scholarly journals Acute Paraxanthine Ingestion Improves Cognition and Short-Term Memory and Helps Sustain Attention in a Double-Blind, Placebo-Controlled, Crossover Trial

Nutrients ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 3980
Author(s):  
Choongsung Yoo ◽  
Dante Xing ◽  
Drew Gonzalez ◽  
Victoria Jenkins ◽  
Kay Nottingham ◽  
...  
Keyword(s):  
Response Time ◽  
Side Effect ◽  
Repeated Measures ◽  
Short Term Memory ◽  
Short Term ◽  
Double Blind ◽  
Term Memory ◽  
Double Blind Placebo ◽  
Psychomotor Vigilance ◽  
Over Time

This study examined the effects of acute paraxanthine (PXN) ingestion on markers of cognition, executive function, and psychomotor vigilance. In a randomized, double blind, placebo-controlled, crossover, and counterbalanced manner, 13 healthy male and female participants were randomly assigned to consume a placebo (PLA) or 200 mg of PXN (ENFINITY™, Ingenious Ingredients, L.P.). Participants completed stimulant sensitivity and side effect questionnaires and then performed the Berg Wisconsin Card Sorting Test (BCST), the Go/No-Go test (GNG), the Sternberg task test (STT), and the psychomotor vigilance task test (PVTT). Participants then ingested one capsule of PLA or PXN treatment. Participants completed side effect and cognitive function tests after 1, 2, 3, 4, 5, and 6 h after ingestion of the supplement. After 7 days, participants repeated the experiment while consuming the alternative treatment. Data were analyzed by general linear model (GLM) univariate analyses with repeated measures using body mass as a covariate, and by assessing mean and percent changes from baseline with 95% confidence intervals (CIs) expressed as means (LL, UL). PXN decreased BCST errors (PXN −4.7 [−0.2, −9.20], p = 0.04; PXN −17.5% [−36.1, 1.0], p = 0.06) and perseverative errors (PXN −2.2 [−4.2, −0.2], p = 0.03; PXN −32.8% [−64.4, 1.2], p = 0.04) at hour 6. GNG analysis revealed some evidence that PXN ingestion better maintained mean accuracy over time and Condition R Round 2 response time (e.g., PXN −25.1 [−52.2, 1.9] ms, p = 0.07 faster than PLA at 1 h), suggesting better sustained attention. PXN ingestion improved STT two-letter length absent and present reaction times over time as well as improving six-letter length absent reaction time after 2 h (PXN −86.5 ms [−165, −7.2], p = 0.03; PXN −9.0% [−18.1, 0.2], p = 0.05), suggesting that PXN enhanced the ability to store and retrieve random information of increasing complexity from short-term memory. A moderate treatment x time effect size (ηp2 = 0.08) was observed in PVTT, where PXN sustained vigilance during Trial 2 after 2 h (PXN 840 ms [103, 1576], p = 0.03) and 4 h (PXN 1466 ms [579, 2353], p = 0.002) compared to PL. As testing progressed, the response time improved during the 20 trials and over the course of the 6 h experiment in the PXN treatment, whereas it significantly increased in the PL group. The results suggest that acute PXN ingestion (200 mg) may affect some measures of short-term memory, reasoning, and response time to cognitive challenges and help sustain attention.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

scholarly journals Dopamine affects short-term memory corruption over time in Parkinson’s disease

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Sean James Fallon ◽  
Matthew Gowell ◽  
Maria Raquel Maio ◽  
Masud Husain
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Over Time

Learning and Memory Impairment with Benzhexol

1987 ◽  
Vol 21 (4) ◽  
pp. 612-614 ◽  
Author(s):  
Cherrie A. Galletly ◽  
Colin D. Field
Keyword(s):  
Single Dose ◽  
Motor Skills ◽  
Memory Impairment ◽  
Short Term Memory ◽  
Double Blind Trial ◽  
Short Term ◽  
Double Blind ◽  
Term Memory ◽  
New Learning ◽  
Significant Impairment

A double-blind trial to determine the effects of a single dose of 2 mg benzhexol on cognitive functioning was undertaken using normal volunteers. Ninety minutes after the drug or placebo was taken, subjects completed a battery of psychological tests designed to measure learning, memory and motor skills. Benzhexol ingestion was associated with significant impairment of short-term memory and slowing of the rate of new learning.

The effect of beverages varying in glycaemic load on postprandial glucose responses, appetite and cognition in 10–12-year-old school children

2012 ◽  
Vol 110 (3) ◽  
pp. 529-537 ◽  
Author(s):  
Emily Brindal ◽  
Danielle Baird ◽  
Amy Slater ◽  
Vanessa Danthiir ◽  
Carlene Wilson ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Cognitive Function ◽  
Energy Intake ◽  
Short Term Memory ◽  
Glucose Monitoring ◽  
Postprandial Glucose ◽  
Short Term ◽  
Double Blind ◽  
Term Memory ◽  
Glycaemic Load

Reducing glycaemic index (GI) and glycaemic load (GL) inconsistently improves aspects of cognitive function and appetite in children. Whether altering the GL by lowering carbohydrate relative to protein and fat has a role in these effects is unknown. Therefore, we assessed the differential effects of beverages varying in GL and dairy composition on appetite, energy intake and cognitive function in children. A total of forty children (10–12 years) completed a double-blind, randomised, crossover trial, receiving three isoenergetic drinks (approximately 1100 kJ): a glucose beverage (GI 100, GL 65), a full milk beverage (GI 27, GL 5) and a half milk/glucose beverage (GI 84, GL 35). For 3 h post-consumption, subjective appetite and cognitive performance (speed of processing, memory, attention and perceptual speed) were measured hourly. At completion, each child was provided a buffet-style lunch and energy intake was calculated. Blood glucose was objectively measured using the Continuous Glucose Monitoring System. Blood glucose AUC values were significantly different between the drinks (P< 0·001), but did not sustain above the baseline for 3 h for any drink. Mixed modelling revealed no effect of beverage on subjective appetite or energy intake. Participant sex and drink GL significantly interacted for short-term memory (P< 0·001). When girls consumed either milk-containing beverage, they recalled 0·7–0·8 more words compared with 0·5 less words after the glucose drink (P≤ 0·014). Altering GL of drinks by reducing carbohydrate and increasing protein did not affect appetite or cognition in children. Girls may demonstrate improved short-term memory after consuming beverages with higher protein and lower GL.

(Eye) tracking short-term memory over time

Aphasiology ◽  
2012 ◽  
Vol 26 (3-4) ◽  
pp. 536-555 ◽  
Author(s):  
C. Papagno ◽  
E. Bricolo ◽  
D. Mussi ◽  
R. Daini ◽  
C. Cecchetto
Keyword(s):  
Eye Tracking ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Over Time

scholarly journals Temporal dynamics of implicit memory underlying serial dependence

2020 ◽  
Author(s):  
Cristiano Moraes Bilacchi ◽  
Esaú Ventura Pupo Sirius ◽  
André M. Cravo ◽  
Raymundo Machado de Azevedo Neto
Keyword(s):  
Short Term Memory ◽  
Temporal Dynamics ◽  
Response Variability ◽  
Serial Dependence ◽  
Short Term ◽  
Term Memory ◽  
Memory Mechanism ◽  
Current Information ◽  
Visuomotor Task ◽  
Over Time

AbstractSerial dependence is the effect in which the immediately preceding trial influences participants’ responses to the current stimulus. But for how long does this bias last in the absence of interference from other stimuli? Here, we had 20 healthy young adult participants (12 women) perform a coincident timing task using different inter-trial intervals to characterize the serial dependence effect as the time between trials increases. Our results show that serial dependence abruptly decreases after 1 s inter-trial interval, but it remains pronounced after that for up to 8 s. In addition, participants’ response variability slightly decreases over longer intervals. We discuss these results in light of recent models suggesting that serial dependence might rely on a short-term memory trace kept through changes in synaptic weights, which might explain its long duration and apparent stability over time.Statement of RelevanceRecent perceptual and motor experiences bias human behavior. For this serial bias to take place, the brain must keep information for at least the time between events to blend past and current information. Understanding the temporal dynamics of such memory traces might shed light into the short-term memory mechanism and integration of prior and current information. Here, we characterized the temporal dynamics of the serial biases that emerge in a visuomotor task by varying the length of the interval between successive events. Our results show response biases are still present even after intervals as long as 8 s and that participants’ response variability decreases over time. Serial dependence thus seems to rely on a memory mechanism that is both long lasting in the absence of interference and stable.

scholarly journals Persistent activity in a recurrent circuit underlies courtship memory in Drosophila

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Xiaoliang Zhao ◽  
Daniela Lenek ◽  
Ugur Dag ◽  
Barry J Dickson ◽  
Krystyna Keleman
Keyword(s):  
Dopaminergic Neurons ◽  
Mushroom Body ◽  
Neural Circuits ◽  
Short Term Memory ◽  
Persistent Activity ◽  
Short Term ◽  
Present Evidence ◽  
Term Memory ◽  
Time Periods ◽  
Over Time

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBγ), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory.

scholarly journals A Deep Learning Integrated Lee–Carter Model

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 33 ◽  
Author(s):  
Andrea Nigri ◽  
Susanna Levantesi ◽  
Mario Marino ◽  
Salvatore Scognamiglio ◽  
Francesca Perla
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Mortality Rates ◽  
Short Term ◽  
Term Memory ◽  
The Future ◽  
Long Short Term Memory ◽  
Carter Model ◽  
Over Time

In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the κ t parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model κ t shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of κ t series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns.

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