Perceptions of randomness in binary sequences: Normative, heuristic, or both?

When people consider a series of random binary events, such as tossing an unbiased coin and recording the sequence of heads (H) and tails (T), they tend to erroneously rate sequences with less internal structure or order (such as HTTHT) as more probable than sequences containing more structure or order (such as HHHHH). This is traditionally explained as a local representativeness effect: Participants assume that the properties of long sequences of random outcomes—such as an equal proportion of heads and tails, and little internal structure—should also apply to short sequences. However, recent theoretical work has noted that the probability of a particular sequence of say, heads and tails of length n, occurring within a larger (>n) sequence of coin flips actually differs by sequence, so P(HHHHH) < P(HTTHT). In this alternative account, people apply rational norms based on limited experience. We test these accounts. Participants in Experiment 1 rated the likelihood of occurrence for all possible strings of 4, 5, and 6 observations in a sequence of coin flips. Judgments were better explained by representativeness in alternation rate, relative proportion of heads and tails, and sequence complexity, than by objective probabilities. Experiments 2 and 3 gave similar results using incentivized binary choice procedures. Overall the evidence suggests that participants are not sensitive to variation in objective probabilities of a sub-sequence occurring; they appear to use heuristics based on several distinct forms of representativeness

Evidence of a Goal-Directed Process in Human Pavlovian-Instrumental Transfer

© 2017 APA, all rights reserved). Cues that signal rewards can motivate reward-seeking behaviors, even for outcomes that are not currently desired. Three experiments examined this phenomenon, using an outcome-selective Pavlovian-instrumental transfer (PIT) design and an outcome devaluation procedure. In Experiment 1, participants learned to perform one response to earn crisps points and another response to earn popcorn points. One outcome was then devalued by adulterating it to make it taste unpleasant. On test, overall response choice was biased toward the outcome that had not been devalued, indicating goal-directed control. Stimuli that signaled crisps and popcorn also biased instrumental response choice toward their respective outcomes (a PIT effect). Most importantly, the strength of this bias was not influenced by the devaluation manipulation. In contrast, Experiment 2 demonstrated that when stimuli signaled equal probability of the two outcomes, cue-elicited response choice was sensitive to the devaluation manipulation. Experiment 3 confirmed this conclusion by demonstrating a selective avoidance of the cued, devalued outcome. Together, these data support a goal-directed model of PIT in which expected outcome probability and value make independent contributions to response choice. (PsycINFO Database Recor

Attention and associative learning in humans: An integrative review

This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (learned predictiveness). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (learned value). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that uncertainty may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that learned predictiveness and learned value modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either goal-directed or stimulus-driven. Rather, it suggests a new kind of “derived” attention

Anterior Temporal Lobe Tracks the Formation of Prejudice

Despite advances in understanding the brain structures involved in the expression of stereotypes and prejudice, little is known about the brain structures involved in their acquisition. Here, we combined fMRI, a task involving learning the valence of different social groups, and modeling of the learning process involved in the development of biases in thinking about social groups that support prejudice. Participants read descriptions of valenced behaviors performed by members of novel social groups, with majority groups being more frequently encountered during learning than minority groups. A model-based fMRI analysis revealed that the anterior temporal lobe tracked the trial-by-trial changes in the valence associated with each group encountered in the task. Descriptions of behavior by group members that deviated from the group average (i.e., prediction errors) were associated with activity in the left lateral pFC, dorsomedial pFC, and lateral anterior temporal cortex. Minority social groups were associated with slower acquisition rates and more activity in the ventral striatum and ACC/dorsomedial pFC compared with majority groups. These findings provide new insights into the brain regions that (a) support the acquisition of prejudice and (b) detect situations in which an individual's behavior deviates from the prejudicial attitude held toward their group

Delayed disengagement of attention from distractors signalling reward

Attention refers to the set of cognitive mechanisms that facilitate the prioritization of incoming sensory information. Existing research suggests that motivationally salient stimuli, such as those associated with reward, are prioritized by the attention system and that this prioritization occurs independently of an observer's goals. Specifically, studies of visual search have shown that stimuli signalling the availability of monetary reward are more likely to capture eye movements, even when participants are motivated to ignore such stimuli. In the current study we ask whether reward magnitude influences only the likelihood that stimuli will capture spatial attention, or whether reward also influences the ease with which people can disengage attention from a location when they are motivated to move their attention elsewhere. Three experiments examined the time taken to disengage from a centrally presented distractor that signalled the availability of high or low reward. We found that participants took longer to move their eyes away from a high-reward distractor, even though this came at financial cost (Experiment 1), that participants were unable to suppress a high-reward distractor consistently presented at the central location (Experiment 2), that slower responding was not due to behavioural freezing in the presence of a signal of high reward (Experiment 3), and that slower responding persisted even when rewards were no longer available (Experiment 4). These results indicate that reward modulates attentional disengagement: signals of high reward hold attention for longer, even when this is counterproductive for performance of ongoing tasks. Our findings further highlight the role of reward in the conflict between automatic and goal-directed attentional processing

Selective attention moderates the relationship between attentional capture by signals of nondrug reward and illicit drug use

BACKGROUND: The current study examined whether cognitive control moderates the association between (non-drug) reward-modulated attentional capture and use of alcohol and other drugs (AOD). METHODS: Participants were 66 university students who completed an assessment including questions about AOD use, a visual search task to measure value-modulated attentional capture, and a goal-directed selective attention task as a measure of cognitive control. RESULTS: The association between the effect of value-modulated attentional capture and illicit drug use was moderated by level of cognitive control. Among participants with lower levels of cognitive control, value-modulated attentional capture was associated with illicit drug use. This was not the case among participants with higher levels of cognitive control, who instead showed a significant association between illicit drug use and self-reported impulsivity, as well as alcohol use. CONCLUSIONS: These results provide support for models that view addictive behaviours as resulting from interaction and competition between automatic and more reflective processes. That is, the mechanisms that ultimately drive addictive behaviour may differ between people low or high in cognitive control. This has important implications for understanding the development and maintenance of substance use disorders and potentially their treatment and prevention