Learning a New Selection Rule in Visual and Frontal Cortex

Cerebral Cortex, August 2016;26: 3611–3626 doi:10.1093/cercor/bhw155 Advance Access Publication Date: 6 June 2016 Original Article ORIGINAL ARTICLE Learning a New Selection Rule in Visual and Frontal Cortex Chris van der Togt1, Liviu Stănişor1, Arezoo Pooresmaeili1, Larissa Albantakis2, Gustavo Deco3, and Pieter R. Roelfsema1,4,5 Department of Vision and Cognition, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, The Netherlands, 2Madison School of Medicine, Department of Psychiatry, University of Wisconsin, 6001 Research Park Boulevard, Madison, WI 53719, USA, 3 Dept. de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, C\ Tanger, 122-140, 08018 Barcelona, Spain, 4Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands and 5Psychiatry Department, Academic Medical Center, 1105 AZ Amsterdam, The Netherlands Address correspondence to email: Chris van der Togt and Liviu Stănişor contributed equally to this work. Abstract How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the visual (area V1) and frontal cortex (area FEF) of monkeys. We devised a new iconselection task. On each day, the monkeys saw 2 new icons (small pictures) and learned which one was relevant. We rewarded eye movements to a saccade target connected to the relevant icon with a curve. Neurons in visual and frontal cortex coded the monkey’s choice, because the representation of the selected curve was enhanced. Learning delayed the neuronal selection signals and we uncovered the cause of this delay in V1, where learning to select the relevant icon caused an early suppression of surrounding image elements. These results demonstrate that the learning of a new rule causes a transition from fast and random decisions to a more considerate strategy that takes additional time and they reveal the contribution of visual and frontal cortex to the learning process. Key words: frontal eye fields, learning, primary visual cortex, visual attention, visual routines Introduction Imagine that you want to learn a new game. There are multiple ways to learn the rules. For example, you can choose to study the manual. However, in many cases, the best way to learn is to try to optimize your strategy while playing, making many errors at the start. Virtually in all games, you will have to learn to attend features that matter, for example, the shape of the chess pieces or the symbols on cards, and to ignore the rest. However, how do you distribute your attention and make decisions when you do not yet know the rules? In previous work, researchers have gained insight into the neuronal mechanisms underlying sensory decisions. Many studies have focused on the parietal and motor cortex in tasks © The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact 1 3612 | Cerebral Cortex , 2016, Vol. 26, No. 8 conditions. Finally, we could vary the difficulty of the task to investigate how this factor influences the activity in V1 and FEF. With this design, we aimed to address the following questions. 1) How are eye movements selected if the rule is unknown? 2) Are random guesses associated with selection signals in the visual and frontal cortex? 3) What is the influence of learning on the time course of selection signals in the visual and frontal cortex? When the monkeys learned the new rule, we observed a transition from extremely fast but random decisions at the start of a session to slower and accurate decisions as the monkeys became proficient. The longer decision times were associated with a more gradual buildup of activity in the frontal cortex and with the later emergence of selection signals in the visual cortex. Our V1 recordings revealed a cause of the delay, because the learning induced a brief suppression of activity in the vicinity of the relevant icon allotting additional time for more considerate decisions in frontal cortex. Materials and Methods Three monkeys (A, J, and G) participated in this study. We recorded 2 datasets, one in FEF of monkeys A and J, and the other one in V1 of monkeys A and G. In a first operation, we implanted a head holder to stabilize the head and inserted a gold ring under the conjunctiva of one eye for the measurement of eye position. For the FEF recordings, we performed a separate surgery to make a trepanation over area FEF and to place a recording chamber. Before surgery, the FEF was localized with a magnetic resonance imaging scan and once the chamber was in place we confirmed its location by eliciting saccadic eye movements with microstimulation (generally <50 μA). For the V1 recordings, we chronically implanted arrays of 4 × 5 or 5 × 5 electrodes (Blackrock Microsystems). The surgical procedures were performed under aseptic conditions and general anesthesia. Details of the surgical procedures and the postoperative care have been described previously (Roelfsema et al. 1998; Khayat et al. 2009). All procedures complied with the US National Institutes of Health Guidelines for the Care and Use of Laboratory Animals were approved by the institutional animal care and use committee of the Royal Netherlands Academy of Arts and Sciences of the Netherlands. The stimuli were presented on a monitor with a diagonal of 35.5 cm (14 inch), a resolution of 1024 by 768 pixels, and a refresh rate of 100 Hz at a distance of 75 cm from the monkey’s eyes. The objects that appeared on the screen were colorful figures with a size of approximately 1.0°; we will refer to these as “icons” (Fig. 1). The saccade targets were red disks with a diameter of 0.8° connected to the icons by a curve that was 2 pixels wide (0.05°). The eye position was measured using either the double magnetic induction technique built in house (Bour et al. 1984) (sampling rate 1 kHz) or an infrared camera system (Thomas Recording: ET-49B, sampling rate 250 Hz). Behavioral Task The animals performed a forced choice task where they had to select one of 2 icons and then made an eye movement to a circular disk that was connected to this icon by a curve (Fig. 1A). We presented a new (unfamiliar) pair of icons during every recording session and the monkey had to learn which of these 2 icons was associated with reward. A trial started as soon as the monkey’s eye position was within a 3°×3° square (...truncated)