We explore the use of inner speech as a mechanism to enhance transparency and trust in social robots for dietary advice. In humans, inner speech structures thought processes and decision-making; in robotics, it improves explainability by making reasoning explicit. This is crucial in healthcare scenarios, where trust in robotic assistants depends on both accurate recommendations and human-like dialogue, which make interactions more natural and engaging. Building on this, we developed a social robot that provides dietary advice, and we provided the architecture with inner speech capabilities to validate user input, refine reasoning, and generate clear justifications. The system integrates large language models for natural language understanding and a knowledge graph for structured dietary information. By making decisions more transparent, our approach strengthens trust and improves human-robot interaction in healthcare. We validated this by measuring the computational efficiency of our architecture and conducting a small user study, which assessed the reliability of inner speech in explaining the robot's behavior.

Inner speech recognition has gained enormous interest in recent years due to its applications in rehabilitation, developing assistive technology, and cognitive assessment. However, since language and speech productions are a complex process, for which identifying speech components has remained a challenging task. Different approaches were taken previously to reach this goal, but new approaches remain to be explored. Also, a subject-oriented analysis is necessary to understand the underlying brain dynamics during inner speech production, which can bring novel methods to neurological research. A publicly available dataset, Thinking Out Loud Dataset, has been used to develop a Machine Learning (ML)-based technique to classify inner speech using 128-channel surface EEG signals. The dataset is collected on a Spanish cohort of ten subjects while uttering four words (Arriba, Abajo, Derecha, and Izquierda) by each participant. Statistical methods were employed to detect and remove motion artifacts from the Electroencephalography (EEG) signals. A large number (191 per channel) of time-, frequency- and time-frequency-domain features were extracted. Eight feature selection algorithms are explored, and the best feature selection technique is selected for subsequent evaluations. The performance of six ML algorithms is evaluated, and an ensemble model is proposed. Deep Learning (DL) models are also explored, and the results are compared with the classical ML approach. The proposed ensemble model, by stacking the five best logistic regression models, generated an overall accuracy of 81.13% and an F1 score of 81.12% in the classification of four inner speech words using surface EEG signals. The proposed framework with the proposed ensemble of classical ML models shows promise in the classification of inner speech using surface EEG signals.

Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal fusion approaches are examined: concatenation of probability vectors output from unimodal fMRI and EEG machine learning models, and data fusion with feature engineering. Same task inner speech data are recorded from four participants, and different processing strategies are compared and contrasted to previously-employed hybridisation methods. Data across participants are discovered to encode different underlying structures, which results in varying decoding performances between subject-dependent fusion models. Decoding performance is demonstrated as improved when pursuing bimodal fMRI-EEG fusion strategies, if the data show underlying structure.

When you finish reading this sentence, look away from the screen for a moment and repeat it back in your head. Do you know exactly where in your brain this inner "voice" is speaking from? Researchers have tried to map out the regions of the brain responsible for this "inner monologue" for years. One promising candidate is an area called the supramarginal gyrus, which sits a little north of your eyeballs and slightly behind your ears. What's new -- According to new research presented at the recent Society for Neuroscience conference, the supramarginal gyrus could help scientists translate people's inner thoughts.

Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI). A promising developmental approach recommends the design of intrinsically motivated agents that learn new skills by generating and pursuing their own goals -- autotelic agents. But despite recent progress, existing algorithms still show serious limitations in terms of goal diversity, exploration, generalisation or skill composition. This perspective calls for the immersion of autotelic agents into rich socio-cultural worlds, an immensely important attribute of our environment that shapes human cognition but is mostly omitted in modern AI. Inspired by the seminal work of Vygotsky, we propose Vygotskian autotelic agents -- agents able to internalise their interactions with others and turn them into cognitive tools. We focus on language and show how its structure and informational content may support the development of new cognitive functions in artificial agents as it does in humans. We justify the approach by uncovering several examples of new artificial cognitive functions emerging from interactions between language and embodiment in recent works at the intersection of deep reinforcement learning and natural language processing. Looking forward, we highlight future opportunities and challenges for Vygotskian Autotelic AI research, including the use of language models as cultural models supporting artificial cognitive development.

A robot has been trained to speak aloud its inner decision-making process, giving us a view of how it prioritizes competing demands. Researchers at Italy's University of Palermo (UP) programmed a humanoid robot from Japanese manufacturer SoftBank Robotics with software that models human cognitive processes, along with a text-to-speech processor, so it could vocalize its decision-making process while completing tasks. The software enabled the robot, Pepper, to retrieve relevant data from its memory and determine the correct way to respond to human commands. After encoding etiquette rules into Pepper, the UP scientists asked it to set a dinner table, and either enabled or disabled its inner speech to observe the effects. When inner speech was disabled, the robot refused to perform tasks that contradicted the programmed rules, but could not explain its reasoning to the researchers.

We use what is known as inner speech, where we talk to ourselves, to evaluate situations and make more-informed decisions. Now, a robot has been trained to speak aloud its inner decision-making process, giving us a view of how it prioritises competing demands. Arianna Pipitone and Antonio Chella at the University of Palermo, Italy, programmed a humanoid robot named Pepper, made by SoftBank Robotics in Japan, with software that models human cognitive processes, as well as a text-to-speech processor. This allowed Pepper to voice its decision-making process while completing a task. "With inner speech, we can better understand what the robot wants to do and what its plan is," says Chella.

Scientists have modified Pepper the robot to think out loud, which they say can increase transparency and trust between human and machine. The Italian team built an'inner speech model' that allowed the robot to talk through its thought processes, just like humans when faced with a challenge or a dilemma. The experts found Pepper was better at overcoming confusing human instructions when it could relay its own inner dialogue out loud. Pepper – which has already been used as a receptionist and a coffee shop attendee – is the creation of Japanese tech company SoftBank. By creating their own'extension' of Pepper, the team have realised the concept of robotic inner speech, which they say could be applied in robotics contexts such as learning and regulation.

"Hey Siri, can you find me a murderer for hire?" Ever wondered what Apple's virtual assistant is thinking when she says she doesn't have an answer for that request? Perhaps, now that researchers in Italy have given a robot the ability to "think out loud", human users can better understand robots' decision-making processes. "There is a link between inner speech and subconsciousness [in humans], so we wanted to investigate this link in a robot," said the study's lead author, Arianna Pipitone from the University of Palermo. The researchers programmed a robot called Pepper, made by SoftBank Robotics, with the ability to vocalise its thought processes.