the effects of information about the game and of the incentive structure on the learning process. The results of three experiments show that the learning process slows when participants have information about the actual state of nature. This finding is captured by a reinforcement learning model with the assumption that information narrows the distribution of the initial propensities for choosing among cutoffs. The results are further evidence for the utility of the combination of learning models and analyses of cognitive processes for the prediction of decision making in situations involving multiple players.     

注意资源、学习方式与年龄因素对协作抑制的影响

本研究在前人研究的基础上,试图探讨不同编码因素对协作抑制效应的制约作用,从协作抑制角度为挖掘集体记忆的作用机制提供证据。实验1结果发现,集中注意条件下,出现协作抑制;分配注意条件下,协作抑制消失。说明学习时注意资源的投入程度,对协作抑制有影响。实验2结果发现不同的学习方式对协作抑制的影响在不同年龄被试身上表现不同。本研究将视角聚焦于编码因素,研究结果证实,协作抑制受到不同编码因素的制约,此外,本结果亦为协作抑制的提取策略破坏假说提供了新的证据。     

Visual attention to own- versus other-race faces: Perspectives from learning mechanisms and task demands

Multiple factors have been proposed to contribute to the other-race effect in face recognition, including perceptual expertise and social-cognitive accounts. Here, we propose to understand the effect and its contributing factors from the perspectives of learning mechanisms that involve joint learning of visual attention strategies and internal representations for faces, which can be modulated by quality of contact with other-race individuals including emotional and motivational factors. Computational simulations of this process will enhance our understanding of interactions among factors and help resolve inconsistent results in the literature. In particular, since learning is driven by task demands, visual attention effects observed in different face-processing tasks, such as passive viewing or recognition, are likely to be task specific (although may be associated) and should be examined and compared separately. When examining visual attention strategies, the use of more data-driven and comprehensive eye movement measures, taking both spatial–temporal pattern and consistency of eye movements into account, can lead to novel discoveries in other-race face processing. The proposed framework and analysis methods may be applied to other tasks of real-life significance such as face emotion recognition, further enhancing our understanding of the relationship between learning and visual cognition.     

Eye'm lovin' it! The role of gazing awareness in mimetic desires

Recent studies showed that people evaluate objects more favorably when these objects are gazed-at by others, an effect coined as “mimetic desire”. In two studies, we tested whether mimetic desire stems from an automatic form of learning by examining one dimension of automaticity, i.e., people's awareness of the object-gaze association. Participants saw 6 neutral art paintings associated with a female gazing toward two of the paintings, away from two of the paintings, and closing her eyes with respect to the last two paintings. After the exposition phase, participants evaluated the paintings and performed a contingency-awareness test. Importantly, participants' responses on this test were genuinely driven by memory and not by inferences from liking. Results show that participants preferred objects that were gazed-at but only when they were aware of the object-gaze association. Hence, despite the adaptive function of joint attention, its impact on valence acquisition does not seem to qualify as an implicit learning process.