The Perception of Automation Reliability and Acceptance of Automated Advice

Objective Examine (1) the extent to which humans can accurately estimate automation reliability and calibrate to changes in reliability, and how this is impacted by the recent accuracy of automation; and (2) factors that impact the acceptance of automated advice, including true automation reliability, reliability perception, and the difference between an operator’s perception of automation reliability and perception of their own reliability. Background Existing evidence suggests humans can adapt to changes in automation reliability but generally underestimate reliability. Cognitive science indicates that humans heavily weight evidence from more recent experiences. Method Participants monitored the behavior of maritime vessels (contacts) in order to classify them, and then received advice from automation regarding classification. Participants were assigned to either an initially high (90%) or low (60%) automation reliability condition. After some time, reliability switched to 75% in both conditions. Results Participants initially underestimated automation reliability. After the change in true reliability, estimates in both conditions moved towards the common true reliability, but did not reach it. There were recency effects, with lower future reliability estimates immediately following incorrect automation advice. With lower initial reliability, automation acceptance rates tracked true reliability more closely than perceived reliability. A positive difference between participant assessments of the reliability of automation and their own reliability predicted greater automation acceptance. Conclusion Humans underestimate the reliability of automation, and we have demonstrated several critical factors that impact the perception of automation reliability and automation use. Application The findings have potential implications for training and adaptive human-automation teaming.

A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer’s temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer’s existing belief—a type of ‘confirmation bias’. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.

Proportion of Conflict, Contingency Learning, and Recency Effects in a Stroop Task

Recent research on the relation between learning and cognitive control has assumed that conflict modulates learning, either by increasing arousal and hence improving learning in high conflict situations (Verguts & Notebaert, 2008), or by inducing control, and hence inhibiting the processing of distracters and their eventual association with the imperative responses (Whitehead et al., 2018). We analyze whether the amount of conflict, manipulated through the proportion of congruency in a set of Stroop inducer trials, affects learning of contingencies established on diagnostic trials composed by neutral words associated with color responses. The results reproduced the list-wide proportion of congruency effect on the inducer trials, and showed evidence of contingency learning on the diagnostic trials, but provided no indication that this learning was modulated by the level of conflict. Specific analyses conducted to control for the impact of episodic effects on the expression of learning indicated that contingency effects were not driven by the incremental processes that could be expected by associative learning, but rather they were due to the impact of the most recent trial involving the same distracter. Accordingly, these effects disappeared when tested selectively on trials that required a non-matching response with respect to the previous occurrence of the distracter. We interpret this result in the context of the debate on how learning and memory interact with the processes of cognitive control.

Does the Order of Visiting Destinations Affect Their Recall and Evaluation?

Tourists frequently engage in visiting a sequence of cities, sites, and destinations. Previous psychology studies have shown the impact of order on recall and favorability; key concepts are the serial position effect and primacy and recency influences. A field-based natural experiment collected posttrip responses from 179 international tourists to four major Iranian cities. The researchers examined the relationships between the order of visiting the cities, tourists’ recall and judgment. Results from the manipulations revealed there is a relationship (mainly Primacy) between position in the itinerary and their recall. For evaluative judgments, both primacy and recency effects were linked to order of visiting. The work has implications for the presentations of tourism units in a sequence and sharpens the way we use the expression memorable in tourism research.

Experience Effects in Finance: Foundations, Applications, and Future Directions

This article establishes four key findings of the growing literature on experience effects in finance: (i) the long-lasting imprint of past experiences on beliefs and risk taking; (ii) recency effects; (iii) the domain-specificity of experience effects; and (iv) imperviousness to information that is not experience-based. I first discuss the neuroscientific foundations of experience-based learning and sketch a simple model of its role in the stock market based on Malmendier et al. (2020a, b). I then distill the empirical findings on experience effects in stock-market investment, trade dynamics, and international capital flows, highlighting these four key features. Finally, I contrast models of belief formation that rely on “learned information” with models accounting for the neuroscience evidence on synaptic tagging and memory formation, and provide directions for future research.

People Adjust Recency Adaptively to Environment Structure

Recency effects—giving exaggerated importance to recent outcomes—are a common aspect of decision tasks. In the current study, we explore two explanations of recency-based decision making, that it is (1) a deliberate strategy for adaptive decision making in real-world environments which tend to be dynamic and autocorrelated, and/or (2) a product of processing limitations of working memory. Supporting explanation 1, we found that participants strategically adjusted their recency levels across trials to achieve optimal levels in a range of tasks. Furthermore, they started with default recency values that had high aggregate performance across environments. However, only some correlations between recency values and WM scores were significant, providing no clear conclusion regarding explanation 2. Ultimately, we propose that recency involves a combination of the two—people can strategically change recency within the limits of WM capacities to adapt to external environments.

Intentional and incidental odour-colour binding in working memory

The question of how features are bound together in working memory has become a topic of much research in recent years. However, this is typically focused on visual and/or auditory stimuli. This study applies established feature binding procedures to investigate odour binding in working memory. Across three experiments, memory for intentionally and incidentally formed odour-colour pairings was tested. Experiment 1 showed that following explicit instruction to remember the odour-colour combinations, young adults can recall lists of 3-odour-colour pairings at levels above that of chance and exhibit a recency advantage for the last pairing in the list. In Experiment 2 participants were asked to prioritise the first pairing in the list or treat all list items equally. We observed only limited evidence of prioritisation affecting the serial position function. Experiment 3 explored whether odour-colour binding can be incidentally formed. Using a yes/no recognition procedure, accuracy did not differ for positive test probes that were presented in the same (bound) or different (unbound) colour to encoding (although some weak recency effects were shown with confidence ratings). Taken together, these findings suggest that odour-colour bindings can be formed in working memory, but functionality may be limited compared to that of visual feature binding.