Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

EXPRESS: Redesigning the Exploration of Semantic Dynamics – SSD Account in Light of Regression Design

The Semantic Settling Dynamics model (Armstrong & Plaut, 2016) postulated that the seemingly inconsistent effects of lexical ambiguity are, in fact, a systematic manifestation of the specific dynamics that arise as a consequence of the amount of time spent in processing. The model has thus far been tested by prolonging lexical decision and comparing homonymous, polysemous, and unambiguous words in a factorial design. Here, we kept the strategy of task manipulation but tested the model by using continuous measures as indices of the level of lexical ambiguity and their slopes as indices of the effect size. We expressed the size of the polysemy effect as the slope of the effect of entropy of sense probability distribution and the size of the homonymy effect as the redundancy of sense probability distribution. Comparing lexical decision tasks with the shorter and longer time spent in processing, we observed the predicted decrease in the effect of the polysemy level as well as the predicted increase in the effect of homonymy level.

Expertise Classification of Soccer Goalkeepers in Highly Dynamic Decision Tasks: A Deep Learning Approach for Temporal and Spatial Feature Recognition of Fixation Image Patch Sequences

The focus of expertise research moves constantly forward and includes cognitive factors, such as visual information perception and processing. In highly dynamic tasks, such as decision making in sports, these factors become more important to build a foundation for diagnostic systems and adaptive learning environments. Although most recent research focuses on behavioral features, the underlying cognitive mechanisms have been poorly understood, mainly due to a lack of adequate methods for the analysis of complex eye tracking data that goes beyond aggregated fixations and saccades. There are no consistent statements about specific perceptual features that explain expertise. However, these mechanisms are an important part of expertise, especially in decision making in sports games, as highly trained perceptual cognitive abilities can provide athletes with some advantage. We developed a deep learning approach that independently finds latent perceptual features in fixation image patches. It then derives expertise based solely on these fixation patches, which encompass the gaze behavior of athletes in an elaborately implemented virtual reality setup. We present a CNN-BiLSTM based model for expertise assessment in goalkeeper-specific decision tasks on initiating passes in build-up situations. The empirical validation demonstrated that our model has the ability to find valuable latent features that detect the expertise level of 33 athletes (novice, advanced, and expert) with 73.11% accuracy. This model is a first step in the direction of generalizable expertise recognition based on eye movements.

Advanced Second Language Learners of Mandarin Show Persistent Deficits for Lexical Tone Encoding in Picture-to-Word Form Matching

People who grow up speaking a language without lexical tones typically find it difficult to master tonal languages after childhood. Accumulating research suggests that much of the challenge for these second language (L2) speakers has to do not with identification of the tones themselves, but with the bindings between tones and lexical units. The question that remains open is how much of these lexical binding problems are problems of encoding (incomplete knowledge of the tone-to-word relations) vs. retrieval (failure to access those relations in online processing). While recent work using lexical decision tasks suggests that both may play a role, one issue is that failure on a lexical decision task may reflect a lack of learner confidence about what is not a word, rather than non-native representation or processing of known words. Here we provide complementary evidence using a picture-phonology matching paradigm in Mandarin in which participants decide whether or not a spoken target matches a specific image, with concurrent event-related potential (ERP) recording to provide potential insight into differences in L1 and L2 tone processing strategies. As in the lexical decision case, we find that advanced L2 learners show a clear disadvantage in accurately identifying tone mismatched targets relative to vowel mismatched targets. We explore the contribution of incomplete/uncertain lexical knowledge to this performance disadvantage by examining individual data from an explicit tone knowledge post-test. Results suggest that explicit tone word knowledge and confidence explains some but not all of the errors in picture-phonology matching. Analysis of ERPs from correct trials shows some differences in the strength of L1 and L2 responses, but does not provide clear evidence toward differences in processing that could explain the L2 disadvantage for tones. In sum, these results converge with previous evidence from lexical decision tasks in showing that advanced L2 listeners continue to have difficulties with lexical tone recognition, and in suggesting that these difficulties reflect problems both in encoding lexical tone knowledge and in retrieving that knowledge in real time.

The Effect of Time Pressure on Risky Financial Decisions from Description and Decisions from Experience

Time pressure has been found to impact decision making in various ways, but studies on the effects time pressure in risky financial gambles have been largely limited to description-based decision tasks and to the gain domain. We present two experiments that investigated the effect of time pressure on decisions from description and decisions from experience, across both gain and loss domains. In description-based choice, time pressure decreased risk seeking for losses, whereas for gains there was a trend in the opposite direction. In experience-based choice, no impact of time pressure was observed on risk-taking, suggesting that time constraints may not alter attitudes towards risk when outcomes are learned through experience.

The Effect of Time Pressure on Risky Financial Decisions from Description and Decisions from Experience

Time pressure has been found to impact decision making in various ways, but studies on the effects time pressure in risky financial gambles have been largely limited to description-based decision tasks and to the gain domain. We present two experiments that investigated the effect of time pressure on decisions from description and decisions from experience, across both gain and loss domains. In description-based choice, time pressure decreased risk seeking for losses, whereas for gains there was a trend in the opposite direction. In experience-based choice, no impact of time pressure was observed on risk-taking, suggesting that time constraints may not alter attitudes towards risk when outcomes are learned through experience.

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.

Data Analysis from Two-choice Decision Tasks in Visual Information Processing

Data analysis are important tasks in research. The present study focuses on the analysis of data sets from human eye movement experiments. The results of the experiments were analyzed according to two criteria – gender and age of the participants. The participants were divided into 3 groups, respectively group 1: between 20 and 35 years, group 2: between 36 and 55 years and group 3: between 56 and 85 years. The results showed that 75% of the two-choice decision tasks were solved correctly. This trend was maintained among the participants from group 1 – respectively 75.4%. The participants from group 2 gave more correct answers – respectively 82.2%, but the participants from group 3 gave fewer correct answers – respectively 70.2%. The average value of the response time indicator (of all participants) was 1455 ms. The response time of the participants from groups 1 and 2 was shorter than the average (respectively with 483 ms and 235 ms). The response time of the participants from group 3 was longer than the average (respectively with 626 ms).

Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

Introducing automated systems based on artificial intelligence and machine learning for ethically sensitive decision tasks requires investigating of trust processes in relation to such tasks. In an example of such a task (personnel selection), this study investigates trustworthiness, trust, and reliance in light of a trust violation relating to ethical standards and a trust repair intervention. Specifically, participants evaluated applicant preselection outcomes by either a human or an automated system across twelve personnel selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds five through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to a violation of ethical standards. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). Results showed that participants initially perceived automated systems to be less trustworthy, and had less intention to trust automated systems. Specifically, participants perceived systems to be less able, and flexible, but also less biased – a result that was sustained even in light of unfair bias. Furthermore, in regard to the automated system the trust violation and the trust repair intervention had weaker effects. Those effects were partly stronger when highlighting imperfection for the automated system. We conclude that it is crucial to investigate trust processes in relation to automated systems in ethically sensitive domains such as personnel selection as insights from classical areas of automation might not translate to application contexts where ethical standards are central to trust processes.