Intention regulates conflicting desires in human decision making

It is an ancient insight that human actions are driven by desires. This insight inspired the formulation that a rational agent acts to maximize expected utility (MEU), which has been widely used in psychology for modeling theory of mind and in artificial intelligence (AI) for controlling machines’ actions. Yet, it's rather unclear how humans act coherently when their desires are complex and often conflicting with each other. Here we show desires do not directly control human actions. Instead, actions are regulated by an intention — a deliberate mental state that commits to a fixed future rather than taking the expected utilities of many futures evaluated by many desires. Our study reveals four behavioral signatures of human intention by demonstrating how human sequential decision-making deviates from the optimal policy based on MEU in a navigation task: “Disruption resistance” as the persistent pursuit of an original intention despite an unexpected change has made that intention suboptimal; “Ulysses-constraint of freedom” as the proactive constraint of one’s freedom by avoiding a path that could lead to many futures, similar to Ulysses’s self-binding to resist the temptation of the Siren’s song; “Enhanced legibility” as an active demonstration of intention by choosing a path whose destination can be promptly inferred by a third-party observer; “Temporal leap” as committing to a distant future even before reaching the proximal one. Our results showed how the philosophy of intention can lead to discoveries of human decision-making, which can also be empirically compared with AI algorithms. The findings showing that to define a theory of mind, intention should be highlighted as a distinctive mental state in between desires and actions, for quarantining conflicting desires from the execution of actions.

Here's the twist: How the brain updates naturalistic event memories as our understanding of the past changes

The brain actively reshapes past memories in light of new incoming information. In the current study, we ask how the brain supports this updatinge process during the encoding and recall of naturalistic stimuli. One group of participants watched a movie ("The Sixth Sense") with a cinematic "twist" at the end that dramatically changed the interpretation of previous events. Next, participants were asked to verbally recall the movie events, taking into account the new "twist" information. Most participants updated their recall to incorporate the twist. Two additional groups recalled the movie without having to update their memories during recall: one group never saw the twist; another group was exposed to the twist prior to the beginning of the movie, and thus the twist information was incorporated both during encoding and recall. We found that providing participants with information about the twist beforehand altered neural response patterns during movie-viewing in the default mode network (DMN). Moreover, presenting participants with the twist at the end of the movie changed the neural representation of the previously-encoded information during recall in a subset of DMN regions. Further evidence for this transformation was obtained by comparing the neural activation patterns during encoding and recall and correlating them with behavioral signatures of memory updating. Our results demonstrate that neural representations of past events encoded in the DMN are dynamically integrated with new information that reshapes our memory in natural contexts.

A Feedforward Convolutional Neural Network with a Few Million Neurons Learns from Images to Covertly Attend to Cues and Context like Humans and an Optimal Bayesian Observer

Human behavioral experiments have led to influential conceptualizations of visual attention, such as a serial processor or a limited resource spotlight. There is growing evidence that simpler organisms such as insects show behavioral signatures associated with human attention. Can those organisms learn such capabilities without conceptualizations of human attention? We show that a feedforward convolutional neural network (CNN) with a few million neurons trained on noisy images to detect targets learns to utilize predictive cues and context. We demonstrate that the CNN predicts human performance and gives rise to the three most prominent behavioral signatures of covert attention: Posner cueing, set-size effects in search, and contextual cueing. The CNN also approximates an ideal Bayesian observer that has all prior knowledge about the statistical properties of the noise, targets, cues, and context. The results help understand how even simple biological organisms show human-like visual attention by implementing neurobiologically plausible simple computations.

Behavioral and nociceptor states of inflammatory pain across timescales in 2D and 3D

Inflammatory pain represents a complex state involving sensitization of peripheral and central neuronal signaling. Resolving this high-dimensional interplay at the cellular and behavioral level is key to effective therapeutic development. Here, using the carrageenan model of local inflammation of the hind paw, we determine how carrageenan alters both the physiological state of sensory neurons and behaviors at rapid and continuous timescales. We identify higher excitability of sensory neurons innervating the site of inflammation by profiling their physiological state at different time points. To identify millisecond-resolved sensory-reflexive signatures evoked by inflammatory pain, we used a combination of supervised and unsupervised algorithms, and uncovered abnormal paw placement as a defining behavioral feature. For long-term detection and characterization of spontaneous behavioral signatures representative of affective-motivational pain states, we use computer vision coupled to unsupervised machine learning in an open arena. Using the non-steroidal anti-inflammatory drug meloxicam to characterize analgesic states during rapid and ongoing timescales, we identify a return to pre-injury states of some sensory-reflexive behaviors, but by and large, many spontaneous, affective-motivational pain behaviors remain unaffected. Taken together, this comprehensive exploration across cellular and behavioral dimensions reveals peripheral versus centrally mediated pain signatures that define the inflamed state, providing a framework for scaling the pain experience at unprecedented resolution.

Survival Bias and Crosstalk between Chronological and Behavioral Age: Age- and Genotype-Sensitivity Tests Define Behavioral Signatures in Middle-Aged, Old, and Long-Lived Mice with Normal and AD-Associated Aging

New evidence refers to a high degree of heterogeneity in normal but also Alzheimer’s disease (AD) clinical and temporal patterns, increased mortality, and the need to find specific end-of-life prognosticators. This heterogeneity is scarcely explored in very old male AD mice models due to their reduced survival. In the present work, using 915 (432 APP23 and 483 C57BL/6 littermates) mice, we confirmed the better survival curves in male than female APP23 mice and respective wildtypes, providing the chance to characterize behavioral signatures in middle-aged, old, and long-lived male animals. The sensitivity of a battery of seven paradigms for comprehensive screening of motor (activity and gait analysis), neuropsychiatric and cognitive symptoms was analyzed using a cohort of 56 animals, composed of 12-, 18- and 24-month-old male APP23 mice and wildtype littermates. Most variables analyzed detected age-related differences. However, variables related to coping with stress, thigmotaxis, frailty, gait, and poor cognition better discriminated the behavioral phenotype of male APP23 mice through the three old ages compared with controls. Most importantly, non-linear age- and genotype-dependent behavioral signatures were found in long-lived animals, suggesting crosstalk between chronological and biological/behavioral ages useful to study underlying mechanisms and distinct compensations through physiological and AD-associated aging.

Adaptive learning through temporal dynamics of state representation

Humans adjust their learning rate according to local environmental statistics, however existing models of this process have failed to provide mechanistic links to underlying brain signals. Here, we implement a neural network model that uses latent variables from Bayesian inference to shift a neural context representation that controls the “state” to which feedback is associated. Within this model, behavioral signatures of adaptive learning emerge through temporally selective transitions in active states, which also mimic the evolution of neural patterns in orbitofrontal cortex. Transitions to a previous state after encountering a one-off outlier reduce learning, as observed in humans, and provide a mechanistic interpretation for bidirectional learning signals, such as the p300, that relate to learning differentially according to the source of surprising events. Together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive learning.