Motivation and Value: Effects on Attentional Control and Learning

My dissertation presents two lines of research that examine motivation-cognition interactions. The first focuses on the effects of gain and loss incentive on attentional performance in young and older adults, examines which aspects of attention/cognitive control may be most sensitive to incentive manipulations, and takes steps towards elucidating the cognitive-motivational states and traits that may mediate those effects. When monetary incentives were offered throughout the experiments, they tended to have no effect or a small beneficial effect on the focused attention of young adults, and decreased young adults’ subjective reports of mind-wandering. In contrast, older adults had worse performance and more mind-wandering under incentive, especially loss incentive. Monetary incentives offered in alternating runs reduced the overall performance of both young and older adults compared to groups for which incentive was not offered at all, whereas within the alternating-run groups, performance was worse on the runs without incentive. Additional results from self-report measures suggest that for young adults, decreased performance under incentive may be the result of distraction. In contrast, older adults were more intrinsically motivated, and decreases in motivation under external incentive may underlie their reduced performance. In short, these results demonstrate that incentives may sometimes paradoxically reduce, rather than increase, performance, and that the direction and underlying mechanisms of incentive effects are influenced by factors including age (young vs old) and incentive structure (between- or within-subject manipulation). The second line of research investigates how outcome probability and valence may influence learning as well as subsequent explicit memory. Participants first learned to associate scenes with wins or losses that occurred at high or low probability, with probability thought to influence the “motivational salience” of the scene. The task objective was to maximize the reward (points or points and money) earned in each trial, and the optimal choices are the high probability win scene and the low probability loss scene. Contrary to the common assumption that win and loss outcome associations are learned equally, win associations were learned better than loss associations, suggesting an advantage for learning outcomes with a positive valence. A subsequent recognition task assessed explicit knowledge of the learned value associations. Regardless of learning level or incentive conditions, memory for the association between a scene and its valence and motivational salience was superior for scenes that had previously been the optimal choice (high probability win and low probability loss). However. accurate recognition was significantly better for optimal win scenes than optimal loss scenes. These findings indicate that learning to select the optimal choice is dissociable from explicit knowledge about the outcome contingencies, especially for loss and low probability outcomes. Moreover, motivational salience is represented differentially in explicit memory for win and loss outcomes. Together, this research examines several common assumptions about incentives and motivation in attention, learning, and memory in previous research studies, and demonstrates that the effects are more complex than currently realized. The discussion considers the implications for understanding the mechanisms underlying incentive effects on different types of cognition, as well as the effects of incentive in everyday life.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146000/1/ziyong_1.pd

R : How cognitive selection affects language change

Like biological species, words in language must compete to survive. Previously, it has been shown that language changes in response to cognitive constraints and over time becomes more learnable. Here, we use two complementary research paradigms to demonstrate how the survival of existing word forms can be predicted by psycholinguistic properties that impact language production. In the first study, we analyzed the survival of words in the context of interpersonal communication. We analyzed data from a large-scale serial-reproduction experiment in which stories were passed down along a transmission chain over multiple participants. The results show that words that are acquired earlier in life, more concrete, more arousing, and more emotional are more likely to survive retellings. We reason that the same trend might scale up to language evolution over multiple generations of natural language users. If that is the case, the same set of psycholinguistic properties should also account for the change of word frequency in natural language corpora over historical time. That is what we found in two large historical-language corpora (Study 2): Early acquisition, concreteness, and high arousal all predict increasing word frequency over the past 200 y. However, the two studies diverge with respect to the impact of word valence and word length, which we take up in the discussion. By bridging micro-level behavioral preferences and macro-level language patterns, our investigation sheds light on the cognitive mechanisms underlying word competition

The Performance of Pleural Fluid T-SPOT.TB Assay for Diagnosing Tuberculous Pleurisy in China: A Two-Center Prospective Cohort Study

The performance of T-SPOT.TB (T-SPOT) assay in diagnosing pleural tuberculosis (plTB) is inconsistent. In this study, we compared the performance of peripheral blood (PB) and pleural fluid (PF) T-SPOT assay in diagnosing plTB. Between July 2017 and March 2018, 218 and 210 suspected plTB patients were prospectively enrolled from Wuhan (training) and Guangzhou (validation) cohort, respectively. PB T-SPOT, PF T-SPOT, and other conventional tests were simultaneously performed. Our data showed the performance of PB T-SPOT in diagnosing plTB was limited, especially with low sensitivity. However, the results of early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) in PF T-SPOT were significantly increased compared with those in PB T-SPOT in plTB patients. If using 76 as the cutoff value of MAX (the larger of ESAT-6 and CFP-10) in Wuhan cohort, the sensitivity and specificity of PF T-SPOT to diagnose plTB were 89.76 and 96.70%, respectively. The diagnostic accuracy of PF T-SPOT was better than other routine tests such as pathogen detection methods and biochemical markers. The diagnostic accuracy of PF T-SPOT in Guangzhou cohort was similar to that in Wuhan cohort, with a sensitivity and specificity of 91.07 and 94.90%, respectively. Furthermore, CD4+ T cells were more activated in PF compared with PB, and the frequency of mycobacterium tuberculosis-specific CD4+ T cells in PF was significantly higher than that in PB in plTB patients. In conclusion, the performance of PF T-SPOT is obviously better than PB T-SPOT or other laboratory tests, which suggests that PF T-SPOT assay has been of great value in the diagnosis of pleural tuberculosis

Determination of reference intervals of serum levels of human epididymis protein 4 (HE4) in Chinese women

The detailed numbers of subjects from different centers. (DOCX 17.3 kb

Ziyong Lin's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

Losing Money and Motivation: Effects of Loss Incentives on Motivation and Metacognition in Younger and Older Adults

Incentives are usually expected to increase motivation and the engagement of cognitive control, and to thereby improve performance on cognitively-demanding tasks. However, a closer read of the literature suggests that incentive effects on performance can be elusive. Further, although loss incentives are common in everyday life, most laboratory studies focus on gain effects. Different theoretical perspectives offer competing predictions for the effects of loss incentives, especially in older adults: The intuitive prediction is that loss incentives should improve performance. In contrast, Socioemotional Selectivity Theory and the age-related positivity effect (Carstensen & Mikels, 2005) would predict that older adults should be largely immune to loss-incentive effects. However, Selective Engagement Theory (Hess, 2014) and the Strength and Vulnerability Integration Theory (Charles, 2010) suggest that losses might increase the ‘perceived costs’ for older adults, and thus lead to disengagement and worse performance. Moreover, most studies use changes in performance or other measures as de facto indices of motivation, rather than measuring motivation directly. To address these gaps in the literature, we examined the effects of loss incentives on measures related to subjective engagement, motivation, and meta-cognition as well as working memory. Even though loss incentive did not impact performance, our findings were most consistent with the idea that loss incentives increase the perceived demands of a task and lead to disengagement. The loss incentive also increased the absolute metacognitive accuracy. Despite the lack of age differences in some measures of the incentive effect, the post-task questionnaires suggest different reasons for arriving at these results (distraction vs de-motivation) in younger versus older adults. The results suggest that the effects (or lack thereof) of incentive on performance may reflect factors other than motivation per se

Asymmetrical Learning and Memory for Acquired Gain versus Loss Associations

Data for Lin, Cabrera-Haro, & Reuter-Lorenz (2020

Review and Updates on the Diagnosis of Tuberculosis

Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection

Explaining Valence Asymmetries in Value Learning: A Reinforcement Learning Account

To understand how acquired value impacts how we perceive and process stimuli, psychologists have developed the Value Learning Task (VLT; e.g., Raymond & O’Brien, 2009). The task consists of a series of trials in which participants attempt to maximize accumulated winnings as they make choices from a pair of presented images associated with probabilistic win, loss, or no-change outcomes. Despite the task having a symmetric outcome structure for win and loss pairs, people learn win associations better than loss associations (Lin, Cabrera-Haro, & Reuter-Lorenz, 2020). This asymmetry could lead to differences when the stimuli are probed in subsequent tasks, compromising inferences about how acquired value affects downstream processing. We investigate the nature of the asymmetry using a standard error-driven reinforcement learning model with a softmax choice rule. Despite having no special role for valence, the model yields the asymmetry observed in human behavior, whether the model parameters are set to maximize empirical fit, or task payoff. The asymmetry arises from an interaction between a neutral initial value estimate and a choice policy that exploits while exploring, leading to more poorly discriminated value estimates for loss stimuli. We also show how differences in estimated individual learning rates help to explain individual differences in the observed win-loss asymmetries, and how the final value estimates produced by the model provide a simple account of a post-learning explicit value categorization task