A predisposed motor bias shapes individuality in vocal learning.

The development of individuality during learned behavior is a common trait observed across animal species; however, the underlying biological mechanisms remain understood. Similar to human speech, songbirds develop individually unique songs with species-specific traits through vocal learning. In this study, we investigate the developmental and molecular mechanisms underlying individuality in vocal learning by utilizing F1 hybrid songbirds (Taeniopygia guttata cross with Taeniopygia bichenovii), taking an integrating approach combining experimentally controlled systematic song tutoring, unbiased discriminant analysis of song features, and single-cell transcriptomics. When tutoring with songs from both parental species, F1 hybrid individuals exhibit evident diversity in their acquired songs. Approximately 30% of F1 hybrids selectively learn either song of the two parental species, while others develop merged songs that combine traits from both species. Vocal acoustic biases during vocal babbling initially appear as individual differences in songs among F1 juveniles and are maintained through the sensitive period of song vocal learning. These vocal acoustic biases emerge independently of the initial auditory experience of hearing the biological father's and passive tutored songs. We identify individual differences in transcriptional signatures in a subset of cell types, including the glutamatergic neurons projecting from the cortical vocal output nucleus to the hypoglossal nuclei, which are associated with variations of vocal acoustic features. These findings suggest that a genetically predisposed vocal motor bias serves as the initial origin of individual variation in vocal learning, influencing learning constraints and preferences.

Challenging the negative learning bias hypothesis of depression: reversal learning in a naturalistic psychiatric sample.

BACKGROUND: Classic theories posit that depression is driven by a negative learning bias. Most studies supporting this proposition used small and selected samples, excluding patients with comorbidities. However, comorbidity between psychiatric disorders occurs in up to 70% of the population. Therefore, the generalizability of the negative bias hypothesis to a naturalistic psychiatric sample as well as the specificity of the bias to depression, remain unclear. In the present study, we tested the negative learning bias hypothesis in a large naturalistic sample of psychiatric patients, including depression, anxiety, addiction, attention-deficit/hyperactivity disorder, and/or autism. First, we assessed whether the negative bias hypothesis of depression generalized to a heterogeneous (and hence more naturalistic) depression sample compared with controls. Second, we assessed whether negative bias extends to other psychiatric disorders. Third, we adopted a dimensional approach, by using symptom severity as a way to assess associations across the sample. METHODS: We administered a probabilistic reversal learning task to 217 patients and 81 healthy controls. According to the negative bias hypothesis, participants with depression should exhibit enhanced learning and flexibility based on punishment v. reward. We combined analyses of traditional measures with more sensitive computational modeling. RESULTS: In contrast to previous findings, this sample of depressed patients with psychiatric comorbidities did not show a negative learning bias. CONCLUSIONS: These results speak against the generalizability of the negative learning bias hypothesis to depressed patients with comorbidities. This study highlights the importance of investigating unselected samples of psychiatric patients, which represent the vast majority of the psychiatric population.

Drug Target Identification with Machine Learning: How to Choose Negative Examples.

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases' statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

Meditation experience predicts negative reinforcement learning and is associated with attenuated FRN amplitude.

Focused attention meditation (FAM) practices are cognitive control exercises where meditators learn to maintain focus and attention in the face of distracting stimuli. Previous studies have shown that FAM is both activating and causing plastic changes to the mesolimbic dopamine system and some of its target structures, particularly the anterior cingulate cortex (ACC) and striatum. Feedback-based learning also depends on these systems and is known to be modulated by tonic dopamine levels. Capitalizing on previous findings that FAM practices seem to cause dopamine release, the present study shows that FAM experience predicts learning from negative feedback on a probabilistic selection task. Furthermore, meditators exhibited attenuated feedback-related negativity (FRN) as compared with nonmeditators and this effect scales with meditation experience. Given that reinforcement learning and FRN are modulated by dopamine levels, a possible explanation for our findings is that FAM practice causes persistent increases in tonic dopamine levels which scale with amount of practice, thus altering feedback processing.

Concern for Others Leads to Vicarious Optimism.

An optimistic learning bias leads people to update their beliefs in response to better-than-expected good news but neglect worse-than-expected bad news. Because evidence suggests that this bias arises from self-concern, we hypothesized that a similar bias may affect beliefs about other people's futures, to the extent that people care about others. Here, we demonstrated the phenomenon of vicarious optimism and showed that it arises from concern for others. Participants predicted the likelihood of unpleasant future events that could happen to either themselves or others. In addition to showing an optimistic learning bias for events affecting themselves, people showed vicarious optimism when learning about events affecting friends and strangers. Vicarious optimism for strangers correlated with generosity toward strangers, and experimentally increasing concern for strangers amplified vicarious optimism for them. These findings suggest that concern for others can bias beliefs about their future welfare and that optimism in learning is not restricted to oneself.

Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations.

Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021-2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI's superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

Interaction of phonological biases and frequency in learning a probabilistic language pattern.

We examine how learning a phonological rule in an artificial language interacts with morphological and lexical learning. We exposed adult participants to an artificial language in which noun plurals were marked by one of two prefix forms (ba- or ni-), one of which also triggered a velar palatalization rule (e.g., singular kimu, plural ni-chimu). In some conditions, the rule additionally created homophony. We also manipulated the relative frequency of the two prefix variants. The results showed that participants shifted away from using the rule-triggering prefix (ni-), but only when it was already the less frequent prefix. We attribute this effect to a paradigm uniformity bias leading participants to avoid phonological alternations (particularly in the stem). When the rule created homophony between lexical items, participants were less able to learn the rule, but it did not affect their choice of prefix. We attribute this effect to homophony avoidance interfering with participants' ability to extract the phonological generalization.

Phonetically Grounded Structural Bias in Learning Tonal Alternations.

This study investigates the hypothesis that tone alternation directionality becomes a basis of structural bias for tone alternation learning, where "structural bias" refers to a tendency to prefer uni-directional tone deletions to bi-directional ones. Two experiments were conducted. In the first, Mandarin speakers learned three artificial languages, with bi-directional tone deletions, uni-directional, left-dominant deletions, and uni-directional, right-dominant deletions, respectively. The results showed a learning bias toward uni-directional, right-dominant patterns. As Mandarin tone sandhi is right-dominant while Cantonese tone change is lexically restricted and does not have directionality asymmetry, a follow-up experiment trained Cantonese speakers either on left- or right-dominant deletions to see whether the right-dominant preference was due to L1 transfer from Mandarin. The results of the experiment also showed a learning bias toward right-dominant patterns. We argue that structural simplicity affects tone deletion learning but the simplicity should be grounded on phonetics factors, such as syllables' contour-tone bearing ability. The experimental results are consistent with the findings of a survey on other types of tone alternation's directionality, i.e., tone sandhi across 17 Chinese varieties. This suggests that the directionality asymmetry found across different tone alternations reflects a phonetically grounded structural learning bias.

Negative Learning Bias in Depression Revisited: Enhanced Neural Response to Surprising Reward Across Psychiatric Disorders.

BACKGROUND: Prior work has proposed that major depressive disorder (MDD) is associated with a specific cognitive bias: patients with depression seem to learn more from punishment than from reward. This learning bias has been associated with blunting of reward-related neural responses in the striatum. A key question is whether negative learning bias is also present in patients with MDD and comorbid disorders and whether this bias is specific to depression or shared across disorders. METHODS: We employed a transdiagnostic approach assessing a heterogeneous group of (nonpsychotic) psychiatric patients from the MIND-Set (Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders) cohort with and without MDD but also with anxiety, attention-deficit/hyperactivity disorder, and/or autism (n = 66) and healthy control subjects (n = 24). To investigate reward and punishment learning, we employed a deterministic reversal learning task with functional magnetic resonance imaging. RESULTS: In contrast to previous studies, patients with MDD did not exhibit impaired reward learning or reduced reward-related neural activity anywhere in the brain. Interestingly, we observed consistently increased neural responses in the bilateral lateral prefrontal cortex of patients when they received a surprising reward. This increase was not specific to MDD, but generalized to anxiety, attention-deficit/hyperactivity disorder, and autism. Critically, increased prefrontal activity to surprising reward scaled with transdiagnostic symptom severity, particularly that associated with concentration and attention, as well as the number of diagnoses; patients with more comorbidities showed a stronger prefrontal response to surprising reward. CONCLUSIONS: Prefrontal enhancement may reflect compensatory working memory recruitment, possibly to counteract the inability to swiftly update reward expectations. This neural mechanism may provide a candidate transdiagnostic index of psychiatric severity.

The cultural evolution of cultural evolution.

What makes fast, cumulative cultural evolution work? Where did it come from? Why is it the sole preserve of humans? We set out a self-assembly hypothesis: cultural evolution evolved culturally. We present an evolutionary account that shows this hypothesis to be coherent, plausible, and worthy of further investigation. It has the following steps: (0) in common with other animals, early hominins had significant capacity for social learning; (1) knowledge and skills learned by offspring from their parents began to spread because bearers had more offspring, a process we call CS1 (or Cultural Selection 1); (2) CS1 shaped attentional learning biases; (3) these attentional biases were augmented by explicit learning biases (judgements about what should be copied from whom). Explicit learning biases enabled (4) the high-fidelity, exclusive copying required for fast cultural accumulation of knowledge and skills by a process we call CS2 (or Cultural Selection 2) and (5) the emergence of cognitive processes such as imitation, mindreading and metacognition-'cognitive gadgets' specialized for cultural learning. This self-assembly hypothesis is consistent with archaeological evidence that the stone tools used by early hominins were not dependent on fast, cumulative cultural evolution, and suggests new priorities for research on 'animal culture'. This article is part of the theme issue 'Foundations of cultural evolution'.

Neutralization and homophony avoidance in phonological learning.

Previous research has suggested that homophony avoidance plays a role in constraining language change; in particular, phonological contrasts are less likely to be neutralized if doing so would greatly increase the amount of homophony in the language. Most of the research on homophony avoidance has focused on the history of real languages, comparing attested and unattested (hypothetical) phonological changes. In this study, we take a novel approach by focusing on the language learner. Using an artificial language learning paradigm, we show that learners are less likely to acquire neutralizing phonological rules compared to non-neutralizing rules, but only if the neutralizing rules create homophony between lexical items encountered during learning. The results indicate that learners are biased against phonological patterns that create homophony, which could have an influence on language change. The results also suggest that lexical learning and phonological learning are highly integrated.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment.

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages.

Conditioned social dominance threat: observation of others' social dominance biases threat learning.

Social groups are organized along dominance hierarchies, which determine how we respond to threats posed by dominant and subordinate others. The persuasive impact of these dominance threats on mental and physical well-being has been well described but it is unknown how dominance rank of others bias our experience and learning in the first place. We introduce a model of conditioned social dominance threat in humans, where the presence of a dominant other is paired with an aversive event. Participants first learned about the dominance rank of others by observing their dyadic confrontations. During subsequent fear learning, the dominant and subordinate others were equally predictive of an aversive consequence (mild electric shock) to the participant. In three separate experiments, we show that participants' eye-blink startle responses and amygdala reactivity adaptively tracked dominance of others during observation of confrontation. Importantly, during fear learning dominant vs subordinate others elicited stronger and more persistent learned threat responses as measured by physiological arousal and amygdala activity. Our results characterize the neural basis of learning through observing conflicts between others, and how this affects subsequent learning through direct, personal experiences.

Infants generalize from just (the right) four words.

Infants in the lab can generalize from 2min of language-like input. Given that infants might fail to fully encode so much input, how many examples do they actually need? And if infants only encode a subset of their input at one time, does generalization change when that subset supports multiple generalizations? Exp. 1 asked whether 11-month-olds generalize the relation between two consonants in a word when just four input words provided non-conflicting vs. partially conflicting support for a phonological feature-based generalization. Infants learned under both conditions, although the latter appears to be more difficult. Exp. 2 asked whether infants' robust learning reflects a bias toward feature-based generalizations. Infants failed to generalize when input provided completely conflicting support for two generalizations. Together, the data suggest that infants are able to generalize from much less input than previously observed, but generalization depends on the specific subset of the input they encounter.