Jami Pekkanen

Jami Pekkanen
University of Helsinki | HY · Department of Digital Humanities

PhD in Cognitive Science

About

45
Publications
11,710
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384
Citations
Citations since 2016
37 Research Items
350 Citations
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2016201720182019202020212022020406080
2016201720182019202020212022020406080
2016201720182019202020212022020406080

Publications

Publications (45)
Article
Full-text available
Gaze behavior during visual tracking consists of a combination of pursuit and saccadic movements. When the tracked object is intermittently occluded, the role of smooth pursuit is reduced, with a corresponding increase in the role of saccades. However, studies of visual tracking during occlusion have focused only on the first few saccades, usually...
Article
Full-text available
The fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expect...
Article
Full-text available
Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly...
Article
Full-text available
Skillful behavior requires the anticipation of future action requirements. This is particularly true during high-speed locomotor steering where solely detecting and correcting current error is insufficient to produce smooth and accurate trajectories. Anticipating future steering requirements could be supported using "model-free" prospective signals...
Article
Full-text available
According to radical enactivists, cognitive sciences should abandon the representational framework. Perceptuomotor cognition and action control are often provided as paradigmatic examples of nonrepresentational cognitive phenomena. In this article, we illustrate how motor and action control are studied in research that uses reinforcement learning a...
Chapter
Full-text available
Vahvistusoppimisalgoritmit ovat nykyisin hyvin suosittu tutkimuksessa käytettävien algoritmien ryhmä. Vahvistusoppimisalgoritmit etsivät mahdollisimman hyviä lopputuloksia tuottavia toimintakäytäntöjä (engl. ”action policy”). Niitä on sovellettu esimerkiksi robottikäsien liikkeiden suunnitteluun, autonomisten ajoneuvojen reittien valintaan ja pörss...
Preprint
Human behavior and interaction in road traffic is highly complex, with many open scientifi?c questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly...
Article
Full-text available
Automated vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better under...
Article
Full-text available
Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results...
Technical Report
Full-text available
The interACT project aims to understand how interactions unfold between road users, in order to ensure the safe integration of automated vehicles (AVs) into mixed traffic environments. This document describes the final evaluation of the expected impacts of the interACT solutions in terms of traffic safety, flow, road design and road users’ subjecti...
Preprint
Full-text available
Automated Vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better under...
Preprint
Full-text available
It remains a huge challenge to create Automated Vehicles (AVs) that are able to respond safely in all possible circumstances. Silent failures will occur when an AV fails to keep within the safety envelope and does not detect this failure or alert the human driver. To ensure AV safety, it is crucial to have a better understanding of human capabiliti...
Article
Full-text available
It is well-established how visual stimuli and self-motion in laboratory conditions reliably elicit retinal-image-stabilizing compensatory eye movements (CEM). Their organization and roles in natural-task gaze strategies is much less understood: are CEM applied in active sampling of visual information in human locomotion in the wild? If so, how? And...
Technical Report
Full-text available
Automated Vehicles (AVs) have seen rapid technological development over the last decade and will soon be deployed on public roads. However, road traffic is unlikely to become fully automated in the near future. Instead, AVs will share the road space with human road users (HRUs), including cyclists, pedestrians and drivers. A major challenge in the...
Article
Full-text available
When negotiating bends car drivers perform gaze polling: their gaze shifts between guiding fixations (GFs; gaze directed 1–2 s ahead) and look-ahead fixations (LAFs; longer time headway). How might this behavior change in autonomous vehicles where the need for constant active visual guidance is removed? In this driving simulator study, we analyzed...
Article
Full-text available
In complex dynamic tasks such as driving it is essential to be aware of potentially important targets in peripheral vision. While eye tracking methods in various driving tasks have provided much information about drivers’ gaze strategies, these methods only inform about overt attention and provide limited grounds to assess hypotheses concerning cov...
Conference Paper
Full-text available
Drift diffusion (or evidence accumulation) models have found widespread use in the modelling of simple decision tasks. Extensions of these models, in which the model’s instantaneous drift rate is not fixed but instead allowed to vary over time as a function of a stream of perceptual inputs, have allowed these models to account for more complex sens...
Preprint
Drift diffusion (or evidence accumulation) models have found widespread use in the modelling of simple decision tasks. Extensions of these models, in which the model’s instantaneous drift rate is not fixed but instead allowed to vary over time as a function of a stream of perceptual inputs, have allowed these models to account for more complex sens...
Article
Full-text available
A major unresolved question in understanding visually guided locomotion in humans is whether actions are driven solely by the immediately available optical information (model-free online control mechanisms), or whether internal models have a role in anticipating the future path. We designed two experiments to investigate this issue, measuring spont...
Thesis
Full-text available
This thesis is an inquiry into how humans use their imperfect perception, limited attention and action under uncertainty to successfully conduct time-critical tasks. This is done in four studies. The experimental task in the first two is car following while being visually distracted. The third study presents a new method for analyzing eye-movement...
Article
Full-text available
We present a computational model of intermittent visual sampling and locomotor control in a simple yet representative task of a car driver following another vehicle. The model has a number of features that take it beyond the current state of the art in modelling natural tasks, and driving in particular. First, unlike most control theoretical models...
Preprint
Full-text available
Tekoälyalgoritmien kehityksestä, tietokoneiden laskentatehon ja muistikapasiteetin kasvamisesta huolimatta-tai osittain juuri niiden takia-tekoälyyn ja sen kehittämiseen liittyy useita avoimia kysymyksiä. Osa on perustavia ja syvällisiä tieteellisiä kysymyksiä inhimillisen älyn ja koneälyn luonteesta, osa tekoälysovelluksiin liittyviä laajoja yhtei...
Article
Full-text available
We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position tim...
Article
Full-text available
Variation in longitudinal control in driving has been discussed in both traffic psychology and transportation engineering. Traffic psychologists have concerned themselves with “driving style”, a habitual form of behavior marked by it’s stability, and its basis in psychological traits. Those working in traffic microsimulation have searched for quant...
Data
Jerk vs. time headway—Accelerating and decelerating case. These figures represent the linear fit for the accelerating and decelerating cases for the subject averages of jerk and time headway. The lines are Ordinary Least Squares (OLS) fits along with their confidence intervals at CL 95%. (PDF)
Data
Participants’ background information. Table describing the relevant background information collected on the 15 participants. Driving experiences are self-reported estimates, with 8 discretized categories for lifetime experience (from “Less than 1000 km” to “Over 1 000 000 km”) and 9 categories for the last 12 months (from “None” to “Over 50 000 km”...
Data
Mean jerk and mean time headway with gender. Scatter plot showing mean time headway on the x-axis and mean jerk on the y-axis. Male participants in orange, females in green. (PDF)
Data
Figs A-L. Per-transition average acceleration figures. For completeness, we provide figures for all the per-transition average accelerations. (PDF)
Data
Tables A-C. PCA details. Details of the Principal Component Analysis for variables containing subject averages (N = 15)—mean acceleration, mean jerk and mean time headway. They include eigenvalues, eigenvectors and the loadings for each component. (PDF)
Data
First two PCA components with gender. Scatter plot showing the first two PCA components, with gender of the participants differentiated by colour. Should the two groups form distinct clusters, one would be able to see them here. No such pattern arises with this amount of participants. (PDF)
Data
(http://doi.org/10.6084/m9.figshare.4498613.v1) Annotated eye tracking movie, one of five, on one highly experienced driver (a driving school instructor) driving on a low-standard road. From: Lappi Otto, Rinkkala Paavo, Pekkanen Jami (2017). Systematic Observation of an Expert Driver's Gaze Strategy—An On-Road Case Study, Frontiers in Psychology,...
Article
Full-text available
In this paper we present and qualitatively analyze an expert driver’s gaze behaviour in natural driving on a real road, with no specific experimental task or instruction. Previous eye tracking research on naturalistic tasks has revealed recurring patterns of gaze behaviour that are surprisingly regular and repeatable. Lappi (doi: 10.1016/j.neubiore...
Article
Full-text available
Car following (CF) models used in traffic engineering are often criticized for not incorporating “human factors” well known to affect driving. Some recent work has addressed this by augmenting the CF models with the Task-Capability Interface (TCI) model, by dynamically changing driving parameters as function of driver capability. We examined assump...
Article
Full-text available
Objective: Studies based on accident statistics generally suggest that the presence of a passenger reduces adult drivers' accident risk. However, passengers have been reported to be a source of distraction in a remarkable portion of distraction related crashes. Although the effect of passengers on driving performance has been studied extensively,...
Article
Full-text available
Several steering models in the visual science literature attempt to capture the visual strategies in curve driving. Some of them are based on steering points on the future path (FP), others on tangent points (TP). It is, however, challenging to differentiate between the models' predictions in real-world contexts. Analysis of optokinetic nystagmus (...
Article
Full-text available
Moving in natural environments is guided by looking where you are going. When entering a bend, car drivers direct their gaze toward the inside of the curve, in the region of the curve apex. This behavior has been analyzed in terms of both "tangent point models," which posit that drivers are looking at the tangent point (TP), and "future path models...
Article
Full-text available
For nearly 20 years, looking at the tangent point on the road edge has been prominent in models of visual orientation in curve driving. It is the most common interpretation of the commonly observed pattern of car drivers looking through a bend, or at the apex of the curve. Indeed, in the visual science literature, visual orientation towards the ins...
Data
Full-text available
Supplementary methods, supplementary results, including individual trial data, and supplementary discussion outlining a more detailed qualitative comparison of various gaze strategies and their predictions. (PDF)

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Projects

Projects (4)
Project
Humans are very efficient in many complex dynamic tasks. Apparently easy and simple activities like picking a cup, walking in a crowd or driving are in fact underpinned by sophisticated information processing, of which we are not usually aware. This it is starkly revealed in artificial intelligence and robotics, and humans still vastly outperform computers on such sensorimotor tasks. This suggests techniques for organizing perception and action discovered by the human brain during development and evolution that could be highly valuable to the development of future AI. We develop a unified computational model of visual target interception and avoidance - a core human sensorimotor capacity. More life-like robotics, autonomous vehicles, aerospace pilot and driver training, and sports psychology are applications of such knowledge. The fundamental interest lies in the revealing the ways our brain allows us to interact with complex dynamic situations so efficiently and effortlessly.
Archived project
Funded by the Academy of Finland, the project MULSIMCO produced new knowledge of driver behavior in car-following. Researchers of Transportation Engineering in Aalto University and the University of Helsinki Traffic Research Unit studied car following behavior in a 3D VR driving simulator and field experiments. The results showed that the desired time gap of and individual driver strongly correlated with accelerative behavior so that drivers keeping shorter time gaps accelerated and braked more forcefully. The time gap and accelerations can be described as driving intensity, which is a characteristic of the individual's driving style. The time gap is also tightly connected with observation behavior: drivers who keep a longer "safety margin" glance at the leading vehicle less often. These dependencies were modelled with a computational model that uses machine learning methods to describe a driver's cognition. Some microsimulation results were obtained (but not yet published), development of the nanosimulation model for modelling multi-agent traffic scenarios is ongoing. In addition to empirical findings, the project supported a substantial amount of infrastructure development in numerous Open Source Code projects(see https://github.com/jampekka?tab=repositories). These include: - TRUsas integrated signal acquisition system for laboratory and field measurements of multidimensional physiological data - webtrajsim 3D VR driving simulator environment used in the experiments - NSLR segmentation algorithm used of eye movement event detection - SCvideonaxu video-annotation tool The computational driver model developed in this project forms the basis of a new project (consortium UPP-PERFORMANCE, between UH and National Defense University Finland). There it will be extended and applied to modelling Situation Awareness in complex dynamic control tasks. The project supported researcher mobility between Aalto University and Chalmers Tekniska Högskola, and between the University of Helsinki and the University of Leeds and Université de Nantes.
Project
Project TRANSITION will use sophisticated laboratory-based measures (including advanced vehicle simulators) to examine drivers re-engaging with the vehicle after a period of AV control. We will determine the capability of drivers regaining steering control under conditions that simulate various types of visual and cognitive load (e.g. driving at night, and/or when looking away at a satellite navigation system). These findings will be used to identify situations where drivers are particularly vulnerable to making steering errors, and develop the TRANSITION model of AV-Human transitions that will inform improvements to the design and implementation of AV systems.