ADHD-associated risk taking is linked to exaggerated views of the benefits of positive outcomes

Attention deficit and hyperactivity disorder (ADHD) is often assumed to be associated with increased engagement in risk-taking behaviors. The current study sought to understand the mental processes underlying this association using a theory-driven behavioral economics perspective. Psychological risk-return models suggest that risk and benefit are inherently subjective, and risk taking is best understood as the interplay between cognitions and motivations regarding the benefits and risks of alternatives. A sample of 244 adults was assessed for ADHD symptoms. The likelihood of engagement in a range of risky behaviors (e.g., driving without wearing a seat belt), the magnitude of perceived benefit and risk ascribed to these behaviors, and benefit and risk attitudes of each participant were extracted from the Domain Specific Risk Taking (DOSPERT) scales. ADHD symptoms were correlated with more risky behaviors and perception of greater benefits from engaging in these behaviors, but were not correlated with risk perception. Mediation analysis revealed that the association between ADHD symptoms and engagement in risk taking was mediated by perceived benefits. These findings highlight the idea that people with high level ADHD symptoms tend to engage in risky behaviors because they find such behavior particularly appealing, rather than because they seek risk per se

Vowel similarity, connectionist models, and syllable structure in motor programming of speech

Using a response-priming procedure, five experiments examined the effects of vowel similarity on the motor programming of spoken syllables. In this procedure, subjects prepared to produce a pair of spoken syllables as rapidly as possible, but sometimes had to produce the syllables in reverse order instead. The spoken responses consisted of consonant-vowel-consonant (CVC) syllables whose medial vowels were /i/, /I/, /[lambda]/, and /[alpha]/. Performance was measured as a function of the phonetic relationship between the vowels in a syllable pair. Longer response latencies occurred for syllable pairs that contained similar vowels (e.g., /i/ and /I/) than for syllable pairs that contained dissimilar vowels (e.g., /i/ and /[lambda]/). This inhibitory vowel-similarity effect occurred regardless of whether the initial consonants of the syllables in a pair were the same or different. However, it decreased substantially when the final consonants of the paired syllables were different. These results suggest that a lateral-inhibition mechanism may modulate the motor programming of vowels during speech production. They also provide evidence for the integrity of vowel-consonant (VC) subunits in syllables.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28725/1/0000546.pd

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs’ facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network’s attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

The scientific payload of the Ultraviolet Transient Astronomy Satellite (ULTRASAT)

The Ultraviolet Transient Astronomy Satellite (ULTRASAT) is a space-borne near UV telescope with an unprecedented large field of view (200 sq. deg.). The mission, led by the Weizmann Institute of Science and the Israel Space Agency in collaboration with DESY (Helmholtz association, Germany) and NASA (USA), is fully funded and expected to be launched to a geostationary transfer orbit in Q2/3 of 2025. With a grasp 300 times larger than GALEX, the most sensitive UV satellite to date, ULTRASAT will revolutionize our understanding of the hot transient universe, as well as of flaring galactic sources. We describe the mission payload, the optical design and the choice of materials allowing us to achieve a point spread function of ~10arcsec across the FoV, and the detector assembly. We detail the mitigation techniques implemented to suppress out-of-band flux and reduce stray light, detector properties including measured quantum efficiency of scout (prototype) detectors, and expected performance (limiting magnitude) for various objects.Comment: Presented in the SPIE Astronomical Telescopes + Instrumentation 202

Sensitization and accessibility of information during memory incubation.

Some of the phenomena surrounding commonplace incidents of memory blocks (temporary inability to retrieve stored target items) raise intriguing questions about memory processes. First, people who experience a memory block can judge the likelihood that continued memory search will succeed (feelings-of-knowing judgments). Second, it is not unusual for people to "stumble" onto relevant information in the midst of some unrelated activity, or to experience "flash" recall long after the question that precipitated the memory block has been removed from consciousness. Such experiences of unexpected flash recall resemble the phenomenon of incubation and illumination during problem solving. The memory-sensitization hypothesis introduced in this dissertation states that following an unsuccessful attempt to recall a stored item related to some problem, a person's memory is sensitized to information bearing on this item, thereby leading to more efficient assimilation of relevant information and cues encountered in the environment. Five experiments are reported to examine memory mechanisms involved in memory sensitization, the activation and metacognition of inaccessible stored information, and the encoding and assimilation of target information. The experimental procedure involved a battery of cognitive tasks. Participants first attempted to recall some rare English words cued by dictionary definitions. For the target words that could not be recalled initially, the participants rated their feelings of knowing. During subsequent tests of word perception and recognition memory, reaction time and accuracy of responses for target words and irrelevant control words were measured. In some experiments, retest on the definition questions occurred 24 hours following the first definition task. The results revealed that the initial failure to recall target words cued by the definitions sensitized the participants to subsequent occurrences of the targets and conceptually related words. Comparison between the results for target words and control words showed faster reaction times for targets that had elicited strong feelings of knowing and also more solid encoding of targets. Feeling-of-knowing judgments correlated positively with the magnitude of these sensitization effects. The implications of these results for cognitive processes such as memory updating, acquisition of cognitive skills, decision making, and problem solving is discussed.Ph.D.Experimental psychologyUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162151/1/8907176.pd

Receiving Other People's Advice: Influence and Benefit

Seeking advice is a basic practice in making real life decisions. Until recently, however, little attention has been given to it in either empirical studies or theories of decision making. The studies reported here investigate the influence of advice on judgment and the consequences of advice use for judgment accuracy. Respondents were asked to provide final judgments on the basis of their initial opinions and advice presented to them. The respondents’ weighting policies were inferred. Analysis of the these policies show that (a) the respondents tended to place a higher weight on their own opinion than on the advisor's opinion (the self/other effect); (b) more knowledgeable individuals discounted the advice more; (c) the weight of advice decreased as its distance from the initial opinion increased; and (d) the use of advice improved accuracy significantly, though not optimally. A theoretical framework is introduced which draws in part on insights from the study of attitude change to explain the influence of advice. Finally the usefulness of advice for improving judgment accuracy is considered.